Data-driven Healthcare using Affordable Sensing - Screening, Diagnosis and Therapy

Arpan PalHead, Embedded Systems and Robotics

TCS ResearchTata Consultancy Services, India

2TCS Confidential

Developed Countries

Elderly people - 44.7 M (2013), double by 2060

Invasive and costly diagnosis

One size fits all Diagnostic / Treatment protocols

Some diseases yet to have a cure

Developing Countries

Capacity - not enough doctors per patient

Reachability – specialized primary care not available

Affordability - majority cannot afford to pay the cost

http://www.aoa.acl.gov/Aging_Statistics/index.aspx

Problems of the New Age and the New World

3TCS Confidential

Next Generation Health

Keeping People Healthy

Monitoring

Alert generation

Identifying Disease Onset

Better Diagnostics

Predictive CareTreating Full Fledged Disease

Conventional and New Care Protocols

Reaction To Disease

Prevention of Disease

Disease Treatment

Promote Wellness

Now Future Improvements Across the Spectrum from Diseased to Healthy

4TCS Confidential

Application Use Cases

Accurate, Affordable, Accessible, Adaptive diagnostics & therapeutics

Screening / Diagnosis

Coronary Artery Disease (CAD)

Hypertension

Therapy

Physical rehab for Stroke Patients

Future Plan• Diabetes• Chronic Obstructive

Pulmonary Disease (COPD)• Neurorehab

HeartSense RehabBox

5TCS Confidential

CAD – also a global hazard

IHD [ischemic heart disease, also known as CAD] causes more deaths and disability and incurs greater economic costs than any other illness in the developed world. . . [and it] is likely to become the most common cause of death worldwide by 2020 - Antman et al., 2008

DoctorSpeak“Only Existing conclusive diagnostic procedures is coronary angiogram - invasive, potentially harmful, costly. It is important to diagnose this preventive condition of CAD with affordable non-invasive mass screening.”

Dr. K M Mandana - Consultant, Fortis Hospital, KolkataMCh (Cardiovascular & Thoracic Surgery), Fellow of European Board of Cardiothoracic Surgeons

CAD – Motivation

Need for Early Screening by detecting/predicting early onset of CAD

6TCS Confidential

Singapore–10, USA -14, UK - 9

Hypertension – Motivation

2011 2012 2013 2014

206 197 189 181

India – Maternal Mortality Rate per 100K Live Berths

data.worldbank.org/indicator/SH.STA.MMRT

Causes of maternal deaths 1. Direct Causes: 81%

Severe Bleeding 25%

Sepsis 15%

Unsafe abortions 13%

Eclampsia 12%

Obstructed Labour 8%

Other direct causes * 8%

2. Indirect Causes ** 19%

* Other direct causes: Ectopic pregnancy, embolism, anesthesia related.** Indirect Causes: Malaria, Anemia, Heart Diseases.

Rehana Kausar, “Maternal Mortality in India - Magnitude, Causes and Concerns”, Indian Journal for the Practising Doctor, Vol. 2, No. 2 (2005-05 - 2005-06)http://www.indmedica.com/journals.php/mailt?journalid=3&issueid=58&articleid=722&action=article

Eclampsia - a condition in which one or more convulsions occur in a pregnant woman suffering from high blood pressure, often followed by coma and posing a threat to the health of mother and baby.

Need for Early Screening of Hypertensive Mothers – control of Hypertension by medication significantly reduces complications

Also need for Hypertensive Screening in general

Hypertension – also a global hazardUSA Figures• Hypertension - leading cause of heart disease and stroke• 1/3rd have hypertension, another 1/3rd prehypertension• Only 54% of hypertensive people have it under control• Total cost to Healthcare - US$48.6 billion each year.

http://www.cdc.gov/dhdsp/data_statistics/fact_sheets/fs_bloodpressure.htm

7TCS Confidential

HeartSense Technology

Systolic Peak

Diastolic Trough

Acquisition and Pre-processing

Feature Engineering and Extraction, Cardiovascular

Models

Classification (includes metadata

like age, gender, height, weight, BMI)

Photoplethysmogram (PPG) Phonocardiogram (PCG)

8TCS Confidential

TCUP - TCS IoT Platform

Ready and DeployedOngoing Field Trials

Photodetector or Camera Microphone

PPG

Cardiovascular Models

PCG

Expert Doctor

Real-time ViewCAD Screening

Event AlertingMaternal Hypertension

Screening

HeartSense Architecture

Advanced Analytics

Cardiovascular Models

9TCS Confidential

CAD – Pilot Study Results

CAD- Own Collected dataset (20 non-CAD + 15

CAD)- 15 healthy adults with no known

history of cardiac problem at TCS Kolkata

- 5 patients but non-CAD subjects from Fortis)

