Enhanced classification of heartbeat electrocardiogram signals using a long short-term memory–convolutional neural network ensemble: paving the way for preventive healthcare

Alharbi, Njud S., Jahanshahi, Hadi, Yao, Qijia, Bekiros, Stelios and Moroz, Irene (2023) Enhanced classification of heartbeat electrocardiogram signals using a long short-term memory–convolutional neural network ensemble: paving the way for preventive healthcare. Mathematics, 11 (18). ISSN 2227-7390

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Abstract

In the rapidly evolving field of medical diagnosis, the accurate and prompt interpretation of heartbeat electrocardiogram (ECG) signals have become increasingly crucial. Despite the presence of recent advances, there is an exigent need to enhance the accuracy of existing methodologies, especially given the profound implications such interpretations can have on patient prognosis. To this end, we introduce a novel ensemble comprising Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models to enable the enhanced classification of heartbeat ECG signals. Our approach capitalizes on LSTM’s exceptional sequential data learning capability and CNN’s intricate pattern recognition strength. Advanced signal processing methods are integrated to enhance the quality of raw ECG signals before feeding them into the deep learning model. Experimental evaluations on benchmark ECG datasets demonstrate that our proposed ensemble model surpasses other state-of-the-art deep learning models. It achieves a sensitivity of 94.52%, a specificity of 96.42%, and an accuracy of 95.45%, highlighting its superior performance metrics. This study introduces a promising tool for bolstering cardiovascular disease diagnosis, showcasing the potential of such techniques to advance preventive healthcare.

Item Type:	Article
Official URL:	https://www.mdpi.com/journal/mathematics
Additional Information:	© 2023 The Authors
Divisions:	LSE Health
Subjects:	Q Science > QA Mathematics R Medicine > RC Internal medicine R Medicine > RA Public aspects of medicine
Date Deposited:	21 May 2024 14:48
Last Modified:	21 Nov 2024 07:00
URI:	http://eprints.lse.ac.uk/id/eprint/123558

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