Automated arrhythmia detection from electrocardiogram signal using stacked restricted Boltzmann machine model

Significant advances in deep learning techniques have made it possible to offer technologically advanced methods to detect cardiac abnormalities. In this study, we have proposed a new deep learning based Restricted Boltzmann machine (RBM) model for the classification of arrhythmias from Electrocardiogram (ECG) signal. The work is divided into three phases where, in the first phase, signal processing is performed, including the normalization of the heartbeats as well as the segmentation of the heartbeats. In the second phase, the stacked RBM model is implemented which extracts the essential features from the ECG signal. Finally, a SoftMax activation function is used that classifies the ECG signal into four types of heartbeat classes according to ANSI/AAMA standards. This stacked RBM model is offered as three types of experiments, patient independent data classification for multi-class, patient independent data for binary classification, and patient specific classification. The best result was obtained using patient independent binary classification with an overall accuracy of 99.61%. For Patient Independent Multi Class classification, accuracy obtained was 98.61% and for patient specific data, the accuracy was 95.13%. The experimental results shows that the developed RBM model has better performance in terms of accuracy, sensitivity and specificity as compared to work mentioned in the other research papers. Article highlights The proposed RBM model is skilled to automatically classify ECG heartbeat according to the ANSI- AAMI standards with accuracy, Recall, specificity. The performance of the RBM model to correctly classify heartbeat classes was found to be improved. The model is fully automatic, hence there is no requirement of additional system like feature extraction, feature selection, and classification.