IEEE Final Year Projects 2011-2012 :: Elysium Technologies Pvt Ltd::Biomedicalnew

Elysium Technologies Private Limited ISO 9001:2008 A leading Research and Development Division Madurai | Chennai | Trichy | Coimbatore | Kollam| Singapore Website: elysiumtechnologies.com, elysiumtechnologies.info Email: [email protected]

IEEE Final Year Project List 2011-2012

Madurai

Elysium Technologies Private Limited

230, Church Road, Annanagar,

Madurai , Tamilnadu – 625 020.

Contact : 91452 4390702, 4392702, 4394702.

eMail: [email protected]

Trichy


3rd

Floor,SI Towers,

15 ,Melapudur , Trichy,

Tamilnadu – 620 001.

Contact : 91431 - 4002234.


Kollam


Surya Complex,Vendor junction,

kollam,Kerala – 691 010.

Contact : 91474 2723622.


A b s t r a c t

BIO-MEDICAL 2011 - 2012

01 A Bayesian Hierarchical Correlation Model for fMRI Cluster Analysis

Data-driven cluster analysis is potentially suitable to search for, and discriminate between, distinct response signals in

blood oxygenation level-dependent functional magnetic resonance imaging (BOLD fMRI), which appear during

cerebrovascular disease. In contrast to model-driven methods, which test for a particular BOLD signal whose shape must be

given beforehand, datadriven methods generate a set of BOLD signals directly from the fMRI data by clustering voxels into

groups with correlated time signals. Here, we address the problem of selecting only the clusters that represent genuine

responses to the experimental stimulus by modeling the correlation structure of the clustered data using a Bayesian

hierarchical model. The model is empirically justified by demonstrating the hierarchical organization of the voxel

correlations after cluster analysis. BOLD signal discrimination is demonstrated using: 1) simulations that contain multiple

pathological BOLD response signals; and 2) fMRI data acquired during an event-related motor task. These demonstrations

are compared with results from a model-driven method based on the general linear model. Our simulations show that the

data-driven method can discriminate between the BOLD response signals, while themodeldriven method only finds one

signal. For fMRI, the data-driven method distinguishes between the BOLD signals appearing in the sensorimotor cortex and

those in basal ganglia and putamen, while themodel-drivenmethod combines these signals into one activation map.We

conclude that the proposed data-driven method provides an objective framework to identify and discriminate between

distinct BOLD response signals.

02 A Brain-Deformation Framework Based on a Linear Elastic Model and Evaluation Using Clinical Data

In image-guided neurosurgery, brain tissue displacement and deformation during neurosurgical procedures are a major

source of error. In this paper, we implement and evaluate a linear-elastic-model-based framework for correction of brain

shift using clinical data from five brain tumor patients. The framework uses a linear elastic model to simulate brain-shift

behavior. The model is driven by cortical surface deformations, which are tracked using a surface-tracking algorithm

combined with a laser-range scanner. The framework performance was evaluated using displacements of anatomical

landmarks, tumor contours and self-defined evaluation parameters. The results show that tumor deformations predicted by

the present framework agreed well with the ones observed intraoperatively, especially in the parts of the larger

deformations. On average, a brain shift of 3.9 mm and a tumor margin shift of 4.2 mm were corrected to 1.2 and 1.3 mm,

respectively. The entire correction process was performed in less than 5 min. The data from this study suggest that the

technique is a suitable candidate for intraoperative brain-deformation correction

03 A Classification Tree Approach for Cardiac Ischemia Detection Using Spatiotemporal Information from Three Standard ECG Leads

The accurate noninvasive diagnosis of cardiac ischemia remains a great challenge. To this end, the ECG is the main source

of information, and personal health systems may now embed intelligence for enabling any citizen to self-record an ECG

anywhere at any time. Our objective is to find a decision-support approach that makes best use of these resources. A new

classification tree based on conditions combinations competition (T-3C) is proposed for building a multibranch tree of

combined decision rules, and its performance is compared to usual methods based either on discriminant analysis or on

1



Madurai




Contact : 91452 4390702, 4392702, 4394702.


Trichy


3rd

Floor,SI Towers,



Contact : 91431 - 4002234.


Kollam




Contact : 91474 2723622.


classification trees. Moreover, we assessed with these methods, the diagnosis content for ischemia detection of the

spatiotemporal ECG information that can be retrieved either from the standard 12-lead ECG or from only the three

orthogonal leads subset (I, II, and V2), easy to set-up in selfcare. The diagnostic accuracy of 14 decision-making strategies

was compared for ischemia detection induced by angioplasty on a test set from a study population of 90 patients. The best

performance is obtained with the T-3C algorithm on three-lead ECG, reaching 98% of sensitivity and of specificity, thus

exceeding 23% of the diagnostic accuracy of the recommended and currently used standard ECG criteria.

04 A Linear Correction for Principal Component Analysis of Dynamic Fluorescence Diffuse Optical Tomography Images

The analysis of dynamic fluorescence diffuse optical tomography (D-FDOT) is important both for drug delivery research and

for medical diagnosis and treatment. The low spatial resolution and complex kinetics, however, limit the ability of FDOT in

resolving drug distributions within small animals. Principal component analysis (PCA) provides the capability of detecting

and visualizing functional structures with different kinetic patterns from D-FDOT images. A particular challenge in using

PCA is to reduce the level of noise in D-FDOT images. This is particularly relevant in drug study, where the time-varying

fluorophore concentration (drug concentration) will result in the reconstructed images containing more noise and,

therefore, affect the performance of PCA. In this paper, a new linear corrected method is proposed for modeling these time-

varying fluorescence measurements before performing PCA. To evaluate the performance of the new method in resolving

drug biodistribution, the metabolic processes of indocyanine green within mouse is dynamically simulated and used as the

input data of PCA. Simulation results suggest that the principal component (PC) images generated using the new method

improve SNR and discrimination capability, compared to the PC images generated using the uncorrected D-FDOT images.

05 A Minimally Invasive Antenna for Microwave Ablation Therapies: Design, Performances, and Experimental Assessment

A new coaxial antenna for microwave ablation therapies is proposed. The antenna design includes a miniaturized choke and

an arrowhead cap to facilitate antenna insertion into the tissues. Antenna matching and the shape and dimension of the area

of ablated tissue (thermal lesion) obtained in ex vivo conditions are evaluated both numerically and experimentally, finding

an optimal agreement between numerical and experimental data. Results showthat the antenna is wellmatched, and that it is

able to produce a thermal lesion with an average length of 6.5 cm and an average diameter of 4.5 cm in ex vivo bovine liver

when irradiates 60 W for 10 min. Finally, the dependence of antenna performances on possible changes in the antenna’s

structure is investigated, finding an optimal stability with respect to manufacturing tolerances and highlighting the

fundamental role played by the antenna’s choke.

06 A New Measure of Movement Symmetry in Early Parkinson’s Disease Patients Using Symbolic Processing of Inertial Sensor Data

Movement asymmetry is one of the motor symptoms associated with Parkinson’s disease (PD). Therefore, being able to

detect and measure movement symmetry is important for monitoring the patient’s condition. The present paper introduces a

novel symbol based symmetry index calculated from inertial sensor data. The method is explained, evaluated, and compared

to six other symmetrymeasures. Thesemeasures were used to determine the symmetry of both upper and lower limbs during

walking of 11 early-to-mid-stage PD patients and 15 control subjects. The patients included in the study showed minimal

motor abnormalities according to the unified Parkinson’s disease rating scale (UPDRS). The symmetry indices were used to

classify subjects into two different groups corresponding to PD or control. The proposed method presented high sensitivity

and specificity with an area under the receiver operating characteristic (ROC) curve of 0.872, 9% greater than the second

best method. The proposed method also showed an excellent intraclass correlation coefficient (ICC) of 0.949, 55% greater

than the second best method. Results suggest that the proposed symmetry index is appropriate for this particular group of

patients.

2



Madurai




Contact : 91452 4390702, 4392702, 4394702.


Trichy


3rd

Floor,SI Towers,



Contact : 91431 - 4002234.


Kollam




Contact : 91474 2723622.


07 A Portable Image Overlay Projection Device for Computer-Aided Open Liver Surgery

Image overlay projection is a form of augmented reality that allows surgeons to view underlying anatomical structures

directly on the patient surface. It improves intuitiveness of computer-aided surgery by removing the need for sight diversion

between the patient and a display screen and has been reported to assist in 3-D understanding of anatomical structures and

the identification of target and critical structures. Challenges in the development of image overlay technologies for surgery

remain in the projection setup. Calibration, patient registration, view direction, and projection obstruction remain unsolved

limitations to image overlay techniques. In this paper, we propose a novel, portable, and handheld-navigated image overlay

device based onminiature laser projection technology that allows images of 3-D patient-specific models to be projected

directly onto the organ surface intraoperatively without the need for intrusive hardware around the surgical site. The device

can be integrated into a navigation system, thereby exploiting existing patient registration and model generation solutions.

The position of the device is tracked by the navigation system’s position sensor and used to project geometrically correct

images from any position within the workspace of the navigation system. The projector was calibrated using modified

camera calibration techniques and images for projection are rendered using a virtual camera defined by the projectors

extrinsic parameters. Verification of the device’s projection accuracy concluded a mean projection error of 1.3 mm. Visibility

testing of the projection performed on pig liver tissue found the device suitable for the display of anatomical structures on

the organ surface. The feasibility of use within the surgical workflow was assessed during open liver surgery. We show that

the device could be quickly and unobtrusively deployed within the sterile environment

08 A Radio-Frequency Coupling Network for Heating of Citrate-Coated Gold Nanoparticles for Cancer Therapy: Design and Analysis

Gold nanoparticles (GNPs) are nontoxic, can be functionalized with ligands, and preferentially accumulate in tumors. We

have developed a 13.56-MHz RF-electromagnetic field (RFEM) delivery system capable of generating high E-field strengths

required for noninvasive, noncontact heating of GNPs. The bulk heating and specific heating rates were measured as a

function of NP size and concentration. It was found that heating is both size and concentration dependent, with 5 nm

particles producing a 50.6 ± 0.2 ◦C temperature rise in 30 s for 25 µg/mL gold (125 W input). The specific heating rate was

also size and concentration dependent, with 5 nm particles producing a specific heating rate of 356 ± 78 kW/g gold at 16

µg/mL (125 W input). Furthermore, we demonstrate that cancer cells incubated with GNPs are killed when exposed to 13.56

MHz RF-EM fields. Compared to cells that were not incubated with GNPs, three out of four RF-treated groups showed a

significant enhancement of cell death with GNPs (p < 0.05). GNP-enhanced cell killing appears to require temperatures above

50 ◦C for the experimental parameters used in this study. Transmission electron micrographs showextensive

vacuolizationwith the combination of GNPs andRF treatment.

09 A Real-time Heart Rate Analysis for a Remote Millimeter Wave I–Q Sensor

This paper analyzes heart rate (HR) information from physiological tracings collected with a remote millimeter wave (mmW)

I–Q sensor for biometric monitoring applications. A parameter optimization method based on the nonlinear Levenberg–

Marquardt algorithm is used. The mmW sensor works at 94 GHz and can detect the vital signs of a human subject from a few

to tens of meters away. The reflected mmW signal is typically affected by respiration, body movement, background noise,

and electronic system noise. Processing of the mmW radar signal is, thus, necessary to obtain the true HR. The down-

converted received signal in this case consists of both the real part (I-branch) and the imaginary part (Q-branch), which can

be considered as the cosine and sine of the received phase of the HR signal. Instead of fitting the converted phase angle

signal, the method directly fits the real and imaginary parts of the HR signal, which circumvents the need for phase

unwrapping. This is particularly useful when the SNR is low. Also, the method identifies both beat-to-beat HR and individual

3



Madurai




Contact : 91452 4390702, 4392702, 4394702.


Trichy


3rd

Floor,SI Towers,



Contact : 91431 - 4002234.


Kollam




Contact : 91474 2723622.


heartbeat magnitude, which is valuable for some medical diagnosis applications. The mean HR here is compared to that

obtained using the discrete Fourier transform.

