Classification of QRS pattern by an associative memory model

Author

Lin, Kang-Ping ; Chang, Walter H.

Author_Institution

Chung Yuan Christian Univ., Ching Li, Taiwan

fYear

1989

fDate

9-12 Nov 1989

Firstpage

2017

Abstract

A feature-extraction method based on linear prediction for classification of QRS in an associative memory model is described. The feature extraction process converts each QRS pattern to a pulse-code train that describes only -1, 0, and +1 states. In order to recognize the feature of a QRS pattern, a two-layer feedforward neural net model is provided. The model shows the operation of each input node as well as a real neuron´s three typical states: resting [0], excitatory [+], and inhibitory [-1]. The model performs well for arrhythmia detection

Keywords

computerised pattern recognition; content-addressable storage; electrocardiography; medical diagnostic computing; neural nets; physiological models; waveform analysis; QRS pattern; arrhythmia detection; associative memory model; classification; electrocardiogram monitoring systems; excitatory; feature-extraction method; inhibitory; input node; linear prediction; pulse-code train; resting; two-layer feedforward neural net model; Associative memory; Biomedical engineering; Computerized monitoring; Electrocardiography; Feature extraction; Joining processes; Neural networks; Pattern analysis; Pattern recognition; Predictive models;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 1989. Images of the Twenty-First Century., Proceedings of the Annual International Conference of the IEEE Engineering in

Conference_Location

Seattle, WA

Type

conf

DOI

10.1109/IEMBS.1989.96573

Filename

96573