Abstract: The greatest technique to track the functionality and health of the cardiovascular system and spot diseases associated with it is to use ECG signals. The ECG pattern reflects irregular heartbeats, and these abnormal signals are referred to as ARRHYTHMIAS. The need of the hour is growing for automated ECG arrhythmia signal categorization and identification that delivers faster and more precise results .Different machine learning techniques have been used to improve the models speed and durability as well as the accuracy of the findings. The architectures and datasets used have received a lot of attention, but preparing the data is also crucial. In this study, a pre-processing method that greatly increases the ECG classification accuracy of deep learning models is proposed alone with a modified deep learning architecture that increases training stability. The system can achieve accuracy levels of more than 99% with this pre-processing method and deep learning model without over fitting the model.
Keywords: Electrocardiogram (ECG), Deep learning (or deep neural network), Convolutional Neural Network (CNN) model, ARRHYTHMIAS, Activation techniques, epoch, validation accuracy.
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DOI:
10.17148/IJARCCE.2025.14722
