Anton Chernihovskyi

State-of-the-art of seizure prediction.

Summary: Although there are numerous studies exploring basic neuronal mechanisms that are likely to be associated with seizures, to date no definite information is available as to how, when, or why a seizure occurs in humans. The fact that seizures occur without warning in the majority of cases is one of the most disabling aspects of epilepsy. If it were possible to identify preictal precursors from the EEG of epilepsy patients, therapeutic possibilities and quality of life could improve dramatically. The last three decades have witnessed a rapid increase in the development of new EEG analysis techniques that appear to be capable of defining seizure precursors. Since the 1970s, studies on seizure prediction have advanced from preliminary descriptions of preictal phenomena and proof of principle studies via controlled studies to studies on continuous multiday recordings. At present, it is unclear whether prospective algorithms can predict seizures. If prediction algorithms are to be used in invasive seizure intervention techniques in humans, they must be proven to perform considerably better than a random predictor. The authors present an overview of the field of seizure prediction, its history, accomplishments, recent controversies, and potential for future development.

Effect of inhibitory diffusive coupling on frequency-selectivity of excitable media simulated with Cellular Neural Networks

We present a biologically inspired approach to time series analysis by means of nonlinear excitable media simulated with cellular neural networks. Following main principles of biophysical and neuronal mechanisms underlying sound processing in mammals we develop a method for the noise-tolerant instantaneous detection of transient spectral patterns. We also show an influence of the lateral inhibition on the frequency selectivity of excitable media

MEASURING SYNCHRONIZATION WITH NONLINEAR EXCITABLE MEDIA

We examine a possible utilization of the recently proposed method of signal-induced excitation waves in nonlinear excitable media as a means for the noise-tolerant detection of zero-lag phase synchronization in very noisy time series. We show that in cases, where a relatively strong noise contamination aggravates the direct application of phase-based measures of synchronization, it is nevertheless possible to detect synchronization phenomena.

Estimating Generalized Synchronization in Brain Electrical Activity from Epilepsy Patients with Cellular Nonlinear Networks

We present a method for estimating the degree of generalized synchronization between long-lasting multichannel recordings of brain electrical activity from epilepsy patients. Using the nonlinear interdependency measure N as an estimator for generalized synchronization and the parallel computing power of a cellular nonlinear network (CNN) with polynomial-type template functions we show that an accurate approximation of N, detecting changes over several days, is possible

Detecting Structural Alterations in the Brain using a Cellular Neural Network based Classification of Magnetic Resonance Images

The ability to quantify structural attributes using cellular neural networks (CNN) has been shown for a wide range of objects. We here introduce an application that allows the detection of structural alterations in the human brain. Using a CNN-based classification approach we show that a defined class of abnormalities - the so called hippocampal sclerosis - can be detected in T1-weighted magnetic resonance images. Our findings indicate that CNN may prove valuable for a computer-aided diagnosis and classification of images generated by medical imaging systems

Analysis of synchronization phenomena in broadband signals with nonlinear excitable media

We apply the method of frequency-selective excitation waves in excitable media to characterize synchronization phenomena in interacting complex dynamical systems by measuring coincidence rates of induced excitations. We relax the frequency-selectivity of excitable media and demonstrate two applications of the method to signals with broadband spectra. Findings obtained from analyzing time series of coupled chaotic oscillators as well as electroencephalographic (EEG) recordings from an epilepsy patient indicate that this method can provide an alternative and complementary way to estimate the degree of phase synchronization in noisy signals.

Analysis of synchronization phenomena in human electroencephalograms with nonlinear excitable media

We apply the method of frequency-selective excitation waves in excitable media to characterize synchronization phenomena in interacting complex dynamical systems by measuring coincidence rates of induced excitations. We analyzed time series of coupled nonlinear model systems and multi-channel, multi-day electroencephalographic recordings from epilepsy patients that we used as local perturbations to our media. We show that the method allows one to estimate the degree of zero-lag phase synchronization even in noisy signals and can be applied for the analysis of human electroencephalograms with the aim of epileptic seizure prediction.