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A neural network-based decision making tool was developed for on-line classification of various neuromuscular diseases. Intramuscular electromyographic (EMG) signals were collected using microcomputer-based real-time data acquisition system during voluntary human muscle contraction. The data was collected from three different group of patients. A number of signal processing techniques such as: Autoregressive (AR) modeling, Short Time Fourier Transform (STFT), Wigner-Ville Distribution (WVD), and Chaos analysis methods were applied to these signals. A pair of signal processing features extracted from each of these methods was used to train a neural network using backpropagation algorithm. MATLAB based software was developed and interfaced with Aspirin/MIGRAINS, a neural network develop ment and training tool. A set of data from eight patients of three Merent patient groups (normal, neuropathy, and myopathy) was used for training and testing the neural network. The signal processing features h m six data of each different known patient group were used as inputfoutput to train the network. The rest two sets of the data of each patient group were used to check the validity of the classification made by the network. The results agree with other methods of classiikation and are promosing for on-line dassiikation and diagnosis of various neuromuscular diseases.

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