Epileptic seizure prediction using the spatiotemporal correlation structure of intracranial EEG

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2011, Pages 665-668

  Williamson, James R ^a   Bliss, Daniel W ^a   Browne, David W ^a  

^a MIT LINCOLN LABORATORY   (United States)

Author keywords

EEG Signal Processing; Epilepsy; Multivariate Features; Seizure Prediction; Support Vector Machines

Indexed keywords

EEG SIGNAL PROCESSING; EPILEPSY; MULTIVARIATE FEATURES; SEIZURE PREDICTION; SUPPORT VECTOR;

ALGORITHMS; COVARIANCE MATRIX; ELECTROPHYSIOLOGY; MACHINE COMPONENTS; PRINCIPAL COMPONENT ANALYSIS; SIGNAL PROCESSING; SPEECH COMMUNICATION; SUPPORT VECTOR MACHINES;

FORECASTING;

EID: 80051641173     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2011.5946491     Document Type: Conference Paper

References (7)

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2. Y. Park, et al. Seizure prediction with spectral power of time/space-differential EEG signals using cost-sensitive support vector machine. In IEEE International Conference on Acoustics, Speech and Signal Processing, 2010.
3. P. Mirowski, et al. Classification of patterns of EEG synchronization for seizure prediction. Clinical neurophysiology, vol. 120, 2009, pp 1927-1940.
4. https://epilepsy.uni-freiburg.de/freiburg-seizureprediction-project/ eeg-database.
5. http://svmlight.joachims.org.
6. K. Schindler, et al. Assessing seizure dynamics by analysing the correlation structure of multichannel interacranial EEG. Brain, vol. 130, 2007, pp. 65-77.
7. M.Winterhalder, et al. Spatio-temporal patientindividual assessment of synchronization changes for epileptic seizure prediction. Clinical Neurophysiology, vol. 117, 2006, pp. 2399-2413.