A decision support system for diagnosis of neuromuscular disorders using DWT and evolutionary support vector machines
Support vector machines (SVMs) have been widely used in many pattern recognition problems. Generally, the performance of SVM classifiers is affected by the selection of the kernel parameters. However, SVM does not offer the mechanism for proper setting of their control parameters. The objective of this research is to optimize the parameters without degrading the SVM classification accuracy in diagnosis of neuromuscular disorders. An evolutionary approach for designing an SVM-based classifier (ESVM) by optimization of automatic parameter tuning using genetic algorithm is proposed. To illustrate and evaluate the efficiency of ESVM, a typical application to EMG signals classification using normal, myopathic, and neurogenic datasets is adopted. 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 was extracted from the sub-bands to represent the distribution of wavelet coefficients. It is shown that ESVM can obtain a high accuracy of 97% using tenfold cross-validation for the EMG datasets. ESVM is developed as an efficient tool, so that various SVMs can be used conveniently as the core of ESVM for diagnosis of neuromuscular disorders.
parameter tuning discrete wavelet transform (dwt) electromyography (emg) evolutionary support vector machines (esvms) genetic algorithms (gas)