Epileptic seizure prediction using relative spectral power features.
Details
Serval ID
serval:BIB_BD46A60BF4EC
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Epileptic seizure prediction using relative spectral power features.
Journal
Clinical neurophysiology
ISSN
1872-8952 (Electronic)
ISSN-L
1388-2457
Publication state
Published
Issued date
02/2015
Peer-reviewed
Oui
Volume
126
Number
2
Pages
237-248
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Publication Status: ppublish
Abstract
Prediction of epileptic seizures can improve the living conditions for refractory epilepsy patients. We aimed to improve sensitivity and specificity of prediction methods, and to reduce the number of false alarms.
Relative combinations of sub-band spectral powers of electroencephalogram (EEG) recordings across all possible channel pairs were utilized for tracking gradual changes preceding seizures. By using a specifically developed feature selection method, a set of best candidate features were fed to support vector machines in order to discriminate cerebral state as preictal or non-preictal.
Proposed algorithm was evaluated on continuous long-term multichannel scalp and invasive recordings (183 seizures, 3565 h). The best results demonstrated a sensitivity of 75.8% (66 out of 87 seizures) and a false prediction rate of 0.1h(-1). Performance was validated statistically, and was superior to that of analytical random predictor.
Applying machine learning methods on a reduced subset of proposed features could predict seizure onsets with high performance.
Our method was evaluated on long-term continuous recordings of overall about 5 months, contrary to majority of previous studies using short-term fragmented data. It is of very low computational cost, while providing acceptable levels of alarm sensitivity and specificity.
Relative combinations of sub-band spectral powers of electroencephalogram (EEG) recordings across all possible channel pairs were utilized for tracking gradual changes preceding seizures. By using a specifically developed feature selection method, a set of best candidate features were fed to support vector machines in order to discriminate cerebral state as preictal or non-preictal.
Proposed algorithm was evaluated on continuous long-term multichannel scalp and invasive recordings (183 seizures, 3565 h). The best results demonstrated a sensitivity of 75.8% (66 out of 87 seizures) and a false prediction rate of 0.1h(-1). Performance was validated statistically, and was superior to that of analytical random predictor.
Applying machine learning methods on a reduced subset of proposed features could predict seizure onsets with high performance.
Our method was evaluated on long-term continuous recordings of overall about 5 months, contrary to majority of previous studies using short-term fragmented data. It is of very low computational cost, while providing acceptable levels of alarm sensitivity and specificity.
Keywords
Adolescent, Adult, Algorithms, Artificial Intelligence, Electroencephalography/methods, Epilepsy/diagnosis, Epilepsy/physiopathology, Female, Humans, Male, Middle Aged, Predictive Value of Tests, Support Vector Machine, Young Adult, Classification, Epileptic seizure prediction, Feature reduction, Relative spectral power
Pubmed
Web of science
Create date
06/07/2021 14:28
Last modification date
04/05/2024 6:07