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Recurrence Plot-Based Approach for Cardiac Arrhythmia Classification Using Inception-ResNet-v2

The present study addresses the cardiac arrhythmia (CA) classification problem using the deep learning (DL)-based method for electrocardiography (ECG) data analysis. Recently, various DL techniques have been utilized to classify arrhythmias, with one typical approach to developing a one-dimensional...

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Autores principales: Zhang, Hua, Liu, Chengyu, Zhang, Zhimin, Xing, Yujie, Liu, Xinwen, Dong, Ruiqing, He, Yu, Xia, Ling, Liu, Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8165394/
https://www.ncbi.nlm.nih.gov/pubmed/34079470
http://dx.doi.org/10.3389/fphys.2021.648950
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author Zhang, Hua
Liu, Chengyu
Zhang, Zhimin
Xing, Yujie
Liu, Xinwen
Dong, Ruiqing
He, Yu
Xia, Ling
Liu, Feng
author_facet Zhang, Hua
Liu, Chengyu
Zhang, Zhimin
Xing, Yujie
Liu, Xinwen
Dong, Ruiqing
He, Yu
Xia, Ling
Liu, Feng
author_sort Zhang, Hua
collection PubMed
description The present study addresses the cardiac arrhythmia (CA) classification problem using the deep learning (DL)-based method for electrocardiography (ECG) data analysis. Recently, various DL techniques have been utilized to classify arrhythmias, with one typical approach to developing a one-dimensional (1D) convolutional neural network (CNN) model to handle the ECG signals in the time domain. Although the CA classification in the time domain is very prevalent, current methods’ performances are still not robust or satisfactory. This study aims to develop a solution for CA classification in two dimensions by introducing the recurrence plot (RP) combined with an Inception-ResNet-v2 network. The proposed method for nine types of CA classification was tested on the 1st China Physiological Signal Challenge 2018 dataset. During implementation, the optimal leads (lead II and lead aVR) were selected, and then 1D ECG segments were transformed into 2D texture images by the RP approach. These RP-based images as input signals were passed into the Inception-ResNet-v2 for CA classification. In the CPSC, Georgia, and the PTB_XL ECG databases of the PhysioNet/Computing in Cardiology Challenge 2020, the RP-based method achieved an average F1-score of 0.8521, 0.8529, and 0.8862, respectively. The results suggested the excellent generalization ability of the proposed method. To further assess the performance of the proposed method, we compared the 2D RP-image-based solution with the published 1D ECG-based works on the same dataset. Also, it was compared with two traditional ECG transform into 2D image methods, including the time waveform of the ECG recordings and time-frequency images based on continuous wavelet transform (CWT). The proposed method achieved the highest average F1-score of 0.844, with only two leads of the 12-lead ECG original data, which outperformed other works. Therefore, the promising results indicate that the 2D RP-based method has a high clinical potential for CA classification using fewer lead ECG signals.
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spelling pubmed-81653942021-06-01 Recurrence Plot-Based Approach for Cardiac Arrhythmia Classification Using Inception-ResNet-v2 Zhang, Hua Liu, Chengyu Zhang, Zhimin Xing, Yujie Liu, Xinwen Dong, Ruiqing He, Yu Xia, Ling Liu, Feng Front Physiol Physiology The present study addresses the cardiac arrhythmia (CA) classification problem using the deep learning (DL)-based method for electrocardiography (ECG) data analysis. Recently, various DL techniques have been utilized to classify arrhythmias, with one typical approach to developing a one-dimensional (1D) convolutional neural network (CNN) model to handle the ECG signals in the time domain. Although the CA classification in the time domain is very prevalent, current methods’ performances are still not robust or satisfactory. This study aims to develop a solution for CA classification in two dimensions by introducing the recurrence plot (RP) combined with an Inception-ResNet-v2 network. The proposed method for nine types of CA classification was tested on the 1st China Physiological Signal Challenge 2018 dataset. During implementation, the optimal leads (lead II and lead aVR) were selected, and then 1D ECG segments were transformed into 2D texture images by the RP approach. These RP-based images as input signals were passed into the Inception-ResNet-v2 for CA classification. In the CPSC, Georgia, and the PTB_XL ECG databases of the PhysioNet/Computing in Cardiology Challenge 2020, the RP-based method achieved an average F1-score of 0.8521, 0.8529, and 0.8862, respectively. The results suggested the excellent generalization ability of the proposed method. To further assess the performance of the proposed method, we compared the 2D RP-image-based solution with the published 1D ECG-based works on the same dataset. Also, it was compared with two traditional ECG transform into 2D image methods, including the time waveform of the ECG recordings and time-frequency images based on continuous wavelet transform (CWT). The proposed method achieved the highest average F1-score of 0.844, with only two leads of the 12-lead ECG original data, which outperformed other works. Therefore, the promising results indicate that the 2D RP-based method has a high clinical potential for CA classification using fewer lead ECG signals. Frontiers Media S.A. 2021-05-17 /pmc/articles/PMC8165394/ /pubmed/34079470 http://dx.doi.org/10.3389/fphys.2021.648950 Text en Copyright © 2021 Zhang, Liu, Zhang, Xing, Liu, Dong, He, Xia and Liu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Physiology
Zhang, Hua
Liu, Chengyu
Zhang, Zhimin
Xing, Yujie
Liu, Xinwen
Dong, Ruiqing
He, Yu
Xia, Ling
Liu, Feng
Recurrence Plot-Based Approach for Cardiac Arrhythmia Classification Using Inception-ResNet-v2
title Recurrence Plot-Based Approach for Cardiac Arrhythmia Classification Using Inception-ResNet-v2
title_full Recurrence Plot-Based Approach for Cardiac Arrhythmia Classification Using Inception-ResNet-v2
title_fullStr Recurrence Plot-Based Approach for Cardiac Arrhythmia Classification Using Inception-ResNet-v2
title_full_unstemmed Recurrence Plot-Based Approach for Cardiac Arrhythmia Classification Using Inception-ResNet-v2
title_short Recurrence Plot-Based Approach for Cardiac Arrhythmia Classification Using Inception-ResNet-v2
title_sort recurrence plot-based approach for cardiac arrhythmia classification using inception-resnet-v2
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8165394/
https://www.ncbi.nlm.nih.gov/pubmed/34079470
http://dx.doi.org/10.3389/fphys.2021.648950
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