Key result
Novel deep learning model improves multilabel ECG classification with an ~88% average F1 score.
Why the study?
Most automated ECG classification studies focus on single-label problems, whereas actual clinical ECG records often contain multiple diseases simultaneously.
Does the CSA-MResNet deep learning model improve automated multilabel classification of clinical 12-lead ECGs compared to standard benchmark architectures?
Does the CSA-MResNet deep learning model improve automated multilabel classification of clinical 12-lead ECGs compared to standard benchmark architectures?
The CSA-MResNet algorithm demonstrates high performance in automated multilabel ECG classification, offering a potential AI tool for rapid clinical screening of complex concurrent cardiac arrhythmias.
Automatic classification of ECG is very important for early prevention and auxiliary diagnosis of cardiovascular disease patients. In recent years, many studies based on ECG have achieved good results, most of which are based on single-label problems; one record corresponds to one label. However, in actual clinical applications, an ECG record may contain multiple diseases at the same time. Therefore, it is very important to study the multilabel ECG classification. In this paper, a multiscale residual deep neural network CSA-MResNet model based on the channel spatial attention mechanism is proposed. Firstly, the residual network is integrated into a multiscale manner to obtain the characteristics of ECG data at different scales and then increase the channel spatial attention mechanism to better focus on more important channels and more important ECG data fragments. Finally, the model is used to classify multilabel in large databases. The experimental results on the multilabel CCDD show that the CSA-MResNet model has an average F1 score of 88.2% when the multilabel classification of 9 ECGs is performed. Compared with the benchmark model, the F1 score of CSA-MResNet in the multilabel ECG classification increased by up to 1.7%. And, in the model verification on another database HF-challenge, the final average F1 score is 85.8%. Compared with the state-of-the-art methods, CSA-MResNet can help cardiologists perform early-stage rapid screening of ECG and has a certain generalization performance, providing a feasible analysis method for multilabel ECG classification.
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Wang et al. (2021) studied Multilabel ECG classification (n=190,000). CSA-MResNet model vs. Benchmark models was evaluated on Average F1 score for multilabel classification of 9 ECG categories. The proposed CSA-MResNet deep learning model achieved an average F1 score of 88.2% for multilabel classification of 9 ECG categories, an increase of up to 1.7% compared with the benchmark model.
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