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
Link To Document :
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