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Classification enhancible grey relational analysis for cardiac arrhythmias discrimination

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Abstract

This paper proposes a method for electrocardiogram (ECG) heartbeat recognition using classification enhancible grey relational analysis (GRA). The ECG beat recognition can be divided into a sequence of stages, starting with feature extraction and then according to characteristics to identify the cardiac arrhythmias including the supraventricular ectopic beat, bundle branch ectopic beat, and ventricular ectopic beat. Gaussian wavelets are used to enhance the features from each heartbeat, and GRA performs the recognition tasks. With the MIT-BIH arrhythmia database, the experimental results demonstrate the efficiency of the proposed non-invasive method. Compared with artificial neural network, the test results also show high accuracy, good adaptability, and faster processing time for the detection of heartbeat signals.

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Acknowledgment

This work is supported in part by the National Science Council of Taiwan under contract number: NSC 93-2614-E-244-001 (December 1 2004–July 31 2005). The author would like to thank associate editors of Medical & Biological Engineering & Computing and reviewers for reviewing the manuscript and providing the suggestion.

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  1. Department of Electrical Engineering, Kao-Yuan University, Kaohsiung, Taiwan

    Chia-Hung Lin

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  1. Chia-Hung Lin
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Correspondence to Chia-Hung Lin.

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Lin, CH. Classification enhancible grey relational analysis for cardiac arrhythmias discrimination. Med Bio Eng Comput 44, 311–320 (2006). https://doi.org/10.1007/s11517-006-0027-3

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  • DOI: https://doi.org/10.1007/s11517-006-0027-3

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