A Novel Approach for ECG Classification Using Probability Continuous Wavelet Transform and Alexnet-Deep Neural Network

Abstract

Arrhythmia identification using electrocardiograms (ECGs) is a critical component of biomedical signal processing and pattern recognition. The classification of ECG arrhythmias is presented in this article using features extracted from the Extreme Value distributor of the probability density function (PDF), also training model built using the Alexnet architecture of a conventional neural network (CNN). A continuous wavelet transform (CWT) was used to convert the features space to a two-dimensional image. Additionally, a comparison of the two approaches was conducted. In the first case, we used CWT to convert ECG data to two-dimensional images. In the second case, features were extracted from PDF files and converted to the image domain using a CWT. With a learning rate of 0.001 and batch size of 250, the accuracy of tests was 98.67 %. The results indicated that the proposed method is more accurate than the conventional method when it comes to feature extraction