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

•An algorithm that has 100% sensitivity to find spontaneous seizures in rats with epilepsy after traumatic brain injury.•Algorithm is 70 times faster than an experienced technician in screening the seizures.•A novel tool to speed up antiepileptogenesis studies after traumatic brain injury. Labor int...

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Veröffentlicht in:Journal of neuroscience methods 2018-09, Vol.307, p.37-45
Hauptverfasser: Andrade, Pedro, Paananen, Tomi, Ciszek, Robert, Lapinlampi, Niina, Pitkänen, Asla
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Sprache:eng
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Zusammenfassung:•An algorithm that has 100% sensitivity to find spontaneous seizures in rats with epilepsy after traumatic brain injury.•Algorithm is 70 times faster than an experienced technician in screening the seizures.•A novel tool to speed up antiepileptogenesis studies after traumatic brain injury. 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. 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. 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. The algorithm had 100% sensitivity. It performed slightly better and was substantially faster than investigator-performed visual analysis. We present a novel seizure detection algorithm for automated detection of seizures in a rat model of post-traumatic epilepsy.
ISSN:0165-0270
1872-678X
DOI:10.1016/j.jneumeth.2018.06.015