SDEMG: Score-based Diffusion Model for Surface Electromyographic Signal Denoising
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909156055711744 |
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| author | Liu, Yu-Tung Wang, Kuan-Chen Liu, Kai-Chun Peng, Sheng-Yu Tsao, Yu |
| author_facet | Liu, Yu-Tung Wang, Kuan-Chen Liu, Kai-Chun Peng, Sheng-Yu Tsao, Yu |
| contents | Surface electromyography (sEMG) recordings can be influenced by electrocardiogram (ECG) signals when the muscle being monitored is close to the heart. Several existing methods use signal-processing-based approaches, such as high-pass filter and template subtraction, while some derive mapping functions to restore clean sEMG signals from noisy sEMG (sEMG with ECG interference). Recently, the score-based diffusion model, a renowned generative model, has been introduced to generate high-quality and accurate samples with noisy input data. In this study, we proposed a novel approach, termed SDEMG, as a score-based diffusion model for sEMG signal denoising. To evaluate the proposed SDEMG approach, we conduct experiments to reduce noise in sEMG signals, employing data from an openly accessible source, the Non-Invasive Adaptive Prosthetics database, along with ECG signals from the MIT-BIH Normal Sinus Rhythm Database. The experiment result indicates that SDEMG outperformed comparative methods and produced high-quality sEMG samples. The source code of SDEMG the framework is available at: https://github.com/tonyliu0910/SDEMG |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_03808 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SDEMG: Score-based Diffusion Model for Surface Electromyographic Signal Denoising Liu, Yu-Tung Wang, Kuan-Chen Liu, Kai-Chun Peng, Sheng-Yu Tsao, Yu Signal Processing Machine Learning Surface electromyography (sEMG) recordings can be influenced by electrocardiogram (ECG) signals when the muscle being monitored is close to the heart. Several existing methods use signal-processing-based approaches, such as high-pass filter and template subtraction, while some derive mapping functions to restore clean sEMG signals from noisy sEMG (sEMG with ECG interference). Recently, the score-based diffusion model, a renowned generative model, has been introduced to generate high-quality and accurate samples with noisy input data. In this study, we proposed a novel approach, termed SDEMG, as a score-based diffusion model for sEMG signal denoising. To evaluate the proposed SDEMG approach, we conduct experiments to reduce noise in sEMG signals, employing data from an openly accessible source, the Non-Invasive Adaptive Prosthetics database, along with ECG signals from the MIT-BIH Normal Sinus Rhythm Database. The experiment result indicates that SDEMG outperformed comparative methods and produced high-quality sEMG samples. The source code of SDEMG the framework is available at: https://github.com/tonyliu0910/SDEMG |
| title | SDEMG: Score-based Diffusion Model for Surface Electromyographic Signal Denoising |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2402.03808 |