title: Unsupervised learning technique for surface electromyogram denoising from power line interference and baseline wander.
abstract: We present a novel approach to single-channel power line interference (PLI) and baseline wander (BW) removal from surface electromyograms (EMG). It is based on non-negative matrix factorization (NMF) using a priori knowledge about the interferences. It performs a linear decomposition of the input signal spectrogram into non-negative components, which represent the PLI, BW and EMG spectrogram estimates. They all exhibit very different time-frequency patterns: PLI and BW are both sparse whereas EMG is noise-like. Initialization of the classical NMF algorithm with accurately designed PLI, BW and EMG structures and a carefully adjusted matrix decomposition rank increases the separation performance. The comparative study suggests that the proposed method outperforms two state-of-the-art reference methods.
authors.forename: Maciej
authors.initials: M
authors.lastname: Niegowski
authors.forename: Miroslav
authors.initials: M
authors.lastname: Zivanovic
authors.forename: Marisol
authors.initials: M
authors.lastname: Gomez
authors.forename: Pablo
authors.initials: P
authors.lastname: Lecumberri
mesh_terms: D000465:Algorithms; D004576:Electromyography / Q000592:standards; D012815:Signal Processing, Computer-Assisted