10 A Self-Powered Telemetry System to Estimate the Postoperative Instability of a Knee Implant

Estimating in vivo the life span of a total knee replacement prosthesis is currently done by estimating the polyethylene (PE) wear

rate from measurement of the femorotibial distance using X-ray photographies. This efficient method requires, however, waiting for

few years to obtain a readout. This letter proposes using another metric that can be obtained within a couple of months of surgery,

namely the center of pressure (COP). This metric represents the point, where the axial force applies the most onto the tibial tray.

The displacement of the COP with respect to its ideal position can be used to estimate the wear and the life span of the PE. This

requires the implant to be fitted with a telemetry system described briefly. The proposed method is supported by measures and

simulations.

11 A Web-Based System for Home Monitoring of Patients With Parkinson’s Disease Using Wearable Sensors

This letter introduces MercuryLive, a platform to enable home monitoring of patients with Parkinson’s disease (PD) using

wearable sensors.MercuryLive contains three tiers: a resourceaware data collection engine that relies upon wearable

sensors, web services for live streaming and storage of sensor data, and a web-based graphical user interface client with

video conferencing capability. Besides, the platform has the capability of analyzing sensor (i.e., accelerometer) data to

reliably estimate clinical scores capturing the severity of tremor, bradykinesia, and dyskinesia. Testing results showed an

average data latency of less than 400 ms and video latency of about 200 ms with video frame rate of about 13 frames/s when

800 kb/s of bandwidth were available and we used a 40% video compression, and data feature upload requiring 1 min of

extra time following a 10 min interactive session. These results indicate that the proposed platform is suitable to monitor

patients with PD to facilitate the titration of medications in the late stages of the disease.

12 A Web-Based System for the Quantitative and Reproducible Assessment of Clinical Indexes From the Retinal Vasculature

A novel system for the vascular tree identification and the quantitative estimation of arteriolar venular ratio clinical index in

retinal fundus images is presented. The system is composed of a module for automatic vascular tracking, an interactive

editing interface to correct errors and set the required parameters of analysis, and a module for the computation of clinical

indexes. The system was organized as a client–server structure to allow clinicians and researchers from all over the world to

work remotely. The system was evaluated by three graders analyzing 30 fundus images. The evaluation of the Pearson’s

correlation coefficient and p-value of a paired t-test for each pair of graders demonstrates the high reproducibility of the

measures provided by the system.

13 Improving Adaptive Sleep–Wake Discrimination for Wearable Devices

Sleep/wake classification systems that rely on physiological signals suffer from intersubject differences that make accurate

classification with a single, subject-independent model difficult. To overcome the limitations of intersubject variability, we

suggest a novel online adaptation technique that updates the sleep/wake classifier in real time. The objective of the present

study was to evaluate the performance of a newly developed adaptive classification algorithm that was embedded on a

wearable sleep/wake classification system called SleePic. The algorithm processed ECG and respiratory effort signals for

the classification task and applied behavioral measurements (obtained from accelerometer and press-button data) for the

automatic adaptation task.Whentrained as a subject-independent classifier algorithm, the SleePic device was only able to

4



Madurai




Contact : 91452 4390702, 4392702, 4394702.


Trichy


3rd

Floor,SI Towers,



Contact : 91431 - 4002234.


Kollam




Contact : 91474 2723622.


correctly classify 74.94 ± 6.76% of the humanrated sleep/wake data. By using the suggested automatic adaptation method,

the mean classification accuracy could be significantly improved to 92.98 ± 3.19%. A subject-independent classifier based on

activity data only showed a comparable accuracy of 90.44 ± 3.57%. We demonstrated that subject-independent models used

for online sleep–wake classification can successfully be adapted to previously unseen subjects without the intervention of

human experts or off-line calibration.

14 An Adaptive Kalman Filter for ECG Signal Enhancement

The ongoing trend of ECG monitoring techniques to become more ambulatory and less obtrusive generally comes at the

expense of decreased signal quality. To enhance this quality, consecutive ECG complexes can be averaged triggered on the

heartbeat, exploiting the quasi-periodicity of the ECG. However, this averaging constitutes a tradeoff between improvement

of the SNR and loss of clinically relevant physiological signal dynamics. Using a Bayesian framework, in this paper, a

sequential averaging filter is developed that, in essence, adaptively varies the number of complexes included in the

averaging based on the characteristics of the ECG signal. The filter has the form of an adaptive Kalman filter. The adaptive

estimation of the process and measurement noise covariances is performed by maximizing the Bayesian evidence function

of the sequential ECG estimation and by exploiting the spatial correlation between several simultaneously recorded ECG

signals, respectively. The noise covariance estimates thus obtained render the filter capable of ascribing more weight to

newly arriving data when these data contain morphological variability, and of reducing this weight in cases of no

morphological variability. The filter is evaluated by applying it to a variety of ECG signals. To gauge the relevance of the

adaptive noise-covariance estimation, the performance of the filter is compared to that of a Kalman filter with fixed, (a

posteriori) optimized noise covariance. This comparison demonstrates that, without using a priori knowledge on signal

characteristics, the filter with adaptive noise estimation performs similar to the filter with optimized fixed noise covariance,

favoring the adaptive filter in cases where no a priori information is available or where signal characteristics are expected to

fluctuate.

15 An Asynchronous P300 BCI with SSVEP-Based Control State Detection

In this paper, an asynchronous brain–computer interface (BCI) system combining the P300 and steady-state visually an

evoked potentials (SSVEPs) paradigm is proposed. The information transfer is accomplished using P300 event-related

potential paradigm and the control state (CS) detection is achieved using SSVEP, overlaid on the P300 base system. Offline

and online experiments have been performed with ten subjects to validate the proposed system. It is shown to achieve fast

and accurate CS detection without significantly compromising the performance. In online experiments, the system is found

to be capable of achieving an average data transfer rate of 19.05 bits/min, with CS detection accuracy of about 88%.

16 Arrhythmia Discrimination in Implantable Cardioverter Defibrillators Using SupportMachines Representation of Electrograms

Arrhythmia classification remains a major challenge for appropriate therapy delivery in implantable cardioverter

defibrillators (ICDs). The purpose of this paper is to present a new algorithm for arrhythmia discrimination based on a

statistical classification by support vector machines of a novel 2-D representation of electrograms (EGMs) named spatial

projection of tachycardia (SPOT) EGMs. SPOT-based discrimination algorithm provided sensitivity and specificity of 98.8%

and 91.3%, respectively, on a test database. A simplified version of the algorithm is also presented, which can be directly

implemented in the ICD.

17 Assessments of Alterations in the Electrical Impedance of Muscle After Experimental Nerve Injury via Finite-Element Analysis

The surface measurement of electrical impedance of muscle, incorporated as the technique of electrical

5



Madurai




Contact : 91452 4390702, 4392702, 4394702.


Trichy


3rd

Floor,SI Towers,



Contact : 91431 - 4002234.


Kollam




Contact : 91474 2723622.


impedancemyography (EIM), provides a noninvasive approach for evaluating neuromuscular diseases, including

amyotrophic lateral sclerosis.However, the relationship between alterations in surface impedance and the electrical

properties of muscle remains uncertain. In order to investigate this further, a group of healthy adult rats, a group of rats two

weeks postsciatic crush, and a group of animals six months postcrush underwent EIM of the gastrocnemius–soleus

complex. The animals were then killed and the conductivity and permittivity of the extracted muscle measured. Finite-

element models based on MRI data were then constructed for each group. The characteristic EIM parameter, 50 kHz phase

(±standard error), obtained with surface impedance measurements was 17.3◦ ± 0.3◦ for normal animals, 13.8◦ ± 0.7◦ for

acutely injured animals, and 16.1◦ ± 0.5◦ for chronically injured animals. The models predicted parallel changes with phase

values of 24.3◦, 18.8◦, and 21.2◦ for the normal, acute, and chronic groups, respectively. Other multifrequency impedance

parameters showed similar alterations. These results confirm that surface impedance measurements taken in conjunction

with anatomical data and finite-element models may offer a noninvasive approach for assessing biophysical alterations in

muscle in neuromuscular disease states.

.18 Automated Segmentation of Cells With IHC Membrane Staining

This study presents a fully automatedmembrane segmentation technique for immunohistochemical tissue images with

membrane staining, which is a critical task in computerized immunohistochemistry (IHC). Membrane segmentation is

particularly tricky in immunohistochemical tissue images because the cellular membranes are visible only in the stained

tracts of the cell, while the unstained tracts are not visible. Our automated method provides accurate segmentation of the

cellularmembranes in the stained tracts and reconstructs the approximate location of the unstained tracts using nuclear

membranes as a spatial reference. Accurate cell-by-cell membrane segmentation allows per cell morphological analysis and

quantification of the target membrane proteins that is fundamental in several medical applications such as cancer

characterization and classification, personalized therapy design, and for any other applications requiring cell morphology

characterization. Experimental results on real datasets from different anatomical locations demonstrate the wide

applicability and high accuracy of our approach in the context of IHC analysis.

19 Automated Segmentation of the Lumbar Pedicle in CT Images for Spinal Fusion Surgery

Exact information about the shape of a lumbar pedicle can increase operation accuracy and safety during computeraided

spinal fusion surgery, which requires extreme caution on the part of the surgeon, due to the complexity and delicacy of the

procedure. In this paper, a robust framework for segmenting the lumbar pedicle in computed tomography (CT) images is

presented. The framework that has been designed takes a CT image, which includes the lumbar pedicle as input, and

provides the segmented lumbar pedicle in the form of 3-D voxel sets. This multistep approach begins with 2-D dynamic

thresholding using local optimal thresholds, followed by procedures to recover the spine geometry in a high curvature

environment. A subsequent canal reference determination using proposed thinning-based integrated cost is then performed.

Based on the obtained segmented vertebra and canal reference, the edge of the spinal pedicle is segmented. This framework

has been tested on 84 lumbar vertebrae of 19 patients requiring spinal fusion. It was successfully applied, resulting in an

average success rate of 93.22% and a final mean error of 0.14±0.05 mm. Precision errors were smaller than 1% for spine

pedicle volumes. Intra- and interoperator precision errors were not significantly different.

20 Automatic and Unsupervised Snore Sound Extraction From Respiratory Sound Signals

In this paper, an automatic and unsupervised snore detection algorithm is proposed. The respiratory sound signals of 30

patients with different levels of airway obstruction were recorded by twomicrophones: one placed over the trachea (the

tracheal microphone), and the other was a freestanding microphone (the ambient microphone). All the recordings were done

simultaneously with full-night polysomnography during sleep. The sound activity episodes were identified using the vertical

6



Madurai




Contact : 91452 4390702, 4392702, 4394702.


Trichy


3rd

Floor,SI Towers,



Contact : 91431 - 4002234.


Kollam




Contact : 91474 2723622.


box (V-Box) algorithm. The 500-Hz subband energy distribution and principal component analysis were used to extract

discriminative features from sound episodes. An unsupervised fuzzy C-means clustering algorithm was then deployed to

label the sound episodes as either snore or no-snore class, which could be breath sound, swallowing sound, or any other

noise. The algorithm was evaluated using manual annotation of the sound signals. The overall accuracy of the proposed

algorithm was found to be 98.6% for tracheal sounds recordings, and 93.1% for the sounds recorded by the ambient

microphone.

21 Automatic Detection of Obstructive Sleep Apnea Using Speech Signals

Obstructive sleep apnea (OSA) is a common disorder associated with anatomical abnormalities of the upper airways that

affects 5% of the population. Acoustic parameters may be influenced by the vocal tract structure and soft tissue properties.

We hypothesize that speech signal properties of OSA patients will be different than those of control subjects not having

OSA. Using speech signal processing techniques, we explored acoustic speech features of 93 subjects who were recorded

using a text-dependent speech protocol and a digital audio recorder immediately prior to polysomnography study. Following

analysis of the study, subjects were divided into OSA (n = 67) and non-OSA (n = 26) groups. A Gaussian mixture model-

based system was developed to model and classify between the groups; discriminative features such as vocal tract length

and linear prediction coefficients were selected using feature selection technique. Specificity and sensitivity of 83% and 79%

were achieved for the male OSA and 86% and 84% for the female OSA patients, respectively. We conclude that acoustic

features from speech signals during wakefulness can detect OSA patients with good specificity and sensitivity. Such a

system can be used as a basis for future development of a tool for OSA screening.

