Mobile monitoring applied to the chronic diseases An expandable multisensor platform

eHealth Day Sierre, 6. June 2014

Awarded by the European Commission as Europe's ´best eHealth SMEs´ 2013

Overview

June 2014 Proprietary Information Biovotion 2

Wearable monitoring

Biophysics & Physiology

Sensors & Algorithms

Actionable interface

Attachment

Markets & Applications

From hospital care to home care

June 2014 Proprietary Information Biovotion 3

  Tight monitoring analogue to hospital   Adequate «infrastructure»   Continuous data   Integration into existing «ICT» solutions

Hospital admission

Intensive hospital care

Non-critical hospital care

Patient home care

Example COPD

  G7 >34M COPD patients*, becoming 3rd leading cause of death. Economic burden >$40B (NIH)

  ~20% of all acute hospital admissions, 24% readmission rate

  7.5% of COPD patients with major handicap in every day life

  Medical treatment limited, reduced level of function, inactivity, frustration and social isolation >40% CVD

* WHO (2010)

Proprietary Information Biovotion 4 June 2014

Fully wearable, continuous & portable

medical device

Simple wearable consumer devices

Simple portable medical devices Complex

stationary Medical Devices

Market developments

5

Typically spot monitoring ‘Moderate’ accuracy

Limited selection of vital signs Ergonomic focus ‘Lower’ accuracy

Full range of vital sign parameters Sophisticated algorithms Reduced movement ‘High’ accuracy

Combine ergonomy/pricing/accuracy and mobility towards new level of wearable monitoring devices incl. eco system

June 2014 5 Proprietary Information Biovotion

VSM 1-3: Parameters today

June 2014 Proprietary Information Biovotion 6

VSM1 (6 sensor signals) - Main vital signs**   Heart rate   Blood oxygenation   Cutaneous blood perfusion/volume   Temperature   Movement

Additional parameters***   Heart rate variability   Energy expenditure   Respiratory rate   Stress   Sleep   Fall

VSM2 (13 sensor signals) - to include water VSM3 (19 sensor signals) - to include glucose

*** Extensive IP portfolio existing, device shown above features a total of 19 different sensor signals *** Performance on par with standard hospital systems *** Expected to be part of VSM 1

*

Ecosystem propositions

Core

Portal Sensor

Person

Provider Payer

Core

Portal Sensor

Person

Provider Payer

Core

Portal Sensor

Person

Provider Payer

Core

Portal Sensor

Person

Provider Payer

«Consumer»

«Corporate Health» «Captive/Capitation»

«Additional Health»

Proprietary Information Biovotion 7 June 2014

Biovotion eco system and services*   Attachment concept   Sensor design   Algorithms   Functionalities   Actionable events

»» Reliable monitoring

View VSM data via cloud

Monitor collects vital signs, displays status. Sophisticated

functionalities **

** Stepwise market introduction, basic parts of overall concept expected to be available for testing in Q4/2014 ** Based on standardised elements also for efficient integration into existing eco systems or connection to support infrastructures

June 2014 8

User support centre**

Health monitoring (customised eco system)   Generational support, healthy living   Fitness & lifestyle, quality of sleep

Medical monitoring (customised eco system)   Pre hospital - critical injury, paramedic, ambulance, triage   In hospital (low acuity, ambulatory patients)   Out of hospital - disease specific support, 30 day monitoring,

long term condition monitoring

VSM/components worn on upper

arm or wrist

Secure platform of VSM data/ evaluation. Sophisticated

functionalities

Eco system to offer different levels of subscription services

Proprietary Information Biovotion

Example - Overnight sleep healthy

June 2014 Proprietary Information Biovotion 9

  Mainly constant heart rate with minor cycle visible

  Little movement   Cycling temperature changes   Constant blood oxygenation   Sleep phases

Hea

rt ra

te [b

pm]

Mov

emen

t ind

icat

or

SaO

2 [%

]

SvO

2 [%

]

Ski

n Te

mp

[°C

]

Per

fusi

on [%

]

