Reading Between the Leads: Local Lead-Attention Based Classification of Electrocardiogram Signals
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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Datasets
PhysioNet Challenge 2021
Results from the Paper
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| ECG Classification | PhysioNet Challenge 2021 | Local Lead Attention | PhysioNet Challenge score 2021 | 0.521 | # 1 |
