Key result
Multilayer neural networks combining convolutional and temporal architectures accurately classify ECG signals for arrhythmia detection.
Why the study?
Traditional ECG analysis requires significant time and high specialist qualification, prompting the need for automated systems to rapidly and accurately interpret ECG data.
Does the use of convolutional neural networks improve the accuracy of classifying ECG signals compared to other architectures?
Does the use of convolutional neural networks improve the accuracy of classifying ECG signals compared to other architectures?
Convolutional neural networks show high accuracy in automated ECG arrhythmia classification, offering potential for clinical application.
Currently, given the increasing number of cardiovascular diseases, the relevance of fast and accurate analysis of electrocardiograms (ECG) is growing significantly. Traditional methods of ECG analysis require significant time costs and high qualifications of specialists, which stimulates the development and implementation of automated systems capable of effectively interpreting ECG data. The article investigates the use of neural networks for ECG analysis, which allows significantly increasing the speed and accuracy of diagnosis. The development and analysis of the effectiveness of a neural network model capable of recognizing and classifying the main arrhythmias from ECG data is proposed. The article presents the results of comparing several neural network architectures, including convolutional and recurrent neural networks, on a set of ECG data with various pathologies. The analysis showed that the use of convolutional neural networks (CNN) demonstrates high accuracy in classifying ECG signals, which provides significant potential for clinical application. Fig.: 1. Bibliography: 9 titles.
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Кrylova et al. (2025) studied Cardiac arrhythmias. Multilayer neural network architecture (CNN and TCN) vs. Traditional ECG analysis methods was evaluated on Accuracy of ECG signal classification. A multilayer neural network architecture combining convolutional and temporal convolutional networks demonstrates high accuracy in classifying ECG signals for arrhythmia detection.
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