@inproceedings{28b011cba73e4b62be1b6abee0c746d3,
title = "Seizure prediction with spectral power of time/space-differential EEG signals using cost-sensitive support vector machine",
abstract = "A patient-specific seizure prediction algorithm is proposed using a classifier to differentiate preictal from interictal ECoG signals. Spectral power of ECoG processed in four different fashions are used as features: raw, time-differential, space-differential, and time/space-differential ECoG. The features are classified using cost-sensitive support vector machines by the double cross-validation methodology. The proposed algorithm has been applied to ECoG recordings of 18 patients in the Freiburg EEG database, totaling 80 seizures and 437-hour-long interictal recordings. Classification with the feature obtained from time/space-differential ECoG demonstrates performance of 86.25% sensitivity and 0.1281 false positives per hour in out-of-sample testing.",
keywords = "Classification, EEG signal processing, Epilepsy, Seizure prediction, Support vector machine",
author = "Yun Park and Theoden Netoff and Keshab Parhi",
year = "2010",
doi = "10.1109/ICASSP.2010.5494922",
language = "English (US)",
isbn = "9781424442966",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5450--5453",
booktitle = "2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings",
note = "2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 ; Conference date: 14-03-2010 Through 19-03-2010",
}