Algorithm for automatic detection of spontaneous seizures in rats with post-traumatic epilepsy.

Background: Labor intensive electroencephalogram (EEG) analysis is a major bottleneck to identifying anti-epileptogenic treatments in experimental models of post-traumatic epilepsy. We aimed to develop an algorithm for automated seizure detection in experimental post-traumatic epilepsy.

New Method: Continuous (24/7) 1-month-long video-EEG monitoring with three epidural screw electrodes was started 154 d after lateral fluid-percussion induced traumatic brain injury (TBI; n = 97) or sham-injury (n = 29) in adult male Sprague-Dawley rats. First, an experienced researcher screened a total of 90,720 h of digitized recordings on a computer screen to annotate the occurrence of spontaneous seizures. The same files were then analyzed using an algorithm in Spike2 (ver.9), which searching for temporally linked power peaks (14-42 Hz) in all three EEG channels, and then positive events were marked as a probable seizures. Finally, an experienced researcher confirmed all seizure candidates visually on the computer screen.

Results: Visual analysis identified 197 seizures in 29 rats. Automatic detection identified 4346 seizure candidates in 109 rats, of which 202 in the same 29 rats were true positives, resulting in a false positive rate of 0.046/h or 1.10/d. The algorithm demonstrated 5% specificity and 100% sensitivity. The algorithm analyzed 1-month 3-channel EEG in 7 cohorts in 2 h, whereas analysis by an experienced technician took ∼500 h.

Comparison With Existing Methods: The algorithm had 100% sensitivity. It performed slightly better and was substantially faster than investigator-performed visual analysis.

Conclusions: We present a novel seizure detection algorithm for automated detection of seizures in a rat model of post-traumatic epilepsy.