Example – Sleep apnoea patient

June 2014 Proprietary Information Biovotion 10

June 2014 Proprietary Information Biovotion 11

» » monitoring in motion » easy to use » accurate » robust

HR

SAT

CBP

CBV

Temp

Mov

RR

HRV

Biovotion AG | Technoparkstr. 1 | 8005 Zurich | Switzerland | www.biovotion.com | info@biovotion.com

COMPASS: COntinuous Multi-variate monitoring for

Patients Affected by chronic obstructive pulmonary diSeaSe

  CTI Project 15888.1   Partners:

  Biovotion

  Mr Stephan Bachofen   HES-SO Sierre, E-Health Unit

  Dr Stefano Bromuri (Deputy Project Manager, PI)   Mr Thomas Hofer   Dr Michael Schumacher

  Running From April 2014 to April 2016.

June 2014 12

COMPASS: Challenges

  Challenges:   Standardisation of the communication stack according to the

Continua Alliance standards to ensure interoperability.   Signal compression and analysis at the mobile application level to

minimise the power requirements of the system   Machine learning algorithm for

  Prediction of exacerbation of the COPD condition.   Provide rehabilitation advices for the patient in COPD.

  HL7 CDA R2, to interface to existing care management solutions.   Test on real patients.

June 2014 13

COMPASS: General Architecture

June 2014 14

COMPASS: Interoperability using CONTINUA

  Continua Care for Devices:   Based on IEEE 11073

  Medical / Health care device communications standards   Enables communications between point of care devices and

remote servers   Client-related health care information, vitals   Equipment-related identity, performance and functional

status   Supports three domains

  Disease Management,   Health and Fitness,   Living Independence

June 2014 15

Our Current Focus in the CONTINUA Stack

June 2014 16

COMPASS: Feature Extraction and Data Compression

  Lossless data compression: It is a class of data compression algorithms that allows the original data to be perfectly reconstructed from the compressed data.

  Lossy data compression: it permits reconstruction only of an approximation of the original data, though this usually allows for improved compression rates (and therefore smaller sized files).

  No free lunch: there is no such thing as the universal compression algorithm, some algorithms work differently in different settings.

June 2014 17

COMPASS: Lossless Compression

June 2014 18

DE   DEF   DD  INF  

DE  =  Delta  Encoding   DEF  =  Deflate   INF  =  Inflate   DD  =  Delta  Decoding  

0 100 200 300 400 500 600 700

0.7

0.8

0.9

1

0 100 200 300 400 500 600 700−0.5

0

0.5

1

0 100 200 300 400 500 600 700

0.7

0.8

0.9

1

COMPASS: Lossless Compression

June 2014 19

You  start  with  a  signal  

You  end  with  the    same  signal  

Compression  rate  =  10%  

Apply  the    Process  

COMPASS: Lossy Compression using Compressive Sensing

June 2014 20

is  uniquely  determined  by    

is  random     with  high  probability  Donoho,  2006  and  Candès  et.  al.,  2006  

NP-­‐hard  

Convex  and  tractable  

Greedy  algorithms:  OMP,  FOCUSS,  etc.  

Donoho,  2006  and  Candès  et.  al.,  2006  

Tropp,  Co6er  et.  al.  Chen  et.  al.  and  many  other  

Compressed  sensing  (2003/4  and  on)  –  Main  results  

Donoho  and  Elad,  2003  

COMPASS: Compressive Sensing Schema

June 2014 Proprietary Information Biovotion 21

S  P  A  R  S  I  F  Y  

Ax  =  y   x0  =  A’y    

T  R  A  N  S  M  I  T  

s  y  x  

D  E  S  P  A  R  S  I  F  Y  

x  is  sparse   y<<x  

O  P  T  I  M  I  Z  E  

x0  s  

COMPASS: CS First Attempt example

June 2014 22

RED:  Original  Signal  

BLUE:  Recovered  Signal  

Compression  Rate  =  20%  

RMSE  =  0.0097  

Future Work

  Finish the CONTINUA stack for the transmission   Define two compression modules:

  LOSSLESS Compression Module   Lossy Compression Module

  Use the features Extracted with CS to perform Machine Learning Tasks.

June 2014 23

Thank You For your Attention

Questions?

June 2014 24