eprintid: 18857
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/88/57
datestamp: 2024-06-04 14:11:17
lastmod: 2024-06-04 14:11:17
status_changed: 2024-06-04 14:04:17
type: article
metadata_visibility: show
creators_name: Ahmed, A.A.
creators_name: Ali, W.
creators_name: Abdullah, T.A.A.
creators_name: Malebary, S.J.
title: Classifying Cardiac Arrhythmia from ECG Signal Using 1D CNN Deep Learning Model
ispublished: pub
note: cited By 30
abstract: Blood circulation depends critically on electrical activation, where any disturbance in the orderly pattern of the heart�s propagating wave of excitation can lead to arrhythmias. Diagnosis of arrhythmias using electrocardiograms (ECG) is widely used because they are a fast, inexpensive, and non-invasive tool. However, the randomness of arrhythmic events and the susceptibility of ECGs to noise leads to misdiagnosis of arrhythmias. In addition, manually diagnosing cardiac arrhythmias using ECG data is time-intensive and error-prone. With better training, deep learning (DL) could be a better alternative for fast and automatic classification. The present study introduces a novel deep learning architecture, specifically a one-dimensional convolutional neural network (1D-CNN), for the classification of cardiac arrhythmias. The model was trained and validated with real and noise-attenuated ECG signals from the MIT-BIH dataset. The main aim is to address the limitations of traditional electrocardiograms (ECG) in the diagnosis of arrhythmias, which can be affected by noise and randomness of events, leading to misdiagnosis and errors. To evaluate the model performance, the confusion matrix is used to calculate the model accuracy, precision, recall, f1 score, average and AUC-ROC. The experiment results demonstrate that the proposed model achieved outstanding performance, with 1.00 and 0.99 accuracies in the training and testing datasets, respectively, and can be a fast and automatic alternative for the diagnosis of arrhythmias. © 2023 by the authors.
date: 2023
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147872474&doi=10.3390%2fmath11030562&partnerID=40&md5=30d34fd03199e88301033299033779ec
id_number: 10.3390/math11030562
full_text_status: none
publication: Mathematics
volume: 11
number: 3
refereed: TRUE
citation:   Ahmed, A.A. and Ali, W. and Abdullah, T.A.A. and Malebary, S.J.  (2023) Classifying Cardiac Arrhythmia from ECG Signal Using 1D CNN Deep Learning Model.  Mathematics, 11 (3).