Investigation of Specificity of Parkinson's Disease Features Obtained Using the Method of Cerebral Cortex Electrical Activity Analysis Based on Wave Trains

摘要

In recent years, spindle-shaped electrical activity became interesting for researchers looking for new methods of time-frequency electroencephalogram (EEG) analysis. 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 EEG are alpha, beta, and sleep spindles. We analyze any kinds of wave train electrical activity of the brain in a wide frequency range. We have developed a new method for analyzing wave train electrical activity of the cerebral cortex based on wavelet analysis and ROC analysis that enables to study the detailed time-frequency features of EEG in patients with neurodegenerative diseases such as Parkinson's disease (PD). The idea of the method is to find local maxima in a wavelet spectrogram and to calculate various characteristics describing these maxima (called wave trains): the leading frequency, the duration (the full-width on the half-maximum of the peak in the spectrogram, FWHM), the bandwidth (FWHM), the number of wave trains per second. Then we conduct statistical analysis of these characteristics. In our previous papers, frequency ranges were found where the quantity of wave trains per second differs between a group of patients in early stage of PD and a group of healthy volunteers. In this paper, the specificity of these PD features is investigated in comparison with the patients with essential tremor (ET).