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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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-04-01
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| Series: | Frontiers in Physiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2022.840011/full |
| _version_ | 1818474947739123712 |
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| author | Xiaohong Ye Yuanqi Huang Qiang Lu |
| author_facet | Xiaohong Ye Yuanqi Huang Qiang Lu |
| author_sort | Xiaohong Ye |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-14T04:43:48Z |
| format | Article |
| id | doaj.art-9e072196bec84ce6915d0903b6630eea |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-04-14T04:43:48Z |
| publishDate | 2022-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-9e072196bec84ce6915d0903b6630eea2022-12-22T02:11:32ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2022-04-011310.3389/fphys.2022.840011840011Automatic Multichannel Electrocardiogram Record Classification Using XGBoost Fusion ModelXiaohong Ye0Yuanqi Huang1Qiang Lu2Chengyi University College, Jimei University, Xiamen, ChinaSchool of Physical Education and Sport Science, Fujian Normal University, Fuzhou, ChinaSchool of Science, Jimei University, Xiamen, ChinaThere 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.https://www.frontiersin.org/articles/10.3389/fphys.2022.840011/fullelectrocardiogram (ECG)classification algorithmphysiological signal processingbioengineeringmodel fusionextreme gradient boosting (xgboost) |
| spellingShingle | Xiaohong Ye Yuanqi Huang Qiang Lu Automatic Multichannel Electrocardiogram Record Classification Using XGBoost Fusion Model Frontiers in Physiology electrocardiogram (ECG) classification algorithm physiological signal processing bioengineering model fusion extreme gradient boosting (xgboost) |
| 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 | electrocardiogram (ECG) classification algorithm physiological signal processing bioengineering model fusion extreme gradient boosting (xgboost) |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2022.840011/full |
| work_keys_str_mv | AT xiaohongye automaticmultichannelelectrocardiogramrecordclassificationusingxgboostfusionmodel AT yuanqihuang automaticmultichannelelectrocardiogramrecordclassificationusingxgboostfusionmodel AT qianglu automaticmultichannelelectrocardiogramrecordclassificationusingxgboostfusionmodel |