ECG Classification using Deep Learning

Abstract

Abstract: The importance of ECG categorization has grown significantly, as demonstrated by the many contemporary medical applications that address this issue. A plethora of machine learning (ML) tools are already available for the analysis and classification of ECG data. The primary drawbacks of these machine learning outcomes are the utilization of heuristic handcrafted or manufactured features with shallow feature learning architectures, or the acquisition of numerical data using image processing. The use of CNN, which recognizes and captures specific patterns in images, is one suggested solution for this problem utilizing deep learning architecture. Recurrent neural networks (RNN) and their variant LSTM are advantageous in the ECG classification task due to their implicit capacity to effectively capture temporal dependencies and noise while providing interpretability of hidden states. In contrast to human results, these Deep Learning Techniques yield results about the employment of Recurrent Neural Networks with LSTM layers and Convolutional Neural Networks without any featureengineered procedure and competing classification accuracy