Electrocardiogram analysis with adaptive feature selection and support vector machines

Wen Chung Kao, Chun Kuo Yu, Chia Ping Shen, Wei Hsin Chen, Pei Yung Hsiao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

Electrocardiogram (ECG) analysis is one of the most important approaches to cardiac arrhythmia detection. In this paper, we propose an ECG analysis approach with adaptive feature selection and support vector machines (SVMs). Many wavelet transform-based coefficients are used as candidates, but only a few coefficients are selected for classification problem of each class pair. In addition, the several variation classes are partitioned into two or more subclasses to improve the training efficiency of SVMs. The experimental results show that the proposed ECG analysis approach can obtain high recognition rate and reliable results.

Original languageEnglish
Title of host publicationAPCCAS 2006 - 2006 IEEE Asia Pacific Conference on Circuits and Systems
Pages1783-1786
Number of pages4
DOIs
Publication statusPublished - 2006 Dec 1
EventAPCCAS 2006 - 2006 IEEE Asia Pacific Conference on Circuits and Systems - , Singapore
Duration: 2006 Dec 42006 Dec 6

Publication series

NameIEEE Asia-Pacific Conference on Circuits and Systems, Proceedings, APCCAS

Other

OtherAPCCAS 2006 - 2006 IEEE Asia Pacific Conference on Circuits and Systems
CountrySingapore
Period06/12/406/12/6

Keywords

  • And SVM
  • ECG
  • Electrocardiogram
  • Support Vector Machines

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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  • Cite this

    Kao, W. C., Yu, C. K., Shen, C. P., Chen, W. H., & Hsiao, P. Y. (2006). Electrocardiogram analysis with adaptive feature selection and support vector machines. In APCCAS 2006 - 2006 IEEE Asia Pacific Conference on Circuits and Systems (pp. 1783-1786). [4145758] (IEEE Asia-Pacific Conference on Circuits and Systems, Proceedings, APCCAS). https://doi.org/10.1109/APCCAS.2006.342164