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

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

  1. 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. 2. Overview June 2014 Proprietary Information Biovotion 2 Wearable monitoring Biophysics & Physiology Sensors & Algorithms Actionable interface Attachment Markets & Applications
  3. 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. 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. 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. 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. 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. 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. 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. 10. Example – Sleep apnoea patient June 2014 Proprietary Information Biovotion 10
  11. 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. 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. 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
  14. 14. COMPASS: General Architecture June 2014 14
  15. 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. 16. Our Current Focus in the CONTINUA Stack June 2014 16
  17. 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. 18. COMPASS: Lossless Compression June 2014 18 DE   DEF   DD  INF   DE  =  Delta  Encoding   DEF  =  Deflate   INF  =  Inflate   DD  =  Delta  Decoding  
  19. 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. 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. 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. 22. COMPASS: CS First Attempt example June 2014 22 RED:  Original  Signal   BLUE:  Recovered  Signal   Compression  Rate  =  20%   RMSE  =  0.0097  
  23. 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. 24. Thank You For your Attention Questions? June 2014 24

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