22 Automatic Optic Disc Detection From Retinal Images by a Line Operator

Under the framework of computer-aided eye disease diagnosis, this paper presents an automatic optic disc (OD) detection

technique. The proposed technique makes use of the unique circular brightness structure associated with the OD, i.e., the

OD usually has a circular shape and is brighter than the surrounding pixels whose intensity becomes darker gradually with

their distances from the OD center. A line operator is designed to capture such circular brightness structure, which

evaluates the image brightness variation along multiple line segments of specific orientations that pass through each retinal

image pixel. The orientation of the line segment with the minimum/maximum variation has specific pattern that can be used

to locate the OD accurately. The proposed technique has been tested over four public datasets that include 130, 89, 40, and

81 images of healthy and pathological retinas, respectively. Experiments show that the designed line operator is tolerant to

different types of retinal lesion and imaging artifacts, and an average OD detection accuracy of 97.4% is obtained.

.23 Automatic Tracking of Muscle Fascicles in Ultrasound Images Using Localized Radon Transform

Ultrasound images of muscle fascicles have been widely used to investigate muscle properties under static/dynamic and

pathologic conditions. Fascicle images are usually detected and measured manually, which is subjective and time

consuming, especially when dealing with large number of images. In this study, an automatic linear extraction method based

on localized Radon transform and revoting strategy is proposed to detect and track muscle fascicles in ultrasound images.

The performance of the proposed method is compared to another automatic linear feature extraction method of revoting

Hough transform using both simulated images generated by Field II and clinical images from two human subjects. The

proposed tracking method is further validated using experimental data. Both the simulation and experimental results show

that the proposed method is robust in the presence of speckle noise, accurate in terms of orientation and position

measurement, and feasible for analyzing clinical data.

7



Madurai




Contact : 91452 4390702, 4392702, 4394702.


Trichy


3rd

Floor,SI Towers,



Contact : 91431 - 4002234.


Kollam




Contact : 91474 2723622.


24 Basilar-Membrane Responses to Broadband Noise Modeled Using Linear Filters With Rational Transfer Functions

Basilar-membrane responses to white Gaussian noise were recorded using laser velocimetry at basal sites of the chinchilla

cochlea with characteristic frequencies near 10 kHz and first-order Wiener kernels were computed by cross correlation of the

stimuli and the responses. The presence or absence of minimum-phase behavior was explored by fitting the kernels with

discrete linear filters with rational transfer functions. Excellent fits to the kernels were obtained with filters with transfer

functions including zeroes located outside the unit circle, implying nonminimum-phase behavior. These filters accurately

predicted basilar-membrane responses to other noise stimuli presented at the same level as the stimulus for the kernel

computation. Fits with all-pole and other minimumphase discrete filters were inferior to fits with nonminimum-phase filters.

Minimum-phase functions predicted from the amplitude functions of theWiener kernels by Hilbert transforms were different

from the measured phase curves. These results, which suggest that basilar-membrane responses do not have the minimum-

phase property, challenge the validity of models of cochlear processing, which incorporate minimum-phase behavior.

25 Bayesian Regularization Applied to Ultrasound Strain Imaging

Noise artifacts due to signal decorrelation and reverberation are a considerable problem in ultrasound strain imaging. For

block-matching methods, information from neighboring matching blocks has been utilized to regularize the estimated

displacements. We apply a recursive Bayesian regularization algorithm developed by Hayton et al. [Artif. Intell., vol. 114, pp.

125– 156, 1999] to phase-sensitive ultrasound RF signals to improve displacement estimation. The parameter of

regularization is reformulated, and its meaning examined in the context of strain imaging. Tissue-mimicking experimental

phantoms and RF data incorporating finite-element models for the tissue deformation and frequency-domain ultrasound

simulations are used to compute the optimal parameter with respect to nominal strain and algorithmic iterations. The optimal

strain regularization parameter was found to be twice the nominal strain and did not vary significantly with algorithmic

iterations. The technique demonstrates superior performance overmedian filtering in noise reduction at strains 5%and

higher for all quantitative experiments performed. For example, the strain SNR was 11 dB higher than that obtained using

amedian filter at 7% strain. It has to be noted that for applied deformations lower than 1%, since signal decorrelation errors

are minimal, using this approach may degrade the displacement image.

26 Breaking the Fixed-Arrival-Time Restriction in Reaching Movements of Neural Prosthetic Devices

We routinely generate reaching arm movements to function independently. For paralyzed users of upper extremity neural

prosthetic devices, flexible, high-performance reaching algorithms will be critical to restoring quality-of-life. Previously,

algorithms called real-time reach state equations (RSE) were developed to integrate the user’s plan and execution-related

neural activity to drive reaching movements to arbitrary targets. Preliminary validation under restricted conditions

suggested that RSE might yield dramatic performance improvements. Unfortunately, real-world applications of RSE have

been impeded because the RSE assumes a fixed, known arrival time. Recent animal-based prototypes attempted to break the

fixed-arrival-time assumption by proposing a standard model (SM) that instead restricted the user’s movements to a fixed,

known set of targets. Here, we leverage general purpose filter design (GPFD) to break both of these critical restrictions,

freeing the paralyzed user to make reaching movements to arbitrary target sets with various arrival times and definitive

stopping. In silico validation predicts that the new approach, GPFD-RSE, outperforms the SM while offering greater

flexibility. We demonstrate the GPFD-RSE against SM in the simulated control of an overactuated 3-D virtual robotic arm with

a real-time inverse kinematics engine.

8



Madurai




Contact : 91452 4390702, 4392702, 4394702.


Trichy


3rd

Floor,SI Towers,



Contact : 91431 - 4002234.


Kollam




Contact : 91474 2723622.


27 Changes in Body-Surface Electrocardiograms From Geometric Remodeling With Obesity

Both diabetes and obesity cause cardiac dysfunction. To separate consequences of geometric changes due to obesity from

electrophysiological ones, we investigated how changes in cardiac and torso geometry affected body-surface ECGs. For this

study, we modified the realistic heart and torso models of the simulation package ECGSIM. ECGs were calculated from

action potentials on the heart surface using our bidomain forward-problem solution. These ECGs were studied using

spectral- and principalcomponent analyses and isopotential and energy maps. We found relative errors over the body-

surface during the QT interval of 12%, 14%, and 68% for hypertrophy of the heart, extension of the abdomen, and heart

displacement with obesity, respectively. The major change to the standard 12-lead set also occurred with heart

displacement. The mean relative error over the QT interval in the precordial leads was 78% with heart displacement. These

results demonstrate the limitations of using standard lead sets to characterize electrocardiographic changes in obese

subjects and point to the need for more inclusive measures, such as body-surface mapping and inverse

electrocardiography, to describe electrical remodeling in the presence of habitus changes due to obesity

28 Classification of Paroxysmal and Persistent Atrial Fibrillation in Ambulatory ECG Recordings

The problem of classifying short atrial fibrillatory segments in ambulatory ECG recordings as being either paroxysmal or

persistent is addressed by investigating a robust approach to signal characterization. The method comprises preprocessing

estimation of the dominant atrial frequency for the purpose of controlling the subbands of a filter bank, computation of the

relative subband (harmonics) energy, and the subband sample entropy. Using minimum-error-rate classification of different

feature vectors, a data set consisting of 24-h ambulatory recordings from 50 subjects with either paroxysmal (26) or

persistent (24) atrial fibrillation (AF) was analyzed on a 10-s segment basis; a total of 212,196 segments were classified. The

best performance in terms of area under the receiver operating characteristic curve was obtained for a feature vector defined

by the subband sample entropy of the dominant atrial frequency and the relative harmonics energy, resulting in a value of

0.923, whereas that of the dominant atrial frequency was equal to 0.826. It is concluded that paroxysmal and persistent AFs

can be discriminated from short segments with good accuracy at any time of an ambulatory recording.

29 Comparison of RootMUSIC and Discrete Wavelet Transform Analysis of Doppler Ultrasound Blood Flow Waveforms I Diabetes

The earliest signs of cardiovascular disease occur in microcirculations. Changes to mechanical and structural properties of

these small resistive vessels alter the impedance to flow, subsequent reflected waves, and consequently, flow waveform

morphology. In this paper, we compare two frequency analysis techniques: 1) rootMUSIC and 2) the discretewavelet

transform (DWT) to extract features of flow velocity waveform morphology captured using Doppler ultrasound from the

ophthalmic artery (OA) in 30 controls and 38 age and sex matched Type I diabetics. Conventional techniques for

characterizing Doppler velocity waveforms, such as mean velocity, resistive index, and pulsatility index, revealed no

significant differences between the groups. However, rootMUSIC and the DWT provided highly correlated results with the

spectral content in bands 2–7 (30–0.8 Hz) significantly elevated in the diabetic group (p< 0.05). The spectral distinction

between the groups may be attributable to manifestations of underlying pathophysiological processes in vascular

impedance and consequent wave reflections, with bands 5 and 7 related to age. Spectral descriptors of OA blood velocity

waveforms are better indicators of preclinical microvascular abnormalities in Type I diabetes than conventional measures.

Although highly correlated DWT proved slightly more discriminatory than rootMUSIC and has the advantage of extending to

subheart rate frequencies, which may be of interest.

9



Madurai




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Trichy


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Contact : 91431 - 4002234.


Kollam




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30 Continuous Intra-Arterial Blood pH Monitoring by a Fiber-Optic Fluorosensor

In the Continuous intra-arterial blood pH monitoring is highly desirable in clinical practice. However, devices with

appreciable accuracy are still not commercially available to date. In this study, we present a fiber-optic fluorosensor that can

be used to continuously and accurately measure blood pH changes. The pH sensor is developed based on a proton-

sensitive fluorescence dye, N-allyl-4-(4_-methyl-piperazinyl)-1,8-naphthalimide, which is bonded covalently to an optical fiber

through heat polymerization. Fluorescence intensity was recorded after the sensor was exposed to different pH buffer

solutions or intra-arterial blood in rabbits. Fluorescence intensity with emission peak at 510 nm decreased immediately as

the blood pH increased. Linear and reproducible responses were observed when pH ranges from 6.8 to 8.0 with resolution of

0.03 pH units. The correlation coefficient between the pH sensor and the conventional blood gas analyzer was 0.93 in vivo (n

= 75, p < 0.001) with a bias and precision of −0.02 ± 0.08 pH units. The pH sensor was stable during measurement for at least

72 h. The pH sensor is not sensitive to fluctuations of various ions’ concentrations and plasma osmosis at

pathophysiological limits, suggesting that it is useful for the continuous measurement of blood pH at various clinical

settings.

.31 Continuous Intra-Arterial Blood pH Monitoring by a Fiber-Optic Fluorosensor

Continuous intra-arterial blood pH monitoring is highly desirable in clinical practice. However, devices with appreciable

accuracy are still not commercially available to date. In this study, we present a fiber-optic fluorosensor that can be used to

continuously and accurately measure blood pH changes. The pH sensor is developed based on a proton-sensitive

fluorescence dye, N-allyl-4-(4_-methyl-piperazinyl)-1,8-naphthalimide, which is bonded covalently to an optical fiber through

heat polymerization. Fluorescence intensity was recorded after the sensor was exposed to different pH buffer solutions or

intra-arterial blood in rabbits. Fluorescence intensity with emission peak at 510 nm decreased immediately as the blood pH

increased. Linear and reproducible responses were observed when pH ranges from 6.8 to 8.0 with resolution of 0.03 pH

units. The correlation coefficient between the pH sensor and the conventional blood gas analyzer was 0.93 in vivo (n = 75, p

< 0.001) with a bias and precision of −0.02 ± 0.08 pH units. The pH sensor was stable during measurement for at least 72 h.

The pH sensor is not sensitive to fluctuations of various ions’ concentrations and plasma osmosis at pathophysiological

limits, suggesting that it is useful for the continuous measurement of blood pH at various clinical settings.

