Matteo Pastorino - Remote daily activity of parkinson’s disease patients the akinesia assessment

Remote daily activity of Parkinson’s disease patients: the Akinesia assessment .

Matteo Pastorino Technical University of Madrid

WTHS_2011 Valencia, December 2011

WTHS_2011 | Valencia| December 2011 1

Parkinson’s Disease (PD)

Parkinson’s Disease (PD) is a degenerative, progressive disorder that affects nerve cells in deep parts of the brain called the basal ganglia and the substantia nigra.

Nerve cells in the substantia nigra produce the neurotransmitter dopamine and are responsible for relaying messages that plan and control body movement. Causes: Genetic, environmental factors …


Parkinson’s Disease (PD) - Symptoms

Movement disorders

Bradykinesia

Akinesia

Rigidity

Tremor

Dyskinesia

Freezing of Gait

Cognitive and behavioural disorders

Dementia

Depression

Hallucination

Sensory, sleep and emotional problems


Parkinson’s Disease (PD) - Akinesia

Akinesia (α a, "absence", κίνησις kinēsis, “movement") represents the most promising motor progression marker of the disease.

Characteristics: defined as absence of movement. This is a condition in which any automatic movement or action, including gestures, blinking or swallowing actions are limited and their frequency decreases, although the elemental motor functions are maintained and can be performed voluntarily. Various aspects appear to contribute to the self-initiation of movements: Causes: reduced dopaminergic input to the striatum. Such changes also cause bradykinesia, rigidity, tremor and postural instability, although the underlying mechanisms leading to these symptoms are still not understood. Treatment: dopamine precursor levodopa is the most efficient treatment for the improvement of Parkinson´s disease signs and symptoms. However, abnormal involuntary movements (dyskinesia) are motor fluctuations that occur in the majority of PD’s patients undergoing this treatment.


Parkinson’s Disease (PD) – ON\OFF

Specific Chronic Neurodegenerative Diseases Progressive loss of motion ability (due to muscle weakening)

Appearance of new motion symptoms (new muscles affected)

Inability to move (at later stages)

Parkinson’s Disease:

Progression is restricted with treatment

Daily motion status is fluctuating due to

treatment

Dyskinesia


Monitoring & Assessment TODAY

Every 4-6 months or As instructed

Patient visits Clinic

Clinician tries to Reconstruct the patient status

Throughout day and night

Clinician PERFORMs UPDRS or other tests to identify

Current patient status

Treatment Adjustment

Made from visits observations & subjective

assessment


Parkinson Disease & p-Health solutions

24h monitoring

Test Devices

Other Info

Every day

Patient at home

Treatment Adjustment

24 h objective status assessment

ALERTS!

Based on objective observation

Immediate Response


PD & p-Health solutions: OBJECTIVES

Short Term

• 24h objective assessment of patient status

• Detection of dosage wearing-off

• Adjustment of therapy according to personal characteristics and reaction • Medication schedule/dosage • Food Intake

• Detection of changes in patient reaction to therapy

• All patient info at-a-glance and detailed info one-click away

Long Term

• Objective therapy assessment

• Analysis of symptoms progression in time

• Recognition of changes in therapeutic response


PERFORM Project

This work is part of the PERFORM project, partially funded by the European Commission

under the 7th framework programme www.perform-project.eu

Consortium:


PERFORM Architecture

Monitor Patient

24h monitoring

Test Devices

Other Info

Detect & Quantify Symptoms & Gait Build

Patient Specific disease profile

Assess Disease Progress

Suggest Treatment Changes

New Treatment Regime


Central Hospital Unit

Clinician

Administrator

External Resources

Cent

ral U

nit C

omm

unic

ator

PERFORM Repository

Interoperability Manager Ce

ntra

l Uni

t Inf

orm

atio

n Ha

ndle

r

User Database Login Manager Account Manager

Alert Manager

Index of Processed Info Patient List

Clinical Decision Support Systems

Patient Modelling

Gait

On – Off

LID

Tremor

Bradykinesia

Early Wearing Off

Medication Change

Stability-Worsening

Patient Management

PERFORM Architecture

Local Base Unit

Logger

User-Hardware Interface

Fall Detector

Alert Manager

Device Controller

Test Processor

Scheduler In

form

atio

n Ha

ndle

r

Freezing of Gait

Gait

Tremor

LID

Activity

Bradykinesia Repository

Wearable Sensors

Communicator

Action Tremor

Patient GUI

Professional GUI

Frequent falls


Akinesia

Local Base Unit Processes the patient signals

acquired; Detects the targeted patient

symptoms (e.g.: tremor, levodopa induced dyskinesia, Akinesia,..).

