Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
IEEE Intelligent Systems
Analysis of neuromuscular disorders using statistical and entropy metrics on surface EMG
WSEAS Transactions on Signal Processing
Processing body sensor data streams for continuous physiological monitoring
Proceedings of the international conference on Multimedia information retrieval
Integrating neuromuscular and cyber systems for neural control of artificial legs
Proceedings of the 1st ACM/IEEE International Conference on Cyber-Physical Systems
An approach of soft computing applications in clinical neurology
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Classifying Epilepsy Diseases Using Artificial Neural Networks and Genetic Algorithm
Journal of Medical Systems
Performance index assessment of intelligent computing methods in EMG-based neuromuscular diseases
International Journal of Knowledge Engineering and Soft Data Paradigms
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In this study, the fast Fourier transform (FFT) analysis was applied to EMG signals recorded from ulnar nerves of 59 patients to interpret data. The data of the patients were diagnosed by the neurologists as 19 patients were normal, 20 patients had neuropathy and 20 patients had myopathy. The amount of FFT coefficients had been reduced by using principal components analysis (PCA). This would facilitate calculation and storage of EMG data. PCA coefficients were applied to multilayer perceptron (MLP) and support vector machine (SVM) and both classified systems of performance values were computed. Consequently, the results show that SVM has high anticipation level in the diagnosis of neuromuscular disorders. It is proved that its test performance is high compared with MLP.