32 Control of Action Potential Duration Alternans in Canine Cardiac Ventricular Tissue

Cardiac electrical alternans, characterized by a beatto- beat alternation in action potential waveform, is a naturally occurring

phenomenon, which can occur at sufficiently fast pacing rates. Its presence has been putatively linked to the onset of

cardiac reentry, which is a precursor to ventricular fibrillation. Previous studies have shown that closed-loop alternans

control techniques that apply a succession of externally administered cycle perturbations at a single site provide limited

spatially-extended alternans elimination in sufficiently large cardiac substrates. However, detailed experimental

investigations into the spatial dynamics of alternans control have been restricted to Purkinje fiber studies. A complete

understanding of alternans control in the more clinically relevant ventricular tissue is needed. In this paper, we study the

spatial dynamics of alternans and alternans control in arterially perfused canine right ventricular preparations using an

optical mapping system capable of high-resolution fluorescence imaging. Specifically, we quantify the spatial efficacy of

alternans control along 2.5 cm of tissue, focusing on differences in spatial control between different subregions of tissue.

We demonstrate effective control of spatially-extended alternans up to 2.0 cm, with control efficacy attenuating as a function

of distance. Our results provide a basis for future investigations into electrode-based control interventions of alternans in

cardiac tissue.

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33 Depth of Anesthesia During Multidrug Infusion: Separating the Effects of Propofol and Remifentanil Using the Spectral Features of EEG

General anesthesia is usually induced with a combination of drugs. In addition to the hypnotic agent, such as propofol,

opioids are often used due to their synergistic hypnotic and analgesic properties. However, the effects of opioids on the EEG

changes and the clinical state of the patient during anesthesia are complex and hinder the interpretation of the EEG-based

depth of anesthesia indexes. In this paper, a novel technology for separating the anesthetic effects of propofol and an

ultrashort-acting opioid, remifentanil, using the spectral features of EEG is proposed. By applying a floating search method,

a well-performing feature set is achieved to estimate the effects of propofol during induction of anesthesia and to classify

whether or not remifentanil has been coadministered. It is shown that including the detection of the presence of opioids to

the estimated effect of propofol significantly improves the determination of the clinical state of the patient, i.e., if the patient

will respond to a painful stimulation.

34 Detection of Viruses Via Statistical Gene Expression Analysis

We develop a new Bayesian construction of the elastic net (ENet), with variational Bayesian analysis. This modeling

framework is motivated by analysis of gene expression data for viruses, with a focus on H3N2 and H1N1 influenza, as well as

Rhino virus and RSV (respiratory syncytial virus). Our objective is to understand the biological pathways responsible for the

host response to such viruses, with the ultimate objective of developing a clinical test to distinguish subjects infected by

such viruses from subjects with other symptom causes (e.g., bacteria). In addition to analyzing these new datasets, we

provide a detailed analysis of the Bayesian ENet and compare it to related models.

.

35 Development of a Flexible System for Measuring Muscle Area Using Ultrasonography

Muscular strength can be estimated by quantification of muscle area. For this purpose, we developed a flexible measuring

system for muscle area using ultrasonography. This method is completely safe and is particularly suitable for elderly people

because the subjects are not required to perform any muscular contraction during measurement. The ultrasound probe is

installed on a mechanical arm, and continuously scans fragmental images along the body surface.Awide-area cross-

sectional image is then constructed using the measured images. The link mechanism is very flexible, enabling the operator

to measure images for any body posture and body site. Use of the spatial compounding method reduces speckle and artifact

noise in the resultant cross-sectional images. The operator can observe individual muscles (extensor, flexor muscle, etc.) in

detail. We conducted experiments to evaluate the performance of the system. In the experiments, the position of the

ultrasound probe was calculated with high accuracy according to the link posture. In addition, a high degree of correlation

was verified between MR images and those of the developed system. We observed a reduction in noise due to use of the

spatial compounding method, and propose a new calibration method for correcting the measured muscle area, which were

slightly deformed by the contact pressure of the ultrasound probe. Finally, we examined the relation between muscular area

and muscular strength in young and middle-aged subjects. The results of these experiments confirm that the developed

system can estimate muscular strength based on muscular area.

36 Directed Differential Connectivity Graph of Interictal Epileptiform Discharges

In this paper, we study temporal couplings between interictal events of spatially remote regions in order to localize the

leading epileptic regions from intracerebral EEG (iEEG). We aim to assess whether quantitative epileptic graph analysis

during interictal period may be helpful to predict the seizure onset zone of ictal iEEG. Using wavelet transform, cross-

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correlation coefficient, and multiple hypothesis test, we propose a differential connectivity graph (DCG) to represent the

connections that change significantly between epileptic and nonepileptic states as defined by the interictal events.

Postprocessings based on mutual information and multiobjective optimization are proposed to localize the leading epileptic

regions through DCG. The suggested approach is applied on iEEG recordings of five patients suffering from focal epilepsy.

Quantitative comparisons of the proposed epileptic regions within ictal onset zones detected by visual inspection and using

electrically stimulated seizures, reveal good performance of the present method.

37 Driver Drowsiness Classification Using Fuzzy Wavelet-Packet-Based Feature-Extraction Algorithm

Driver drowsiness and loss of vigilance are a major cause of road accidents. Monitoring physiological signals while driving

provides the possibility of detecting and warning of drowsiness and fatigue. The aim of this paper is to maximize the amount

of drowsiness-related information extracted from a set of electroencephalogram (EEG), electrooculogram (EOG), and

electrocardiogram (ECG) signals during a simulation driving test. Specifically, we develop an efficient fuzzy mutual-

information (MI)- based wavelet packet transform (FMIWPT) feature-extraction method for classifying the driver drowsiness

state into one of predefined drowsiness levels. The proposed method estimates the requiredMI using a novel approach

based on fuzzy memberships providing an accurate-information content-estimation measure. The quality of the extracted

features was assessed on datasets collected from 31 drivers on a simulation test. The experimental results proved the

significance of FMIWPT in extracting features that highly correlate with the different drowsiness levels achieving a

classification accuracy of 95%–97% on an average across all subjects.

.38 Duodenum Identification Mechanism for Capsule Endoscopy

The aim of this study is to implement a duodenum identificationmechanism for capsule endoscopes because commercially

available capsule endoscopes sometimes present a false negative diagnosis of the duodenum. One reason for the false

negative diagnosis is that the duodenum is the fastest moving part within the gastrointestinal tract and the current frame

rate of the capsule is not fast enough. When the capsule can automatically identify that it is in the duodenum, the frame rate

of the capsule can be temporarily increased to reduce the possibility of a false negative diagnosis. This study proposes a

mechanism to identify the duodenum using capacitive proximity sensors that can distinguish the surrounding tissue and

transmit data using RF communication. The implemented capsule (D11 mm × L22 mm) was smaller than the commercially

available capsule endoscopes, and power consumption was as low as 0.642 mW. Preexperiments were conducted to select

an appropriate electrode width in order to increase the signal-to-noise ratio (SNR), and in vitro experiments were conducted

to verify whether the implemented capsule could identify the duodenum within 3 s. The experiment showed that the

identification rate of duodenum was 93% when the velocity of the capsule was less than 1 cm/s.

39 Dynamic Brain Phantom for Intracranial Volume Measurements

Knowledge of intracranial ventricular volume is important for the treatment of hydrocephalus, a disease in which

cerebrospinal fluid (CSF) accumulates in the brain. Current monitoring options involve MRI or pressure monitors (InSite,

Medtronic). However, there are no existing methods for continuous cerebral ventricle volume measurements. In order to test

a novel impedance sensor for direct ventricular volume measurements, we present a model that emulates the expansion of

the lateral ventricles seen in hydrocephalus. To quantify the ventricular volume, sensor prototypes were fabricated and

tested with this experimental model. Fluidwas injected andwithdrawn cyclically in a controlledmanner and volume

measurements were tracked over 8 h. Pressure measurements were also comparable to conditions seen clinically. The

results from the bench-top model served to calibrate the sensor for preliminary animal experiments. A hydrocephalic rat

model was used to validate a scaled-down, microfabricated prototype sensor. CSF was removed from the enlarged ventricles

and a dynamic volume decrease was properly recorded. This method of testing new designs on brain phantoms prior to

animal experimentation accelerates medical device design by determining sensor specifications and optimization in a

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rational process.

40 Evaluation of Scaffolds based on α-Tricalcium Phosphate Cements for Tissue Engineering Applications

Growth of cells in 3-D porous scaffolds has gained importance in the field of tissue engineering. The scaffolds guide cellular

growth, synthesize extracellular matrix and other biological molecules, and make the formation of tissues and functional

organs easier. The aim of this study is to use α-tricalcium phosphate cement in order to obtain new types of scaffolds with

the aid of paraffin spheres as pore generators. The porosity of the scaffolds produced with paraffin spheres was analyzed

and compared to the literature, and the study of scaffold permeability using the Forchheimer equation allowed the analysis

of pore interconnectivity. In vitro tests showed the behavior of scaffolds in solutions of simulated body fluid, and viability

and cell proliferation were also evaluated. The results show the potential use of the materials developed for scaffolds for use

in tissue engineering applications.

41 Fast Technique for Noninvasive Fetal ECG Extraction

This letter describes a fast and very simple algorithm for estimating the fetal electrocardiogram (FECG). It is based on

independent component analysis, but we substitute its computationally demanding calculations for a much simpler

procedure. The resulting method consists of two steps: 1) a dimensionality reduction step and 2) a computationally light

postprocessing stage used to enhance the FECG signal.

42 FEM-Based 3-D Tumor Growth Prediction for Kidney Tumor

It is important to predict the tumor growth so that appropriate treatment can be planned in the early stage. In this letter, we

propose a finite-element method (FEM)-based 3-D tumor growth prediction system using longitudinal kidney tumor images.

To the best of our knowledge, this is the first kidney tumor growth prediction system. The kidney tissues are classified into

three types: renal cortex, renal medulla, and renal pelvis. The reaction–diffusion model is applied as the tumor growth

model. Different diffusion properties are considered in the model: the diffusion for renal medulla is considered as

anisotropic, while those of renal cortex and renal pelvis are considered as isotropic. The FEM is employed to solve the

diffusion model. Themodel parameters are estimated by the optimization of an objective function of overlap accuracy using a

hybrid optimization parallel search package. The proposed method was tested on two longitudinal studies with seven time

points on five tumors. The average true positive volume fraction and false positive volume fraction on all tumors is 91.4%

and 4.0%, respectively. The experimental results showed the feasibility and efficacy of the proposed method.

43 Finite-Element-Based Discretization and Regularization Strategies for 3-D Inverse Electrocardiography

We consider the inverse electrocardiographic problem of computing epicardial potentials from a body-surface potential map.

We study how to improve numerical approximation of the inverse problem when the finite-element method is used. Being ill-

posed, the inverse problem requires different discretization strategies from its corresponding forward problem. We propose

refinement guidelines that specifically address the ill-posedness of the problem. The resulting guidelines necessitate the use

of hybrid finite elements composed of tetrahedra and prism elements. Also, in order to maintain consistent numerical quality

when the inverse problem is discretized into different scales, we propose a new family of regularizers using the variational

principle underlying finiteelement methods. These variational-formed regularizers serve as an alternative to the traditional

Tikhonov regularizers, but preserves the L2 norm and thereby achieves consistent regularization in multiscale simulations.

The variational formulation also enables a simple construction of the discrete gradient operator over irregularmeshes, which

is difficult to define in traditional discretization schemes.We validated our hybrid element technique and the variational

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regularizers by simulations on a realistic 3-D torso/heart model with empirical heart data. Results show that discretization

based on our proposed strategies mitigates the ill-conditioning and improves the inverse solution, and that the variational

formulation may benefit a broader range of potential-based bioelectric problems.

