Comparison of AM-FM features with standard features for the classification of surface electromyographic signals

C. I. Christodoulou, P. A. Kaplanis, V. Murray, M. S. Pattichis, C. S. Pattichis

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

2 Citas (Scopus)

Resumen

In this work AM-FM features extracted from surface electromyographic (SEMG) signals were compared with standard time and frequency domain features, for the classification of neuromuscular disorders at different force levels. SEMG signals were recorded from a total of 40 subjects: 20 normal and 20 abnormal cases, at 10%, 30%, 50%, 70% and 100% of maximum voluntary contraction (MVC), from the biceps brachii muscle. For the classification, three classifiers were used: (i) the statistical K-nearest neighbour (KNN), (ii) the neural self-organizing map (SOM) and (iii) the neural support vector machine (SVM). For all classifiers the leave-one-out methodology was implemented for the classification of the SEMG signals into normal or pathogenic. The test results reached a classification success rate of 77% for the AM-FM features whereas standard features failed to provide any meaningful results on the given dataset.

Idioma originalInglés
Título de la publicación alojadaXII Mediterranean Conference on Medical and Biological Engineering and Computing 2010, MEDICON 2010
Páginas69-72
Número de páginas4
DOI
EstadoPublicada - 2010
Publicado de forma externa
Evento12th Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2010 - Chalkidiki, Grecia
Duración: 27 may. 201030 may. 2010

Serie de la publicación

NombreIFMBE Proceedings
Volumen29
ISSN (versión impresa)1680-0737

Conferencia

Conferencia12th Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2010
País/TerritorioGrecia
CiudadChalkidiki
Período27/05/1030/05/10

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