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

Overview

Journal PLoS One

Specialties General Medicine
Science

Date 2014 Apr 22

PMID 24747432

Citations 15

Authors

Guanzheng Liu

Lei Wang

Qian Wang

Guangmin Zhou

Ying Wang

Qing Jiang

Affiliations

Soon will be listed here.

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.

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