Leon D. Iasemidis

Epileptic seizure prediction and control

Epileptic seizures are manifestations of epilepsy, a serious brain dynamical disorder second only to strokes. Of the worlds /spl sim/50 million people with epilepsy, fully 1/3 have seizures that are not controlled by anti-convulsant medication. The field of seizure prediction, in which engineering technologies are used to decode brain signals and search for precursors of impending epileptic seizures, holds great promise to elucidate the dynamical mechanisms underlying the disorder, as well as to enable implantable devices to intervene in time to treat epilepsy. There is currently an explosion of interest in this field in academic centers and medical industry with clinical trials underway to test potential prediction and intervention methodology and devices for Food and Drug Administration (FDA) approval. This invited paper presents an overview of the application of signal processing methodologies based upon the theory of nonlinear dynamics to the problem of seizure prediction. Broader application of these developments to a variety of systems requiring monitoring, forecasting and control is a natural outgrowth of this field.

Quadratic Binary Programming and Dynamical System Approach to Determine the Predictability of Epileptic Seizures

Epilepsy is one of the most common disorders of the nervous system. The progressive entrainment between an epileptogenic focus and normal brain areas results to transitions of the brain from chaotic to less chaotic spatiotemporal states, the epileptic seizures. The entrainment between two brain sites can be quantified by the T-index from the measures of chaos (e.g., Lyapunov exponents) of the electrical activity (EEG) of the brain. By applying the optimization theory, in particular quadratic zero-one programming, we were able to select the most entrained brain sites 10 minutes before seizures and subsequently follow their entrainment over 2 hours before seizures. In five patients with 3–24 seizures, we found that over 90% of the seizures are predictable by the optimal selection of electrode sites. This procedure, which is applied to epilepsy research for the first time, shows the possibility of prediction of epileptic seizures well in advance (19.8 to 42.9 minutes) of their occurrence.

Information Flow and Application to Epileptogenic Focus Localization From Intracranial EEG

Transfer entropy (TE) is a recently proposed measure of the information flow between coupled linear or nonlinear systems. In this study, we suggest improvements in the selection of parameters for the estimation of TE that significantly enhance its accuracy and robustness in identifying the direction and the level of information flow between observed data series generated by coupled complex systems. We show the application of the improved TE method to long (in the order of days; approximately a total of 600 h across all patients), continuous, intracranial electroencephalograms (EEG) recorded in two different medical centers from four patients with focal temporal lobe epilepsy (TLE) for localization of their foci. All patients underwent ablative surgery of their clinically assessed foci. Based on a surrogate statistical analysis of the TE results, it is shown that the identified potential focal sites through the suggested analysis were in agreement with the clinically assessed sites of the epileptogenic focus in all patients analyzed. It is noteworthy that the analysis was conducted on the available whole-duration multielectrode EEG, that is, without any subjective prior selection of EEG segments or electrodes for analysis. The above, in conjunction with the use of surrogate data, make the results of this analysis robust. These findings suggest a critical role TE may play in epilepsy research in general, and as a tool for robust localization of the epileptogenic focus/foci in patients with focal epilepsy in particular.

Homeostasis of Brain Dynamics in Epilepsy: A Feedback Control Systems Perspective of Seizures

In an effort to understand basic functional mechanisms that can produce epileptic seizures, some key features are introduced in coupled lumped-parameter neural population models that produce “seizure”-like events and dynamics similar to the ones during the route of the epileptic brain towards seizures. In these models, modified from existing ones in the literature, internal feedback mechanisms are incorporated to maintain the normal low level of synchronous behavior in the presence of coupling variations. While the internal feedback is developed using basic feedback systems principles, it is also functionally equivalent to actual neurophysiological mechanisms such as homeostasis that act to maintain normal activity in neural systems that are subject to extrinsic and intrinsic perturbations. Here it is hypothesized that a plausible cause of seizures is a pathology in the internal feedback action; normal internal feedback quickly regulates an abnormally high coupling between the neural populations, whereas pathological internal feedback can lead to “seizure”-like high amplitude oscillations. Several external seizure-control paradigms, that act to achieve the operational objective of maintaining normal levels of synchronous behavior, are also developed and tested in this paper. In particular, closed-loop “modulating” control with predefined stimuli, and closed-loop feedback decoupling control are considered. Among these, feedback decoupling control is the consistently successful and robust seizure-control strategy. The proposed model and remedies are consistent with a variety of recent observations in the human and animal epileptic brain, and with theories from nonlinear systems, adaptive systems, optimization, and neurophysiology. The results from the analysis of these models have two key implications, namely, developing a basic theory for epilepsy and other brain disorders, and the development of a robust seizure-control device through electrical stimulation and/or drug intervention modalities.

