Population
ECG signals from the PhysioNet Challenge 2017 dataset
Comparison
Scalograms fused with scattering and statistical features vs scalograms alone across diverse deep learning architectures
Design
Machine learning model development and validation study
Key result
A hybrid FusionViT architecture combining scalograms with scattering and statistical features achieved an accuracy of 0.8623 and F1-score of 0.8528 for ECG arrhythmia detection.
Authors
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Enables potential wearable ECG arrhythmia detection; leaves open prospective clinical validation before adoption.
Absolute Event Rate: 0.8623% vs 0.859%
A hybrid wavelet-deep learning architecture combining visual and statistical signal features achieves high accuracy and efficiency for ECG arrhythmia detection, suitable for wearable devices.
Thapa et al. (2025) studied Arrhythmia. Hybrid wavelet-deep learning architecture (FusionViT) vs. Standard deep learning architectures (ViT, ResNet-18, SimpleCNN, CNNTransformer) was evaluated on Accuracy for ECG rhythm classification. A hybrid FusionViT architecture combining scalograms with scattering and statistical features achieved an accuracy of 0.8623 and F1-score of 0.8528 for ECG arrhythmia detection.
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