A new approach to detect congestive heart failure using short-term heart rate variability measures

Guanzheng Liu, Lei Wang, Qian Wang, Guangmin Zhou, Ying Wang, Qing Jiang, 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.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1. The CHF classification algorithm based…
Figure 1. The CHF classification algorithm based on support vector machine.
Figure 2. Three nonstandard features between patients…
Figure 2. Three nonstandard features between patients with CHF and healthy people; Mean ±one standard deviation was plotted.
SUM_TD was a nonstandard time domain feature; SUM_FD was a nonstandard frequency domain feature; SUM_IE was a nonstandard non-line feature.
Figure 3. The SVM classifier from different…
Figure 3. The SVM classifier from different input feature vectors.
SUM_TD was a nonstandard time domain feature; SUM_FD was a nonstandard frequency domain feature; SUM_IE was a nonstandard non-line feature.

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