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 low-and 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. INDEX TERMS Congestive heart failure (CHF), heart rate variability (HRV), Hilbert-Huang transform (HHT), instantaneous frequency (IF), multi-frequency components analysis (MFCA).

Early Detection of Sudden Cardiac Death by Using Ensemble Empirical Mode Decomposition-Based Entropy and Classical Linear Features From Heart Rate Variability Signals

“…Therefore, EEMDbased entropy HRV signals analysis provides a new way of assessing the complexity of the rhythm variation of ANS so as to unearth significant clinical information related to diseases. Li et al (2019) proposed a novel descriptor, namely sliding trend fuzzy approximate entropy (SITr-fApEn), based on the empirical mode decomposition (EMD) method for analyzing ANS with obstructive sleep apnea (Li et al, 2019), and Pan et al (2019) introduced a multi-frequency components entropy (MFC-En) based on EMD for CHF classification. MFC-En was verified to be a useful tool for CHF measurement by evaluating the irregularity of rhythm variations of the ANS.…”

Empirical Mode Decomposition as a Novel Approach to Study Heart Rate Variability in Congestive Heart Failure Assessment

“…The IMF components are able to represent the energy in specific frequency bands when the central frequency lies within the band limits and the standard deviations are less than twenty percent outside the boundary [ 41 ]. By the Hilbert transform (HT), instantaneous attributes of different time series were calculated [ 25 , 42 ]. The instantaneous frequency is no longer affected by the unnecessary fluctuation caused by the asymmetric waveform thanks to the modified requirements, which makes EMD more suitable for nonstationary signals [ 37 ].…”

“…The instantaneous frequency is no longer affected by the unnecessary fluctuation caused by the asymmetric waveform thanks to the modified requirements, which makes EMD more suitable for nonstationary signals [ 37 ]. Furthermore, Pan et al extracted instantaneous frequency of IMFs and demonstrated their correspondence with four physiological subsystems through HT [ 25 ]. The results explained why IMFs were meaningful and could stand for the energy in specific frequency bands.…”

“…Anas et al revealed the similarity between IMFs and ECG signals and exploited EMD to separate ventricular fibrillation from other cardiac pathologies [ 24 ]. Pan et al decomposed four subsystems from HRV signals with EMD method, of which the correspondence with the four frequency bands (VLF, LF, HF and VHF) was further validated by the Hilbert-Huang transform (HHT) [ 25 ].…”

A Prototype Photoplethysmography Electronic Device that Distinguishes Congestive Heart Failure from Healthy Individuals by Applying Natural Time Analysis

“…Pan et al [60] (see Table 3) Multi-Frequency Components Entropy (or LF/HF) 63.6% to 79.5% (or 86.4%)…”