ArticleJournal of medical systems2018
A Novel Wavelet Transform-Homogeneity Model for Sudden Cardiac Death Prediction Using ECG Signals.
Article in Journal of medical systems, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled 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
- IoT-based emergency cardiac death risk rescue alert system.MethodsX · 2024Article
- Accurate Prediction of Sudden Cardiac Death Based on Heart Rate Variability Analysis Using Convolutional Neural Network.Medicina (Kaunas, Lithuania) · 2023Article
- Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review.JMIR medical informatics · 2021Article
- ECG-Based Identification of Sudden Cardiac Death through Sparse Representations.Sensors (Basel, Switzerland) · 2021Article
- A New Methodology Based on EMD and Nonlinear Measurements for Sudden Cardiac Death Detection.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Sudden cardiac death (SCD) is one of the main causes of death among people. A new methodology is presented for predicting the SCD based on ECG signals employing the wavelet packet transform (WPT), a signal processing technique, homogeneity index (HI), a nonlinear measurement for time series signals, and the Enhanced Probabilistic Neural Network classification algorithm. The effectiveness and usefulness of the proposed method is evaluated using a database of measured ECG data acquired from 20 SCD and 18 normal patients. The proposed methodology presents the following significant advantages: (1) compared with previous works, the proposed methodology achieves a higher accuracy using a single nonlinear feature, HI, thus requiring low computational resource for predicting an SCD onset in real-time, unlike other methodologies proposed in the literature where a large number of nonlinear features are used to predict an SCD event; (2) it is capable of predicting the risk of developing an SCD event up to 20 min prior to the onset with a high accuracy of 95.8%, superseding the prior 12 min prediction time reported recently, and (3) it uses the ECG signal directly without the need for transforming the signal to a heart rate variability signal, thus saving time in the processing.
Indexed as
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30117048What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.