44 Hilbert–Huang-Based Tremor Removal to Assess Postural Properties From Accelerometers

Tremor is one of the symptoms of several disorders of the central and peripheral nervous system, such as Parkinson’s

disease (PD). The impairment of postural control is another symptom of PD. The conventional method of posture analysis

uses force plates, but accelerometers can be a valid and reliable alternative. Both these measurement techniques are

sensitive to tremor.Tremor affects postural measures and may thus lead to misleading results or interpretations. Linear low-

pass filters (LPFs) are commonly employed for tremor removal. In this study, an alternative method, based on Hilbert–Huang

transformation (HHT), is proposed. We examined 20 PD subjects, with and without tremor, and 20 control subjects. We

compared the effectiveness of LPF and HHT-based filtering on a set of postural parameters extracted from acceleration

signals. HHT has the advantage of providing a filter, which with no a priori knowledge, efficiently manages the nonlinear,

nonstationary interference due to tremor, and beyond tremor, gives descriptive measures of postural function. Some of the

differences found using LPF can instead be ascribed to inefficient noise/tremor suppression. Filter order and cutoff

frequency are indeed critical when subjects exhibit a tremorous behavior, in which case LPF parameters should be chosen

very carefully.

45 Identification and Control for Automated Regulation of Hemodynamic Variables During Hemodialysis

This paper proposes a novel model-based control methodology for a computer-controlled hemodialysis system, designed to

maintain the hemodynamic stability of end-stage renal failure patients undergoing fluid removal during hemodialysis. The

first objective of this paper is to introduce a linear parameter varying system to model the hemodynamic response of

patients during hemodialysis. Ultrafiltration rate (UFR) and dialysate sodium concentration (DSC) are imposed as the inputs,

and the model computes the relative blood volume (RBV), percentage change in heart rate (∆HR), and systolic blood

pressure (SBP) during the course of hemodialysis. The model parameters were estimated based on data collected from 12

patients undergoing 4 profiled hemodialysis sessions. The modeling results demonstrated that the proposed model could be

useful for estimating the individual patient’s hemodynamic behavior during hemodialysis. Based on the model, the second

objective is to implement a computer-controlled hemodialysis system for the regulation of RBV and HR during hemodialysis

while maintaining SBP within stable range. The proposed controller is based on a model predictive control approach utilizing

pre-defined constraints on the control inputs (UFR and DSC) as well as the output (SBP). The designed control system was

experimentally verified on four patients. The results demonstrated that the proposed computer-controlled hemodialysis

system regulated the RBV and HR of the patients according to individual reference profiles with an average mean square

error of 0.24% and 2.6%, respectively, and thus can be potentially useful for ensuring the stability of patients undergoing

hemodialysis by avoiding sudden changes in hemodynamic variables.

46 Improved Pressure–Frequency Sensing Subxiphoid Pericardial Access System: Performance Characteristics During In Vivo Testing

We have designed, synthesized, and tested an improved version of our original subxiphoid access system intended to

facilitate epicardial electrophysiology. The new version of the system incorporates a precision fiber-optic pressure sensor

and a novel signal analysis algorithm for identifying pressure–frequency signatures which, in the clinical setting, may allow

for safer access to the pericardial space. Following in vivo studies on ten adult canine models, we analyzed 215 pressure–

frequency measurements made at the distal tip of the access needle, of which 98 were from nonpericardial, 112 were from

pericardial, and five were from ventricular locations. The needle locations as identified by the algorithm were significantly

different from each other (p < 0.01), and the algorithm had improved performance when compared to a standard fast Fourier

transform (FFT) analysis of the same data. Moreover, the structure of the algorithm can potentially overcome the time lags

14



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intrinsic to FFT analysis such that the needle’s location can be determined in nearreal time. Hydrodynamic pressure–

frequencymeasurementsmade during traversal of the pericardial membrane revealed a distinct change in signal structure

between the pericardial and nonpericardial anatomy. We present and discuss the design principles, details of construction,

and performance characteristics of this system.

47 Incremental Fuzzy Mining of Gene Expression Data for Gene Function Prediction

Due to the complexity of the underlying biological processes, gene expression data obtained from DNA microarray

technologies are typically noisy and have very high dimensionality and these make the mining of such data for gene function

prediction very difficult. To tackle these difficulties, we propose to use an incremental fuzzy mining technique called

incremental fuzzy mining (IFM). By transforming quantitative expression values into linguistic terms, such as highly or lowly

expressed, IFM can effectively capture heterogeneity in expression data for pattern discovery. It does so using a fuzzy

measure to determine if interesting association patterns exist between the linguistic gene expression levels. Based on these

patterns, IFM can make accurate gene function predictions and these predictions can be made in such a way that each gene

can be allowed to belong to more than one functional class with different degrees of membership. Gene function prediction

problem can be formulated both as classification and clustering problems, and IFM can be used either as a classification

technique or together with existing clustering algorithms to improve the cluster groupings discovered for greater prediction

accuracies. IFM is characterized also by its being an incremental data mining technique so that the discovered patterns can

be continually refined based only on newly collected data without the need for retraining using the whole dataset. For

performance evaluation, IFM has been tested with real expression datasets for both classification and clustering tasks.

Experimental results show that it can effectively uncover hidden patterns for accurate gene function predictions.

48 Intervention in Biological Phenomena Modeled by S-Systems

Recent years have witnessed extensive research activity in modeling biological phenomena as well as in developing

intervention strategies for such phenomena. S-systems, which offer a good compromise between accuracy and

mathematical flexibility, are a promising framework for modeling the dynamical behavior of biological phenomena. In this

paper, two different intervention strategies, namely direct and indirect, are proposed for the S-system model. In the indirect

approach, the prespecified desired values for the target variables are used to compute the reference values for the control

inputs, and two control algorithms, namely simple sampled-data control and model predictive control (MPC), are developed

for transferring the control variables from their initial values to the computed reference ones. In the direct approach, a MPC

algorithm is developed that directly guides the target variables to their desired values. The proposed intervention strategies

are applied to the glycolytic–glycogenolytic pathway and the simulation results presented demonstrate the effectiveness of

the proposed schemes.

49 Model-Based Human Circadian Phase Estimation Using a Particle Filter

We present a method for tracking an individual’s circadian phase that integrates dynamic models of circadian physiology

with physiological measurements in a Bayesian statistical framework. A model of the circadian pacemaker’s response to

light exposure is transformed into a nonlinear state-space model with a circadian phase state. The probability distribution of

the circadian phase is estimated by a particle filter that predicts changes over time based on themodel, and performs

updates with information gained fromphysiologicalmeasurements. Simulations demonstrate how probability distributions

allow flexible initialization of model states and enable statistical quantification of entrainment and divergence properties of

the circadian pacemaker. The combined use of sleep–wake scheduling data and physiological measurements is

demonstrated in a case study highlighting advantages for addressing the challenge of noninvasive ambulatory monitoring of

circadian physiology.

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50 Modeling and Percept of Transcorneal Electrical Stimulation in Humans

Retinal activation via transcorneal electrical stimulation (TcES) in normal humans was investigated by comparing subject

perception, model predictions, and brain activation patterns. The preferential location of retinal stimulation was predicted

from3-D admittancemodeling. Visual cortex activation was measured using positron emission tomography (PET) and 18

Ffluorodeoxyglucose (FDG). Two different corneal electrodes were investigated: DTL-Plus and ERG-Jet. Modeling results

predicted preferential stimulation of the peripheral, inferior, nasal retina during right eye TcES using DTL-Plus, but more

extensive activation of peripheral, nasal hemiretina using ERG-Jet. The results from human FDG PET study using both

corneal electrodes showed areas of visual cortex activation that consistently corresponded with the reported phosphene

percept and modeling predictions. ERG-Jet was able to generate brighter phosphene percept than DTL-Plus and elicited

retinotopically mapped primary visual cortex activation. This study demonstrates that admittance modeling and PET imaging

consistently predict the perceived location of electrically elicited phosphenes produced during TcES.

51 Modeling Conserved Structure Patterns for Functional Noncoding RNA

This RNA regulation has been increasingly recognized as a potential and perhaps overlooked genetics of higher

organisms. Noncoding RNAs (ncRNA) may play various catalytic and regulatory roles in the genetic operating system.

Recent studies using comparative genomics andmolecular genetics show evidence of the presence of varied ncRNAs.

Unlike protein coding genes, there is a lack of comparable information or outstanding signal for ncRNAs. Traditional

computational linguistics show limitations in modeling complicated secondary structures and prevent us from

identifying structure–function relationships of ncRNAs. This paper presents a novel approach, based on a set of

distance constraints, to model the predicted RNA secondary structures. Further, a filtering schema is presented to

identify matched models for the queried secondary structures.

52 Modeling Study of the Light Stimulation of a Neuron Cell With Channelrhodopsin-2 Mutants

Channelrhodopsin-2 (ChR2) has become a widely used tool for stimulating neurons with light. Nevertheless, the underlying

dynamics of the ChR2-evoked spikes are still not yet fully understood. Here, we develop a model that describes the response

of ChR2-expressing neurons to light stimuli and use the model to explore the light-to-spike process. We show that an

optimal stimulation yield is achieved when the optical energies are delivered in short pulses. The model allows us to

theoretically examine the effects of using various types of ChR2 mutants. We show that while increasing the lifetime and

shuttering speed of ChR2 have limited effect, reducing the threshold irradiance by increased conductance will eliminate

adaptation and allow constant dynamic range. The model and the conclusion presented in this study can help to interpret

experimental results, design illumination protocols, and seek improvement strategies in the nascent ontogenetic field..

53 Modified Kinematic Technique for Measuring Pathological Hyperextension and Hypermobility of the Interphalangeal Joints

Dynamic finger joint motion is difficult to measure using optical motion analysis techniques due to the limited surface

area allowed for adequate marker placement. This paper describes an extension of a previously validated kinematic

measurement technique using a reduced surface marker set and outlines the required calculations based on a specific

surface marker placement to calculate flexion/extension and hyperextension of the metacarpophalangeal, proximal

interphalangeal, and distal interphalangeal joints. The modified technique has been assessed for accuracy using a

series of static reference frames (absolute residual error = ±3.7◦, cross correlation between new method and reference

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frames; r = 0.99). The method was then applied to a small group of participantswith rheumatoid arthritis (seven females,

one male; mean age = 62.8 years ± 12.04) and illustrated congruent strategies of movement for a participant and a large

range of finger joint movement over the sample (5.8–71.1◦, smallest to largest active range of motion). This method used

alongside the previous paper [1] provides a comprehensive, validated method for calculating 3-D wrist, hand, fingers,

and thumb kinematics to date and provides a valuable measurement tool for clinical research.

54 MRI-Compatible Intensity-Modulated Force Sensor for Cardiac Catheterization Procedures

This paper presents a novel, magnetic resonance imaging (MRI)-compatible, force sensor suitable for cardiac

catheterization procedures. The miniature, fiber-optic sensor is integrated with the tip of a catheter to allow the detection

of interaction forces with the cardiac walls. The optical fiber light intensity is modulated when a force acting at the

catheter tip deforms an elastic element, which, in turn, varies the distance between a reflector and the optical fiber. The

tip sensor has an external diameter of 9 Fr (3mm) and can be used during cardiac catheterization procedures. The

sensor is able to measure forces in the range of 0–0.85 N, with relatively small hysteresis. A nonlinear method for

calibration is used and real-time MRI in vivo experiments are carried out, to prove the feasibility of this low-cost sensor,

enabling the detection of catheter-tip contact forces under dynamic conditions.

55 Multiclass Filters by a Weighted Pairwise Criterion for EEG Single-Trial Classification

The filtering technique for dimensionality reduction of multichannel electroencephalogram (EEG) recordings,modeled

using common spatial patterns and its variants, is commonly used in two-class brain–computer interfaces (BCI). For a

multiclass problem, the optimization of certain separability criteria in the output space is not directly related to the

classification error of EEG single-trial segments. In this paper, we derive a new discriminant criterion, termed weighted

pairwise criterion (WPC), for optimizing multiclass filters by minimizing the upper bound of the Bayesian error that is

intentionally formulated for classifying EEG single-trial segments. The WPC approach pays more attention to close class

pairs that are more likely to be misclassified than far away class pairs that are already well separated. Moreover, we

extend WPC by integrating temporal information of EEG series. Computationally, we employ the rank-one update and

power iteration technique to optimize the proposed discriminant criterion. The experiments of multiclass classification

on the datasets of BCI competitions demonstrate the efficacy of the proposed method.

