A5048694866- EEG and Brain-Computer Interfaces
- Heart Rate Variability and Autonomic Control
- Epilepsy research and treatment
- Functional Brain Connectivity Studies
- Neural dynamics and brain function
- Human Health and Disease
- Pharmacological Effects and Toxicity Studies
- Blind Source Separation Techniques
- Genetics and Neurodevelopmental Disorders
- Advanced Chemical Sensor Technologies
- Peptidase Inhibition and Analysis
- Economic Issues in Ukraine
- International Science and Diplomacy
- Psychosomatic Disorders and Their Treatments
- Complex Systems and Time Series Analysis
- Non-Invasive Vital Sign Monitoring
- ECG Monitoring and Analysis
- Neonatal and fetal brain pathology
- Machine Learning in Bioinformatics
- Fractal and DNA sequence analysis
- Control Systems and Identification
- RNA modifications and cancer
- Chemical Reactions and Isotopes
- Psychology of Development and Education
- Neural Networks and Applications
Kyiv City Clinical Oncology Center
2015-2025
Kyiv Medical University
2020
State Institution "Ukrainian Research Institute of Medical Rehabilitation and Resort Therapy of Ministry of Health of Ukraine"
2001
Background. In modern society, there is a significant increase in the number of neuropsychiatric disorders among children. Modern methods genetic testing play an important role diagnosing diseases nervous system and neurodevelopmental disorders. The purpose was to analyze results studies children with various neurological evaluate their effectiveness improving diagnostic approaches. Materials methods. One hundred sixty aged 0–18 years (average age 6.7 years) were included study. Among them,...
Abstract CHD2‐related epilepsy is characterized by early‐onset photosensitive myoclonic with developmental delay and a high rate of pharmacoresistance. We sought to evaluate the efficacy acetazolamide (ACZ) in epilepsy, due ACZ's unexpected our first patient harboring pathogenic CHD2 variant. collected patients from different Eastern European countries drug‐resistant who were then treated ACZ. Patients underwent video EEG before during ACZ treatment. In zebrafish model ictal‐like events...
In this work we explore the potential of combining standard time and frequency domain indexes with novel information measures, to characterize pre- post-ictal heart rate variability (HRV) in epileptic children, aim differentiating focal generalized epilepsy regarding autonomic control mechanisms.We analyze short-term HRV 37 children suffering from or epilepsy, monitored 10 s, 300 600 s 1800 both before after seizure episodes. Nine are computed (mean, deviation normal-to-normal intervals,...
This study introduces a framework for the information-theoretic analysis of brain functional connectivity performed at level electroencephalogram (EEG) sources. The combines use common spatial patterns to select EEG components which maximize variance between two experimental conditions, simultaneous implementation vector autoregressive modeling (VAR) with independent component describe joint source dynamics and their projection scalp, computation information measures (information storage,...
The present study proposes new method for epileptic seizure prediction based on heart rate variability (HRV) analysis and one-class support vector machines (SVM) technique. Methods: Excessive neural activity in preictal period affects not only brain activity, but also autonomic nervous system, that HRV. proposed distinguishes eight features, analyzes matrix of eigenvalues eigenvectors, makes one class SVM. Results: was tested data collected from 31 patients. Total amount intervals is 232,...
This work is devoted to the prediction of epileptic seizures using heart rate variability (HRV) characteristics. Several HRV features were extracted (statistical, spectral, histogram, polynomial approximation coefficients) for various durations sliding time windows and lengths preictal intervals. The data from 14 subjects with generalized was used. Support Vector Machine exploited as a classifier. Leave-One-Group-Out validation, yielded following values classifier performance: AUC = 0.7622,...
In this contribution, several classifiers are employed to study patient-specific epileptic seizure prediction quality using intracranial electroencephalogram signal (iEEG) for dogs and humans suffering from epilepsy. New approach extraction of correlation-based features in sliding time window within the EEG epoch is proposed. Classification performance was evaluated by area under receiver operating characteristic curve (AUC). Influence duration on results classification studied. For humans,...
In this work, partial information decomposition (PID) was applied to the time series of heart rate and EEG amplitude variability investigate dynamical interactions in brain-heart coupling before after epileptic seizures. From ECG signals collected on 23 children suffering from focal epilepsy, RR intervals variance at ipsilateral contralateral temporal electrodes were computed four different windows Static PID used obtain redundant, unique synergistic components total shared between variance....
