Key result
Local lead-attention model improves ECG classification ~6% over the winning solution using fewer parameters.
Why the study?
Application of self-attention models in time-domain ECG signal analysis has been limited due to the lesser need for global receptive fields.
Absolute Event Rate: 0.521% vs 0.493%
A novel local lead-attention transformer model improves multi-class ECG classification performance while significantly reducing parameter size compared to state-of-the-art convolution models.
May improve efficiency of automated ECG classification; leaves open prospective clinical validation before practice adoption.
Self-attention models have emerged as powerful tools in both computer vision and Natural Language Processing (NLP) domains.However, their application in timedomain Electrocardiogram (ECG) signal analysis has been limited, primarily due to the lesser need for global receptive fields.In this study, we present a novel approach utilizing local self-attention to address multi-class classification tasks using the PhysioNet/Computing in Cardiology Challenge 2021 dataset, encompassing 26 distinct classes across six different datasets.We introduce an innovative concept called "local lead-attention" to capture features within a single lead and across multiple configurable leads.The proposed architecture achieves an F1 score of 0.521 on the challenge's validation set, marking a 5.67% improvement over the winning solution.Remarkably, our model accomplishes this performance boost with only one-third of the total parameter size, amounting to 2.4 million parameters.
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Manimaran et al. (2023) studied Arrhythmias. Local Lead-Attention model vs. ISIBrno winning solution (convolution-only approach) was evaluated on F-Measure (F1 score). The local lead-attention model achieved an F1 score of 0.521 for ECG classification, marking a 5.67% improvement over the winning solution while using 63% fewer parameters.
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