Key result
ECG signal processing completely distinguishes high-risk SCD patients from healthy controls up to 6 minutes prior.
Why the study?
Sudden Cardiac Death leads to the killing of millions of people worldwide every year, motivating methods to predict it using electrocardiogram signal processing.
Does an algorithm using non-linear ECG features and Hybrid-RBF classifiers accurately predict sudden cardiac death in high-risk individuals?
Does an algorithm using non-linear ECG features and Hybrid-RBF classifiers accurately predict sudden cardiac death in high-risk individuals?
A machine learning approach using Hybrid-RBF classifiers on ECG heart rate variability features can accurately predict sudden cardiac death up to 6 minutes prior to the event.
ECG-based SCD prediction may inform future risk models; leaves open prospective validation before any clinical adoption.
Sudden Cardiac Death (SCD) leads to the killing of millions of people worldwide every year. In this article, sudden cardiac death is predicted by utilizing electrocardiogram signal processing. For this purpose, after extracting the signal of heart rate variations from the electrocardiogram signal, temporal and non-linear features have been extracted. In the next step, by applying LDA to the combined feature vector, the feature dimensions are reduced and finally, healthy people and high-risk people are classified through Hybrid-RBF classifiers. The obtained results show that there are features in the signal of heart rate variations related to risk-taking individuals near the occurrence of sudden cardiac death, that completely distinguish them from healthy persons. It has also been shown that from 6 minutes before the occurrence of cardiac death, this increase in the probability of risk is quite evident, so that as we get closer to the occurrence of the accident, the probability of its occurrence also increases, and this is enough time to adopt strategies to prevent it. The simulation results achieved by the data available in the MIT-BIH database prove the ability of the presented methods to achieve accurate diagnosis.
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Koliji et al. (2023) studied Sudden Cardiac Death. ECG signal processing with non-linear features and Hybrid-RBF classifiers vs. Healthy persons was evaluated on Prediction of sudden cardiac death. ECG signal processing using non-linear features and Hybrid-RBF classifiers completely distinguished individuals at high risk of sudden cardiac death from healthy persons up to 6 minutes before the event.
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