Comput. Biol. Med. - Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders.

Tópicos

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Resumo

Support vector machine (SVM) is an extensively used machine learning method with many biomedical signal classification applications. In this study, a novel PSO-SVM model has been proposed that hybridized the particle swarm optimization (PSO) and SVM to improve the EMG signal classification accuracy. This optimization mechanism involves kernel parameter setting in the SVM training procedure, which significantly influences the classification accuracy. The experiments were conducted on the basis of EMG signal to classify into normal, neurogenic or myopathic. In the proposed method the EMG signals were decomposed into the frequency sub-bands using discrete wavelet transform (DWT) and a set of statistical features were extracted from these sub-bands to represent the distribution of wavelet coefficients. The obtained results obviously validate the superiority of the SVM method compared to conventional machine learning methods, and suggest that further significant enhancements in terms of classification accuracy can be achieved by the proposed PSO-SVM classification system. The PSO-SVM yielded an overall accuracy of 97.41% on 1200 EMG signals selected from 27 subject records against 96.75%, 95.17% and 94.08% for the SVM, the k-NN and the RBF classifiers, respectively. PSO-SVM is developed as an efficient tool so that various SVMs can be used conveniently as the core of PSO-SVM for diagnosis of neuromuscular disorders.

Resumo Limpo

support vector machin svm extens use machin learn method mani biomed signal classif applic studi novel psosvm model propos hybrid particl swarm optim pso svm improv emg signal classif accuraci optim mechan involv kernel paramet set svm train procedur signific influenc classif accuraci experi conduct basi emg signal classifi normal neurogen myopath propos method emg signal decompos frequenc subband use discret wavelet transform dwt set statist featur extract subband repres distribut wavelet coeffici obtain result obvious valid superior svm method compar convent machin learn method suggest signific enhanc term classif accuraci can achiev propos psosvm classif system psosvm yield overal accuraci emg signal select subject record svm knn rbf classifi respect psosvm develop effici tool various svms can use conveni core psosvm diagnosi neuromuscular disord

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