A multitier deep learning model for arrhythmia detection

Hammad, M., Iliyasu, A. M., Subasi, A., Ho, E. S. L. and Abd El-Latif, A. A. (2021) A multitier deep learning model for arrhythmia detection. IEEE Transactions on Instrumentation and Measurement, 70, 2502809. (doi: 10.1109/TIM.2020.3033072)

Full text not currently available from Enlighten.


An electrocardiograph (ECG) is employed as a primary tool for diagnosing cardiovascular diseases (CVDs). ECG signals provide a framework to probe the underlying properties and enhance the initial diagnosis obtained via traditional tools and patient-doctor dialogs. Notwithstanding its proven utility, deciphering large data sets to determine appropriate information remains a challenge in ECG-based CVD diagnosis and treatment. Our study presents a deep neural network (DNN) strategy to ameliorate the aforementioned difficulties. Our strategy consists of a learning stage where classification accuracy is improved via a robust feature extraction protocol. This is followed by using a genetic algorithm (GA) process to aggregate the best combination of feature extraction and classification. Comparison of the performance recorded for the proposed technique alongside state-of-the-art methods reported the area shows an increase of 0.94 and 0.953 in terms of average accuracy and F1 score, respectively. The outcomes suggest that the proposed model could serve as an analytic module to alert users and/or medical experts when anomalies are detected.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Ho, Dr Edmond S. L
Authors: Hammad, M., Iliyasu, A. M., Subasi, A., Ho, E. S. L., and Abd El-Latif, A. A.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:IEEE Transactions on Instrumentation and Measurement
ISSN (Online):1557-9662
Published Online:26 October 2020

University Staff: Request a correction | Enlighten Editors: Update this record