Expert System Design Based on Wavelet Transform and Non-Linear Feature Selection

Abstract

Electromyography (EMG) signal is electrical manifestation of neuromuscular activation due to which physiological processes are accessible which cause the muscle to generate force and produce movement and help us to interact with the world. EMGs have large variation and nonstationary properties. There are two issues in the classification of EMG signals. One is the feature selection, and the other is classifier design. In EMGs diseases recognition, the first and the most important step is feature extraction. In this paper, we have selected Symlet of order five mother wavelet for EMG signal analysis and later we have selected eight features to classify EMG signals of isometric contraction for two different abnormalities namely ALS (Amyotrophic Lateral Sclerosis) which is coming under Neuropathy and Myopathy and the classification approach is termed as Muscular Atrophy Diagnostic Approach (MADA) by authors. From the experimental results, waveform length is the best feature comparing with the other features. Root mean square, spectrogram, kurtosis, entropy and power are other useful augmenting features.