Electromyography Parameter Variations with Electrocardiography Noise
Electromyograms (EMG signals) may be contaminated by electrocardiographic (ECG)
signals that cannot be easily separated with traditional filters, because both signals have …
signals that cannot be easily separated with traditional filters, because both signals have …
Wearable Active Electrode for sEMG Monitoring Using Two‐Channel Brass Dry Electrodes with Reduced Electronics
Gel‐based electrodes are employed to record sEMG signals for prolonged periods. These
signals are used for the control of myoelectric prostheses, clinical analysis, or sports …
signals are used for the control of myoelectric prostheses, clinical analysis, or sports …
Analysis of Electromyography (EMG) Signal Processing with Filtering Techniques
The paper presents the Analysis of Electromyography (EMG) Signal Processing with
Filtering Techniques. The problem in this study is how to consider the filtering techniques for …
Filtering Techniques. The problem in this study is how to consider the filtering techniques for …
Implementation of the Process for Contamination in Electromyography (EMG) Signal by Using Noise Removal Techniques
The paper describes the analysis of electromyography (EMG) signals using noise removal
techniques. The problem in this study is to consider a noise removal technique for basic …
techniques. The problem in this study is to consider a noise removal technique for basic …
Arrhythmia Classification Based on Multi-Input Convolutional Neural Network with Attention Mechanism
M Zhang, W Luo, H **, B Zheng - Available at SSRN 5007477 - papers.ssrn.com
Arrhythmia can lead to various cardiovascular complications, including stroke, heart failure,
and cardiac arrest. Recent advancements in deep learning for electrocardiogram (ECG) …
and cardiac arrest. Recent advancements in deep learning for electrocardiogram (ECG) …