Compression of Surface Emg Signalswith Algebraic Code Excited Linear Prediction

In this paper we investigate a lossy coding technique for surface EMG signals which is based on the algebraic code excited linear prediction (ACELP) paradigm, widely used for speech signal coding. The algorithm was adapted to the EMG characteristics and tested on both simulated and experimental signals. A fixed compression ratio of 87.3% was chosen. On simulated signals, the mean square error in signal reconstruction and the percentage error in average rectified value after compression were 10.43 % and 5.52 %, respectively. On experimental signals, they were 6.74% and 3.11%. The mean power spectral frequency and third order power spectral moment were estimated with relative error smaller than 1.36% and 1.70%, respectively, for simulated signals, and 3.74% and 2.28% for experimental signals. It was concluded that the proposed coding scheme can be effectively used for high rate, low distortion and low-delay compression of surface EMG signals