stephan bachofen - mobile monitoring applied to the chronic diseases - e-health 6.6.14
TRANSCRIPT
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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
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Overview
June 2014 Proprietary Information Biovotion 2
Wearable monitoring
Biophysics & Physiology
Sensors & Algorithms
Actionable interface
Attachment
Markets & Applications
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From hospital care to home care
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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
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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)
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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
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VSM 1-3: Parameters today
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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
*
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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»
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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
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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
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Example - Overnight sleep healthy
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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 [%
]
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Example – Sleep apnoea patient
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» » 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 | [email protected]
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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.
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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.
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COMPASS: General Architecture
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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
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Our Current Focus in the CONTINUA Stack
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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.
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COMPASS: Lossless Compression
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DE DEF DD INF
DE = Delta Encoding DEF = Deflate INF = Inflate DD = Delta Decoding
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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
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You start with a signal
You end with the same signal
Compression rate = 10%
Apply the Process
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COMPASS: Lossy Compression using Compressive Sensing
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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
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COMPASS: Compressive Sensing Schema
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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
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COMPASS: CS First Attempt example
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RED: Original Signal
BLUE: Recovered Signal
Compression Rate = 20%
RMSE = 0.0097
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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.
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Thank You For your Attention
Questions?
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