Jundishapur Journal of Medical Sciences

Jundishapur Journal of Medical Sciences

A Multimodal Convolutional Neural Networks (CNN) Approach for Prediction of Sudden Cardiac Death Versus Congestive Heart Failure

Document Type : Original Article

Authors
sara tavazo 1
Farideh Ebrahimi 2
1 Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Shariati Ave., Babol, Iran
2 Assistant Professor of Electrical and Computer Engineering, Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Shariati Ave., Babol, Iran..
10.22118/jsmj.2025.515064.3885
Abstract
Background and Objectives Sudden cardiac death (SCD) remains one of the leading causes of mortality worldwide. The purpose of this study is to predict SCD early and to distinguish it from congestive heart failure (CHF), which presents with similar electrocardiographic patterns.
Subjects and Methods In this study, shallow and fully automated models based on convolutional neural networks (CNNs) are presented to predict SCD. In these models the electrocardiogram (ECG) signal is given as input to the model, and classification is performed at the output of the model.
Results The first model, which is based on one-dimensional CNNs, could predict SCD with an accuracy of 96%. In the next step, by adding data related to CHF, an accuracy of 97.85 was obtained in the three-class classification (normal, before SCD, and CHF). In the second model, which is based on two-dimensional CNNs, first, each part of the ECG signals was converted into two-dimensional images using continuous wavelet transform (CWT) and given as input to the model. This model was able to predict SCD with 99% accuracy.
Conclusion The proposed fully automated CNN-based models demonstrated high predictive accuracy for SCD within one hour before its occurrence.
Keywords
Electrocardiogram (ECG)
Convolutional Neural Networks (CNNs)
Sudden Cardiac Death (SCD)
Congestive Heart Failure (CHF)
Subjects
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  • Receive Date 01 May 2025
  • Revise Date 15 November 2025
  • Accept Date 19 November 2025