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Automatic Multichannel Electrocardiogram Record Classification Using XGBoost Fusion Model

There is an increasing demand for automatic classification of standard 12-lead electrocardiogram signals in the medical field. Considering that different channels and temporal segments of a feature map extracted from the 12-lead electrocardiogram record contribute differently to cardiac arrhythmia d...

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Autores principales: Ye, Xiaohong, Huang, Yuanqi, Lu, Qiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9049587/
https://www.ncbi.nlm.nih.gov/pubmed/35492618
http://dx.doi.org/10.3389/fphys.2022.840011
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author Ye, Xiaohong
Huang, Yuanqi
Lu, Qiang
author_facet Ye, Xiaohong
Huang, Yuanqi
Lu, Qiang
author_sort Ye, Xiaohong
collection PubMed
description There is an increasing demand for automatic classification of standard 12-lead electrocardiogram signals in the medical field. Considering that different channels and temporal segments of a feature map extracted from the 12-lead electrocardiogram record contribute differently to cardiac arrhythmia detection, and to the classification performance, we propose a 12-lead electrocardiogram signal automatic classification model based on model fusion (CBi-DF-XGBoost) to focus on representative features along both the spatial and temporal axes. The algorithm extracts local features through a convolutional neural network and then extracts temporal features through bi-directional long short-term memory. Finally, eXtreme Gradient Boosting (XGBoost) is used to fuse the 12-lead models and domain-specific features to obtain the classification results. The 5-fold cross-validation results show that in classifying nine categories of electrocardiogram signals, the macro-average accuracy of the fusion model is 0.968, the macro-average recall rate is 0.814, the macro-average precision is 0.857, the macro-average F1 score is 0.825, and the micro-average area under the curve is 0.919. Similar experiments with some common network structures and other advanced electrocardiogram classification algorithms show that the proposed model performs favourably against other counterparts in F1 score. We also conducted ablation studies to verify the effect of the complementary information from the 12 leads and the auxiliary information of domain-specific features on the classification performance of the model. We demonstrated the feasibility and effectiveness of the XGBoost-based fusion model to classify 12-lead electrocardiogram records into nine common heart rhythms. These findings may have clinical importance for the early diagnosis of arrhythmia and incite further research. In addition, the proposed multichannel feature fusion algorithm can be applied to other similar physiological signal analyses and processing.
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spelling pubmed-90495872022-04-29 Automatic Multichannel Electrocardiogram Record Classification Using XGBoost Fusion Model Ye, Xiaohong Huang, Yuanqi Lu, Qiang Front Physiol Physiology There is an increasing demand for automatic classification of standard 12-lead electrocardiogram signals in the medical field. Considering that different channels and temporal segments of a feature map extracted from the 12-lead electrocardiogram record contribute differently to cardiac arrhythmia detection, and to the classification performance, we propose a 12-lead electrocardiogram signal automatic classification model based on model fusion (CBi-DF-XGBoost) to focus on representative features along both the spatial and temporal axes. The algorithm extracts local features through a convolutional neural network and then extracts temporal features through bi-directional long short-term memory. Finally, eXtreme Gradient Boosting (XGBoost) is used to fuse the 12-lead models and domain-specific features to obtain the classification results. The 5-fold cross-validation results show that in classifying nine categories of electrocardiogram signals, the macro-average accuracy of the fusion model is 0.968, the macro-average recall rate is 0.814, the macro-average precision is 0.857, the macro-average F1 score is 0.825, and the micro-average area under the curve is 0.919. Similar experiments with some common network structures and other advanced electrocardiogram classification algorithms show that the proposed model performs favourably against other counterparts in F1 score. We also conducted ablation studies to verify the effect of the complementary information from the 12 leads and the auxiliary information of domain-specific features on the classification performance of the model. We demonstrated the feasibility and effectiveness of the XGBoost-based fusion model to classify 12-lead electrocardiogram records into nine common heart rhythms. These findings may have clinical importance for the early diagnosis of arrhythmia and incite further research. In addition, the proposed multichannel feature fusion algorithm can be applied to other similar physiological signal analyses and processing. Frontiers Media S.A. 2022-04-14 /pmc/articles/PMC9049587/ /pubmed/35492618 http://dx.doi.org/10.3389/fphys.2022.840011 Text en Copyright © 2022 Ye, Huang and Lu. 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
Ye, Xiaohong
Huang, Yuanqi
Lu, Qiang
Automatic Multichannel Electrocardiogram Record Classification Using XGBoost Fusion Model
title Automatic Multichannel Electrocardiogram Record Classification Using XGBoost Fusion Model
title_full Automatic Multichannel Electrocardiogram Record Classification Using XGBoost Fusion Model
title_fullStr Automatic Multichannel Electrocardiogram Record Classification Using XGBoost Fusion Model
title_full_unstemmed Automatic Multichannel Electrocardiogram Record Classification Using XGBoost Fusion Model
title_short Automatic Multichannel Electrocardiogram Record Classification Using XGBoost Fusion Model
title_sort automatic multichannel electrocardiogram record classification using xgboost fusion model
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9049587/
https://www.ncbi.nlm.nih.gov/pubmed/35492618
http://dx.doi.org/10.3389/fphys.2022.840011
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