Electromyography based Detection of Neuropathy Disorder using Reduced Cepstral Feature

Background/Objectives: Neuropathy is a disorder which will be detected using Electromyography (EMG) signals. A new transformation based wavelet decomposition method is proposed in this work to categorize normal EMG signals from abnormal neuropathy disorder signals. Methods/Statistical Analysis: Transformation technique is applied to convert the signals into frequency map. Wavelet decomposition method decomposes transformed signal into set of various levels of coefficients. Cepstral feature have been applied to extract meaningful properties and Minimum Redundancy Maximum Relevance (MRMR) method has been applied to reduce dimensionality of cepstral features. Findings: The KNN classifier is used to discriminate neuropathy disorder from healthy Electromyography signals. The results shows better classification accuracy using cepstral feature set. Entire signal has been subdivided into 20 and 40 sub segments for better features. Coefficients for five levels have been extracted where 40 sub segment features shows better classification accuracy than 20 sub segments. In some cases, 3rd and 5th level coefficients of 20 sub segments shows better classification. Application/Improvements: This study helps to detect abnormal EMG signal from normal patterns which helps radiologist for better prediction of various disorders based on EMG signals.