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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-07-01
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| Series: | SoftwareX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352711022000814 |
| _version_ | 1798020736338624512 |
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| author | Nestor Alexander Zermeño-Campos Daniel Cuevas-González Juan Pablo García-Vázquez Roberto López-Avitia Miguel Enrique Bravo-Zanoguera Marco A. Reyna Arnoldo Díaz-Ramírez |
| author_facet | Nestor Alexander Zermeño-Campos Daniel Cuevas-González Juan Pablo García-Vázquez Roberto López-Avitia Miguel Enrique Bravo-Zanoguera Marco A. Reyna Arnoldo Díaz-Ramírez |
| author_sort | Nestor Alexander Zermeño-Campos |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-04-11T17:02:29Z |
| format | Article |
| id | doaj.art-4caa3e4dee494d889acf7c110055ad4a |
| institution | Directory Open Access Journal |
| issn | 2352-7110 |
| language | English |
| last_indexed | 2024-04-11T17:02:29Z |
| publishDate | 2022-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | SoftwareX |
| spelling | doaj.art-4caa3e4dee494d889acf7c110055ad4a2022-12-22T04:13:07ZengElsevierSoftwareX2352-71102022-07-0119101124PÉEK: A cloud-based application for automatic electrocardiogram pre-diagnosisNestor Alexander Zermeño-Campos0Daniel Cuevas-González1Juan Pablo García-Vázquez2Roberto López-Avitia3Miguel Enrique Bravo-Zanoguera4Marco A. Reyna5Arnoldo Díaz-Ramírez6Facultad de Ingeniería, Universidad Autónoma de Baja California (UABC), MyDCI, Mexicali B.C., MexicoFacultad de Ingeniería, Universidad Autónoma de Baja California (UABC), MyDCI, Mexicali B.C., Mexico; Instituto de Ingeniería, Universidad Autónoma de Baja California (UABC), MyDCI, Mexicali, B.C., MexicoFacultad de Ingeniería, Universidad Autónoma de Baja California (UABC), MyDCI, Mexicali B.C., Mexico; Corresponding author.Facultad de Ingeniería, Universidad Autónoma de Baja California (UABC), MyDCI, Mexicali B.C., MexicoFacultad de Ingeniería, Universidad Autónoma de Baja California (UABC), MyDCI, Mexicali B.C., Mexico; Instituto Tecnológico de Mexicali (ITM), Mexicali B.C., MexicoInstituto de Ingeniería, Universidad Autónoma de Baja California (UABC), MyDCI, Mexicali, B.C., MexicoInstituto Tecnológico de Mexicali (ITM), Mexicali B.C., MexicoElectrocardiogram (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).http://www.sciencedirect.com/science/article/pii/S2352711022000814ArrhythmiaElectrocardiogramECGDeep learningConvolutional neural networks |
| spellingShingle | Nestor Alexander Zermeño-Campos Daniel Cuevas-González Juan Pablo García-Vázquez Roberto López-Avitia Miguel Enrique Bravo-Zanoguera Marco A. Reyna Arnoldo Díaz-Ramírez PÉEK: A cloud-based application for automatic electrocardiogram pre-diagnosis SoftwareX Arrhythmia Electrocardiogram ECG Deep learning Convolutional neural networks |
| title | PÉEK: A cloud-based application for automatic electrocardiogram pre-diagnosis |
| title_full | PÉEK: A cloud-based application for automatic electrocardiogram pre-diagnosis |
| title_fullStr | PÉEK: A cloud-based application for automatic electrocardiogram pre-diagnosis |
| title_full_unstemmed | PÉEK: A cloud-based application for automatic electrocardiogram pre-diagnosis |
| title_short | PÉEK: A cloud-based application for automatic electrocardiogram pre-diagnosis |
| title_sort | peek a cloud based application for automatic electrocardiogram pre diagnosis |
| topic | Arrhythmia Electrocardiogram ECG Deep learning Convolutional neural networks |
| url | http://www.sciencedirect.com/science/article/pii/S2352711022000814 |
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