Ann Varghese
@aisat.ac.in
Assistant Professor, Department of Electronics and Communication Engineering
Albertian Institute of Science and Technology
RESEARCH, TEACHING, or OTHER INTERESTS
Artificial Intelligence, Signal Processing, Engineering, Multidisciplinary
Scopus Publications
Scholar Citations
Scholar h-index
Scopus Publications
Ann Varghese, M.S. Midhun, and James Kurian
Inderscience Publishers
Ann Varghese, Suraj Kamal, and James Kurian
Springer Science and Business Media LLC
Ann Varghese, Midhun Muraleedharan Sylaja, and James Kurian
Walter de Gruyter GmbH
Abstract Arrhythmias are irregular heartbeats that may be life-threatening. Proper monitoring and the right care at the right time are necessary to keep the heart healthy. Monitoring electrocardiogram (ECG) patterns on continuous monitoring devices is time-consuming. An intense manual inspection by caregivers is not an option. In addition, such an inspection could result in errors and inter-variability. This article proposes an automated ECG beat classification method based on deep neural networks (DNN) to aid in the detection of cardiac arrhythmias. The data collected by an Internet of Things enabled ECG monitoring device are transferred to a server. They are analysed by a deep learning model, and the results are shared with the primary caregiver. The proposed model is trained using the MIT-BIH ECG arrhythmia database to classify into four classes: normal beat (N), left bundle branch block beat (L), right bundle branch block beat (R), and premature ventricular contraction (V). The received data are sampled with an overlapping sliding window and divided into an 80:20 ratio for training and testing, with tenfold cross-validation. The proposed method achieves higher accuracy with a simple model without any preprocessing when compared with previous works. For the train and test sets, we achieved accuracy rates of 99.09 and 99.03%, respectively. A precision, recall, and F1 scores of 0.99 is obtained. The proposed model achieves its goal of developing a simple and accurate ECG monitoring system with improved performance. This simple and efficient deep learning approach for heartbeat classification could be applied in real-time telehealth monitoring systems.