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A New Approach to Detect Congestive Heart Failure Using Short-Term Heart Rate Variability Measures

Author

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  • Guanzheng Liu
  • Lei Wang
  • Qian Wang
  • GuangMin Zhou
  • Ying Wang
  • Qing Jiang

Abstract

Heart rate variability (HRV) analysis has quantified the functioning of the autonomic regulation of the heart and heart's ability to respond. However, majority of studies on HRV report several differences between patients with congestive heart failure (CHF) and healthy subjects, such as time-domain, frequency domain and nonlinear HRV measures. In the paper, we mainly presented a new approach to detect congestive heart failure (CHF) based on combination support vector machine (SVM) and three nonstandard heart rate variability (HRV) measures (e.g. SUM_TD, SUM_FD and SUM_IE). The CHF classification model was presented by using SVM classifier with the combination SUM_TD and SUM_FD. In the analysis performed, we found that the CHF classification algorithm could obtain the best performance with the CHF classification accuracy, sensitivity and specificity of 100%, 100%, 100%, respectively.

Suggested Citation

  • Guanzheng Liu & Lei Wang & Qian Wang & GuangMin Zhou & Ying Wang & Qing Jiang, 2014. "A New Approach to Detect Congestive Heart Failure Using Short-Term Heart Rate Variability Measures," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-8, April.
    • Handle: RePEc:plo:pone00:0093399
    DOI: 10.1371/journal.pone.0093399
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