A Multitier Deep Learning Model for Arrhythmia Detection

Hammad, Mohamed, Iliyasu, Abdullah M., Subasi, Abdulhamit, Ho, Edmond and El-Latif, Ahmed A Abd (2020) A Multitier Deep Learning Model for Arrhythmia Detection. IEEE Transactions on Instrumentation and Measurement, 70. p. 9239355. ISSN 0018-9456

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Official URL: https://doi.org/10.1109/tim.2020.3033072


Electrocardiograph (ECG) is employed as a primary tool for diagnosing cardiovascular diseases (CVD) in the hospital, which often helps in the early detection of such ailments. ECG signals provide a framework to probe the underlying properties and enhance the initial diagnosis obtained via traditional tools and patient-doctor dialogues. It provides cardiologists with inferences regarding more serious cases. Notwithstanding its proven utility, deciphering large datasets 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. This is followed using a genetic algorithm (GA) process to aggregate the best combination of feature extraction and classification. The MIT-BIH Arrhythmia was employed in the validation to identify five arrhythmia categories based on the association for the advancement of medical instrumentation (AAMI) standard. The performance of the proposed technique alongside state-of-the-art in the area shows an increase of 0.94 and 0.953 in terms of average accuracy and F1 score, respectively. The proposed model could serve as an analytic module to alert users and/or medical experts when anomalies are detected in the acquired ECG data in a smart healthcare framework.

Item Type: Article
Uncontrolled Keywords: AAMI standard, Arrhythmia Detection, Cardiovascular Diseases, Deep Neural Network, E-healthcare devices, Electrocardiogram (ECG), Genetic Algorithm
Subjects: B900 Others in Subjects allied to Medicine
G600 Software Engineering
G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Rachel Branson
Date Deposited: 14 Oct 2020 10:13
Last Modified: 31 Jul 2021 14:47
URI: http://nrl.northumbria.ac.uk/id/eprint/44509

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