56 Multiple-Object 2-D–3-D Registration for Noninvasive Pose Identification of Fracture Fragments

This paper presents a multiple-object 2-D–3-D registration technique for noninvasively identifying the poses of fracture

fragments in the space of a preoperative treatment plan. The plan is made by manipulating and aligning computer

models of individual fracture fragments that are segmented from a diagnostic computed tomography. The registration

technique iteratively updates the treatment plan and matches its digitally reconstructed radiographs to a small number

of intraoperative fluoroscopic images. The proposed approach combines an image similarity metric that integrates edge

information with mutual information, and a global–local optimization scheme, to deal with challenges associated with

the registration of multiple small fragments and limited imaging orientations in the operating room. The method is easy

to use as minimum user interaction is required. Experiments on simulated fractures and two distal radius fracture

phantoms demonstrate clinically acceptable target registration errors with capture range as large as 10 mm.

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57 Neural Control of Posture During Small Magnitude Perturbations: Effects of Aging and Localized Muscle Fatigue

This study investigated the effects of aging and localized muscle fatigue on the neural control of upright stance during

small postural perturbations. Sixteen young (aged 18–24 years) and 16 older (aged 55–74 years) participants were

exposed to small magnitude, anteriorly-directed postural perturbations before and after fatiguing exercises (lumbar

extensors and ankle plantar flexors). A single degree of freedom model of the human body was used to simulate

recovery kinematics following the perturbations. Central to the model was a simulated neural controller that multiplied

time-delayed kinematics by invariant feedback gains. Feedback gains and time delay were optimized for each participant

based on measured kinematics, and a novel delay margin analysis was performed to assess system robustness. A

10.9% longer effective time delay (p = 0.010) was found among the older group, who also showed a greater reliance upon

velocity feedback information (31.1% higher differential gain, p = 0.001) to control upright stance. Based on delay

margins, older participants adopted a more robust control scheme to accommodate the small perturbations, potentially

compensating for longer time delays or degraded sensory feedback. No fatigue-induced changes in neural controller

gains, time delay, or delay margin were found in either age group, indicating that integration of this feedback information

was not altered by muscle fatigue. The sensitivity of this approach to changes with fatigue may have been limited by

model simplifications.

58 Novel Dry Polymer Foam Electrodes for Long-Term EEG Measurement

A novel dry foam-based electrode for long-term EEG measurement was proposed in this study. In general, the

conventional wet electrodes are most frequently used for EEG measurement. However, they require skin preparation and

conduction gels to reduce the skin–electrode contact impedance. The aforementioned procedures when wet electrodes

were used usually make trouble to users easily. In order to overcome the aforesaid issues, a novel dry foam electrode,

fabricated by electrically conductive polymer foam covered by a conductive fabric, was proposed. By using conductive

fabric, which provides partly polarizable electric characteristic, our dry foam electrode exhibits both polarization and

conductivity, and can be used to measure biopotentials without skin preparation and conduction gel. In addition, the

foam substrate of our dry electrode allows a high geometric conformity between the electrode and irregular scalp

surface to maintain low skin–electrode interface impedance, even under motion. The experimental results presented that

the dry foam electrode performs better for long-termEEGmeasurement, and is practicable for daily life applications.

59 On Decomposing Stimulus and Response Waveforms in Event-Related Potentials Recordings

Event-related potentials (ERPs) reflect the brain activities related to specific behavioral events, and are obtained by

averaging across many trial repetitions with individual trials aligned to the onset of a specific event, e.g., the onset of

stimulus (s-aligned) or the onset of the behavioral response (r-aligned). However, the s-aligned and r-aligned ERP

waveforms do not purely reflect, respectively, underlying stimulus (S-) or response (R-) component waveform, due to

their cross-contaminations in the recorded ERP waveforms. Zhang [J. Neurosci. Methods, 80, pp. 49–63, 1998] proposed

an algorithm to recover the pure S-component waveform and the pure R-component waveform from the s-aligned and r-

aligned ERP average waveforms—however, due to the nature of this inverse problem, a direct solution is sensitive to

noise that disproportionally affects low-frequency components, hindering the practical implementation of this algorithm.

Here, we apply the Wiener deconvolution technique to deal with noise in input data, and investigate a Tikhonov

regularization approach to obtain a stable solution that is robust against variances in the sampling of reaction-time

distribution (when number of trials is low). Our method is demonstrated using data from a Go/NoGo experiment about

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image classification and recognition.was not altered by muscle fatigue. The sensitivity of this approach to changes with

fatigue may have been limited by model simplifications.

60 Open- and Closed-Loop Multiobjective Optimal Strategies for HIV Therapy Using NSGA-II

In this paper, multiobjective open- and closed-loop optimal treatment strategies for HIV/AIDS are presented. It is

assumed that highly active antiretroviral therapy is available for treatment of HIV infection. Amount of drug usage and

the quality of treatment are defined as two objectives of a biobjective optimization problem, and Nondominated Sorting

Genetic Algorithm II is used to solve this problem. Open- and closed-loop control strategies are used to produce optimal

control inputs, and the Pareto frontiers obtained from these two strategies are compared. Pareto frontier, resulted from

the optimization process, suggests a set of treatment strategies, which all are optimal from a perspective, and can be

used in different medical and economic conditions. Robustness of closed-loop system in the presence of measurement

noises is analyzed, assuming various levels of noise

.61 Optimizing the Channel Selection and Classification Accuracy in EEG-Based BCI

Multichannel EEG is generally used in brain– computer interfaces (BCIs), whereby performing EEG channel selection 1)

improves BCI performance by removing irrelevant or noisy channels and 2) enhances user convenience from the use of

lesser channels. This paper proposes a novel sparse common spatial pattern (SCSP) algorithm for EEG channel

selection. The proposed SCSP algorithm is formulated as an optimization problem to select the least number of

channels within a constraint of classification accuracy. As such, the proposed approach can be customized to yield the

best classification accuracy by removing the noisy and irrelevant channels, or retain the least number of channels

without compromising the classification accuracy obtained by using all the channels. The proposed SCSP algorithm is

evaluated using two motor imagery datasets, one with a moderate number of channels and another with a large number

of channels. In both datasets, the proposed SCSP channel selection significantly reduced the number of channels, and

outperformed existing channel selection methods based on Fisher criterion, mutual information, support vector

machine, common spatial pattern, and regularized common spatial pattern in classification accuracy. The proposed

SCSP algorithm also yielded an average improvement of 10% in classification accuracy compared to the use of three

channels (C3, C4, and Cz).

62 Prediction of Biomechanical Properties of Trabecular Bone in MR Images With Geometric Features and Support Vector Regression

Whole knee joint MR image datasets were used to compare the performance of geometric trabecular bone features and

advanced machine learning techniques in predicting biomechanical strength propertiesmeasured on the corresponding

ex vivo specimens. Changes of trabecular bone structure throughout the proximal tibia are indicative of several

musculoskeletal disorders involving changes in the bone quality and the surrounding soft tissue. Recent studies have

shown that MR imaging also allows non-invasive 3-D characterization of bone microstructure. Sophisticated features

like the scaling index method (SIM) can estimate local structural and geometric properties of the trabecular bone and

may improve the ability of MR imaging to determine local bone quality in vivo. A set of 67 bone cubes was extracted

from knee specimens and their biomechanical strength estimated by the yield stress (YS) [inMPa] was determined

throughmechanical testing. The regional apparent bone volume fraction (BVF) and SIM derived features were calculated

for each bone cube. A linear multiregression analysis (MultiReg) and a optimized support vector regression (SVR)

algorithm were used to predict the YS from the image features. The prediction accuracy was measured by the root

mean square error (RMSE) for each image feature on independent test sets. The best prediction result with the lowest

prediction error of RMSE = 1.021 MPa was obtained with a combination of BVF and SIM features and by using SVR. The

prediction accuracy with only SIM features and SVR (RMSE= 1.023 MPa) was still significantly better than BVF alone

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and MultiReg (RMSE=1.073MPa). The current study demonstrates that the combination of sophisticated bone structure

features and supervised learning techniques can improve MR-based determination of trabecular bone quality.

.63 Quantification of Restitution Dispersion From the Dynamic Changes of the T-Wave Peak to End, Measured at the Surface ECG

Action potential duration restitution (APDR) curves present spatial variations due to the electrophysiological

heterogeneities present in the heart. Enhanced spatial APDR dispersion in ventricle has been suggested as an

arrhythmic risk marker. In this study, we propose a method to noninvasively quantify dispersion of APDR slopes at

tissue level by making only use of the surface electrocardiogram (ECG). The proposed estimate accounts for rate

normalized differences in the steady-state T -wave peak to T -wave end interval (Tpe). A methodology is developed for its

computation, which includes compensation for the Tpe memory lag after heart-rate (HR) changes. The capability of the

proposed estimate to reflect APDR dispersion is assessed using a combination of ECG signal processing, and

computational modeling and simulation. Specifically, ECG recordings of control subjects undergoing a tilt test trial are

used to measure that estimate, while its capability to provide a quantification of APDR dispersion at tissue level is

assessed by using a 2-D ventricular tissue simulation. From this simulation, APDR dispersion, denoted as ∆αSIM, is

calculated, and pseudo-ECGs are derived. Estimates of APDR dispersion measured from the pseudo-ECGs show to

correlate with∆αSIM, being the mean relative error below 5%. A comparison of the ECGestimates obtained fromtilt test

recordings and the ∆αSIM values measured in silico simulations at tissue level show that differences between them are

below 20%, which is within physiological variability limits. Our results provide evidence that the proposed estimate is a

noninvasive measurement of APDR dispersion in ventricle. Additional results fromthis study confirm that Tpe adapts to

HR changes much faster than the QT interval.

65 Quasi-Monte Carlo Estimation Approach for Denoising MRI Data Based on Regional Statistics

An important post processing step for MR data is noise reduction. Noise in MR data is difficult to suppress due to its

signal-dependence. To address this issue, a novel stochastic approach to noise reduction for MR data is presented. The

estimation of the noise-free signal is formulated as a general Bayesian least-squares estimation problem and solved

using a quasi-Monte Carlo method that takes into account the statistical characteristics of the underlying noise and the

regional statistics of the observed signal in a data-adaptive manner. A set of experiments were performed to compare

the proposed quasi-Monte Carlo estimation (QMCE) method to state-of-the-art wavelet-based MR noise reduction

(WAVE) and nonlocal means MR noise reduction (NLM) methods using MR data volumes with synthetic noise, as well as

real noise-contaminated MR data. Experimental results show that QMCE is capable of achieving state-of-the-art

performance when compared to WAVE and NLM methods quantitatively in SNR, mean structural similarity (MSSIM), and

contrast measures. Visual comparisons show that QMCE provides effective noise suppression, while better preserving

tissue structural boundaries and restoring contrast..

66 Rayleigh Mixture Model for Plaque Characterization in Intravascular Ultrasound

Vulnerable plaques are the major cause of carotid and coronary vascular problems, such as heart attack or stroke. A

correct modeling of plaque echomorphology and composition can help the identification of such lesions. The Rayleigh

distribution is widely used to describe (nearly) homogeneous areas in ultrasound images. Since plaques may contain

tissues with heterogeneous regions, more complex distributions depending on multiple parameters are usually needed,

such as Rice, K or Nakagami distributions. In such cases, the problem formulation becomesmore complex, and the

optimization procedure to estimate the plaque echomorphology is more difficult. Here, we propose to model the tissue

echomorphology by means of a mixture of Rayleigh distributions, known as the Rayleigh mixture model (RMM). The

problem formulation is still simple, but its ability to describe complex textural patterns is very powerful. In this paper, we

present a method for the automatic estimation of the RMM mixture parameters by means of the expectation

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maximization algorithm, which aims at characterizing tissue echomorphology in ultrasound (US). The performance of

the proposed model is evaluated with a database of in vitro intravascular US cases. We show that the mixture

coefficients and Rayleigh parameters explicitly derived from the mixture model are able to accurately describe different

plaque types and to significantly improve the characterization performance of an already existing methodology.