Abstract Two patients with insular and striatal postnatal scar had epileptic spasms (ES) that were asymmetrical the only seizure type, whereas none of usual ictal symptoms seizures occurred. Ictal electroencephalography (EEG) showed high-amplitude slow-wave characteristic ES. Vigabatrin remained efficient for over 4 years one patient right into third decade other one. Such ES are distinct from infantile late onset spasms. Furthermore, these observations suggest in epilepsy triggers...
Abstract Objective To investigate the repercussions of war in Ukraine on people with epilepsy (PWE), focusing access to health care, seizure control, quality life (QoL), psychological distress, anxiety, and depression; identify key factors influencing these measures. Methods Consecutive PWE, ≥18 years age, presenting one seven centers across were invited complete a self‐administered survey 2023. The gathered information clinical demographic aspects, geographic displacement, care medications....
In this research, the study of functional connectivity between sources electroencephalogram (EEG) activity assessed for different classes (well before seizure, preictal and post-ictal) was performed. EEG recordings were acquired from 12 subjects with focal epilepsy. Then, ten common spatial patterns (CSP) obtained segments describing 95% Riemannian distance pairs classes, followed by estimation multivariate autoregressive (MVAR) models' coefficients. The MVAR models further used to extract...
This study proposes the new method for epileptic seizure onset detection based on heart rate variability. The one- class support vector machine technique is used as a core part of proposed approach. To extract RR intervals from ECG signal Pan-Tompkins used. Then six time domain features are calculated: number intervals, mean value, standard deviation, root-mean-square difference, variance and pairs whose lengths differ more than 50 ms. preprocessing phase singular value decomposition...
To evaluate the interaction between epilepsy-related brain activities and heart rate dynamics, we analyze electroencephalogram (EEG) variability (HRV) using detrended moving-average cross-correlation analysis (DMCA) for pre- postictal periods in subjects with focal epilepsy. The DMCA was applied to 5 min. long of beat-to-beat intervals power spectral density time series, located 10 before after seizures. Statistically significant differences were found δ (0.5–4 Hz) band seizure, which shows...
Introduction. The influence of EEG derivation scheme on performance epileptic seizure prediction is considered in the paper. Comparison for different schemes conducted. Database recordings from 20 patients (between 1 and 25 years old) suffering epilepsy was used. Correlation coefficients between channels signal extracted with combination window preictal lengths were used as features. Support vector machine to classify data into interictal classes. Epileptic seizures evaluated using area...
Introduction. Brain electrical activity signals (or EEG) by their very nature are non-stationary time series. This basically allows applying a set of mathematical-statistical analysis methods to them. One the most common for signal analyzing is construction autoregressive mathematical models and parameters in order obtain additional information about itself or causality between signals. In multivariate (MVAR) modeling EEG, main issue optimal choice model order. this work, approach selecting...
In the present work problem of optimal bin number selection for equidistant Mutual Information (MI) estimator between electroencephalogram (EEG) and cardiorhythmogram (CRG) is addressed. previously developed method selected based on finding an MI values range numbers. With application to real raw EEG CRG signals it was found that closely placed or symmetrical channels data can be applied, true value with proposed method. calculation are not significantly coupled, cannot estimated small...
The paper presents the results of analysis emotional state patients with epilepsy and depressions in interictal period. Was analyzed entire spectrum aff ective manifestations (positive negative reactions), were separately investigated structural features anxiety. It has been established that structure epilepsy, both without, is noted presence ectivity form reactions tension, anxiety, anger, frustration. specifi c character lies not so much representation states, but range their severity....
The paper presents the results of a comprehensive study characteristics psychopathological state patients with epilepsy and depressions various genesis (organic, psychogenic endogenous) in interi ctal period. Were investigated severity structure manifestations, as well separately level anxiety depending on form depression comparative aspect without signs depression. According to selected forms depression, are analyzed structural features depressive symptoms. Was established that...
In order to determine the psychological characteristics of personality patients with epilepsy and comorbidity depressions, were analised characterological features, defining socio-psychological adaptation, forms manifestation aggressiveness in organic, psychogenic endogenous depressions interictal period epilepsy. It has been established that depression are characterized by anadaptive nature personal organization: persistent pessimism, decreased contact tendencies, rigidity affective...