Support vector machine in computer aided clinical electromyography

Author

Xie, Hong-bo ; Wang, Zhi-zhong ; Huang, Hai ; Qing, Chuan

Author_Institution

Dept. of Biomed. Eng., Shanghai Jiaotong Univ., China

Volume

2

fYear

2003

fDate

2-5 Nov. 2003

Firstpage

1106

Abstract

Motor unit action potentials (MUAPs) recorded during routine electromyography (EMG) examination provides important information for the assessment of neuromuscular disorders. In this preliminary study, support vector machines (SVMs) based on multi-class classifier is activated for the identification of normal subjects and patients suffering from motor neuron diseases (MND) and myopathies (MVO). The results in experiments prove the classification validity of SVMs which guarantee high generalization ability on the testing samples. Furthermore, its performance is compared with a back-propagation (BP) neural network. More excellent recognition accuracy indicates the potential of the SVMs techniques in clinical neuromuscular disorders evaluation.

Keywords

backpropagation; electromyography; medical diagnostic computing; neural nets; neuromuscular stimulation; support vector machines; EMG; backpropagation neural network; clinical neuromuscular disorders evaluation; electromyography examination; motor neuron diseases; motor unit action potential; multiclass classifier; myopathies; support vector machines; Biomedical computing; Diseases; Electromyography; Machine learning; Muscles; Neural networks; Neuromuscular; Neurons; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2003 International Conference on

Print_ISBN

0-7803-8131-9

Type

conf

DOI

10.1109/ICMLC.2003.1259649

Filename

1259649