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

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

JOURNAL=Frontiers in Physiology

VOLUME=Volume 11 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2020.00118

DOI=10.3389/fphys.2020.00118

ISSN=1664-042X

ABSTRACT=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 performances of ensemble empirical mode decomposition (EEMD) based entropy features on SCD identification, and 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 including 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 referred to as EEMD-based entropy features. Additionally, an automated scheme combined with 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) 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 k-Nearest Neighbor 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%, sensitivity of 97.5%, specificity of 94.4% 14 minute 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 on 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 affected by SCD.