Applications of artificial neural networks for ECG signal detection and classification.

Abstract:

:The authors have investigated potential applications of artificial neural networks for electrocardiographic QRS detection and beat classification. For the task of QRS detection, the authors used an adaptive multilayer perceptron structure to model the nonlinear background noise so as to enhance the QRS complex. This provided more reliable detection of QRS complexes even in a noisy environment. For electrocardiographic QRS complex pattern classification, an artificial neural network adaptive multilayer perceptron was used as a pattern classifier to distinguish between normal and abnormal beat patterns, as well as to classify 12 different abnormal beat morphologies. Preliminary results using the MIT/BIH (Massachusetts Institute of Technology/Beth Israel Hospital, Cambridge, MA) arrhythmia database are encouraging.

journal_name

J Electrocardiol

authors

Hu YH,Tompkins WJ,Urrusti JL,Afonso VX

subject

Has Abstract

pub_date

1993-01-01 00:00:00

pages

66-73

eissn

0022-0736

issn

1532-8430

journal_volume

26 Suppl

pub_type

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