Key result
BLF-Net outperforms state-of-the-art deep learning methods for multiclass arrhythmia classification across six tasks.
Why the study?
Existing deep learning and attention mechanism methods for ECG classification assign weights to time points rather than evaluating ECG signals at the heartbeat level.
Does the BLF-Net algorithm improve multiclass arrhythmia classification performance compared to existing methods using the PTB-XL database?
Does the BLF-Net algorithm improve multiclass arrhythmia classification performance compared to existing methods using the PTB-XL database?
A novel beat-level fusion network (BLF-Net) improves automated multiclass arrhythmia classification from ECG signals compared to existing deep learning methods.
Answers are grounded in this paper and cite their sources.
May support attention-enhanced DL models for ECG arrhythmia detection in research; leaves open prospective clinical validation.
Cardiovascular disease (CVD) is one of the most severe diseases threatening human life. Electrocardiogram (ECG) is an effective way to detect CVD. In recent years, many methods have been proposed to detect arrhythmia using 12-lead ECG. In particular, deep learning methods have been proven to be effective and have been widely used. The attention mechanism has attracted extensive attention in many fields in a series of deep learning methods. Off-the-shelf solutions based on deep learning and attention mechanism for ECG classification mostly give weights to time points. None of the existing methods were considered using the attention mechanism dealing with ECG signals at the level of heartbeats. In this paper, we propose a beat-level fusion net (BLF-Net) for multiclass arrhythmia classification by assigning weights at the heartbeat level, according to the contribution of the heartbeat to diagnostic results. This algorithm consists of three steps: (1) segmenting the long ECG signal into short beats; (2) using a neural network to extract features from heartbeats; and (3) assigning weights to features extracted from heartbeats using an attention mechanism. We test our algorithm on the PTB-XL database and have superiority over state-of-the-art performance on six classification tasks. Besides, the principle of this architecture is clarified by visualizing the weight of the attention mechanism. The proposed BLF-Net is shown to be useful and automatically provides an effective network structure for arrhythmia classification, which is capable of aiding cardiologists in arrhythmia diagnosis.
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Jing et al. (2023) studied Arrhythmia. Beat-level fusion net (BLF-Net) vs. State-of-the-art methods was evaluated on Multiclass arrhythmia classification performance on six classification tasks. The proposed beat-level fusion net (BLF-Net) demonstrated superiority over state-of-the-art performance on six classification tasks for multiclass arrhythmia classification using the PTB-XL database.
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