title: Classification of patterns of EEG synchronization for seizure prediction.
abstract: OBJECTIVE
  Research in seizure prediction from intracranial EEG has highlighted the usefulness of bivariate measures of brainwave synchronization. Spatio-temporal bivariate features are very high-dimensional and cannot be analyzed with conventional statistical methods. Hence, we propose state-of-the-art machine learning methods that handle high-dimensional inputs.
  
  
  METHODS
  We computed bivariate features of EEG synchronization (cross-correlation, nonlinear interdependence, dynamical entrainment or wavelet synchrony) on the 21-patient Freiburg dataset. Features from all channel pairs and frequencies were aggregated over consecutive time points, to form patterns. Patient-specific machine learning-based classifiers (support vector machines, logistic regression or convolutional neural networks) were trained to discriminate interictal from preictal patterns of features. In this explorative study, we evaluated out-of-sample seizure prediction performance, and compared each combination of feature type and classifier.
  
  
  RESULTS
  Among the evaluated methods, convolutional networks combined with wavelet coherence successfully predicted all out-of-sample seizures, without false alarms, on 15 patients, yielding 71% sensitivity and 0 false positives.
  
  
  CONCLUSIONS
  Our best machine learning technique applied to spatio-temporal patterns of EEG synchronization outperformed previous seizure prediction methods on the Freiburg dataset.
  
  
  CONCLUSIONS
  By learning spatio-temporal dynamics of EEG synchronization, pattern recognition could capture patient-specific seizure precursors. Further investigation on additional datasets should include the seizure prediction horizon.
authors.affiliation: Courant Institute of Mathematical Sciences, New York University, 719 Broadway, New York, NY 10003, USA. Electronic address: piotr.mirowski@computer.org.
authors.forename: Piotr
authors.initials: P
authors.lastname: Mirowski
authors.affiliation: Department of Neurological Sciences, 982045 University of Nebraska Medical Center, Omaha, NE 68198, USA.
authors.forename: Deepak
authors.initials: D
authors.lastname: Madhavan
authors.affiliation: Courant Institute of Mathematical Sciences, New York University, 719 Broadway, New York, NY 10003, USA.
authors.forename: Yann
authors.initials: Y
authors.lastname: LeCun
authors.affiliation: New York University Comprehensive Epilepsy Center, 223 East 34th St., New York, NY 10016, USA.
authors.forename: Ruben
authors.initials: R
authors.lastname: Kuzniecky
mesh_terms: D004569:Electroencephalography / Q000145:classification*; D006801:Humans; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D012640:Seizures / Q000145:classification* / Q000175:diagnosis* / Q000503:physiopathology