EMG SIGNAL ANALYSIS ON DETECTION AND DIAGNOSIS OF HEALTHY AND NEUROPATHY MUSCLE DISEASES

Abstract

The Electromyogram (EMG) is a vital and effective tool for muscle monitoring system as well as detection of various abnormalities on behalf of both regular checkup and in case of any emergency. The goal of this project is to provide an improved and more effective way of monitoring the muscles and its disruptions. It also provides a great opportunity to examine the muscles signal deeply considering the different environments. The project is separated into two parts like, the EMG data acquisition and the signal processing. The EMG data acquiring part is responsible to extract the EMG signal from the body and remove the high frequency components and power line noise. The signal processing part can work to filter the signal to remove the background noise. As the recent analyzing technologies are not enough to deal with the sudden abnormalities or even small abnormalities, in this the proposed method wavelet function used to analyzing the data more accurately. The system has been established using the wavelet tool in MATLAB. Because of the availability of statistical information of the EMG data, the system can find out the smallest possible abnormalities even in the harsh conditions. Extracting various statistical parameters along with the other processing techniques including filtering, the proposed technique can monitor the muscles as well as detect any type of abnormalities in a more accurate and actual way which will help to ensure proper diagnosis.