ResNet-Based Deep Learning Approach for Automated ECG Arrhythmia Recognition System

This study introduces a novel approach to classifying electrocardiogram (ECG) signals using deep learning techniques, specifically leveraging TensorFlow and the ResNet50 architecture. The research aims to develop a robust model for accurate ECG signal classification, which is vital for various medical diagnostics and healthcare applications. The methodology includes preprocessing the ECG dataset, constructing a deep neural network model based on ResNet50, and training it on the labelled data. The model's performance is thoroughly evaluated using metrics like accuracy, confusion matrix, classification report, F1-score, and ROC curve analysis. The results show a 92.71% accuracy in ECG signal classification, highlighting the effectiveness of the proposed approach. This research advances signal classification in healthcare, offering a promising tool for the automated analysis and interpretation of ECG signals, ultimately aiding healthcare professionals in the timely diagnosis and treatment of cardiovascular conditions.