Key result
The proposed MLBF-Net achieved an average F1 score of 0.855 on the CPSC 2018 dataset, outperforming previous methods for multi-class arrhythmia classification using 12-lead ECGs.
Why the study?
Previous automatic 12-lead ECG arrhythmia detection methods neglected diverse lead-specific features when concatenating leads, leading to inadequate information learning.
The proposed MLBF-Net architecture provides a promising deep learning solution for automatic multi-class arrhythmia classification by effectively fusing lead-specific and comprehensive features from 12-lead ECGs.
May aid automated arrhythmia screening; leaves open prospective clinical validation before practice adoption.
Automatic arrhythmia detection using 12-lead electrocardiogram (ECG) signal plays a critical role in early prevention and diagnosis of cardiovascular diseases. In the previous studies on automatic arrhythmia detection, most methods concatenated 12 leads of ECG into a matrix, and then input the matrix to a variety of feature extractors or deep neural networks for extracting useful information. Under such frameworks, these methods had the ability to extract comprehensive features (known as integrity) of 12-lead ECG since the information of each lead interacts with each other during training. However, the diverse lead-specific features (known as diversity) among 12 leads were neglected, causing inadequate information learning for 12-lead ECG. To maximize the information learning of multi-lead ECG, the information fusion of comprehensive features with integrity and lead-specific features with diversity should be taken into account. In this paper, we propose a novel Multi-Lead-Branch Fusion Network (MLBF-Net) architecture for arrhythmia classification by integrating multi-loss optimization to jointly learning diversity and integrity of multi-lead ECG. MLBF-Net is composed of three components: 1) multiple lead-specific branches for learning the diversity of multi-lead ECG; 2) cross-lead features fusion by concatenating the output feature maps of all branches for learning the integrity of multi-lead ECG; 3) multi-loss co-optimization for all the individual branches and the concatenated network. We demonstrate our MLBF-Net on China Physiological Signal Challenge 2018 which is an open 12-lead ECG dataset. The experimental results show that MLBF-Net obtains an average [Formula: see text] score of 0.855, reaching the highest arrhythmia classification performance. The proposed method provides a promising solution for multi-lead ECG analysis from an information fusion perspective.
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Zhang et al. (2021) studied Cardiac arrhythmia (n=31,668). MLBF-Net (Multi-Lead-Branch Fusion Network) vs. Previous state-of-the-art machine learning and deep learning models was evaluated on Average F1 score for arrhythmia classification on the CPSC 2018 private test set. The proposed MLBF-Net achieved an average F1 score of 0.855 on the CPSC 2018 dataset, outperforming previous methods for multi-class arrhythmia classification using 12-lead ECGs.
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