A support vector machine approach for AF classification from a short single-lead ECG recording.

Journal: Physiological measurement
Published Date: Jun 25, 2018

Abstract

OBJECTIVE: In this paper, a support vector machine (SVM) approach using statistical features, P wave absence, spectrum features, and length-adaptive entropy are presented to classify ECG rhythms as four types: normal rhythm, atrial fibrillation (AF), other rhythm, and too noisy to classify.

Authors

  • Na Liu
  • Muyi Sun
  • Ludi Wang
    Automation School, Beijing University of Posts and Telecommunications, Beijing 100876, China.
  • Wei Zhou
    Department of Eye Function Laboratory, Eye Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
  • Hao Dang
  • Xiaoguang Zhou
    Automation School, Beijing University of Posts and Telecommunications, Beijing 100876, China.

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