Epileptic seizure prediction using relative spectral power features

Abstract

Objective: 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.

Methods: 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.

Results: 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.

Conclusion: Applying machine learning methods on a reduced subset of proposed features could predict seizure onsets with high performance.

Significance: 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: Classification; Epileptic seizure prediction; Feature reduction; Relative spectral power.