Unsupervised learning technique for surface electromyogram denoising from power line interference and baseline wander (original) (raw)

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015

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.

Pablo Lecumberri hasn't uploaded this paper.

Let Pablo know you want this paper to be uploaded.

Ask for this paper to be uploaded.