6 papers
Prism-SQA: An Interpretable and Adaptable Neural Framework for Surface Electromyography Quality Assessment
Kuan-Chen Wang, Kai-Chun Liu, Ping-Cheng Yeh +2
sEMG is vulnerable to various contaminants that distort signal morphology and spectral content. Accurate signal quality assessment (SQA) is essential for identifying such degradati…
SSEMG-Net: A Spectrogram-Based Mamba Network for Surface Electromyography Denoising
Cheng-Han Shih, Kuan-Chen Wang, Kai-Chun Liu +2
Electrocardiogram (ECG) artifact contamination frequently occurs in surface electromyography (sEMG) when muscles are recorded near the heart. Existing neural network (NN)-based app…
MSECG: Incorporating Mamba for Robust and Efficient ECG Super-Resolution
Jie Lin, I Chiu, Kuan-Chen Wang +4
Electrocardiogram (ECG) signals play a crucial role in diagnosing cardiovascular diseases. To reduce power consumption in wearable or portable devices used for long-term ECG monito…
MSEMG: Surface Electromyography Denoising with a Mamba-based Efficient Network
Yu-Tung Liu, Kuan-Chen Wang, Rong Chao +3
Surface electromyography (sEMG) recordings can be contaminated by electrocardiogram (ECG) signals when the monitored muscle is closed to the heart. Traditional signal processing-ba…
TrustEMG-Net: Using Representation-Masking Transformer with U-Net for Surface Electromyography Enhancement
Kuan-Chen Wang, Kai-Chun Liu, Ping-Cheng Yeh +2
Surface electromyography (sEMG) is a widely employed bio-signal that captures human muscle activity via electrodes placed on the skin. Several studies have proposed methods to remo…
A Non-Intrusive Neural Quality Assessment Model for Surface Electromyography Signals
Cho-Yuan Lee, Kuan-Chen Wang, Kai-Chun Liu +4
In practical scenarios involving the measurement of surface electromyography (sEMG) in muscles, particularly those areas near the heart, one of the primary sources of contamination…