Title :
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
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;
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
DOI :
10.1109/IEMBS.1989.96573
