Investigation of the multiple comparisons problem in the wave train electrical activity analysis of the muscles in Parkinson’s disease patients

Разработан метод анализа всплескообразной электрической активности мышц, основанный на вейвлет-анализе и ROC-анализе, который позволяет изучать частотно-временные особенности сигналов электромиограмм (ЭМГ) и акселерометра (АКС) «треморных» конечностей пациентов с болезнью Паркинсона (БП). Идея метода заключается в поиске локальных максимумов («всплесков») на вейвлет- спектрограмме и вычислении различных характеристик, описывающих эти максимумы: ведущая частота всплеска, длительность всплеска в периодах, ширина полосы частот всплеска, число всплесков в секунду. Анализируется степень отличия группы пациентов от контрольной группы испытуемых в пространстве этих параметров. Для этого используется ROC-анализ. Исследуется функциональная зависимость AUC (площади под ROC-кривой) от значений границ диапазонов рассматриваемых параметров. Применение этого метода позволило выявить новые нейрофизиологические закономерности в диапазоне 3–7 Гц в сигналах ЭМГ и АКС. В этих частотных диапазонах наблюдаются отличия между группой пациентов с БП и контрольной группой испытуемых. Разработанный метод предусматривает перебор большого количества диапазонов выбранных характеристик, поэтому при статистическом оценивании различных гипотез возникает проблема множественного сравнения. Необходимо найти компромисс между степенью детализации изучаемых характеристик и величиной поправки Бонферрони. В работе рассказывается о процедуре проверки статистических гипотез на данных пациентов с БП на ранней стадии. We have developed a new method for analyzing wave train electrical activity of the muscles based on the wavelet analysis and ROC analysis that enables to study the time- frequency features of electromyograms (EMG) and acceleration (ACC) signals of limbs’ tremor in patients with Parkinson’s disease (PD). The idea of the method is to find local maxima (named wave trains) in the wavelet spectrogram and to calculate various characteristics describing these maxima: the leading frequency, the duration in periods, the bandwidth, the number of wave trains per second. The degree of difference of the group of patients from the control group of subjects in the space of these parameters is analyzed. ROC analysis is used for this purpose. The functional dependence of AUC (the area under the ROC curve) on the values of the bounds of the ranges of the parameters under consideration is investigated. This method allows revealing new neurophysiological regularities in the range of 3–7 Hz in the EMG and ACC signals. The differences between the group of patients with PD and the control group of subjects are observed in these frequency ranges. The method involves investigation of a large number of ranges of selected characteristics; therefore a multiple comparisons problem appears during the statistical check of the statistical hypotheses. It is necessary to find a compromise between the degree of detail of the studied characteristics and the magnitude of the Bonferroni correction. The paper describes the statistical check of statistical hypotheses on the data of patients with early Parkinson’s disease.Исследование выполнено при финансовой поддержке РФФИ в рамках научного проекта No 18-37-20021 с привлечением средств государственного задания по теме «Фундаментальные основы радиоэлектронных методов для проблем биомедицины», стипендии Президента РФ моло-дым учёным и аспирантам No СП-5247.2018.4 и поддержано Российской академией наук

An investigation of specificity of features of early stages of Parkinson’s disease obtained using the method of cortex electrical activity analysis based on wave trains

Разработан метод анализа сигналов для детального исследования частотно- временной динамики электрической активности коры головного мозга, основанный на вейвлет-анализе, ROC-анализе и непараметрической статистике. Идея метода заключается в том, что электроэнцефалограмма (ЭЭГ) рассматривается как набор всплесков, то есть, локальных максимумов на вейвлет-спектрограмме ЭЭГ. Всплески рассматриваются как типичные компоненты ЭЭГ, а не как особый вид сигналов ЭЭГ. Вычисляются следующие параметры всплесков: частота, амплитуда, длительность, ширина полосы частот, количество всплесков в секунду. Степень различия между группой пациентов с болезнью Паркинсона первой стадии и группой здоровых добровольцев в пространстве этих параметров оценивается с помощью ROC-анализа, а именно, с помощью анализа функциональной зависимости AUC от границ диапазонов этих параметров. Используя этот метод, мы определили три частотных диапазона, где выявляются различия между группой пациентов и группой здоровых добровольцев. В статье описываются результаты исследования специфичности обнаруженных ранее групповых признаков ранней стадии болезни Паркинсона. A new method of signal analysis based on wavelet analysis, ROCanalysis, and non-parametric statistics for detailed investigation of the timefrequency dynamics of the electrical activity of the cerebral cortex is developed. The idea of the method is in that the electroencephalogram (EEG) is considered as a set of wave trains (WT). WT are detected as local maxima in the wavelet spectrogram of EEG. We consider WT as a typical component of EEG, but not as a special kind of EEG signals. The following parameters of WT are accounted: the frequency, the duration, the bandwidth, the number of WT per second, and PSD. The extent of differences between the group of the first stage Parkinson’s disease patients and the healthy volunteers in the space of these parameters is investigated. ROC-analysis is used for this purpose. The functional dependence of AUC on the boundaries of the ranges of these parameters is analyzed. Using this method, we have identified three frequency ranges, where differences between the group of the patients and the healthy volunteers were discovered. The paper describes the results of investigation of specificity of these features of early stage of Parkinson’s disease.Авторы благодарны Алексею В. Карабанову (Федеральное государственное бюджетное учреждение науки «Научный центр неврологии», Москва) за подбор и медицинское обследование пациентов. Работа выполнена за счёт средств государственного задания № 0030-2015-0189

