Multi-class ECG Signal Classification Using Deep Learning

Electrocardiogram (ECG) signal classification is essential for identifying cardiovascular disorders, and the utilization of deep learning methodologies has demonstrated potential in improving classification precision. In this paper, we evaluate the efficacy of deep learning architectures in categorizing 12-lead ECG signals into distinct cardiac states, utilizing data derived from the CPSP 2018. We pre-processed the ECG signals to normalize the data and employed multiple deep learning architectures, including ResNet 34, to categorize the ECG signals into predetermined categories. The efficacy of these models was assessed using criteria including accuracy, sensitivity, and specificity. The model achieved an overall average F1 score of 80