Vladimir V. Kulish

A signal processing based analysis and prediction of seizure onset in patients with epilepsy

One of the main areas of behavioural neuroscience is forecasting the human behaviour. Epilepsy is a central nervous system disorder in which nerve cell activity in the brain becomes disrupted, causing seizures or periods of unusual behaviour, sensations and sometimes loss of consciousness. An estimated 5% of the world population has epileptic seizure but there is not any method to cure it. More than 30% of people with epilepsy cannot control seizure. Epileptic seizure prediction, refers to forecasting the occurrence of epileptic seizures, is one of the most important but challenging problems in biomedical sciences, across the world. In this research we propose a new methodology which is based on studying the EEG signals using two measures, the Hurst exponent and fractal dimension. In order to validate the proposed method, it is applied to epileptic EEG signals of patients by computing the Hurst exponent and fractal dimension, and then the results are validated versus the reference data. The results of these analyses show that we are able to forecast the onset of a seizure on average of 25.76 seconds before the time of occurrence.

ANALYSIS AND VISUALIZATION OF HUMAN ELECTROENCEPHALOGRAMS SEEN AS FRACTAL TIME SERIES

The paper presents a novel technique of nonlinear spectral analysis. This technique is based on the concept of generalized entropy of a given probability distribution, known as the Renyi entropy. This concept allows defining generalized fractal dimension of encephalogram (EEG) and determining fractal spectra of encephalographic signals. These spectra contain information of both frequency and amplitude characteristics of EEG and can be used together with well-accepted techniques of EEG analysis as an enhancement of the latter. Powered by volume visualization of the brain activity, the method provides new clues for understanding the mental processes in humans.

Novel Tools for Quantification of Brain Responses to Music Stimuli

Therapeutic effect of music is known for long time. Music therapy can be used to improve the current patient’s state or even prevent some deceases of psychosomatic origin. For decades, the basic research methods of music psychology mainly relied on survey with questionnaire or on observations of subjects. Investigating the effect of music through electroencephalograms (EEG) could be more precise and objective. In this paper, we describe fractal dimension model for quantification of brain responses to external stimuli. Human EEG evoked by music stimuli are analysed with the developed software. The proposed technique of processing EEG is based on the Renyi entropy — the concept of generalized entropy of a given probability distribution. It allows defining generalized fractal dimension of EEG. The experiments involved ten subjects, both male and female twenty years old university students. The EEGs were processed by the fractal dimension analysis tools. Analysis of fractal dimension values demonstrates that the method can be used to quantitatively distinguish between different states of the brain. Correlation between music, and subject’s emotion and concentration was studied. Few hypotheses were validated.

An Integral Equation for the Dual-Lag Model of Heat Transfer

An integral relation is obtained between local temperature and local temperature gradient for the dual-lag model of heat transport. which substitutes classical Fourier law at short time scales. Both the heat flux lag and the temperature gradient lag are considered, however, it is shown that only difference between the two affects temperature profile. Being applied at exposed surface of material, the integral equation predicts surface temperature variation for any form of imposed heat flux. The solution is tested considering solid heating by a picosecond laser impulse. Results are compared with the classical solution of the parabolic heat transfer equation and available experimental data.

Analysis of the influence of memory content of auditory stimuli on the memory content of EEG signal

One of the major challenges in brain research is to relate the structural features of the auditory stimulus to structural features of Electroencephalogram (EEG) signal. Memory content is an important feature of EEG signal and accordingly the brain. On the other hand, the memory content can also be considered in case of stimulus. Beside all works done on analysis of the effect of stimuli on human EEG and brain memory, no work discussed about the stimulus memory and also the relationship that may exist between the memory content of stimulus and the memory content of EEG signal. For this purpose we consider the Hurst exponent as the measure of memory. This study reveals the plasticity of human EEG signals in relation to the auditory stimuli. For the first time we demonstrated that the memory content of an EEG signal shifts towards the memory content of the auditory stimulus used. The results of this analysis showed that an auditory stimulus with higher memory content causes a larger increment in the memory content of an EEG signal. For the verification of this result, we benefit from approximate entropy as indicator of time series randomness. The capability, observed in this research, can be further investigated in relation to human memory.

