ArticleFrontiers in physiology2020
Early Detection of Sudden Cardiac Death by Using Ensemble Empirical Mode Decomposition-Based Entropy and Classical Linear Features From Heart Rate Variability Signals.
Article in Frontiers in physiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 52 citations in OpenAlex.
- Artificial intelligence in electrocardiogram signals for sudden cardiac death prediction: a systematic review and meta-analysis.Systematic reviews · 2025Pooled it
- Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies.EBioMedicine · 2023Pooled it
- A novel method for early prediction of sudden cardiac death through nonlinear feature extraction from ECG signals.Physical and engineering sciences in medicine · 2025Article
- Nonlinear Analysis of the Hand and Foot Force-Time Profiles in the Four Competitive Swimming Strokes.Journal of human kinetics · 2024Article
- Heart Rate Variability Measurement through a Smart Wearable Device: Another Breakthrough for Personal Health Monitoring?International journal of environmental research and public health · 2023Review
- Performance of heart rate adjusted heart rate variability for risk stratification of sudden cardiac death.BMC cardiovascular disorders · 2023Article
- Optimal Classification of Atrial Fibrillation and Congestive Heart Failure Using Machine Learning.Frontiers in physiology · 2021Article
- Measures of repolarization variability predict ventricular arrhythmogenesis in heptanol-treated Langendorff-perfused mouse hearts.Current research in physiology · 2021Article
- Effects of Cigarette Smoking on Cardiac Autonomic Responses: A Cross-Sectional Study.International journal of environmental research and public health · 2020Article
- Sudden cardiac arrest: Limitations in risk-stratification and treatment, and the potential for digital technologies and artificial intelligence to improve prediction and outcomes.Progress in cardiovascular diseasesReview
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9 authors at 4 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
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.,
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