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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
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
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.
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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.
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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
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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.
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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
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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
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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