Discrimination of Parkinsonian tremor from essential tremor by implementation of a wavelet-based soft-decision technique on EMG and accelerometer signals
A wavelet-decomposition with soft-decision algorithm is used to estimate an approximate power-spectral density (PSD) of both accelerometer and surface EMG signals for the purpose of discrimination of Parkinson tremor from essential tremor. A soft-decision wavelet-based PSD estimation is used with 256 bands for a signal sampled at 800Hz. The sum of the entropy of the PSD in band 6 (7.81259.375Hz) and band 11 (15.62517.1875Hz) is used as a classification factor. The data has been recorded for diagnostic purposes in the Department of Neurology of the University of Kiel, Germany. Two sets of data are used. The training set, which consists of 21 essential-tremor (ET) subjects and 19 Parkinson-disease (PD) subjects, is used to obtain the threshold value of the classification factor differentiating between the two subjects. The test data set, which consists of 20 ET and 20 PD subjects, is used to test the technique and evaluate its performance. A voting between three results obtained from accelerometer signal and two EMG signals is applied to obtain the final discrimination. A total accuracy of discrimination of 85% is obtained.
discrimination essential tremor soft-decision technique emg accelerometer signals wavelet-decomposition power-spectral density parkinson tremor