Phase Entrainment and Predictability of Epileptic Seizures

Epilepsy is one of the most common disorders of the nervous system, second only to strokes. We have shown in the past that progressive entrainment between an epileptogenic focus and normal brain areas results to transitions of the brain from chaotic to less chaotic spatiotemporal states, the well-known epileptic seizures. The entrainment between two brain sites can be quantified by the T-index between measures of chaos (e.g., Lyapunov exponents) estimated from the brain electrical activity (EEG) at these sites. Recently, by applying optimization theory, and in particular quadratic zero-one programming, selecting the most entrained brain sites 10 minutes before seizures and subsequently tracing their entrainment backward in time over at most 2 hours, we have shown that over 90% of the seizures in five patients with multiple seizures were predictable [23]. In this communication we show that the above procedure, applied to measures of angular frequency in the state space (average rate of phase change of state) estimated from EEG data per recording brain site over time in one of our patients with 24 recorded seizures, produces very similar results about the predictability of the epileptic seizures (87.5%). This finding implies an interrelation of the phase and chaos entrainment in the epileptic brain and may be used to refine procedures for long-term prediction of epileptic seizures as well as to generate a model of the disorder within the framework of dynamical nonlinear systems.

Control of Epileptic Seizures: Models of Chaotic Oscillator Networks

In an effort to understand basic functional mechanisms that can produce epileptic seizures, and strategies for seizure suppression and control, we discuss some key features of theoretical models of networks of coupled chaotic oscillators that produce seizure-like events and bear striking similarities to dynamics of epileptic seizures. We show that a plausible cause of seizures is a pathological feedback in the brain circuitry. These results have interesting physical interpretation and implications for treatment of epilepsy. They also have close ties with a variety of recent practical observations in the human and animal epileptic brain, and with theories from adaptive systems, optimization, and chaos.

Dynamical hysteresis and spatial synchronization in coupled non-identical chaotic oscillators

We identify a novel phenomenon in distinct (namely non-identical) coupled chaotic systems, which we term dynamical hysteresis. This behavior, which appears to be universal, is defined in terms of the system dynamics (quantified for example through the Lyapunov exponents), and arises from the presence of at least two coexisting stable attractors over a finite range of coupling, with a change of stability outside this range. Further characterization via mutual synchronization indices reveals that one attractor corresponds to spatially synchronized oscillators, while the other corresponds to desynchronized oscillators. Dynamical hysteresis may thus help to understand critical aspects of the dynamical behavior of complex biological systems, e.g. seizures in the epileptic brain can be viewed as transitions between different dynamical phases caused by time dependence in the brain’s internal coupling.

Combined Application of Global Optimization and Nonlinear Dynamics to Detect State Resetting in Human Epilepsy

Epilepsy is a common neurological disorder characterized by recurrent seizures, most of which appear to occur spontaneously. Our research, employing novel signal processing techniques based on the theory of nonlinear dynamics, led us to the hypothesis that seizures represent a spatiotemporal state transition in a complex chaotic system. Through the analysis of long-term intracranial EEG recordings obtained in patients with medically intractable seizures, we discovered that seizures were preceded by a preictal transition that evolves over tens of minutes. This transition is followed by a seizure. Following the seizure, the spatiotemporal dynamics appear to be reset. The study of this process has been hampered by its complexity and variability. A major problem was that the transitions involve a subset of brain sites that vary from seizure to seizure, even in the same patient. However, by combining dynamical analytic techniques with a powerful global optimization algorithm for selecting critical electrode sites, we have been able to elucidate important dynamical characteristics underlying human epilepsy. We illustrate the use of these approaches in confirming our hypothesis regarding postictal resetting of the preictal transition by the seizure. It is anticipated that these observations will lead to a better understanding of the physiological processes involved. From a practical perspective, this study indicates that it may be possible to develop novel therapeutic approaches involving carefully timed interventions and reset the preictal transition of the brain well prior to the onset of the seizure.

Measuring the direction and the strength of coupling in nonlinear Systems-a modeling approach in the State space

We present a novel signal processing methodology to determine the direction and the strength of coupling between coupled nonlinear systems. The methodology is based on multivariate local linear prediction in the reconstructed state spaces of the observed variables from each multivariable nonlinear system. Application of the method is illustrated with systems of coupled Rossler and Lorenz oscillators in various coupling configurations. The obtained results are compared with ones produced by the use of the directed transfer function, a model-based method in the time domain. Through a surrogate analysis, it is shown that the proposed method is more reliable than the directed transfer function in identifying the direction and strength of the involved interactions.

Global optimization and spatial synchronization changes prior to epileptic seizures

Epileptic seizures are manifestations of intermittent spatiotemporal transitions of the human brain from chaos to order. In this paper, a comparative study involving a measure of chaos, in particular the short-term Lyapunov exponent (STLmax), a measure of phase (ϕmax) and a measure of energy (E) is carried out to detect the dynamical spatial synchronization changes that precede temporal lobe epileptic seizures. The measures are estimated from intracranial electroencephalographic (EEG) recordings with sub-dural and in-depth electrodes from two patients with focal temporal lobe epilepsy and a total of 43 seizures. Techniques from optimization theory, in particular quadratic bivalent programming, are applied to optimize the performance of the three measures in detecting preictal synchronization. It is shown that spatial synchronization, as measured by the convergence of STLmax, ϕmax and E of critical sites selected by optimization versus randomly selected sites leads to long-term seizure predictability. Finally, it is shown that the seizure predictability period using STlmax is longer than that of the phase or energy synchronization measures. This points out the advantages of using synchronization of the STlmax measure in conjunction with optimization for long-term prediction of epileptic seizures.