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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.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.
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Metadata
- Authors
- Mirowski, P., Madhavan, D., LeCun, Y., & Kuzniecky, R.
- Affiliations
- Courant Institute of Mathematical Sciences (US)
- University of Nebraska Medical Center (US)
- New York University (US)
- Corresponding author
- Mirowski, Piotr
- Venue
- Clinical Neurophysiology
- Publisher
- Elsevier BV
- Published
- 2009-11
- Volume
- 120
- Issue
- 11
- Pages
- 1927-1940
- ISSN
- 1388-2457 (ISSN-L)
- OpenAlex
- W2009902427
- Cited by
- 380
- References
- 45
- Citations by year
- 2026: 13 · 2025: 13 · 2024: 16 · 2023: 19 · 2022: 26 · 2021: 26 · 2020: 34 · 2019: 40 · 2018: 48 · 2017: 34 · 2016: 21 · 2015: 27 · 2014: 16 · 2013: 13 · 2012: 10
- FWCI
- 5.48
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Clinical Neurophysiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Medicine › Clinical Neurology › Epilepsy research and treatment
- Physical Sciences › Computer Science › Artificial Intelligence › Seismology and Earthquake Studies
- Keywords
- seizure prediction, intracranial EEG, EEG synchronization, cross correlation function, wavelet coherence, spatiotemporal patterns, preictal EEG, interictal EEG, convolutional neural networks, support vector machine
- MeSH terms
- Electroencephalography, Humans, Predictive Value of Tests, Seizures, Neural Networks, Computer
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