Welcome to the 3rd International Workshop on Seizure Prediction in Epilepsy!
s for Poster Presentation Abstracts for Poster Presentation by Siegert et al. (Phys. Lett. A 4 75, 998) we estimate deterministic and stochastic parts of the dynamics and define measures that both detect the direction and quantify the strength of the coupling. We numerically test the method using time series from coupled stochastic model systems. Applying our method to intracranially recorded EEG time series from patients suffering from focal epilepsies, we observe interdependencies between EEG signals from different contacts that allow one to identify the epileptic focus even during the interictal state. We discuss possible applications for the detection of pre seizure states. 1.3) A Cluster Computing System for Rapidly Evaluating Seizure Prediction Algorithms Kent Leyde, Mike Bland, Khaled Boulos, Gregory Dunn, David Himes, Frederick Hood, Ryan Seghers, David Snyder, Jim Stearns NeuroVista Corporation Introduction: Seizure prediction algorithms are often computationally complex and require the use of large multi patient intracranial EEG data sets. Measures employed to avoid in sample testing necessitate partitioning data and separating training and testing computations. Rigorous methods are required to characterize algorithm performance. The computational burden of these tasks is a barrier to developing and evaluating seizure prediction algorithms. Methods: A cluster computing system was designed specifically for seizure prediction research. Storage requirements were derived from the desire to study a meaningful population of 50 to 00 patients, with a typical record length of 00 to 00 gigabytes per subject. Computational requirements were derived from the need to execute and assess the results from hundreds of candidate algorithms, comprising signal processing, feature extraction, classification, cross validation, and performance metric calculation tasks. The resulting system includes 0 computing nodes comprising 675 gigaflops of processing power and 40 terabytes of on line storage. Custom software supports automated preparation and management of large quantities of clinical data, rapid implementation and execution of algorithms, rigorous performance analysis, and management of cluster hardware. Results: Over an 8 month period, the cluster computing system was used to analyze approximately 500 candidate algorithms and variants, involving the processing of approximately 5 million patient hours from a data set of 76 patients. Conclusions: Rapid, rigorous, evaluation of candidate seizure prediction algorithms using large data sets and cross validation methods is beyond the capability of presently available individual personal computers, but may be accomplished using cluster computing techniques. 1.4) Developing Seizure Prediction Algorithms Based Upon EMU Recordings: Data Challenges and Solutions David Himes, Mike Bland, Khaled Boulos, Kent Leyde NeuroVista Corporation Introduction: Assembling a database for developing and evaluating seizure prediction algorithms requires data from multiple centers to be converted to a common format and validated. Differences in data collection practices and equipment must be addressed. Methods: In the process of collecting and pre processing patient data, inconsistencies were identified, catalogued, and corrected using a flexible pre processing system. Results: Issues that required correction were: Lead changes within subject due to headbox limitations, investigators sometimes exchanged leads for a given channel within the same recording session. Dropped samples some EEG manufacturers allow system pausing or account for time drift by assigning clock times to sample times, creating discontinuities within the binary data. Labeling inconsistencies physicians and technicians frequently used different channel names within subjects, complicating electrode identification. Non integer sampling frequencies some EEG systems account for clock/sample drift by assigning fractional sampling frequencies, making resampling difficult. Time zone inconsistencies it is not uncommon for data within differing portions of a manufacturer's file set (EEG, video, annotations, patient demographics) to employ differing time standards, potentially causing desynchronization. Anonymization patient information is embedded across many data file types, complicating deidentification efforts. Calibration factor changes within session investigators infrequently alter their headbox gain within sessions, requiring recalibration of binary data to provide a uniform amplitude scale. Conclusions: Significant data pre conditioning is required to facilitate use in algorithm development. The aforementioned system supports mass processing, while allowing a dynamically growing library of corrective measures to be applied to subject files. 