DocumentCode
786763
Title
A low-power network for on-line diagnosis of heart patients
Author
Coggins, Richard ; Jabri, Marwan ; Flower, Barry ; Pickard, Stephen
Author_Institution
Syst. Eng. & Design Autom. Lab., Sydney Univ., NSW, Australia
Volume
15
Issue
3
fYear
1995
fDate
6/1/1995 12:00:00 AM
Firstpage
18
Lastpage
25
Abstract
Implantable cardioverter defibrillators detect and treat dangerous cardiac arrhythmias. Current ICDs, however, cannot distinguish between some potentially fatal arrhythmias and benign conditions. Our system classifies intracardiac electrograms to detect such arrhythmias and uses analog techniques to meet the strict power and area requirements of implantable systems. A robust neural network architecture reduces the impact of noise, drift, and offsets inherent in analog approaches
Keywords
defibrillators; medical signal processing; neural nets; patient monitoring; patient treatment; pattern recognition; cardiac arrhythmias; drift; heart patients; implantable cardioverter defibrillators; low-power network; noise; offsets; on-line diagnosis; robust neural network; Circuits; Electric shock; Heart; Implants; Medical treatment; Morphology; Neural networks; Rhythm; Timing; Very large scale integration;
fLanguage
English
Journal_Title
Micro, IEEE
Publisher
ieee
ISSN
0272-1732
Type
jour
DOI
10.1109/40.387678
Filename
387678
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