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

Pan, Weifeng; He, Aodi; Feng, Kaicheng; Li, Yifan; Wu, Dan; Liu, Guanzheng

doi:10.1109/access.2019.2896342

Cited by 18 publications

(15 citation statements)

References 46 publications

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“…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.…”

Section: Discussionmentioning

confidence: 99%

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

Shi

Geng

et al. 2020

Front. Physiol.

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Sudden cardiac death (SCD), which can deprive a person of life within minutes, is a destructive heart abnormality. Thus, providing early warning information for patients at risk of SCD, especially those outside hospitals, is essential. In this study, we investigated the performances of ensemble empirical mode decomposition (EEMD)-based entropy features on SCD identification. EEMD-based entropy features were obtained by using the following technology: (1) EEMD was performed on HRV beats to decompose them into intrinsic mode functions (IMFs), (2) five entropy parameters, namely Rényi entropy (RenEn), fuzzy entropy (FuEn), dispersion Entropy (DisEn), improved multiscale permutation entropy (IMPE), and Renyi distribution entropy(RdisEn), were computed from the first four IMFs obtained, which were named EEMD-based entropy features. Additionally, an automated scheme combining EEMD-based entropy and classical linear (time and frequency domains) features was proposed with the intention of detecting SCD early by analyzing 14 min (at seven successive intervals of 2 min) heart rate variability (HRV) in signals from a normal population and subjects at risk of SCD. Firstly, EEMDbased entropy and classical linear measurements were extracted from HRV beats, and then the integrated measurements were ranked by various methodologies, i.e., t-test, entropy, receiver-operating characteristics (ROC), Wilcoxon, and Bhattacharyya. Finally, these ranked features were fed into a k-Nearest Neighbor algorithm for classification. Compared with several state-of-the-art methods, the proposed scheme firstly predicted subjects at risk of SCD up to 14 min earlier with an accuracy of 96.1%, a sensitivity of 97.5%, and a specificity of 94.4% 14 min before SCD onset. The simulation results exhibited that EEMD-based entropy estimators showed significant difference between SCD patients and normal individuals and outperformed the classical linear estimators

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Section: Discussionmentioning

confidence: 99%

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

Shi

Geng

et al. 2020

Front. Physiol.

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“…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 ].…”

Section: Discussionmentioning

confidence: 99%

“…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.…”

Section: Discussionmentioning

confidence: 99%

“…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 ].…”

Section: Introductionmentioning

confidence: 99%

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

Chen

Feng

et al. 2019

Entropy

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Congestive heart failure (CHF) is a cardiovascular disease related to autonomic nervous system (ANS) dysfunction and fragmented patterns. There is a growing demand for assessing CHF accurately. In this work, 24-h RR interval signals (the time elapsed between two successive R waves of the QRS signal on the electrocardiogram) of 98 subjects (54 healthy and 44 CHF subjects) were analyzed. Empirical mode decomposition (EMD) was chosen to decompose RR interval signals into four intrinsic mode functions (IMFs). Then transfer entropy (TE) was employed to study the information transaction among four IMFs. Compared with the normal group, significant decrease in TE (*→1; information transferring from other IMFs to IMF1, p < 0.001) and TE (3→*; information transferring from IMF3 to other IMFs, p < 0.05) was observed. Moreover, the combination of TE (*→1), TE (3→*) and LF/HF reached the highest CHF screening accuracy (85.7%) in IBM SPSS Statistics discriminant analysis, while LF/HF only achieved 79.6%. This novel method and indices could serve as a new way to assessing CHF and studying the interaction of the physiological phenomena. Simulation examples and transfer entropy applications are provided to demonstrate the effectiveness of the proposed EMD decomposition method in assessing CHF.

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

Section: Referencementioning

confidence: 99%

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

et al. 2019

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In this paper, a prototype photoplethysmography (PPG) electronic device is presented for the distinction of individuals with congestive heart failure (CHF) from the healthy (H) by applying the concept of Natural Time Analysis (NTA). Data were collected simultaneously with a conventional three-electrode electrocardiography (ECG) system and our prototype PPG electronic device from H and CHF volunteers at the 2nd Department of Cardiology, Medical School of Ioannina, Greece. Statistical analysis of the results show a clear separation of CHF from H subjects by means of NTA for both the conventional ECG system and our PPG prototype system, with a clearly better distinction for the second one which additionally inherits the advantages of a low-cost portable device.

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

Cited by 18 publications

References 46 publications

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

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

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

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

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