Classification of patterns of EEG synchronization for seizure prediction☆
Accepted 2 September 2009.
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.
Significance
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.
aCourant Institute of Mathematical Sciences, New York University, 719 Broadway, New York, NY 10003, USA
bDepartment of Neurological Sciences, 982045 University of Nebraska Medical Center, Omaha, NE 68198, USA
cNew York University Comprehensive Epilepsy Center, 223 East 34th St., New York, NY 10016, USA
Corresponding Author. Address: Courant Institute of Mathematical Sciences, New York University, 719 Broadway, 12th Floor, New York, NY 10003, USA. Tel.: +1 203 278 1803; fax: +1 212 263 8342.
☆ Portions of this manuscript were presented at the 2008 American Epilepsy Society annual meeting and at the 2008 IEEE Workshop on Machine Learning for Signal Processing.
ABSTRACT