Stiliyan Kalitzin

Enhancement of phase clustering in the EEG/MEG gamma frequency band anticipates transitions to paroxysmal epileptiform activity in epileptic patients with known visual sensitivity

A new analytical method for quantifying brain activity from magnetoelectroencephalogram (MEG) and electroencephalogram (EEG) recordings during periodic light stimulation is proposed. It consists in estimating the phase clustering of harmonically related frequency components of a subjects MEG/EEG responses evoked by the light stimulation. The method was developed to test the hypothesis that changes in the dynamics of brain systems in the course of intermittent photic stimulation (IPS) may precede the transition to seizure activity in photosensitive patients. We assumed that such changes would be reflected in the phase of harmonic components of the evoked responses. Thus, we determined the phase clustering for different harmonic components of these MEG/EEG signals. We found that the patients who develop epileptiform discharges during IPS present an enhancement of the phase clustering index at the gamma frequency band, compared with that at the driving frequency. We introduce a quantity-relative phase clustering index (rPCI)-by means of which this enhancement can be quantified. We argue that this quantity reflects the degree of excitability of the underlying dynamical system and it can indicate presence of nonlinear dynamics. rPCI can be applied to detect transitions to epileptic seizure activity in patients with known sensitivity to IPS.

Stimulation-based anticipation and control of state transitions in the epileptic brain

We focus on the implications that the underlying neuronal dynamics might have on the possibility of anticipating seizures and designing an effective paradigm for their control. Transitions into seizures can be caused by parameter changes in the dynamic state or by interstate transitions as occur in multi-attractor systems; in the latter case, only a weak statistical prognosis of the seizure risk can be achieved. Nevertheless, we claim that by applying a suitable perturbation to the system, such as electrical stimulation, relevant features of the systems state may be detected and the risk of an impending seizure estimated. Furthermore, if these features are detected early, transitions into seizures may be blocked. On the basis of generic and realistic computer models we explore the concept of acute seizure control through state-dependent feedback stimulation. We show that in some classes of dynamic transitions, this can be achieved with a relatively limited amount of stimulation.

Topological Numbers and Singularities in Scalar Images: Scale-Space Evolution Properties

Singular points of scalar images in any dimensions are classified by a topological number. This number takes integer values and can efficiently be computed as a surface integral on any closed hypersurface surrounding a given point. A nonzero value of the topological number indicates that in the corresponding point the gradient field vanishes, so the point is singular. The value of the topological number classifies the singularity and extends the notion of local minima and maxima in one-dimensional signals to the higher dimensional scalar images. Topological numbers are preserved along the drift of nondegenerate singular points induced by any smooth image deformation. When interactions such as annihilations, creations or scatter of singular points occurs upon a smooth image deformation, the total topological number remains the same.Our analysis based on an integral method and thus is a nonperturbative extension of the order-by-order approach using sets of differential invariants for studying singular points.Examples of typical singularities in one- and two-dimensional images are presented and their evolution induced by isotropic linear diffusion of the image is studied.

CORTICAL EXCITABILITY AS A POTENTIAL CLINICAL MARKER OF EPILEPSY: A REVIEW OF THE CLINICAL APPLICATION OF TRANSCRANIAL MAGNETIC STIMULATION

Transcranial magnetic stimulation (TMS) can be used for safe, noninvasive probing of cortical excitability (CE). We review 50 studies that measured CE in people with epilepsy. Most showed cortical hyperexcitability, which can be corrected with anti-epileptic drug treatment. Several studies showed that decrease of CE after epilepsy surgery is predictive of good seizure outcome. CE is a potential biomarker for epilepsy. Clinical application may include outcome prediction of drug treatment and epilepsy surgery.

Invertible Apertured Orientation Filters in Image Analysis

A unitary approach for locally apertured orientation analysis of 2D and 3D scalar images is proposed. The size of the local aperture (the scale) needed for the orientation representation induces in general a lost of spatial acuity, or blur. Our construction permits a compensation of the blur by a reconstruction procedure. For this purpose, a special scale-dependent orientation bundle (map of the visual space into function of both position and orientation) is build from the local Gaussian-derivatives jet of a scalar image. In this construction there is an invertible relation between the orientation bundle and the original image. This invertible transformation is used to regain the original acuity in the spatial domain after analyzing orientation features at any given scale.The approach turns out to be highly effective for the detection of elongated structures and for removal of elongated artifacts in 2D images.

