ECG Signal Classification Technique Based on Deep Features Using Differential Evolution Algorithm Extreme Learning Machine (DEA-ELM)

Abstract

The movements of electrocardiogram (ECG) signals are very important in the diagnosis of heart disorders. Machine learning methods are widely used to classify ECG signals. The aim of this work is to contribute to the classification of ECG signals using the Differential Evolution Algorithm Extreme Learning Machine (DGA-ELM).In this paper, a public heart records in Physionet was utilized to classify ECG signals. The pre-processing was applied to eliminate the ECG signals from noise. Then, the ECG signals were converted to spectrograms for the feature extraction stage. A method was used originated on Convolutional Neural Network (CNN) to obtain the attributes of ECG signals. The DGA-ELM algorithm was used to select the best activation function. In this context, the best cost value 79.37% with a sigmoid activation function and 750 iteration in the classification made with DGA-ELM was achieved.