67 Real-Time Multispectral Imager for Home-Based Health Care

Multispectral imaging (MSI) is becoming a powerful tool for tissue abnormality detection. Conventional MSI systems,

however, are not readily suitable for challenges of routine clinical uses due to the fact that they are expensive, bulky,

and time consuming to acquire the data. In this letter we report a novel approach to instrument MSI technology into a

handheld, low-cost, standing alone, real-time operational device that is suitable for home-based health care. It covers

techniques used to produce multiple images at discrete signature wavelengths of tissues with a single shot.

68 Recognizing Architectural Distortion in Mammogram: A Multiscale Texture Modeling Approach with GMM

We propose a generative model for constructing an efficient set of distinctive textures for recognizing architectural

distortion in digital mammograms. In the first layer of the proposed two-layer architecture, the mammogram is analyzed

by a multiscale oriented filter bank to form texture descriptor of vectorized filter responses. Our model presumes that

every mammogram can be characterized by a “bag of primitive texture patterns” and the set of textural primitives (or

textons) is represented by a mixture of Gaussians which builds up the second layer of the proposed model. The

observed textural descriptor in the first layer is assumed to be a stochastic realization of one (hardmapping) ormore

(soft mapping) textural primitive(s) from the second layer. The results obtained on two publicly available datasets,

namely Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM),

demonstrate the efficacy of the proposed approach.

69 Reduced Conductivity Dependence Method for Increase of Dipole Localization Accuracy in the EEG Inverse Problem

The EEG is a neurological diagnostic tool with high temporal resolution. However, when solving the EEG inverse

problem, its localization accuracy is limited because of noise in measurements and available uncertainties of the

conductivity value in the forward model evaluations. This paper proposes the reduced conductivity dependence (RCD)

method for decreasing the localization error in EEG source analysis by limiting the propagation of the uncertain

conductivity values to the solutions of the inverse problem. We redefine the traditional EEG cost function, and in

contrast to previous approaches, we introduce a selection procedure of the EEG potentials. The selected potentials are,

as low as possible, affected by the uncertainties of the conductivity when solving the inverse problem. We validate the

methodology on the widely used three-shell spherical headmodel with a single electrical dipole and multiple dipoles as

source model. The proposed RCD method enhances the source localization accuracy with a factor ranging between 2

and 4, dependent on the dipole location and the noise in measurements

70 Region-Based Current-Source Reconstruction for the Inverse EEG Problem

This paper presents a new method for the reconstruction of current sources for the electroencephalography (EEG)

inverse problem, which produces reconstructed sources, which are confined to a few anatomical regions. The method is

based on a partition of the gray matter into a set of regions, and in the construction of a simple linear model for the

potential produced by feasible source configurations inside each one of these regions. The proposed method computes

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the solution in two stages: in the first one, a subset of active regions is found so that the combined potentials produced

by sources inside them approximate the measured potential data. In the second stage, a detailed reconstruction of the

current sources inside each active region is performed. Experimental results with synthetic data are presented, which

show that the proposed scheme is fast, computationally efficient and robust to noise, producing results that are

competitive with other published methods, especially when the current sources are effectively distributed in few

anatomical regions. The proposed method is also validated with real data from an experiment with visual evoked

potentials.

71 Registration Under Topological Change for CT Colonography

Computed tomography (CT) colonography is a minimally invasive screening technique for colorectal polyps, in which X-

ray CT images of the distended colon are acquired, usually in the prone and supine positions of a single patient.

Registration of segmented colon images from both positions will be useful for computer-assisted polyp detection. We

have previously presented algorithms for registration of the prone and supine colons when both are well distended and

there is a single connected lumen. However, due to inadequate bowel preparation or peristalsis, theremay be collapsed

segments in one or both of the colon images resulting in a topological change in the images. Such changes make

deformable registration of the colon images difficult, and at present, there are no registration algorithms that can

accommodate them. In this paper, we present an algorithm that can perform volume registration of prone/supine colon

images in the presence of a topological change. For this purpose, 3-D volume images are embedded as a manifold in a

4-D space, and the manifold is evolved for nonrigid registration. Experiments using data from 24 patients show that the

proposed method achieves good registration results in both the shape alignment of topologically different colon images

from a single patient and the polyp location estimation between supine and prone colon images.

72 Remote Health Monitoring of Heart Failure With Data Mining via CART Method on HRV Features

Disease management programs, which use no advanced information and computer technology, are as effective as

telemedicine but more efficient because less costly. We proposed a platform to enhance effectiveness and efficiency of

home monitoring using data mining for early detection of any worsening in patient’s condition. These worsenings could

require more complex and expensive care if not recognized. In this letter, we briefly describe the remote health

monitoring platform we designed and realized, which supports heart failure (HF) severity assessment offering functions

of data mining based on the classification and regression tree method. The system developed achieved accuracy and a

precision of 96.39% and 100.00% in detecting HF and of 79.31% and 82.35% in distinguishing severe versus mild HF,

respectively. These preliminary results were achieved on public databases of signals to improve their reproducibility.

Clinical trials involving local patients are still running and will require longer experimentation.

73 Remote Sensing of Heart Rate and Patterns of Respiration on a Stationary Subject Using 94-GHz Millimeter-Wave Interferometry

Using continuous wave, 94-GHz millimeter-wave interferometry, a signal representing chest wall motion can be obtained

that contains both the heart rate and respiration patterns of a human subject.These components have to be separated

fromeach other in the received signal. Our method was to use the quadrature and in-phase components of the signal,

after removing the mean of each, to find the phase, unwrap it, and convert it to a displacement measurement. Using this,

the power spectrum was examined for peaks, which corresponded to the heart rate and respiration rate. The

displacement waveform of the chest was also analyzed for discrete heartbeats using a novel wavelet decomposition

technique.

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74 Retinal Image Analysis Using Curvelet Transform and Multistructure Elements Morphology by Reconstruction

Retinal images can be used in several applications, such as ocular fundus operations as well as human recognition.

Also, they play important roles in detection of some diseases in early stages, such as diabetes, which can be performed

by comparison of the states of retinal blood vessels. Intrinsic characteristics of retinal images make the blood vessel

detection process difficult. Here, we proposed a new algorithm to detect the retinal blood vessels effectively. Due to the

high ability of the curvelet transform in representing the edges, modification of curvelet transform coefficients to

enhance the retinal image edges better prepares the image for the segmentation part. The directionality feature of the

multistructure elements method makes it an effective tool in edge detection. Hence, morphology operators using

multistructure elements are applied to the enhanced image in order to find the retinal image ridges. Afterward,

morphological operators by reconstruction eliminate the ridges not belonging to the vessel tree while trying to preserve

the thin vessels unchanged. In order to increase the efficiency of the morphological operators by reconstruction, they

were applied using multistructure elements. A simple thresholding method alongwith connected components analysis

(CCA) indicates the remained ridges belonging to vessels. In order to utilize CCA more efficiently, we locally applied the

CCA and length filtering instead of considering the whole image. Experimental results on a known database, DRIVE, and

achieving to more than 94% accuracy in about 50 s for blood vessel detection, proved that the blood vessels can be

effectively detected by applying our method on the retinal images.

75 Revealing Action Representation Processes in Audio Perception Using Fractal EEG Analysis

Electroencephalogram (EEG) recordings, and especially the Mu-rhythm over the sensorimotor cortex that relates to the

activation of the mirror neuron system (MNS), were acquired from two subject groups (orchestral musicians and

nonmusicians), in order to explore action representation processes involved in the perception and performance of

musical pieces. Two types of stimuli were used, i.e., an auditory one consisting of an excerpt of Beethoven’s fifth

symphony and a visual one presenting a conductor directing an orchestra performing the same excerpt of the piece.

Three tasks were conducted including auditory stimulation, audiovisual stimulation, and visual stimulation only, and the

acquired signals were processed using fractal [time-dependent fractal dimension (FD) estimation] and statistical

analysis (analysis of variance, Mann-Whitney). Experimental results showed significant differences between the two

groups while desychronization of the Mu-rhythm, which can be linked to MNS activation, was observed during all tasks

for the musicians’ group, as opposed to the nonmusicians’ group who exhibited similar response onlywhen the visual

stimulus was present. The mobility of the conductor was also correlated to the estimated FD signals, showing

significantly higher correlation for the case of musicians compared to nonmusicians’ one. The present study sheds light

upon the difference in action representation in auditory perception between musicians and nonmusicians and paves the

way for better comprehension of the underlying mechanisms of the MNS.

76 Selective Classification for Improved Robustness of Myoelectric Control Under Nonideal Conditions

Recent literature in pattern recognition-based myoelectric control has highlighted a disparity between classification

accuracy and the usability of upper limb prostheses. This paper suggests that the conventionally defined classification

accuracy may be idealistic and may not reflect true clinical performance. Herein, a novel myoelectric control system

based on a selective multiclass one-versus-one classification scheme, capable of rejecting unknown data patterns, is

introduced. This scheme is shown to outperform nine other popular classifiers when compared using conventional

classification accuracy as well as a form of leave-oneout analysis thatmay be more representative of real prosthetic use.

Additionally, the classification scheme allows for real-time, independent adjustment of individual class-pair boundaries

making it flexible and intuitive for clinical use.

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77 Single-Trial Subspace-Based Approach for VEP Extraction

A signal subspace approach for extracting visual evoked potentials (VEPs) from the background electroencephalogram

(EEG) colored noise without the need for a prewhitening stage is proposed.Linear estimation of the clean signal is

performed by minimizing signal distortion while maintaining the residual noise energy below some given threshold. The

generalized eigendecomposition of the covariance matrices of a VEP signal and brain background EEG noise is used to

transform them jointly to diagonal matrices. The generalized subspace is then decomposed into signal subspace and

noise subspace. Enhancement is performed by nulling the components in the noise subspace and retaining the

components in the signal subspace. The performance of the proposed algorithm is tested with simulated and real data,

and compared with the recently proposed signal subspace techniques. With the simulated data, the algorithms are used

to estimate the latencies of P100, P200, and P300 of VEP signals corrupted by additive colored noise at different values

of SNR. With the real data, the VEP signals are collected at Selayang Hospital, Kuala Lumpur, Malaysia, and the

capability of the proposed algorithm in detecting the latency of P100 is obtained and compared with other subspace

techniques. The ensemble averaging technique is used as a baseline for this comparison. The results indicated

significant improvement by the proposed technique in terms of better accuracy and less failure rate..

78 Temporal Sequence Parameters in Isodistributional Surrogate Data: Model and Exact Expressions

In this paper, a set of formulae for the temporal spontaneous baroreceptor reflex (sBRR) sequence parameters in isodis

tributional (ID) surrogate data is derived. This is facilitated by representing successive positive or negative amplitude

changes as a Markov chain model. The obtained analytical tool measures the effect of random fluctuations on the overall

number of sequences, estimated from the original biomedical time series. The formulae are tested using ID surrogates of

systolic blood pressure and pulse-interval signals recorded from 13 healthy male Wistar rats at baseline conditions.

79 Termination of Reentrant Cardiac Action Potential Propagation Using Far-Field Electrical Pacing

Several different types of rapid cardiac rhythm disorders, including atrial and ventricular fibrillation, are likely caused by

multiple, rapidly rotating, action potential (AP) waves. Thus, an electrical pacing therapy, whose effectiveness is based

on being delivered with a particular timing relative to one of these waves, is unlikely to be useful in terminating the

remaining waves. Here, we develop pacing protocols that are designed to terminate rotating waves independently of

when the sequences of stimuli are imposed or where each wave is in its rotation at the time the sequences are initiated.

