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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 4June 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 5Proprietary 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
ProviderPayer
Core
Portal Sensor
Person
ProviderPayer
Core
Portal Sensor
Person
ProviderPayer
Core
Portal Sensor
Person
ProviderPayer
«Consumer»
«Corporate Health» «Captive/Capitation»
«Additional Health»
Proprietary Information Biovotion 7June 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
Heartrate[bpm]
Movementindicator
SaO2[%]
SvO2[%]
SkinTemp[°C]
Perfusion[%]
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

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Stephan Bachofen - Mobile monitoring applied to the chronic diseases - e-health 6.6.14

  • 1. 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
  • 2. Overview June 2014 Proprietary Information Biovotion 2 Wearable monitoring Biophysics & Physiology Sensors & Algorithms Actionable interface Attachment Markets & Applications
  • 3. 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
  • 4. 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 4June 2014
  • 5. 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 5Proprietary Information Biovotion
  • 6. 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 *
  • 7. Ecosystem propositions Core Portal Sensor Person ProviderPayer Core Portal Sensor Person ProviderPayer Core Portal Sensor Person ProviderPayer Core Portal Sensor Person ProviderPayer «Consumer» «Corporate Health» «Captive/Capitation» «Additional Health» Proprietary Information Biovotion 7June 2014
  • 8. 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
  • 9. 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 Heartrate[bpm] Movementindicator SaO2[%] SvO2[%] SkinTemp[°C] Perfusion[%]
  • 10. Example – Sleep apnoea patient June 2014 Proprietary Information Biovotion 10
  • 11. 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
  • 12. 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
  • 13. 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
  • 15. 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
  • 16. Our Current Focus in the CONTINUA Stack June 2014 16
  • 17. 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
  • 18. COMPASS: Lossless Compression June 2014 18 DE   DEF   DD  INF   DE  =  Delta  Encoding   DEF  =  Deflate   INF  =  Inflate   DD  =  Delta  Decoding  
  • 19. 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  
  • 20. 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  
  • 21. 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  
  • 22. COMPASS: CS First Attempt example June 2014 22 RED:  Original  Signal   BLUE:  Recovered  Signal   Compression  Rate  =  20%   RMSE  =  0.0097  
  • 23. 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
  • 24. Thank You For your Attention Questions? June 2014 24