Mathematical Modelling and Prediction of the Effect of Chemotherapy on Cancer Cells.

Cancer is a class of diseases characterized by out-of-control cells’ growth which affect DNAs and make them damaged. Many treatment options for cancer exist, with the primary ones including surgery, chemotherapy, radiation therapy, hormonal therapy, targeted therapy and palliative care. Which treatments are used depends on the type, location, and grade of the cancer as well as the person’s health and wishes. Chemotherapy is the use of medication (chemicals) to treat disease. More specifically, chemotherapy typically refers to the destruction of cancer cells. Considering the diffusion of drugs in cancer cells and fractality of DNA walks, in this research we worked on modelling and prediction of the effect of chemotherapy on cancer cells using Fractional Diffusion Equation (FDE). The employed methodology is useful not only for analysis of the effect of special drug and cancer considered in this research but can be expanded in case of different drugs and cancers.

The analysis of the influence of fractal structure of stimuli on fractal dynamics in fixational eye movements and EEG signal.

One of the major challenges in vision research is to analyze the effect of visual stimuli on human vision. However, no relationship has been yet discovered between the structure of the visual stimulus, and the structure of fixational eye movements. This study reveals the plasticity of human fixational eye movements in relation to the ‘complex’ visual stimulus. We demonstrated that the fractal temporal structure of visual dynamics shifts towards the fractal dynamics of the visual stimulus (image). The results showed that images with higher complexity (higher fractality) cause fixational eye movements with lower fractality. Considering the brain, as the main part of nervous system that is engaged in eye movements, we analyzed the governed Electroencephalogram (EEG) signal during fixation. We have found out that there is a coupling between fractality of image, EEG and fixational eye movements. The capability observed in this research can be further investigated and applied for treatment of different vision disorders.

The relationship between the local temperature and the local heat flux within a one-dimensional semi-infinite domain of heat wave propagation

The relationship between the local temperature and the local heat flux has been established for the homogeneous hyperbolic heat equation. This relationship has been written in the form of a convolution integral involving the modified Bessel functions. The scale analysis of the hyperbolic energy equation has been performed and the dimensionless criterion for the mode of energy transport, similar to the Reynolds criterion for the flow regimes, has been proposed. Finally, the integral equation, relating the local temperature and the local heat flux, has been solved numerically for those processes of surface heating whose time scale is of the order of picoseconds.

The fractal based analysis of human face and DNA variations during aging

Human DNA is the main unit that shapes human characteristics and features such as behavior. Thus, it is expected that changes in DNA (DNA mutation) influence human characteristics and features. Face is one of the human features which is unique and also dependent on his gen. In this paper, for the first time we analyze the variations of human DNA and face simultaneously. We do this job by analyzing the fractal dimension of DNA walk and face during human aging. The results of this study show the human DNA and face get more complex by aging. These complexities are mapped on fractal exponents of DNA walk and human face. The method discussed in this paper can be further developed in order to investigate the direct influence of DNA mutation on the face variations during aging, and accordingly making a model between human face fractality and the complexity of DNA walk.

A mathematical based calculation of a myelinated segment in axons

The brain is a complicated system that controls all of the bodys actions and reactions by receiving and processing different stimuli and producing the proper responses. The brain accomplishes this task using various sensory elements such as neurons. The axon is the most important element of the neuron in terms of signal generation and propagation. Although much effort has been made studying the characteristics of the axon, there is no research that focuses on measuring the length of this element from a mathematical point of view. In this paper, we propose for the first time a new mathematical model of the generation of action potentials in the axon. Using this model and the diffusion phenomenon in axons, we propose a characteristic length for the myelinated segments in axons. This mathematically calculated value is corroborated by comparison with values measured by biologists.