Pedro Andrade1, Tomi Paananen1, Robert Ciszek1, Niina Lapinlampi1, Asla Pitkänen2. 1. A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland. 2. A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland. Electronic address: asla.pitkanen@uef.fi.
Abstract
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.
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.
Authors: Pablo M Casillas-Espinosa; Pedro Andrade; Cesar Santana-Gomez; Tomi Paananen; Gregory Smith; Idrish Ali; Robert Ciszek; Xavier Ekolle Ndode-Ekane; Rhys D Brady; Jussi Tohka; Matthew R Hudson; Piero Perucca; Emma L Braine; Riikka Immonen; Noora Puhakka; Sandy R Shultz; Nigel C Jones; Richard J Staba; Asla Pitkänen; Terence J O'Brien Journal: Epilepsy Res Date: 2019-04-27 Impact factor: 3.045