- 15 angio-proven CAD from Fortis- MIMIC-II matched dataset (ICU)

- 56 CAD and 56 non-CADSensitivity = correctly identify CADSpecificity = correctly identify non-CAD

Own Collected Data MIMIC – II Data

Specificity Sensitivity Specificity Sensitivity

PCG 80% 60% - -

PPG 40% 90% 82% 88%

Fusion 70% 80% - -

1000 patient trial in two hospitals in India – Fortis Hospital in Kolkata and JJ Hospital in Mumbai IRB clearance in place, MoU in progress

Future Plan

10TCS Confidential

Blood Pressure – Pilot Study Results

BP98 subjects at 2 hospitals (Narayana Nethralaya, Bangalore and Primary healthcare centre, Jamla, Gujrat)

- SBP (80-210 mmHg) and DBP (60-130 mmHg)

Error(mmHg)

SBP DBP

OMRON1 iBP2 TCS Model OMRON1 iBP2 TCS Model

< 5 61% 24% 29% 52% 26% 41%

< 10 85% 44% 43% 85% 48% 66%

< 15 94% 59% 66% 96% 70% 84%

• Working with Municipality and Government in Nashik, a semi-urban eco-system• Focusing more on classification for Hypertensive screening and not on actual BP measurement – MAP based• Field Trial with Pregnant mothers planned – need for Longitudinal Study

Future Plan

1. El Assaad, Mohamed A., et al. "Validation of the Omron HEM-907 device for blood pressure measurement." American Journal of Hypertension 15.S3 (2002): 87A-87A.2. Plante, Timothy B., et al. "Validation of the Instant Blood Pressure Smartphone App." JAMA internal medicine (2016) – A study by Johns Hopkins University School of Medicine

11TCS Confidential

HeartSense - Achievements so far

Co

ntr

ibu

tio

n • Low cost Mobile Attachment / wearable • Digital stethoscope

• Smartwatch

• Robust Signal Processing• Outlier Rejection

• Motion Artefact Removal

• Robust Pre-processing

• Cloud based screening and alerting• Supervised Learning for

CAD, Hypertension Classification

• Multi-sensor Fusion

IP • Papers• 15+ in major conferences

(ICASSP, EMBC, MobiHoc, Sensys, ICC, BSN ..)

• Patents• 14 patents filed

• Collaborations• Indian Statistical Institute

Kolkata

• Fortis Hospital, Kolkata

• IIT KGP and IISc planned

Aw

ard

s • Awards • Wearable Tech Award, 2016

• Aegis Graham Bell Award, 2015

• CSI Young Innovator Award, 2015

• Best Demo Award at Sensys 2014

Also working on Diabetes, COPD and Stress

12TCS Confidential

Tele-rehabilitation for Stroke Patients - Motivation

Very expensive devices and high maintenance cost

Existing Quantitative Motion Analysis systems (VICON ) costs approx. $150K – very few hospitals in have it even in metro cities – no access in rural locations

Transportation and Queue at Hospital

No adaptability in exercises and Poor Compliance Checking

13TCS Confidential

TCS RehabBox which will be used for balance, gait and joint range of motion analysis using Kinect sensor

Store Raw Data Patient’s Exercise

Parameter

Patient History

Extract Parameters

Doctor’s Portal

Feedback

Video Conf.

RehabBox - Architecture

• Single Limb Standing (SLS) for Balance – Duration, Vibration, Fall Risk Score

• Gait Analysis – Dynamic Balance, Foot Landing, Stride Length

• Range of Motion (ROM) Analysis for Upper Limb – Joint movement compliance

14TCS Confidential

RehabBox –Pilot Study Results

Single Limb Stance - Balance

• 11 subjects (5 chronic stroke-survivors, 3 females, mean age 64.4 ± 8.2 yrs) - able to stand/walk unaided.

History of fall was present in 80% stroke and 16.6% control subjects.

• SLS duration (SLSD) was significantly low in Stroke vs Control (SLSD =9.5±14.5 sec. vs 55.7±13.6 sec.).

• Both SLSD and Vibration Index (VI) are significantly different in patients with fall vs no-fall history

(SLSD=6.7±9.8 second vs 59.9±13.8; VI=0.68±0.34 vs 0.25±0.16

• 8 healthy subjects (age: 21-36 years, weight: 47kg - 78kg & height: 4’6’’ - 5’9’’, athletic and non-athletic)

• Minimum Absolute Error (w.r.t. GAITRite) for stride length 3.84 cm

ROM Analysis – Improvement in Kinect Joint Motion Estimate

Future Plan – Larger Patient Trials at AMRI Kolkata and Kokilaben Hospital, Mumbai

Gait Analysis – Stride Length

15TCS Confidential

RehaBox - Achievements so far

Co

ntr

ibu

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n • Image Processing• Improvement in Kinect Accuracy

• Joint movement model based RGBD processing to augment Kinect Skeleton Model

• Analytics• Cloud based therapy planning

and compliance

• Accurate Stride length via Gait Modeling and Analysis

• Creation of a Strength and Stability Score fusing SLS Duration and Vibration Index

• Correlate with Fall PropensityIP • Papers

• 5+ in major conferences (ICASSP, EMBC, BioMed, ESPRM, INREM..)

