ECG-NAT combines masked autoencoder pretraining with hierarchical neighborhood attention and dual-loss fine-tuning to reach 88.1% accuracy on ECG classification using just 1% labeled data.
Biometric-based human identification using ensemble-based technique and ECG signals.Applied Sciences, 13(16):9454
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ECG-NAT: A Self-supervised Neighborhood Attention Transformer for Multi-lead Electrocardiogram Classification
ECG-NAT combines masked autoencoder pretraining with hierarchical neighborhood attention and dual-loss fine-tuning to reach 88.1% accuracy on ECG classification using just 1% labeled data.