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

Manhong Shi,Chaoying Zhan,Shumin Ren,Yanwen Jin,Fei Zhu,Rongrong Wu,Hongxin He,Wanchen Geng,Bairong Shen

doi:10.3389/fphys.2020.00118

Abstract

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, EEMD-based 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 in SCD detection, the EEMD-based FuEn and IMPE indexes were particularly useful assessments for identification of patients at risk of SCD and can be used as novel indices to reveal the disorders of rhythm variations of the autonomic nervous system when affected by SCD.

Highlights

Sudden cardiac death (SCD) describes the death of a person who has died from previously known or even unknown cardiac diseases in an unanticipated and abrupt manner, within no more than an hour after the first occurrence of symptoms (Zipes and Wellens, 1998; Chugh, 2001; Myerburg and Castellanos, 2005)
We explored the performance of EEMDbased entropy metrics on SCD detection and proposed an automated SCD scheme based on Ensemble empirical mode decomposition (EEMD) and classical linear methods
We computed the FuEn indexes of different intrinsic mode functions (IMFs) obtained from EEMD decomposition for uncorrected and corrected 1st 2-min interval heart rate variability (HRV) signals, respectively, as shown in Figure 4

Summary

Introduction

Sudden cardiac death (SCD) describes the death of a person who has died from previously known or even unknown cardiac diseases in an unanticipated and abrupt manner, within no more than an hour after the first occurrence of symptoms (Zipes and Wellens, 1998; Chugh, 2001; Myerburg and Castellanos, 2005). Despite the increased usage of public defibrillation devices after collapse, according to the latest data, out-of-hospital survival is at only about 10.4% due to the failure to provide patients with timely care (Vandenberg et al, 2017). These startling figures highlight the significance of early SCD prediction for improving survival rates. The Public Access Defibrillation (PAD) technique is usually used to rescue the dying after the collapse, but for patients outside hospitals it is difficult to provide timely and effective treatment in a short time, and early detection of unanticipated SCD in a person suffering from VF is of vital significance for increasing the survival rate of outof-hospital patients.

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Discussion

Conclusion