Piotr Mirowski, Deepak Madhavan, Yann LeCun, Ruben Kuzniecky.
Clinical Neurophysiology, vol. 120, issue 11, November 2019
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.
IEEE Workshop on Machine Learning and Signal Processing paper:
Comparing SVM and convolutional networks for epileptic seizure prediction from intracranial EEG.