Deep Learning Models for Arrhythmia Detection in IoT Healthcare Applications

Hammad, Mohamed; Abd El-Latif, Ahmed A.; Hussain, Amir; Abd El-Samie, Fathi E.; Gupta, Brij B.; Ugail, Hassan; Sedik, Ahmed

Abstract

In this paper, novel convolutional neural network (CNN) and convolutional long short-term (ConvLSTM) deep learning models (DLMs) are presented for automatic detection of arrhythmia for IoT applications. The input ECG signals are represented in 2D format, and then the obtained images are fed into the proposed DLMs for classification. This helps to overcome most of the problems of the previous machine and deep learning models such as overfitting, and working on more than one lead of ECG signals. We use several publicly available datasets from PhysioNet such as MIT-BIH, PhysioNet 2016 and PhysioNet 2018 for model assessment. Overall accuracies of 97%, 98 %, 94 % and 91 % are obtained on spectrograms of MIT-BIH dataset, compressed MIT-BIH dataset, PhysioNet 2016 dataset, and PhysioNet 2018 dataset, respectively. Compared to the previous works, the proposed framework is more robust and efficient, especially in the case of noisy data.

Citation

Hammad, M., Abd El-Latif, A. A., Hussain, A., Abd El-Samie, F. E., Gupta, B. B., Ugail, H., & Sedik, A. (2022). Deep Learning Models for Arrhythmia Detection in IoT Healthcare Applications. Computers and Electrical Engineering, 100, Article 108011. https://doi.org/10.1016/j.compeleceng.2022.108011

• Journal Article Type: Article
• Acceptance Date: Apr 12, 2022
• Online Publication Date: Apr 28, 2022
• Publication Date: 2022-05
• Deposit Date: Jun 9, 2022
• Journal: Computers and Electrical Engineering
• Print ISSN: 0045-7906
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 100
• Article Number: 108011
• DOI: https://doi.org/10.1016/j.compeleceng.2022.108011
• Public URL: http://researchrepository.napier.ac.uk/Output/2877381