Automatic Detection of Spike and Wave Discharges in the EEG of Genetic Absence Epilepsy Rats From Strasbourg

• Published in: IEEE transactions on biomedical engineering 2009-03, Vol.56 (3), p.706-717
• Main Authors: Van Hese $^{\ast}$, Peter, Martens, Jean-Pierre, Waterschoot, Liesbeth, Boon, Paul, Lemahieu, Ignace
• Format: Article
• Language: English
• Subjects: Algorithms; Animals; Automatic detection; Capacitive sensors; detection; Disease Models, Animal; Electroencephalography; Epilepsy; Epilepsy, Absence - physiopathology; experimental animal models; Fragments; Frequency estimation; genetic absence epilepsy rats from Strasbourg (GAERS); Genetics; Harmonic analysis; Male; Methods; Microbiology; Pattern Recognition, Automated - methods; pitch detection; Rats; Rats, Wistar; Seizing; Selectivity; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Spectrogram; spike and wave discharges (SWDs); Spikes; Testing; time-frequency analysis
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2008.2008858

Genetic absence epilepsy rats from Strasbourg are a strain of Wistar rats in which all animals exhibit spontaneous occurrences of spike and wave discharges (SWDs) in the EEG. In this paper, we propose a novel method for the detection of SWDs, based on the key observation that SWDs are quasi-periodic signals. The method consists of the following steps: 1) calculation of the spectrogram; 2) estimation of the background spectrum and detection of stimulation artifacts; 3) harmonic analysis with continuity analysis to estimate the fundamental frequency; and 4) classification based on the percentage of power in the harmonics to the total power of the spectrum. We evaluated the performance of the novel detection method and six SWD/seizure detection methods from literature on a large database of labeled EEG data consisting of two datasets running to a total duration of more than 26 days of recording. The method outperforms all tested SWD/seizure detection methods, showing a sensitivity and selectivity of 96% and 97%, respectively, on the first test set, and a sensitivity and selectivity of 94% and 92%, respectively, on the second test set. The detection performance is less satisfactory (as for all other methods) for EEG fragments showing more irregular and less periodic SWDs.