James Chris Sackellares

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.

Long-term prospective on-line real-time seizure prediction

OBJECTIVE Epilepsy, one of the most common neurological disorders, constitutes a unique opportunity to study the dynamics of spatiotemporal state transitions in real, complex, nonlinear dynamical systems. In this study, we evaluate the performance of a prospective on-line real-time seizure prediction algorithm in two patients from a common database. METHODS We previously demonstrated that measures of chaos and angular frequency, estimated from electroencephalographic (EEG) signals recorded at critical sites in the cerebral cortex, progressively converge (i.e. become dynamically entrained) as the epileptic brain transits from the asymptomatic interictal state to the ictal state (seizure) (Iasemidis et al., 2001, 2002a, 2003a). This observation suggested the possibility of developing algorithms to predict seizures well ahead of their occurrences. One of the central points in those investigations was the application of optimization theory, specifically quadratic zero-one programming, for the selection of the critical cortical sites. This current study combines that observation with a dynamical entrainment detection method to prospectively predict epileptic seizures. The algorithm was tested in two patients with long-term (107.54h) and multi-seizure EEG data B and C (Lehnertz and Litt, 2004). RESULTS Analysis from the 2 test patients resulted in the prediction of up to 91.3% of the impending 23 seizures, about 89+/-15min prior to seizure onset, with an average false warning rate of one every 8.27h and an allowable prediction horizon of 3h. CONCLUSIONS The algorithm provides warning of impending seizures prospectively and in real time, that is, it constitutes an on-line and real-time seizure prediction scheme. SIGNIFICANCE These results suggest that the proposed seizure prediction algorithm could be used in novel diagnostic and therapeutic applications in epileptic patients.

Performance of a seizure warning algorithm based on the dynamics of intracranial EEG

During the past decade, several studies have demonstrated experimental evidence that temporal lobe seizures are preceded by changes in dynamical properties (both spatial and temporal) of electroencephalograph (EEG) signals. In this study, we evaluate a method, based on chaos theory and global optimization techniques, for detecting pre-seizure states by monitoring the spatio-temporal changes in the dynamics of the EEG signal. The method employs the estimation of the short-term maximum Lyapunov exponent (STL(max)), a measure of the order (chaoticity) of a dynamical system, to quantify the EEG dynamics per electrode site. A global optimization technique is also employed to identify critical electrode sites that are involved in the seizure development. An important practical result of this study was the development of an automated seizure warning system (ASWS). The algorithm was tested in continuous, long-term EEG recordings, 3-14 days in duration, obtained from 10 patients with refractory temporal lobe epilepsy. In this analysis, for each patient, the EEG recordings were divided into training and testing datasets. We used the first portion of the data that contained half of the seizures to train the algorithm, where the algorithm achieved a sensitivity of 76.12% with an overall false prediction rate of 0.17h(-1). With the optimal parameter setting obtained from the training phase, the prediction performance of the algorithm during the testing phase achieved a sensitivity of 68.75% with an overall false prediction rate of 0.15h(-1). The results of this study confirm our previous observations from a smaller number of patients: the development of automated seizure warning devices for diagnostic and therapeutic purposes is feasible and practically useful.

Characterizing nonlinearity in invasive EEG recordings from temporal lobe epilepsy

Abstract Invasive electroencephalographic (EEG) recordings from depth and subdural electrodes, performed in eight patients with temporal lobe epilepsy, are analyzed using a variety of nonlinear techniques. A surrogate data technique is used to find strong evidence for nonlinearities in epileptogenic regions of the brain. Most of these nonlinearities are characterized as “spiking” by a wavelet analysis. A small fraction of the nonlinearities are characterized as “recurrent” by a nonlinear prediction algorithm. Recurrent activity is found to occur in spatio-temporal patterns related to the location of the epileptogenic focus. Residual delay maps, used to characterize “lag-one nonlinearity”, are remarkably stationary for a given electrode, and exhibit striking variations among electrodes. The clinical and theoretical implications of these results are discussed.

