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PhysioWave: A Multi-Scale Wavelet-Transformer for Physiological Signal Representation

Created by
  • Haebom

Author

Yanlong Chen, Mattia Orlandi, Pierangelo Maria Rapa, Simone Benatti, Luca Benini, Yawei Li

Outline

To address the challenges of physiological signal analysis, we propose a wavelet-based approach and, for the first time, introduce a large-scale pre-trained model specialized for EMG and ECG, achieving performance that surpasses existing methods. Furthermore, by integrating EEG models, we build a multimodal framework where each modality is guided through dedicated branches and fused through learnable weighted fusion. This demonstrates potential for contributions to a wide range of biomedical fields, including wearable health monitoring and clinical diagnostics.

Takeaways, Limitations

Takeaways:
Establishing a robust foundation for analyzing various physiological signals by leveraging wavelet-based architecture.
Achieving superior performance compared to existing methods by introducing large-scale pre-trained models specialized for EMG and ECG.
Outperforming existing methods in multimodal tasks by building a multimodal framework that integrates EEG models.
Wearable health monitoring, clinical diagnostics, and other biomedical fields are presented with potential impact.
Limitations:
No direct mention of Limitations in the paper.
Needs to be supplemented through further research.
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