1.5) Does Seizure Prediction Require Discretely Localized Onset? A Comparison of Mesial Temporal and Regional Onset Neocortical Seizures David E. Snyder (1) Frederick R. Hood (1) Jaideep Mavoori (1) Brian Litt (2) (1) NeuroVista Corporation; (2) University of Pennsylvania Introduction: Seizure prediction studies have focused primarily on patients with well localized seizures, and electrodes proximate to the ictal onset zone. Whether seizure prediction is more challenging for patients with extra temporal and/or regional seizure onset remains unexplored. Methods: Continuous subdural EEG recordings of 76 EMU patients were analyzed with a seizure prediction algorithm. The dataset comprises 745 hours of data, with 0 primary seizures (clusters excluded) Of these patients, seizure onsets were regional neocortical in 7, unilateral mesial temporal in 8, and multi focal and/or extra temporal in the remainder. A block wise (90 minute) statistical model employing k fold crossvalidation was used to compute prediction sensitivity and the false positive rate for each patient. The Wilcoxon signed ranks test was used to examine algorithm performance versus a chance predictor (paired rate of alerts) and the KruskallWallis test for differences by seizure onset pattern. Results: Mean sensitivity was 79% (p 0.000 vs. chance) with a false positive rate of 0.08/hr /seizure) Sensitivity did not differ by onset pattern (p 0.77) with 80% for mesial temporal, 88% for regional, and 77% for all others. False positive rate was similarly unaffected (p 0.7 with rates of 0.08/hr for mesial temporal, 0.07/hr for regional, and 0.08/hr for all others .6, .4, .0/seizure, respectively) Conclusions: High sensitivity seizure prediction with clinically acceptable false alert rates has been demonstrated in 4 Abstracts for Poster Presentation 5 Abstracts for Poster Presentation a population of 76 EMU patients. Performance for patients with mesial temporal or regional neocortical onsets did not differ from the population as a whole. 1.6) Implementation of Closed Loop, EEG Triggered Vagus Nerve Stimulation using Patient Specific Seizure Onset Detection from Scalp EEG Ali Shoeb, Trudy Pang, Steven C. Schachter, John Guttag Electrical Engineering and Computer Science Departement Massachusetts Institute of Technology; Departement of Neurology Beth Israel Deaconness Medical Center Boston Massachusetts In a clinical study currently underway at the Beth Israel Deaconness Medical Center in Boston, Massachusetts we are demonstrating the feasibility of automatically triggering the Vagus Nerve Stimulator Therapy System (Cyberonics Inc. in response to real time detection of a neurologist specified (ictal or interictal) abnormality in the scalp EEG. We recently enrolled our first patient, and confirmed that our computerized system reliably and automatically activated the subject's vagus nerve stimulator whenever the patient experienced an inter ictal epileptiform burst that was pre specified by a neurologist. Future results form the study will be presented at the meeting. Our computerized system relies on a patientspecific detection algorithm to differentiate the neurologist specified (ictal or interictal) epileptiform abnormality from the remaining activity in a subject's EEG. The patient specific algorithm classifies a subject's EEG as being consistent or inconsistent with the neurologist specified abnormality using the support vector machine learning algorithm and feature vectors derived from spectral analysis of the EEG. Prior to our clinical study of closed loop, EEG triggered vagus nerve stimulation, we retrospectively evaluated the sensitivity, specificity, and detection latency of our patient specific algorithm using 5 6 hours of continuously recorded scalp EEG from 6 pediatric epilepsy subjects. We noted that our patient specific algorithm detected 4 seizures with a 6.8+.4 second detection latency and 0. 0.7 false alarms per hour. For comparison, the Reveal algorithm (a commercial, patient non specific detector) evaluated on the same data detected 94/ 4 seizures with a 7.8+/0.0 second detection latency and .0+ 5. false alarms per hour. 1.7) Signal Prediction Algorithm by Cellular Nonlinear Networks (CNN) C. Niederhofer, R. Tetzlaff Institute of Applied Physics, Johann Wolfgang Goethe University, Frankfurt/Main, Germany The derivation and analysis of EEG signal feature extraction methods has been treated in several investigations. Although, the results of recent studies indicate that a pre ictal transition to a seizure can be detected, up to now the sensitivity and specificity of the applied methods are not sufficient for an automated seizure prediction. In this contribution we will present recent results obtained by applying a signal prediction algorithm to segmented EEG signals of long term recordings obtained in ECoG and SEEG measurements. The prediction performance of autonomous single layer delay type discrete time CNN (DTCNN) has been analysed for different cell coupling structures and several feedback delays. Firstly, nonlinear feedbacks have been assumed which have been represented b