Heartbeat Classification and Arrhythmia Detection Using a Multi-Model Deep-Learning Technique

Abstract

Cardiac arrhythmias pose a significant danger to human life; therefore, it is of utmost importance to be able to efficiently diagnose these arrhythmias promptly. There exist many techniques for the detection of arrhythmias; however, the most widely adopted method is the use of an Electrocardiogram (ECG). The manual analysis of ECGs by medical experts is often inefficient. Therefore, the detection and recognition of ECG characteristics via machine-learning techniques have become prevalent. There are two major drawbacks of existing machine-learning approaches: (a) they require extensive training time; and (b) they require manual feature selection. To address these issues, this paper presents a novel deep-learning framework that integrates various networks by stacking similar layers in each network to produce a single robust model. The proposed framework has been tested on two publicly available datasets for the recognition of five micro-classes of arrhythmias. The overall classification sensitivity, specificity, positive predictive value, and accuracy of the proposed approach are 98.37%, 99.59%, 98.41%, and 99.35%, respectively. The results are compared with state-of-the-art approaches. The proposed approach outperformed the existing approaches in terms of sensitivity, specificity, positive predictive value, accuracy and computational cost.

A Literature Review: ECG-Based Models for Arrhythmia Diagnosis Using Artificial Intelligence Techniques

“…Although the study 9 includes the treatment of myocardial ischemia, it was selected because there was no interference with arrhythmia diagnosis; the 2 diseases were independently addressed. Irfan et al 10 used a dataset with 13 types of heartbeats, including arrhythmias and myocardial infarctions, which added more variety to the dataset without affecting the performance of the model for arrhythmia diagnosis.…”

“…Ullah et al 39 mentioned segmentation and pre-processing of data with no more details on the used techniques. Irfan et al 10 applied standardization of data (standard scalar unit) and feature reduction with PCA on the UCI dataset, and noise removal with DWT and normalization on the MIT-BIH arrhythmia dataset.…”

“…However, Yang et al 22 showed better accuracy for MIT-BIH database than INCART, from which were extracted only 2 leads (II and V1). Irfan et al 10 and Wang 49 recorded better results on MIT-BIH database because the other databases were highly imbalanced.…”

“…Irfan et al 10 used the publicly available MIT-BIH arrhythmia database and UCI arrhythmia dataset available in the University of California Irvine ML Repository.…”

“…Synthetic minority oversampling technique (SMOTE) is used in literature 10 , 50 to handle the problem of the imbalanced data in the MIT-BIH arrhythmia database and MIT-BIH AF database. SMOTE relies on a k-nearest neighbor algorithm to create new synthetic data.…”