Comparison of seizure detection algorithms in continuously monitored pediatric patients.

SUMMARY: Robust, automated seizure detection has long been an important goal in epilepsy research because of both the possibilities for portable intervention devices and the potential to provide prompter, more efficient treatment while in clinic. The authors present results on how well four seizure detection algorithms (based on principal eigenvalue [EI], total power, Kolmogorov entropy [KE], and correlation dimension) discriminated between ictal and interictal EEG and electrocorticoencephalography (ECoG) from four patients (aged 13 months to 21 years). Test data consisted of 46 to 78 hours of continuously acquired EEG/ECoG for each patient (245 hours total), and the detectors' accuracy was checked against seizures found by a board-certified neurologist and an experienced registered EEG technician. The results were patient-specific: no algorithm performed well on a 13-month-old patient, and no algorithm consistently performed best on the other three patients. One of the metrics (EI) supported the existence of a postictal period of 5 to 15 minutes in the three oldest patients, but no strong evidence of a preictal anticipation was found. Two metrics (EI and KE) cycled continuously with a period of several hours in a 21-year-old patient, highlighting the importance of continuous analysis to differentiate background cycling from anticipation.