ASSESSMENT OF A SCALP EEG-BASED AUTOMATED SEIZURE DETECTION SYSTEM

OBJECTIVE The purpose of this study was to evaluate and validate an offline, automated scalp EEG-based seizure detection system and to compare its performance to commercially available seizure detection software. METHODS The test seizure detection system, IdentEvent™, was developed to enhance the efficiency of post-hoc long-term EEG review in epilepsy monitoring units. It translates multi-channel scalp EEG signals into multiple EEG descriptors and recognizes ictal EEG patterns. Detection criteria and thresholds were optimized in 47 long-term scalp EEG recordings selected for training (47 subjects, ∼3653h with 141 seizures). The detection performance of IdentEvent was evaluated using a separate test dataset consisting of 436 EEG segments obtained from 55 subjects (∼1200h with 146 seizures). Each of the test EEG segments was reviewed by three independent epileptologists and the presence or absence of seizures in each epoch was determined by majority rule. Seizure detection sensitivity and false detection rate were calculated for IdentEvent as well as for the comparable detection software (Persysts Reveal®, version 2008.03.13, with three parameter settings). Bootstrap re-sampling was applied to establish the 95% confidence intervals of the estimates and for the performance comparison between two detection algorithms. RESULTS The overall detection sensitivity of IdentEvent was 79.5% with a false detection rate (FDR) of 2 per 24h, whereas the comparison system had 80.8%, 76%, and 74% sensitivity using its three detection thresholds (perception score) with FDRs of 13, 8, and 6 per 24h, respectively. Bootstrap 95% confidence intervals of the performance difference revealed that the two detection systems had comparable detection sensitivity, but IdentEvent generated a significantly (p<0.05) smaller FDR. CONCLUSIONS The study validates the performance of the IdentEvent™ seizure detection system. SIGNIFICANCE With comparable detection sensitivity, an improved false detection rate makes the automated seizure detection software more useful in clinical practice.

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.

Applications of global optimization and dynamical systems to prediction of epileptic seizures

Seizure occurrences seem to be random and unpredictable. However, recent studies in epileptic patients suggest that seizures are deterministic rather than random. There is growing evidence that seizures develop minutes to hours before clinical onset. Our previous studies have shown that quantitative analysis based on chaos theory of long-term intracranial electroencephalogram (EEG) recordings may enable us to observe the seizures development in advance before clinical onset. The period of seizures development is called a preictal transition period, which is characterized by gradual dynamical changes in EEG signals of critical electrode sites from asymptomatic interictal state to seizure. Techniques used to detect a preictal transition include statistical analysis of EEG signals, optimization techniques, and nonlinear dynamics. In this paper, we herein present optimization techniques, specifically multi-quadratic 0-1 programming, for the selection of the cortical sites that are involved with seizures development during the preictal transition period. The results of this study can be used as a criterion to preselect the critical electrode sites that can be used to predict epileptic seizures.

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.

Data Mining in EEG: Application to Epileptic Brain Disorders

Epilepsy is one of the most common brain disorders. At least 40 million people or about 1% of the population worldwide currently suffer from epilepsy Despite advances in neurology and neuroscience, approximately 25–30% of epileptic patients remain unresponsive to anti-epileptic drug treatment, which is the standard therapy for epilepsy There is a growing body of evidence and interest in predicting epileptic seizures using intracranial electroencephalogram (EEG), which is a tool for evaluating the physiological state of the brain. Although recent studies in the EEG dynamics have been used to demonstrate seizure predictability, the question of whether the brain’s normal and pre-seizure epileptic activities are distinctive or differentiable remains unanswered. In this study, we apply data mining techniques to EEG data in order to verify the classifiability of the brain dynamics. We herein propose a quantitative analysis derived from the chaos theory to investigate the brain dynamics. We employ measures of chaoticity and complexity of EEG signals, including Short-Term Maximum Lyapunov Exponents, Angular Frequency, and Entropy, which were previously shown capable of contemplating dynamical mechanisms of the brain network. Each of these measures can be used to display the state transition toward seizures, in which different states of patients can be classified (normal, pre-seizure, and post-seizure states). In addition, optimization and data mining techniques are herein proposed for the extraction of classifiable features of the brain’s normal and pre-seizure epileptic states from spontaneous EEG. We use these features in study of classification of the brain’s normal and epileptic activities. A statistical cross validation is implemented to estimate the accuracy of the brain state classification. The results of this study indicate that it may be possible to design and develop efficient seizure warning algorithms for diagnostic and therapeutic purposes.

Quantitative analysis on electrooculography (EOG) for neurodegenerative disease

Many studies have documented abnormal horizontal and vertical eye movements in human neurodegenerative disease as well as during altered states of consciousness (including drowsiness and intoxication) in healthy adults. Eye movement measurement may play an important role measuring the progress of neurodegenerative diseases and state of alertness in healthy individuals. There are several techniques for measuring eye movement, Infrared detection technique (IR). Video‐oculography (VOG), Scleral eye coil and EOG. Among those available recording techniques, EOG is a major source for monitoring the abnormal eye movement. In this real‐time quantitative analysis study, the methods which can capture the characteristic of the eye movement were proposed to accurately categorize the state of neurodegenerative subjects. The EOG recordings were taken while 5 tested subjects were watching a short (>120 s) animation clip. In response to the animated clip the participants executed a number of eye movements, including vert...