Multi-Frequency Components Entropy as Novel Heart Rate Variability Indices in Congestive Heart Failure Assessment

Abstract

In this paper, novel heart rate variability (HRV) indices were extracted for the autonomic nervous system (ANS) activity assessment in congestive heart failure (CHF). It has been reported that CHF is a chronic cardiovascular syndrome along with ANS dysfunction, and HRV is a useful tool for ANS assessment. The multi-frequency components Entropy (MFC-En), which is obtained by the Hilbert-Huang transform and the entropy algorithm, was proposed as novel HRV indices for analyzing ANS with CHF. This paper included 24-h HRV signals of 98 subjects collected with Holter (54 healthy, 12 low-risk CHF, and 32 high-risk CHF subjects). The MFC-En indices successfully showed a statistical significance between the control and CHF groups (p <; 0.001). The CHF classification accuracy of the MFC-En was 86.7%, while the ratio of the lowand high-frequency power was only 79.6%. Moreover, statistical significances were found among the control, low-risk CHF, and high-risk CHF groups (p <; 0.01). Therefore, the MFC-En is a useful tool for CHF assessment that revealed the ANS of CHF patient is more activated by measuring the complexity of the rhythms changes of the ANS throughout the day.