Key result
CBi-DF-XGBoost classifies nine common ECG rhythms with ~97% accuracy.
Why the study?
There is an increasing demand for automatic classification of standard 12-lead electrocardiogram signals to improve cardiac arrhythmia detection and classification performance.
A novel XGBoost-based fusion model combining spatial and temporal feature extraction demonstrates high accuracy in automatically classifying 12-lead ECG records into nine common arrhythmias.
May aid automated multi-arrhythmia ECG classification; leaves open prospective validation before clinical adoption.
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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Ye et al. (2022) studied Cardiac arrhythmia (n=9,831). CBi-DF-XGBoost fusion model vs. Other deep learning models (VGG, ResNet, LSTM, etc.) was evaluated on Macro-average F1 score for classifying nine categories of ECG signals. The CBi-DF-XGBoost fusion model classified 12-lead electrocardiogram records into nine common heart rhythms with a macro-average F1 score of 0.825 and a macro-average accuracy of 0.968.
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