Classification of ECG Signals Using Deep Learning

Abstract

Accurate classifi cation of electrocardiogram (ECG) signals is crucial for diagnosing cardiac conditions. In this project, our objective was to classify ECG beats into disease classes using deep learning techniques. We leveraged two primary datasets: the MIT-BIH dataset from PhysioNet and the INCART 12-lead Arrhythmia Database from St. Petersburg, providing a comprehensive basis for our classifi cation models. Our methodology involved a hybrid model combining 1D and 2D convolutional neural networks (CNNs). We applied a 1D CNN architecture to process ECG signals directly and transformed ECG beats into images for a 2D CNN architecture. By incorporating both approaches, we captured temporal and spatial information in the ECG signals. Data augmentation techniques were employed to address imbalanced data distribution and improve model performance.