Neuromuscular disease classification by wavelet decomposition technique

Electromyograph (EMG) is a recording of electrical activity of the skeletal muscles. Various muscle related and neuromuscular diseases are diagnosed by analyzing an EMG signals. In this work, neuromuscular diseases such as amyotrophic lateral sclerosis (ALS), myopathy and normal subjects are analyzed by using discrete wavelet transform (DWT). In time domain analysis, root mean square value is calculated to classify neuromuscular diseases. But DWT based feature extraction scheme gives the best results because in this case, signal analysis is carried out both in time and frequency domain simultaneously. Here an EMG signal is divided into a number of frames and a signal analysis is performed in frame by frame manner. In proposed method, DWT based feature extraction scheme is utilized for feature extraction so as to separate normal person to that of diseased patients. Higher valued DWT coefficients are considered by arranging these coefficients in descending order which are used for feature extraction. Maximum and average value of first five higher valued coefficients is calculated to reduce feature dimension. To demonstrate classification, an EMG data consisting of 5 ALS, 5 myopathy and 5 normal subjects are considered.