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Electrocardiography (ECG) has been a reliable method to monitor the proper functioning of the cardiovascular system for decades. Recently, there has been a lot of research focusing on accurately analysing the heart condition through ECG. In recent days, numerous attempts are being taken to analyse these signals using deep learning algorithms which include the implementation of artificial neural networks like Convolutional Neural Networks, Recurrent Neural Networks and the like. In this context, this chapter intends to present some important techniques for classifying heartbeats based on deep neural networks with 1D CNN. There are five classes of ECG signals (namely, N, S, V, F and Q) based on the AAMI EC57 standard. The primary focus of this chapter is to discuss the techniques to classify ECG signals in those classes with promising accuracy and draw a clear picture of the current state of the art in this sphere of study.

Deep Learning Algorithms for Efficient Analysis of ECG Signals to Detect Heart Disorders