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Using Soft Computer Techniques on Smart Devices for Monitoring Chronic Diseases: the CHRONIOUS case
| Date post: | 29-Aug-2014 |
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Health & Medicine |
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Using Soft Computer Techniques on Smart Devices for Monitoring Chronic Diseases: the CHRONIOUS case
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Where I live?
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Where I live?
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Where I live?
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What did I study?
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Where did I study?
Galileo studied here with different results…
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Where did I work?
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Where did I work?
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Where did I work?
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Where did I work?
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Where did I work?
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Where do I work now?
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Which is part of
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ITALTBS numbers
Presence in 12 countriesIncluding:
AustriaBelgium
FranceGermanyIndia
ItalyNetherlands
PortugalSouth Arabia
Serbia
Spain
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What do I do in TeSAN 1?
R&D IT MANAGER (BASICALLY)
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What do I do 2?
I solve (software) problems
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What I’m here to present ?
Soft computing techniques+
Smart devices+
Chronic diseasesin
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Why Chronious COPD?
COPD: by 2020 3.5 million deaths in the world (at least)
USA 2000: 10 million adults reported COPD
= 8 million physician office and
hospital outpatients visit+
1.5 million emergency visit+
726,000 hospitalizations+
119,000 deaths
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Why Chronious COPD?
First cause for COPD
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Why Chronious COPD?
Second cause for COPD
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Why Chronious COPD?
Another cause of COPD in development countries
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Free advice?
Want to reduce risk of contracting COPD?
DON’T SMOKE!
DON’T DRIVE CARS!
DON’T DO BARBECUE!
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Why Chronious CKD?
In the US, 9.6% of non-institutionalized adults are estimated to have CKD
Reducing the mortality rates associated to the CKD could save 10% of the loss extimated in 8 bilion USD only in development countries
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Why Chronious CKD?
CKD is difficult to treat because of comorbiditiesThe renal functionality becoming worse at every exacerbation
We choose to go to the moon in this decade and do the other things, not because they are easy, but because they are hard (J.F.Kennedy)
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The IDEA!
Combining•Remote sensor framework•Smart device soft computing•Central Decision Support System•Ontology literature search•Rule base inference engine
To monitor elderly (>60) patient affected by •COPD•CDK
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THE CHRONIOUS SCHEMA
BTW: www.chronious.eu
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THE COMMUNICATION FRAMEWORK
WEARABLE•ECG•Respiratory bands•SpO2•Accelerometer•Microphone•Body temperature sensor
Data Handler
PDA
EXTERNAL DEVICES•Weight scale•Blood pressure device•Glucometer•Spirometer•Environmental sensors
Home Patient Monitor(touch screen PC)
CHRONIOUSCENTRAL SYSTEM•Smart data elaboration•Decision Support System• Ontologies• Guidelines
3G
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The PDA
WINDOWS MOBILE 6.5
SQL SERVER CE 2005
.NET FRAMEWORK 3.5
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Algorithms on the PDA
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PREPROCESSING ALGORITHMS ECG
LINEAR FILTERING+
POLINOMYAL FITTING=
REMOVAL BASE LINE WANDERING
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PREPROCESSING ALGORITHMS ECG
Daubechies (DB4) wavelets=
REMOVAL HIGH FREQUENCY NOISE
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PREPROCESSING ALGORITHMS ECG
Filtering ECG with CWT and FWT+
Pan-Tompkins wavelets =
QRS DETECTION
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PREPROCESSING ALGORITHMS RR
Reference inspiration signal+
STFT (windows size 60s)=
CALCULATE REFERENCE RESPIRATION SIGNAL
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CLASSIFICATION SYSTEM
Light rule based expert system
Supervised classification system
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Light rule based
Lifesaving rules extracted from the CDSS
Weight increase by 2% in the last 24 hours
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Supervised classification system
SVM
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Training set
SVM
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CLASSIFICATION SYSTEM
The rules extracted have been validated by clinician
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Controlling the stress index
Bayesian network
Use 9 attributes for evaluating a stress index that can be used to understand if the patient condirion can worse.
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Problems with the PDA
Heavy resource consumption -> bottleneck during preprocessing phase in case of alerting situation
Difficulties on updating the training algorithms
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Last and least
