PÉEK: A cloud-based application for automatic electrocardiogram pre-diagnosis

Electrocardiogram (ECG) visual analysis is a common task performed by a healthcare specialist as a cardiovascular-diseases pre-diagnostic technique. However, when an ECG specialist analyzes long-time duration records (such as a 24-h Holter), the task becomes tiresome, complicated, and a probable err...

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Bibliographic Details
Published in:SoftwareX 2022-07, Vol.19, p.101124, Article 101124
Main Authors: Zermeño-Campos, Nestor Alexander, Cuevas-González, Daniel, García-Vázquez, Juan Pablo, López-Avitia, Roberto, Bravo-Zanoguera, Miguel Enrique, Reyna, Marco A., Díaz-Ramírez, Arnoldo
Format: Article
Language:English
Subjects:
Arrhythmia
Convolutional neural networks
Deep learning
ECG
Electrocardiogram
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Summary:Electrocardiogram (ECG) visual analysis is a common task performed by a healthcare specialist as a cardiovascular-diseases pre-diagnostic technique. However, when an ECG specialist analyzes long-time duration records (such as a 24-h Holter), the task becomes tiresome, complicated, and a probable erroneous diagnostic. This article presents a cloud-based application called PÉEK that helps healthcare specialists automatically detect normal and abnormal heartbeats on ECG registers using Stationary Wavelet Transform (SWT) and Convolutional Neural Networks (CNN). In order to illustrate the functionality of PÉEK, we present the analysis of set ECG traces from the MIT-BIH Arrhythmia Database. This software can detect with a 99.09% accuracy normal heartbeats, premature ventricular contractions (PVC) beats, and others (Premature Atrial Contraction, Left Bundle Branch Block, and Right Bundle Branch Block).
ISSN:2352-7110
2352-7110
DOI:10.1016/j.softx.2022.101124