The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
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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