For each symptom a dedicated submodule : Processes the relevant signals; Detects the symptom episode; the symptom episode:

according to the Unified Parkinson’s Disease Rating Scale

or Other features such as

duration, frequency, erergy and amplitude might also be provided for further clinician review and system evaluation.

Central Hospital Unit Exploits the recorded patient information in order to build a

patient symptom profile.

For each symptom produces a patient profile which describes the patient’s

common symptom features. compared with the patient symptom profile.

If significant differences are found, it might be due to two reasons: temporarily patient behaviour abnormality

or a change in the patient profile. Checks whether a substantial number of similar situations are identified for the last time period for the specific patient and if that occurs, it creates an alert.

Monitoring System

PERFORM Monitoring System

Day Monitoring wearable

Accelerometer Accelerometer

Accelerometer Accelerometer

Gyroscope/ Accelerometer

Accelerometer / Control Unit


PERFORM: Patient Interface


Interface easy to use

Look and feel of the phone dialling pad

Drag and Drop functions

Used to declare subjective estimation of Patient status

Used to receive instructions on life-style interventions (medication/food intake)

PERFORM Technological Innovation

Continuous Patient Monitoring & Assessment

Detection of all symptoms using a single and low cost

sensor setting

Early recognition of disease progression and patient

reaction changes

Assistance in patient management with expert

knowledge based systems

Prognosis of disease evolution according to patient

characteristics


PERFORM: Pilots description

Phase Characteristics # of patients Objective Pilot Sites

1 Data Collection with SHIMMERS

8 healthy + 8 PD

Design the algorithm

Madrid and Pamplona(Spain)

2 Data Collection in

a supervised environment

20 Design the algorithm and train the classifier

Pamplona(Spain) and Ioannina (Greece)

3 Data Collected in a unsupervised

environment 24 Data Collection

Pamplona(Spain) and Ioannina (Greece)

4 Data Collected in a unsupervised

environment 22 Test and Validation

of algorithms Modena (Italy)


Akinesia Algorithm and design


Different modules were created in order to detect and quantify different symptoms

AKINESIA module assesses the amount of movement of the patient in space for any given period of time.

Akinesia: Algorithm design

2 2 2x y z+ +

• Pre-processing: • Resultant Computation: eliminate position dependence

of the sensors given by: •

• Filtering: Akinesia is related with the slowness of the

movement ,therefore we are interested in the low frequencies of the signal: • Band-Pass IIR Butterworth 4th order filter [1÷3] Hz.


Output

Output

Akinesia: Algorithm design

The signal is split in 5 minutes length epochs to evaluate a considerable portion of signal. There is 50% overlapping in epochs to study the whole signal. For each epoch computes Total Amount of Energy for each working sensor.


OUTPUT: The resulting energies of each sensor are combined by using a weighted sum in order to take into account all the possible combination of sensors.

The module is able to recognize automatically the sensor’s setting

Akinesia: ON-OFF Evaluation


→ clear relationship between ON-OFF phases and the akinesia levels. → Strong correlation between the lack of movement and OFF status. →Using the akinesia is possible to discriminate ON and OFF periods in PD patients.

ON ON OFF

Akinesia: Results


The global evaluation of both scenarios demonstrates that it is possible to discriminate ON and OFF periods computing the lack of movement combining the information provided by different modules of the PERFORM system, in this case the activity recognizer and the Akinesia module.

For the analysis of the results, two different scenarios are considered.

Akinesia – NO WALKING periods mean value of the computed akinesia during the

periods when the patient is not walking.

Akinesia – WALKING periods mean value of the computed akinesia during

walking periods.

Conclusions

DATA: Recording of one patient during 4h and are focused only in the akinesia results as

discriminating parameter for the ON – OFF

FUTURE WORK: More exhaustive analysis using all the recordings collected during the pilot phases; Combining the results of all PERFORM classifier outputs Create a complete profile of patients


Useful and Objective tool for the analysis of the akinesia in PD’s patients.

Suitable for clinical practice

Support health professionals in the diagnosis and follow-up of PD patients

PD patients’ quality of life improvement

Discriminating parameter for the ON – OFF condition

Thank you!

Matteo Pastorino Universidad Politécnica de Madrid Life Supporting Technologies [email protected] Skype id: matteo_pasto


Matteo Pastorino - Remote daily activity of parkinson’s disease patients the akinesia assessment

Health & Medicine

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