title: Wavelet analysis for Support Vector Machine classification of motor unit action potentials.
abstract: The paper presents a new method for neuromuscular disorders diagnosis based on analysis of scalograms determined by the Symlet 4 wavelets technique. Obtained results served for extraction of five features, which, after SVM analysis, were reduced to a single decision parameter allowing assigning the investigated cases to one of three groups: myogenic, neurogenic or normal. Software implementation of the method permitted to create a diagnostic tool for EMG investigation aid. The method characterizes high probability of accurate diagnosis of a muscle state with total error of 0.5% - 4 misclassifications out of 780 examined cases.
authors.affiliation: Faculty of Electronics, Military University of Technology, 2 Kaliskiego St., 00-908 Warsaw, Poland. ADobrowolski@wat.edu.pl
authors.forename: Andrzej P
authors.initials: AP
authors.lastname: Dobrowolski
authors.forename: Mariusz
authors.initials: M
authors.lastname: Wierzbowski
authors.forename: Kazimierz
authors.initials: K
authors.lastname: Tomczykiewicz
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D001185:Artificial Intelligence; D004576:Electromyography / Q000379:methods*; D006801:Humans; D009046:Motor Neurons / Q000502:physiology*; D009119:Muscle Contraction / Q000502:physiology*; D018482:Muscle, Skeletal / Q000294:innervation / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D058067:Wavelet Analysis