Cited by 380EEG and Brain-Computer InterfacesEpilepsy research and treatmentSeismology and Earthquake Studies

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Classification of patterns of EEG synchronization for seizure prediction

Mirowski, Piotr, Madhavan, Deepak, LeCun, Yann, Kuzniecky, Ruben·2009·Clinical Neurophysiology

DOI 10.1016/j.clinph.2009.09.002

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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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Support-vector networks
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Self-citation
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Support-Vector Networks
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565
EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis
Delorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371
Detecting strange attractors in turbulence
Takens, F. (1981) · Lecture Notes in Mathematics · doi:10.1007/bfb0091924 · Cited by 10,459 · cited 2×
“Even if not all the variables of a chaotic system are observed, one can theoretically reconstruct the original chaotic attractor, thanks to time-delay embedding of the time series of the limited subset of observed variables, assuming the right embedding dimension and time delay (Takens, 1981).”
Libsvm : A library for support vector machines
Ferrari, V. (2008) · Medical Entomology and Zoology · Cited by 10,123
The Nature of Statistical Learning Theory
Sain, S. R., & Vapnik, V. N. (1996) · Technometrics · doi:10.2307/1271324 · Cited by 5,528

Show all 45 references

Research works printed but not linked: 1

Andrzejak, 2003, Testing the null hypothesis of the non-existence of the pre-seizure state, Phys Rev E, 67

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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
ORCID: Mirowski, Piotr
LeCun, Yann
Kuzniecky, Ruben
Venue: Clinical Neurophysiology
Publisher: Elsevier BV
Published: 2009-11
Volume: 120
Issue: 11
Pages: 1927-1940
ISSN: 1388-2457 (ISSN-L)
DOI: 10.1016/j.clinph.2009.09.002
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
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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Classification of patterns of EEG synchronization for seizure prediction

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