Leveraging ResNet for Efficient ECG Heartbeat Classification

This paper provides a novel approach that uses a modified version of the ResNet architecture to classify heartbeats on an electrocardiogram (ECG). Padding, convolution, max pooling, convolutional blocks, average pooling, and fully linked layers are the six processes in the approach. The MIT-BIH Arrhythmia Database is used to test the approach on five different types of heartbeats: unclassifiable, supraventricular premature, premature ventricular contraction, fusion of ventricular and normal, and normal. The outcomes demonstrate that the suggested approach outperforms other current techniques like LSTM, CNN, and EfficientNet, achieving an accuracy of 98.6%. The performance, restrictions, and future directions of the model are also thoroughly examined in this work. The automated ECG heartbeat categorization using deep learning techniques is one way that the article advances the field of cardiac diagnosis.