Open AccessQuality Controlled EMG Signal Compression using Linear and Non Linear Transforms
Author(s) -
Vibha Aggarwal,
Sandeep Gupta,
Manjeet Singh Patterh,
Lakhwinder Kaur
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.l3826.1081219
Subject(s) - signal compression , computer science , data compression , bandwidth (computing) , compression (physics) , signal (programming language) , linear prediction , dynamic range , signal processing , speech recognition , mathematics , algorithm , telecommunications , computer vision , materials science , composite material , programming language , radar
In today’s era of telemedicine, data and graphical records are required to be transmitted over noisy, power limited and band limited channels. The effective compression is the best alternate to save time and bandwidth. For Electromyogram (EMG) signal, that are huge in data size, must be compressed in such a way so that can be recovered with minimum alterations. This work focused on the tuneable method to compress EMG signals, with linear and non linear transforms. The analysis is based upon compression factor (CF) and percentage root mean square difference (PRD). The results helps to conclude that non linear transform method have precedence over the linear transforms for almost entire range of user defined PRD (UPRD)