These protocols are delivered as far-field stimuli, and therefore are capable of simultaneously influencing all the waves

present. The pacing intervals for these protocols are, in general, of unequal duration and are determined through

examination of the dynamics of AP propagation in a 1-D ring model. Series of two or three stimuli with interstimulus

intervals chosen in this way are shown to be effective in terminating these waves over a wide range of ring

circumferences and AP dynamical parameters. Stimulus sequences of this type may form the basis for developing new

defibrillation protocols to test in experiments ormore realisticmodels of the electrical heart

.80 Textureless Macula Swelling Detection With Multiple Retinal Fundus Images

Retinal fundus images acquired with nonmydriatic digital fundus cameras are versatile tools for the diagnosis of various

retinal diseases. Because of the ease of use of newer camera models and their relatively lowcost, these cameras can be

employed by operators with limited training for telemedicine or point-of-care (PoC) applications. We propose a novel

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technique that uses uncalibrated multiple-view fundus images to analyze the swelling of the macula. This innovation

enables the detection and quantitative measurement of swollen areas by remote ophthalmologists. This capability is not

available with a single image and prone to error with stereo fundus cameras.We also present automatic algorithms to

measure features fromthe reconstructed image,which are useful in PoC automated diagnosis of early macular edema,

e.g., before the appearance of exudation. The technique presented is divided into three parts: first, a preprocessing

technique simultaneously enhances the dark microstructures of the macula and equalizes the image; second, all

available views are registered using nonmorphological sparse features; finally, a dense pyramidal optical flow is

calculated for all the images and statistically combined to build a naive height map of the macula. Results are presented

on three sets of synthetic images and two sets of real-world images. These preliminary tests show the ability to infer a

minimum swelling of 300 µm and to correlate the reconstruction with the swollen location.

81 The Effect of Anthropometric Variations on Acoustical Flow Estimation: Proposing a Novel Approach Flow Estimation Need for Individual

Calibration

Tracheal sound average power is directly related to the breathing flow rate and recently it has attracted considerable

attention for acoustical flow estimation. However, the flow–sound relationship is highly variable among people and it

also changes for the same person at different flow rates. Hence, a robust model capable of estimating flow from tracheal

sounds at different flow rates in a large group of individuals does not exist. In this paper, a model is proposed to

estimate respiratory flow from tracheal sounds. The proposed model eliminates the dependence of the previous

methods on calibrating the model for every individual and at different flow rates. To validate the model, it was applied to

the respiratory sound and flow data of 93 healthy individuals. We investigated the statistical correlation between the

model parameters and anthropometric features of the subjects. The results have shown that gender, height, and

smoking are the most significant factors that affect the model parameters. Hence, we grouped nonsmoker subjects into

four groups based on their gender and height. The average of model parameters in each group was defined as the

group-calibrated model parameters. These models were applied to estimate flow from data of subjects within the same

group and in the other groups. The results show that flow estimation error based on the group-calibrated model is less

than 10%. The low estimation errors confirm the possibility of defining a general flow estimation model for subjectswith

similar anthropometric features with no need for calibrating the model parameters for every individual. This technique

simplifies the acoustical flow estimation in general applications including sleep studies and patients’ screening in health

care facilities.

.82 The Inverse Problem Utilizing the Boundary Element Method for a Nonstandard Female Torso

This paper proposes a new method of rapidly deriving the transfer matrix for the boundary element method (BEM)

forward problem from a tailored female torso geometry in the clinical setting. The method allows rapid calculation of

epicardial potentials (EP) from body surface potentials (BSP). The use of EPs in previous studies has been shown to

improve the successful detection of the life-threatening cardiac condition—acute myocardial infarction.TheMRIscanning

of a cardiac patient in the clinical setting is not practical and other methods are required to accurately deduce torso

geometries for calculation of the transfer matrix. The new method allows the noninvasive calculation of tailored torso

geometries from a standard female torso and five measurements taken from the body surface of a patient. This scaling

of the torso has been successfully validated by carrying out EP calculations on 40 scaled torsos and ten female

subjects. It utilizes the BEM in the calculation of the transfer matrix as the BEM depends only upon the topology of the

surfaces of the torso and the heart, the former can now be accurately deduced, leaving only the latter geometry as an

unknown.

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83 Time-Multiplexed Beamforming for Noninvasive Microwave Hyperthermia Treatment

A noninvasive microwave beamforming strategy is proposed for selective localized heating of biological tissue. The

proposed technique is based on time multiplexing of multiple beamformers. We investigate the effectiveness of the time-

multiplexed beamforming in the context of brain hyperthermia treatment by using a high-fidelity numerical head

phantom of an adult female from the Virtual Family (IT’IS Foundation) as our testbed. An operating frequency of 1 GHz is

considered to balance the improved treatment resolution afforded by higher frequencies against the increased

penetration through the brain afforded by lower frequencies. The exact head geometry and dielectric properties of

biological tissues in the head are assumed to be available for the creation of patient-specific propagation models used

in beamformer design. Electromagnetic and thermal simulations based on the finitedifference time-domain method are

used to evaluate the hyperthermia performance of time-multiplexed beamforming and conventional beamforming

strategies.The proposed time-multiplexing technique is shown to reduce the unintended heating of healthy tissue

without affecting the treatment temperature or volume. The efficacy of the method is demonstrated for target locations in

three different regions of the brain. This approach has the potential to improve microwave-induced localized heating for

cancer treatment via hyperthermia or heat-activated chemotherapeutic drug release.

.84 Towards Brain First-Aid: A Diagnostic Device for Conscious Awareness

When the brain is damaged, evaluating an individual’s level of awareness can be a major diagnostic challenge (Is he or

she in there?). Existing tests typically rely on behavioral indicators, which are incorrect in as many as one out of every

two cases. The current paper presents a diagnostic device that addresses this problem. The technology circumvents

behavioral limitations through noninvasive brain wave measurements (electroencephalography, or EEG). Unlike

traditional EEG, the device is designed for point-of-care use by incorporating a portable, userfriendly, and stable design.

It uses a novel software algorithm that automates subject stimulation, data acquisition/analysis, and the reporting of

results.The test provides indicators for five identifiable levels of neural processing: sensation, perception, attention,

memory, and language. The results are provided as rapidly obtained diagnostic, reliability, validity, and prognostic

scores. The device can be applied to awide variety of patients across a host of different environments. The technology is

designed to be wireless-enabled for remote monitoring and assessment capabilities. In essence, the device is developed

to scan for conscious awareness in order to optimize subsequent patient care.

85 Tracking Biological Cells in Time-Lapse Microscopy: An Adaptive Technique Combining Motion and Topological Features

This paper presents a vision-based method for automatic tracking of biological cells in time-lapse microscopy by

combining themotion features with the topological features of the cells. The automation of tracking frequently faces

problems of segmentation error and of finding correct cell correspondence in consecutive frames, since the cells are of

varying size and shape, and may have uneven movement; these problems become more acute when the cell population

is very high. To reduce the segmentation error, we introduce a cell-detection method based on h-maxima transformation,

followed by the fitting of an ellipse for the nucleus shape. To find the correct correspondence between the detected

cells, the topological features, namely, color compatibility, area overlap and deformation are combined with the motion

features of skewness and displacement. This reduces the ambiguity of matching and constructs accurately the

trajectories of the cell proliferation. Finally, a template-matching-based backward tracking procedure is employed to

recover any break in a cell trajectory that may occur due to the segmentation errors or the presence of a mitosis. The

tracking procedure is tested using a number of different cell sequences with nonuniform illumination, or uneven cell

motion, and is shown to provide high accuracy both in the detection and the tracking of the cells.

26



Madurai




Contact : 91452 4390702, 4392702, 4394702.


Trichy


3rd

Floor,SI Towers,



Contact : 91431 - 4002234.


Kollam




Contact : 91474 2723622.


.86 Tracking of Vessels in Intra-Operative Microscope Video Sequences for Cortical Displacement Estimation

This article presents a method designed to automatically track cortical vessels in intra-operative microscope video

sequences. The main application of this method is the estimation of cortical displacement that occurs during tumor

resection procedures. The method works in three steps. First, models of vessels selected in the first frame of the

sequence are built. These models are then used to track vessels across frames in the video sequence. Finally,

displacements estimated using the vessels are extrapolated to the entire image. The method has been tested

retrospectively on images simulating large displacement, tumor resection, and partial occlusion by surgical instruments

and on 21 video sequences comprising several thousand frames acquired from three patients. Qualitative results show

that the method is accurate, robust to the appearance and disappearance of surgical instruments, and capable of

dealing with large differences in images caused by resection. Quantitative results show a mean vessel tracking error

(VTE) of 2.4 pixels (0.3 or 0.6 mm, depending on the spatial resolution of the images) and an average target registration

error (TRE) of 3.3 pixels (0.4 or 0.8 mm).

87 Transcranial Direct Current Stimulation: Estimation of the Electric Field and of the Current Density in an Anatomical Human Head

Model

This paper investigates the spatial distribution of the electric field and of the current density in the brain tissues induced

by transcranial direct current stimulation of the primary motor cortex. A numericalmethod was applied on a realistic

human head model to calculate these field distributions in different brain structures, such as the cortex, the white

matter, the cerebellum, the hippocampus, the medulla oblongata, the pons, the midbrain, and the thalamus. The

influence of varying the anode area, the cathode area, and the injected current was also investigated. An electrode area

as the one typically used in clinical practice (i.e., both electrodes equal to35 cm2 ) resulted into complex and diffuse

amplitude distributions over all the examined brain structures, with the region of maximum induced field being below or

close to the anode. Variations in either the anode or cathode area corresponded to changes in the field amplitude

distribution in all the brain tissues, with the former variation producing more diffuse effects. Variations in the injected

current resulted, as could be expected, in linearly correlated changes in the field amplitudes.

.88 Unconstrained Estimation Method of Delta-Wave Percentage Included in EEG of Sleeping Subjects

This paper describes an unconstrained pneumatic method of estimating the δ-wave activity of the brain from the

heartbeat signal. Based on experiments showing that transinformation of the heartbeat signal corrupted by body

movement was correlated with the δ-wave activity, we developed a method of estimating the percentage of the δ-wave

included in the EEG from the transinformation. The comb filtering technique was used to obtain accurate

transinformation. We applied the proposed method to young normal subjects to evaluate the method. As a result, the

correlation between the δ-wave included in the EEG and the transinformation was 0.727 and the average error of the

estimates of δ-wave percentage was 14.9%. The δ-wave activity and heartbeat activity were shown to be quantitatively

related. This suggests that sleep depth can be estimated from the δ-wave percentage estimated by unconstrained

measurement of the heartbeat signal of young normal subjects.

89 Wireless Implantable Electronic Platform for Chronic Fluorescent-Based Biosensors

The development of a long-term wireless implantable biosensor based on fluorescence intensity measurement poses a

number of technical challenges, ranging from biocompatibility to sensor stability over time. One of these challenges is

27



Madurai




Contact : 91452 4390702, 4392702, 4394702.


Trichy


3rd

Floor,SI Towers,



Contact : 91431 - 4002234.


Kollam




Contact : 91474 2723622.


the design of a power efficient and miniaturized electronics, enabling the biosensor tomove frombench testing to long

term validation, up to its final application in human beings. In this spirit, we present a wireless programmable electronic

platform for implantable chronic monitoring of fluorescent-based autonomous biosensors. This system is able to

achieve extremely low power operation with bidirectional telemetry, based on the IEEE802.15.4-2003 protocol, thus

enabling over three-year battery lifetime and wireless networking of multiple sensors. During the performance of single

fluorescent-based sensor measurements, the circuit drives a laser diode, for sensor excitation, and acquires the

amplified signals from four different photodetectors. In vitro functionality was preliminarily tested for both glucose and

calcium monitoring, simply by changing the analyte-binding protein of the biosensor. Electronics performance was

assessed in terms of timing, power consumption, tissue exposure to electromagnetic fields, and in vivo wireless

connectivity. The final goal of the presented platform is to be integrated in a complete system for blood glucose level

monitoring that may be implanted for at least one year under the skin of diabetic patients. Results reported in this paper

may be applied to a wide variety of biosensors based on fluorescence intensity measurement.

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IEEE Final Year Projects 2011-2012 :: Elysium Technologies Pvt Ltd::Biomedicalnew

Health & Medicine

Transcript of IEEE Final Year Projects 2011-2012 :: Elysium Technologies Pvt Ltd::Biomedicalnew