Comput. Biol. Med. - Medical decision support system for diagnosis of neuromuscular disorders using DWT and fuzzy support vector machines.

Tópicos

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Resumo

The motor unit action potentials (MUAPs) in an electromyographic (EMG) signal provide a significant source of information for the assessment of neuromuscular disorders. In this work, different types of machine learning methods were used to classify EMG signals and compared in relation to their accuracy in classification of EMG signals. The models automatically classify the EMG signals into normal, neurogenic or myopathic. The best averaged performance over 10 runs of randomized cross-validation is also obtained by different classification models. Some conclusions concerning the impacts of features on the EMG signal classification were obtained through analysis of the classification techniques. The comparative analysis suggests that the fuzzy support vector machines (FSVM) modelling is superior to the other machine learning methods in at least three points: slightly higher recognition rate; insensitivity to overtraining; and consistent outputs demonstrating higher reliability. The combined model with discrete wavelet transform (DWT) and FSVM achieves the better performance for internal cross validation (External cross validation) with the area under the receiver operating characteristic (ROC) curve (AUC) and accuracy equal to 0.996 (0.970) and 97.67% (93.5%), respectively. These results show that the proposed model have the potential to obtain a reliable classification of EMG signals, and to assist the clinicians for making a correct diagnosis of neuromuscular disorders.

Resumo Limpo

motor unit action potenti muap electromyograph emg signal provid signific sourc inform assess neuromuscular disord work differ type machin learn method use classifi emg signal compar relat accuraci classif emg signal model automat classifi emg signal normal neurogen myopath best averag perform run random crossvalid also obtain differ classif model conclus concern impact featur emg signal classif obtain analysi classif techniqu compar analysi suggest fuzzi support vector machin fsvm model superior machin learn method least three point slight higher recognit rate insensit overtrain consist output demonstr higher reliabl combin model discret wavelet transform dwt fsvm achiev better perform intern cross valid extern cross valid area receiv oper characterist roc curv auc accuraci equal respect result show propos model potenti obtain reliabl classif emg signal assist clinician make correct diagnosi neuromuscular disord

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