A Survey on the Advancement of ECG Classification Using Deep Convolutional Neural Network

B. Ashwini, M. Ganesan and R. Lavanya

Classification of Electrocardiograph (ECG) signal has an ultimate position in clinical analysis of heart diseases. In this paper, we have made a survey on advanced way of classifying ECG. It is based on multivariate time series classification. Here combining Dynamic Time Warping (DTW) and Nearest Neighbor (k-NN) classification was found to give a much desired result. Usually as the data point set increases, the time consumption of DTW and 1-NN is very much costly and it is less effective for feature- based classification methods. So, here they are modifying the procedure of outmoded feature established method by a new feature learning techniques. Here they have also claimed about the Multi Channels Deep Convolutional Neural Networks (MCDCNN), as a novel deep learning framework for classifying the recorded ECG beats. In the proposed system, initially every channel learns the features of all specific single variate time series information which will then be combined to give a feature representation to the final outcome layer. Then, these learnt features are given to the Multilayer Perceptron (MLP) for further classification. As a result on carrying out the experiments based on real time data, this model has proven it is more efficient and accurate than all the previous proven models.

Volume 12 | 05-Special Issue

Pages: 1072-1078

DOI: 10.5373/JARDCS/V12SP5/20201859