Invertible Orientation Bundles on 2D Scalar Images

A general approach for multiscale orientation analysis of 2D scalar images is proposed. A scale-dependent orientation bundle (map of the visual space into function of two arguments: position and orientation) is constructed from the local Gaussian-derivatives jet of a scalar image in 2D. It is shown that there exists a class of orientation filters exhibiting an invertible relation between the orientation bundle and the original image in space domain. This invertible transformation is used to regain the original acuity in the spatial domain after analyzing orientation features at any given scale. The approach turns out to be highly effective for the detection of elongated structures.

Postictal generalized EEG suppression is not associated with periictal cardiac autonomic instability in people with convulsive seizures

Purpose:  Postictal generalized EEG suppression (PGES) seems to be a pathophysiologic hallmark in ictal recordings of sudden unexpected death in epilepsy (SUDEP). It has recently been suggested that presence and duration of PGES might be a predictor of SUDEP risk. Little is known about the etiology of PGES.

A computational method for segmenting topological point-sets and application to image analysis

We propose a computational method for segmenting topological subdimensional point-sets in scalar images of arbitrary spatial dimensions. The technique is based on calculating the homotopy class defined by the gradient vector in a subdimensional neighborhood around every image point. This neighborhood is defined as the linear envelope spawned over a given subdimensional vector frame. In the simplest case where the rank of this frame is maximal, we obtain a technique for localizing the critical points. We consider, in particular, the important case of frames formed by an arbitrary number of the first largest by absolute value principal directions of the Hessian. The method then segments positive and and negative ridges as well as other types of critical surfaces of different dimensionalities. The signs of the eigenvalues associated to the principal directions provide a natural labeling of the critical subsets. The result, in general, is a constructive definition of a hierarchy of point-sets of different dimensionalities linked by inclusion relations. Because of its explicit computational nature, the method gives a fast way to segment height ridges or edges in different applications. The defined topological point-sets are connected manifolds and, therefore, our method provides a tool for geometrical grouping using only local measurements. We have demonstrated the grouping properties of our construction by presenting two different cases where an extra image coordinate is introduced.

Computer Assisted Human Follicle Analysis for Fertility Prospects with 3D Ultrasound

Knowledge about the status of the female reproductive system is important for fertility problems and age-related family planning. The volume of these fertility requests in our emancipated society is steadily increasing. Intravaginal 3D ultrasound imaging of the follicles in the ovary gives important information about the ovarian aging, i.e. number of follicles, size, position and response to hormonal stimulation. Manual analysis of the many follicles is laborious and error-prone. We present a multiscale analysis to automatically detect and quantify the number and shape of the patients follicles. Robust estimation of the centres of the follicles in the speckled echographic images is done by calculating so-called winding number of the intensity singularity, i.e. the path integral of the angular increment of the direction of the gradient vector over a closed neighbourhood around the point. The principal edges on 200-500 intensity traces radiating from the detected singularity points are calculated by a multiscale edge focussing technique on 1D winding numbers. They are fitted with 3D spherical harmonic functions, from which the volume and shape parameters are derived.

Automatic Segmentation of Episodes Containing Epileptic Clonic Seizures in Video Sequences

Epilepsy is a neurological disorder characterized by sudden, often unexpected transitions from normal to pathological behavioral states called epileptic seizures. Some of these seizures are accompanied by uncontrolled, often rhythmic movements of body parts when seizure activity propagates to brain areas responsible for the initiation and control of movement. The dynamics of these transitions is, in general, unknown. As a consequence, individuals have to be monitored for long periods in order to obtain sufficient data for adequate diagnosis and to plan therapeutic strategy. Some people may require long-term care in special units to allow for timely intervention in case seizures get out of control. Our goal is to present a method by which a subset of motor seizures can be detected using only remote sensing devices (i.e., not in contact with the subject) such as video cameras. These major motor seizures (MMS) consist of clonic movements and are often precursors of generalized tonic-clonic (convulsive) seizures, sometimes leading to a condition known as status epilepticus, which is an acute life-threatening event. We propose an algorithm based on optical flow, extraction of global group transformation velocities, and band-pass temporal filtering to identify occurrence of clonic movements in video sequences. We show that for a validation set of 72 prerecorded epileptic seizures in 50 people, our method is highly sensitive and specific in detecting video segments containing MMS with clonic movements.