A Novel Wavelet Based Algorithm for Spike and Wave Detection in Absence Epilepsy

Absence seizures are characterized by sudden loss of consciousness and interruption of ongoing motor activities for a brief period of time lasting few to several seconds and up to half a minute. Due to their brevity and subtle clinical manifestations absence seizures are easily missed by inexperienced observers. Accurate evaluation of their high frequency of recurrence can be a challenge even for experienced observers. We present a novel method for detecting and analyzing absence seizures acquired from electroencephalogram (EEG) recordings in patients with absence seizures. Six patients were included in this study; two seizure free, of a total recording time of 26 hours, and four experiencing over 100 seizures within 14.5 hours of total recordings. Our algorithm detected only one false positive finding in the first seizure free patients and 148 of 186 continuous uninterrupted 3Hz spike and wave discharge (SWD) epochs in the rest of the patients. Out of the total 38 missed SWD epochs 28 were ≤ 2.1 sec in duration. The remaining epochs included interrupted 3Hz SWDs. Our proposed algorithm offers an efficient automatic detection scheme that can be used in diagnostic and therapeutic evaluations in patients with absence seizures.