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dc.contributor.authorAndrade, Pedro
dc.contributor.authorPaananen, Tomi
dc.contributor.authorCiszek, Robert
dc.contributor.authorLapinlampi, Niina
dc.contributor.authorPitkänen, Asla
dc.date.accessioned2018-09-13T12:24:48Z
dc.date.available2018-09-13T12:24:48Z
dc.date.issued2018
dc.identifier.urihttps://erepo.uef.fi/handle/123456789/6915
dc.description.abstractBackground 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.
dc.language.isoenglanti
dc.publisherElsevier BV
dc.relation.ispartofseriesJOURNAL OF NEUROSCIENCE METHODS
dc.relation.urihttp://dx.doi.org/10.1016/j.jneumeth.2018.06.015
dc.rightsCC BY-NC-ND https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectelectroencephalogram
dc.subjectepileptogenesis
dc.subjectfourier transformation
dc.subjectlateral fluid-percussion
dc.subjecttraumatic brain injury
dc.titleAlgorithm for automatic detection of spontaneous seizures in rats with post-traumatic epilepsy
dc.description.versionfinal draft
dc.contributor.departmentA.I. Virtanen -instituutti
uef.solecris.id55300573en
dc.type.publicationTieteelliset aikakauslehtiartikkelit
dc.rights.accessrights© Elsevier B.V.
dc.relation.doi10.1016/j.jneumeth.2018.06.015
dc.description.reviewstatuspeerReviewed
dc.format.pagerange37-45
dc.publisher.countryAlankomaat
dc.relation.issn0165-0270
dc.relation.volume307
dc.rights.accesslevelopenAccess
dc.type.okmA1
uef.solecris.openaccessEi


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