Seizure Prediction With Spectral Power of EEG Using Cost-Sensitive Support Vector Machines | Zendy

Yun Park | Zendy; Théoden I. Netoff | Zendy; Keshab K. Parhi | Zendy

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Seizure Prediction With Spectral Power of EEG Using Cost-Sensitive Support Vector Machines

Author(s) -

Yun Park,

Théoden I. Netoff,

Keshab K. Parhi

Publication year - 2010

Publication title -

journal of medical devices

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.242

H-Index - 29

eISSN - 1932-619X

pISSN - 1932-6181

DOI - 10.1115/1.3455144

Subject(s) - ictal , electroencephalography , false positive paradox , support vector machine , pattern recognition (psychology) , feature vector , artificial intelligence , computer science , sensitivity (control systems) , speech recognition , psychology , neuroscience , engineering , electronic engineering

A patient-specific seizure prediction algorithm is proposed using a classifier to differentiate pre-ictal from inter-ictal EEG signals. The spectral power of EEG processed in four different fashions is used as features: raw, time-differential, space-differential, and time/space-differential EEG. The features are classified using cost-sensitive support vector machines by the double cross-validation methodology. The proposed algorithm has been applied to EEG recordings of 18 patients in the Freiburg EEG database, totaling 80 seizures and 437 h long inter-ictal recordings. Classification with the feature obtained from time/space-differential ECoG demonstrates the performance of 86.25% sensitivity and 0.1281 false positives per hour in out-of-sample testing.

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