A multiple-classifier architecture for ECG beat classification

We investigate the use of the modular architecture of multiple clustering based pattern classifiers for ECG beat classification using the MIT/BIH arrhythmia database. The feature space is divided into several regions and individual classifiers are developed for each region separately. Then the outpu...

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Bibliographic Details
Main Authors: Palreddy, S., Yu Hen Hu, Mani, V., Tompkins, W.J.
Format: Conference Proceeding
Language:English
Subjects:
Computer architecture
Electrocardiography
Heart beat
Heart rate variability
Morphology
Pattern classification
Self organizing feature maps
Shape
Spatial databases
Training data
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Description
Summary:We investigate the use of the modular architecture of multiple clustering based pattern classifiers for ECG beat classification using the MIT/BIH arrhythmia database. The feature space is divided into several regions and individual classifiers are developed for each region separately. Then the outputs of these classifiers are combined using two competing combination rules: a winner decides all method and a distance-based combination method. Experiment results indicated that multiple classifier approach yields better sensitivity and classification rate.
ISSN:1089-3555
2379-2329
DOI:10.1109/NNSP.1997.622396