ECG Beat Classifiers: A Journey from ANN To DNN
Manisha Manisha, Sanjeev Kumar Dhull, Krishna Kant Singh
Abstract
Automatic electrocardiogram beat classification is a prevalent field of research among the biomedical and computer science researchers. In the last decade, the research has seen a transition from feature centric classification approach to a classifier centric approach. The performance of conventional approach based classifiers such as probabilistic neural networks, support vector machines, is highly dependent on the performance of pre-processing and feature extraction modules. Whereas, modern approach based classifiers such as deep neural networks, convolutional neural networks, do not need handcrafted features. They can generate characteristic features automatically from raw ECG signal. This paper aims to provide a comparative analysis of conventional and modern approaches. Further, deep learning based classifier models have been investigated to design a road-map for researcher new to this field.