• Patents• 5 patents filed

• Collaborations• AMRI Hospital, Kolkata

• IIT Gandhinagar

Also working on Neurorehab using EEG and GSR

16TCS Confidential

1. Pal, Arpan et. al., "A robust heart rate detection using smart-phone video." In Proceedings of the 3rd ACM MobiHoc workshop on Pervasive wireless healthcare, pp. 43-48. ACM, 2013.

2. Ghose, Avik et. al., "UbiHeld: ubiquitous healthcare monitoring system for elderly and chronic patients." In Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication, pp. 1255-1264. ACM, 2013.

3. Banerjee Rohan et. al., "PhotoECG: Photoplethysmography to Estimate ECG Parameters" In IEEE International Conference on Accoustics, Speech and Signal Processing, 20144. Dutta Choudhury Anirban et al. "Estimating Blood Pressure using Windkessel Model on Photoplethysmogram" in EMBC 2014.5. Visvanathan Aishwarya et al. "Smart Phone Based Blood Pressure Indicator" in MobileHealth workshop of Mobihoc 20146. Visvanathan Aishwarya et al. "Effects of Fingertip Orientation and Flash Location in Smartphone Photoplethysmography“, ICACCI 20147. Banerjee, Rohan et al. "Demo Abstract: HeartSense: Smart Phones to Estimate Blood Pressure from Photoplethysmography" in 11th ACM Conference on Embedded Networked

Sensor Systems (SenSys 2014)8. Dutta Choudhury, Anirban et al. "HeartSense: Estimating Heart rate from Smartphone Photoplethysmogram using Adaptive Filter and Interpolation" in 1st International Conference

on IoT Technologies for HealthCare (HealthyIoT, IoT-360)9. Nasim Ahmed et al. “Feasibility Analysis for Estimation of Blood Pressure and Heart Rate using A Smart Eye Wear”, in WearSys Workshop, Mobisys 201510. Aditi Misra et al. "Novel Peak detection to estimate HRV using Smartphone Audio“ in BSN 201511. "Noise Cleaning and Gaussian Modeling of Smart Phone Photoplethysmogram to improve Blood Pressure Estimation" in ICASSP 201512. Keshaw Dewangan, Arijit Sinharay, Parijat Deshpande, “Design And Simulation Of A Low-cost Digital Stethoscope” in COMSOL 2015 as poster for 30 Oct 201513. Shreyasi Datta et al, “Blood Pressure Estimation from Photoplethysmogram using Latent Parameters” in IEEE ICC 201614. "Rohan Banerjee et al, “Non Invasive Detection of Coronary ArteryDisease using PCG and PPG Signal” in HealthWear 2016 : 1st EAI International Conference on Wearables in

Healthcare"15. Rohan Banerjee et al, "Time-Frequency Analysis of Phonocardiogram for Classifying Heart Disease", Computing in Cardiology 201616. Kingshuk Chakravarty et. al. ,“A Multimodal Therapy Design Toolbox for Gait-Rehabilitation". INEREM 201517. Chakravarty, Kingshuk et. al. ,"Quantification of balance in single limb stance using kinect." In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing

(ICASSP), pp. 854-858. IEEE, 201618. Kingshuk Chakravarty et. al. ,"AN AFFORDABLE GAIT AND POSTURAL BALANCE ANALYSIS SYSTEM USING KINECT FOR REHABILITATION." In BioMed 201619. Sanjana Sinha et. al. ,"Accurate Upper Body Rehabilitation System Using Kinect." Accepted in EMBC 2016 (To Appear in 16-20 Aug 2016)20. Kingshuk Chakravarty et. al. ,"PREDICTING FALL RISK IN STROKE SURVIVORS: A NOVEL MEASUREMENT USING KINECT BASED SYSTEM", Accepted in World Stroke Congress (To

Appear in 26-29 Oct. 2016)

Relevant Publications

17TCS Confidential

Aniruddha SinhaParijat Deshpande

Anirban DuttachaudhuriRohan BanerjeeShreyoshi Dutta

Kingshuk ChakravartyBrojeswar Bhowmick

Sanjana SinhaAvik Ghose

Nasim AhmedAditi Misra

Aiswharya VisvanathanKeshaw Dewangan

Arijit Sinharay

Team

Thank You