Time-frequency dynamics of spike-wave discharges in absence epilepsy patients using wavelet transform

Contains fulltext : 56879.pdf (publisher's version ) (Closed access)1 p

Some peculiarities of time-frequency dynamics of spike-wave discharges in humans and rats

Contains fulltext : 56663.pdf (publisher's version ) (Closed access)Objective - Time–frequency dynamics of spike–wave discharges (SWDs) were investigated in patients with absence seizures and in WAG/Rij rats, a genetic model of absence epilepsy using a specially developed wavelet transform. Methods - Two types of SWDs were analyzed in both species: the most frequently occurring discharges (of minimal 3.6–4.0 s or more) and shorter ones recorded from various cortical regions. Results - The more prolonged discharges had two phases: during the initial part (from tenth of seconds to 1 s) of the seizure the frequency decreased quickly from 5 to 3.5 Hz in patients and from about 15 to 10 Hz in rats. A slower frequency decrease with periodical fluctuations was observed in both species during the second part of the discharge: the frequency decreased towards the end of the discharge to 3 Hz in patients and to 6–7 Hz in rats. The frequency of the short discharges decreased fast during the whole discharge: from 5 to 2–2.5 Hz and from about 15 to 5 Hz in patients and rats, respectively. Conclusions - Comparison of data obtained in patients with typical absence epilepsy and WAG/Rij rats with genetic absence epilepsy revealed that the time–frequency dynamics of SWDs had similar properties but in a different frequency range. Significance - The study of time–frequency dynamics using this specially developed wavelet transform revealed two different types of SWDs, which most likely represent different dynamics in the cortico-thalamo-cortical loop during shorter and more prolonged discharges.8 p

Coexistence of two types of nonconvulsive epilepsy in patients and genetic animal model (WAG/Rij rats) [in Russian]

Item does not contain fulltex

Time-frequency analysis of spike-wave discharges using a modified wavelet transform

The continuous Morlet wavelet transform was used for the analysis of the time-frequency pattern of spike-wave discharges (SWD) as can be recorded in a genetic animal model of absence epilepsy (rats of the WAG/Rij strain). We developed a new wavelet transform that allows to obtain the time-frequency dynamics of the dominating rhythm during the discharges. SWD were analyzed pre- and post-administration of certain drugs. SWD recorded predrug demonstrate quite uniform time-frequency dynamics of the dominant rhythm. The beginning of the discharge has a short period with the highest frequency value (up to 15 Hz). Then the frequency decreases to 7-9 Hz and frequency modulation occurs during the discharge in this range with a period of 0.5-0.7 s. Specific changes of SWD time-frequency dynamics were found after the administration of psychoactive drugs, addressing different brain mediator and modulator systems. Short multiple SWDs appeared under low (0.5 mg/kg) doses of haloperidol, they are characterized by a fast frequency decrease to 5-6 Hz at the end of every discharge. The frequency of the dominant frequency of SWD was not stable in long lasting SWD after 1.0 mg/kg or more haloperidol: then two periodicities were found. Long lasting SWD seen after the administration of vigabatrin showed a stable frequency of the discharge. The EEG after Ketamin showed a distinct 5 s quasiperiodicity. No clear changes of time-frequency dynamics of SWD were found after perilamine. It can be concluded that the use of the modified Morlet wavelet transform allows to describe significant parameters of the dynamics in the time-frequency domain of the dominant rhythm of SWD that were not previously detected