ECG Classification Using Deep Learning

Abstract

ECG Classification or Heartbeat Classification is an important task in cardiology. Deep Learning based methods assist human experts in the diagnosis of cardiac diseases and help save important lives. In this research, a deep learning framework is proposed for classification of cardiac diseases using the heartbeats from ECG data. Multiple models are proposed including CNN, LSTM and Autoencoders. The models are trained on a digitized format of CPEIC Cardiac dataset. First, the ECG images are digitized, segmenting the Lead-II heartbeats and then the digitized signals are passed to the deep learning models. The proposed CNN model achieves highest accuracy of 97%, equivalent to the best accuracy (98%) of image based models with all 12-leads data. The proposed model is highly accurate and fast for inference for real-time and direct monitoring of ECG signals. The self-supervised learning (SSL) based model is experimented on MIT BIH dataset as well and it achieves state-of-the-art results (∼96% accuracy). In the SSL based model, after training on un-labeled data, fine-tuning on only 50% of the training data gives 94.8% accuracy, which cuts the training time by 50% and accuracy is almost the same, making this model computationally efficient.