title: Robust outlier detection in high-density surface electromyographic signals.
abstract: High Density surface Electromyography (HDsEMG) has been applied in both research and clinical applications for non-invasive neuromuscular assessment in several different fields using 2-D array. Proper interpretation of HDsEMG signals requires identifying "good" channels (where there is no short-circuit or bad-contact or major power line interference problem). Recording with many channels usually implies bad-contacts (that introduces large power line interference) and short-circuits (when using gels). In addition to online monitoring the electrode-contact quality, it is necessary to identify "bad" channels, or outliers, prior to the analysis of HDsEMG signal. In this paper we introduce a robust method to identify outliers in a set of monopolar HDsEMG signals recorded from Biceps and Triceps Brachii, Anconeus, Brachioradialis and Pronator Teres. The sensitivity and precision of this method show that this approach is promising.
authors.affiliation: Laboratorio di Ingegneria del Sistema Neuromusculare (LISiN), Dipartimento di Elettronica, Politencnico di Torino, Italy. hamid.marateb@polito.it
authors.forename: H R
authors.initials: HR
authors.lastname: Marateb
authors.forename: M
authors.initials: M
authors.lastname: Rojas-Martinez
authors.forename: M A
authors.initials: MA
authors.lastname: Mananas Villanueva
authors.forename: R
authors.initials: R
authors.lastname: Merletti
mesh_terms: D000465:Algorithms; D016477:Artifacts; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004576:Electromyography / Q000379:methods*; D006801:Humans; D009119:Muscle Contraction / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity