7 citations · 9 across the 9 of their papers we have counts for
11 papers
DiSR-ECG: Residual Shifting Conditional Diffusion for Robust ECG Super-Resolution
Fan-Yi Hsu, I Chiu, Kuan-Chen Wang +2
Electrocardiogram (ECG) signals are essential for arrhythmia diagnosis. With the growing adoption of long-term monitoring via wearable and portable devices, energy-efficient acquis…
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…
Robust Photoplethysmography Signal Denoising via Mamba Networks
I Chiu, Yu-Tung Liu, Kuan-Chen Wang +2
Photoplethysmography (PPG) is widely used in wearable health monitoring, but its reliability is often degraded by noise and motion artifacts, limiting downstream applications such…
Transfer Learning for Keypoint Detection in Low-Resolution Thermal TUG Test Images
Wei-Lun Chen, Chia-Yeh Hsieh, Yu-Hsiang Kao +3
This study presents a novel approach to human keypoint detection in low-resolution thermal images using transfer learning techniques. We introduce the first application of the Time…
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…
MECG-E: Mamba-based ECG Enhancer for Baseline Wander Removal
Kuo-Hsuan Hung, Kuan-Chen Wang, Kai-Chun Liu +4
Electrocardiogram (ECG) is an important non-invasive method for diagnosing cardiovascular disease. However, ECG signals are susceptible to noise contamination, such as electrical i…
SDEMG: Score-based Diffusion Model for Surface Electromyographic Signal Denoising
Yu-Tung Liu, Kuan-Chen Wang, Kai-Chun Liu +2
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…
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…