Improvement in seizure detection performance by automatic adaptation to the EEG of each patient

Abstract

An important problem in the use of automatic seizure detection during long-term epilepsy monitoring is that false detections can be very frequent, often because a paroxysmal but non-epileptiform pattern occurs repeatedly in a particular patient. We therefore introduce a method to reduce such patient-specific false seizure detections. The program “learns” about the false detections occurring in the first day of a prolonged monitoring session and attempts to eliminate similar patterns occurring during the remainder of the session. This method was evaluated in 20 patients having particularly high false detection rates. Seventy EEG sessions from 10 patients with scalp electrodes and 64 sessions from 10 patients with depth electrodes, covering a total of 2600 h were used in the evaluation. False detections were reduced by 61% (50% in scalp recordings and 71% in depth recordings), with only a 5% probability of losing true seizures. The average false detection rate in these patients fell from 3.25/h to 1.26/h. This significant reduction in false detections could also lead to lower detection thresholds and consequently to the detection of more true seizures.