Deep Residual U-Net Autoencoder with Weighted Overlapping Reconstruction for EMG Signal Denoising

Electromyography (EMG) signals, crucial for neuromuscular assessment, are frequently corrupted by noise, impairing signal fidelity and subsequent analysis across diverse applications. Conventional filters often inadequately address non-stationary noise or introduce signal distortion. This paper intr...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Signal Processing Algorithms, Architectures, Arrangements, and Applications Conference proceedings S. 198 - 203
Hauptverfasser:	Mehmood, Atif, Wiora, Jozef
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	Division of Signal Processing and Electronic Syste 17.09.2025
Schlagworte:	Autoencoders Convolution Convolutional Autoencoder (CAE) Deep learning Electromyography Electromyography (EMG) Noise measurement Noise reduction Residual Networks Signal denoising Signal Processing Signal to noise ratio Synthetic data Synthetic Data Generation Training U-Net
ISBN:	9788362065493, 8362065494
ISSN:	2326-0262
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!