Investigation of surface EMG and acceleration signals of limbs’ tremor in parkinson’s disease patients using the method of electrical activity analysis based on wave trains

Abstract

In recent years, spindle-shaped electrical activity became interesting for researchers looking for new methods of time-frequency analysis of electromyograms (EMG) and acceleration (ACC) signals. We call signals of this type as wave trains; a wave train (a wave packet) is an electrical signal that is localized in space, frequency, and time. Examples of wave trains in electroencephalograms (EEG) are alpha spindles, beta spindles, and sleep spindles. We analyze all kinds of wave train electrical activity of the muscles in a wide frequency range. We have developed a new method for analyzing wave train electrical activity of muscles based on wavelet analysis and ROC analysis that enables to study the time-frequency features of EMG and ACC in limbs’ tremor in patients with neurodegenerative diseases such as Parkinson’s disease (PD). The idea of the method is to find local maxima in the wavelet spectrogram and to calculate various characteristics describing these maxima (called wave trains): the leading frequency, the duration in periods (the full-width on the square root of 1/2 of the peak in the spectrogram), the bandwidth (the full-width on the square root of 1/2 of the peak in the spectrogram), the number of wave trains per second. Then we conduct a statistical analysis of these characteristics. In our previous papers, frequency ranges (based on EEG features) were found where the quantity of wave trains per second differs between a group of patients of the early stage of PD and a group of healthy volunteers. In this paper, we search similar frequency ranges based on time-frequency features of EMG and ACC.