Key result
A refined-attention transformer model yielded over a 65% reduction in computational efforts compared with conventional self-attention and achieved 96% accuracy for rhythmic detection.
Why the study?
Current transformer models for ECG arrhythmia detection demand substantial computational resources as sequence length increases, and conventional single-scale tokens struggle to accommodate various arrhythmia patterns.
Does a refined-attention transformer model improve arrhythmia detection accuracy and reduce computational effort compared to conventional self-attention models on ECG sequences?
Does a refined-attention transformer model improve arrhythmia detection accuracy and reduce computational effort compared to conventional self-attention models on ECG sequences?
Answers are grounded in this paper and cite their sources.
A refined self-attention transformer model significantly reduces computational burden while maintaining high accuracy for ECG-based arrhythmia detection.
As the length of electrocardiogram (ECG) sequences increases, most current transformer models demand substantial computational resources for ECG arrhythmia detection. Additionally, conventional single-scale tokens encounter difficulties in accommodating various patterns of arrhythmia. Thus, in this study, a refined-attention transformer model for arrhythmia detection was proposed. Our model introduces two refined attention mechanisms, namely, refined diag- and gated linear attentions, effectively alleviating computational burdens associated with unnecessary correlations between heartbeats. To address rhythmic and beat-pattern arrhythmias, we used two refined transformer models with a collaborative block, leveraging coarse- and fine-grained tokens to capture inter- and intra-heartbeat correlations. The collaborative block between two models facilitates the exchange of rhythm information, thereby improving the accuracy of beat detection. On the MIT-BIH dataset, our refined attentions yield over a 65% reduction in computational efforts compared with conventional self-attention. Notably, our refined transformer models achieve 96% accuracy for rhythmic detection and rank within the top two performers for all types of heartbeat detection. Moreover, the collaborative block enhances the recall by 8.8% and precision by 3.4% for atrial premature detection.
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Tao et al. (2024) studied Arrhythmia. Refined-attention transformer model vs. Conventional self-attention models was evaluated on Computational effort reduction and detection accuracy. A refined-attention transformer model yielded over a 65% reduction in computational efforts compared with conventional self-attention and achieved 96% accuracy for rhythmic detection.
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