Surface EMG Signal Classification for Parkinson's Disease using WCC Descriptor and ANN Classifier

Abstract

To increase the diagnostic accuracy, artificial intelligence techniques can be used as a medical support. The Electromyography (EMG) signals are used in the neuromuscular dysfunction evaluation. The aim of this paper is to construct an automatic system of neuromuscular dysfunction identification in the case of the Parkinson disease based on surface EMG (sEMG) signals. Our proposed system uses artificial neural network method (ANN) to discriminate healthy EMG signals (normal) from abnormal EMG signals (Parkinson). After detecting the EMG activity regions using Fine Modified Adaptive Linear Energy Detecor (FM-ALED) method, Discrete Wavelet Transform (DWT) has been used for feature extraction. An experimental analysis is carried out using ECOTECH's project dataset using principally the Accuracy (Acc). Moreover, a multi-class neural networks classification system combined with the voting rule and Wavelet Cepstral Coefficient (WCC) for healthy and Parkinsonian subjects identification has been developed. The diagnosis accuracy assessment is carried out by conducting various experiments on surface EMG signals. Proposed methodology leads to a classification accuracy of 100%.