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Short Single-Lead ECG Signal Delineation-Based Deep Learning: Implementation in Automatic Atrial Fibrillation Identification

Physicians manually interpret an electrocardiogram (ECG) signal morphology in routine clinical practice. This activity is a monotonous and abstract task that relies on the experience of understanding ECG waveform meaning, including P-wave, QRS-complex, and T-wave. Such a manual process depends on si...

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Autores principales: Tutuko, Bambang, Rachmatullah, Muhammad Naufal, Darmawahyuni, Annisa, Nurmaini, Siti, Tondas, Alexander Edo, Passarella, Rossi, Partan, Radiyati Umi, Rifai, Ahmad, Sapitri, Ade Iriani, Firdaus, Firdaus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8953093/
https://www.ncbi.nlm.nih.gov/pubmed/35336500
http://dx.doi.org/10.3390/s22062329
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author Tutuko, Bambang
Rachmatullah, Muhammad Naufal
Darmawahyuni, Annisa
Nurmaini, Siti
Tondas, Alexander Edo
Passarella, Rossi
Partan, Radiyati Umi
Rifai, Ahmad
Sapitri, Ade Iriani
Firdaus, Firdaus
author_facet Tutuko, Bambang
Rachmatullah, Muhammad Naufal
Darmawahyuni, Annisa
Nurmaini, Siti
Tondas, Alexander Edo
Passarella, Rossi
Partan, Radiyati Umi
Rifai, Ahmad
Sapitri, Ade Iriani
Firdaus, Firdaus
author_sort Tutuko, Bambang
collection PubMed
description Physicians manually interpret an electrocardiogram (ECG) signal morphology in routine clinical practice. This activity is a monotonous and abstract task that relies on the experience of understanding ECG waveform meaning, including P-wave, QRS-complex, and T-wave. Such a manual process depends on signal quality and the number of leads. ECG signal classification based on deep learning (DL) has produced an automatic interpretation; however, the proposed method is used for specific abnormality conditions. When the ECG signal morphology change to other abnormalities, it cannot proceed automatically. To generalize the automatic interpretation, we aim to delineate ECG waveform. However, the output of delineation process only ECG waveform duration classes for P-wave, QRS-complex, and T-wave. It should be combined with a medical knowledge rule to produce the abnormality interpretation. The proposed model is applied for atrial fibrillation (AF) identification. This study meets the AF criteria with RR irregularities and the absence of P-waves in essential oscillations for even more accurate identification. The QT database by Physionet is utilized for developing the delineation model, and it validates with The Lobachevsky University Database. The results show that our delineation model works properly, with 98.91% sensitivity, 99.01% precision, 99.79% specificity, 99.79% accuracy, and a 98.96% F1 score. We use about 4058 normal sinus rhythm records and 1804 AF records from the experiment to identify AF conditions that are taken from three datasets. The comprehensive testing has produced higher negative predictive value and positive predictive value. This means that the proposed model can identify AF conditions from ECG signal delineation. Our approach can considerably contribute to AF diagnosis with these results.
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spelling pubmed-89530932022-03-26 Short Single-Lead ECG Signal Delineation-Based Deep Learning: Implementation in Automatic Atrial Fibrillation Identification Tutuko, Bambang Rachmatullah, Muhammad Naufal Darmawahyuni, Annisa Nurmaini, Siti Tondas, Alexander Edo Passarella, Rossi Partan, Radiyati Umi Rifai, Ahmad Sapitri, Ade Iriani Firdaus, Firdaus Sensors (Basel) Article Physicians manually interpret an electrocardiogram (ECG) signal morphology in routine clinical practice. This activity is a monotonous and abstract task that relies on the experience of understanding ECG waveform meaning, including P-wave, QRS-complex, and T-wave. Such a manual process depends on signal quality and the number of leads. ECG signal classification based on deep learning (DL) has produced an automatic interpretation; however, the proposed method is used for specific abnormality conditions. When the ECG signal morphology change to other abnormalities, it cannot proceed automatically. To generalize the automatic interpretation, we aim to delineate ECG waveform. However, the output of delineation process only ECG waveform duration classes for P-wave, QRS-complex, and T-wave. It should be combined with a medical knowledge rule to produce the abnormality interpretation. The proposed model is applied for atrial fibrillation (AF) identification. This study meets the AF criteria with RR irregularities and the absence of P-waves in essential oscillations for even more accurate identification. The QT database by Physionet is utilized for developing the delineation model, and it validates with The Lobachevsky University Database. The results show that our delineation model works properly, with 98.91% sensitivity, 99.01% precision, 99.79% specificity, 99.79% accuracy, and a 98.96% F1 score. We use about 4058 normal sinus rhythm records and 1804 AF records from the experiment to identify AF conditions that are taken from three datasets. The comprehensive testing has produced higher negative predictive value and positive predictive value. This means that the proposed model can identify AF conditions from ECG signal delineation. Our approach can considerably contribute to AF diagnosis with these results. MDPI 2022-03-17 /pmc/articles/PMC8953093/ /pubmed/35336500 http://dx.doi.org/10.3390/s22062329 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Tutuko, Bambang
Rachmatullah, Muhammad Naufal
Darmawahyuni, Annisa
Nurmaini, Siti
Tondas, Alexander Edo
Passarella, Rossi
Partan, Radiyati Umi
Rifai, Ahmad
Sapitri, Ade Iriani
Firdaus, Firdaus
Short Single-Lead ECG Signal Delineation-Based Deep Learning: Implementation in Automatic Atrial Fibrillation Identification
title Short Single-Lead ECG Signal Delineation-Based Deep Learning: Implementation in Automatic Atrial Fibrillation Identification
title_full Short Single-Lead ECG Signal Delineation-Based Deep Learning: Implementation in Automatic Atrial Fibrillation Identification
title_fullStr Short Single-Lead ECG Signal Delineation-Based Deep Learning: Implementation in Automatic Atrial Fibrillation Identification
title_full_unstemmed Short Single-Lead ECG Signal Delineation-Based Deep Learning: Implementation in Automatic Atrial Fibrillation Identification
title_short Short Single-Lead ECG Signal Delineation-Based Deep Learning: Implementation in Automatic Atrial Fibrillation Identification
title_sort short single-lead ecg signal delineation-based deep learning: implementation in automatic atrial fibrillation identification
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8953093/
https://www.ncbi.nlm.nih.gov/pubmed/35336500
http://dx.doi.org/10.3390/s22062329
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