Key result
A methodology combining complete ensemble empirical mode decomposition and a convolutional neural network predicted sudden cardiac death up to 30 minutes in advance with 97.5% accuracy.
Why the study?
Sudden cardiac death represents 15-20% of global deaths, and ECG variations may allow predicting events 30 minutes in advance.
Does a methodology based on CEEMD and CNN accurately predict sudden cardiac death 30 minutes in advance using ECG records?
Does a methodology based on CEEMD and CNN accurately predict sudden cardiac death 30 minutes in advance using ECG records?
A novel machine learning approach combining CEEMD and CNN can predict sudden cardiac death from ECG records with 97.5% accuracy 30 minutes prior to the event.
Sudden cardiac death (SCD) is a global health problem, which represents 15–20% of global deaths. This type of death can be due to different heart conditions, where ventricular fibrillation has been reported as the main one. These cardiac alterations can be seen in an electrocardiogram (ECG) record, where the heart’s electrical activity is altered. The present research uses these variations to be able to predict 30 min in advance when the SCD event will occur. In this regard, a methodology based on the complete ensemble empirical mode decomposition (CEEMD) method to decompose the cardiac signal into its intrinsic mode functions (IMFs) and a convolutional neural network (CNN) for automatic diagnosis is proposed. Results for the ensemble empirical mode decomposition (EEMD) method and the empirical mode decomposition (EMD) method are also compared. Results demonstrate that the combination of the CEEMD and the CNN is a potential solution for SCD prediction since 97.5% of accuracy is achieved up to 30 min in advance of the SCD event.
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Centeno-Bautista et al. (2023) studied Sudden cardiac death (SCD). Complete ensemble empirical mode decomposition (CEEMD) and convolutional neural network (CNN) vs. Ensemble empirical mode decomposition (EEMD) and empirical mode decomposition (EMD) was evaluated on Prediction of sudden cardiac death 30 minutes in advance (accuracy). A methodology combining complete ensemble empirical mode decomposition and a convolutional neural network predicted sudden cardiac death up to 30 minutes in advance with 97.5% accuracy.
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