Machine Learning
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The conventional neural networks methods of motor unit action potential analysis in clinical Electromyography are mainly based on single feature set model, the diagnosis accuracy of which is not always satisfactory. In order to utilize multiple feature sets to improve diagnosis accuracy, a hybrid decision support system based on fusion of multiple feature sets classification outputs is presented. Back-propagation (BP) neural network is used as single diagnosis model in every feature set, i.e. i) time domain morphological measures, ii) frequency parameters, and iii) time-frequency domain wavelet transform feature set. Then these outputs are combined by a modified fuzzy integral method to obtain the consensus diagnosis result. More excellent diagnosis yield indicates the potential of the proposed multiple feature domain strategies for aiding the neurophysiologist in the early and accurate diagnosis of neuromuscular disorders. The method is also compared with the majority vote combination scheme.