A proposed ResNet plus attention model with concatenated time-domain augmentations reports 99.96%, 99.78%, and 100% accuracy on UCI EEG, MIT-BIH ECG, and PTB ECG, but no ablation or split protocol supports the state-of-the-art claim.
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A Novel Data Augmentation Strategy for Robust Deep Learning Classification of Biomedical Time-Series Data: Application to ECG and EEG Analysis
A proposed ResNet plus attention model with concatenated time-domain augmentations reports 99.96%, 99.78%, and 100% accuracy on UCI EEG, MIT-BIH ECG, and PTB ECG, but no ablation or split protocol supports the state-of-the-art claim.