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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


Mohamed Hammad

Ahmed A. Abd El-Latif

Fathi E. Abd El-Samie

Brij B. Gupta

Hassan Ugail

Ahmed Sedik


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.

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
Public URL