Detecting temporal lobe seizures in ultra long-term subcutaneous EEG using algorithm-based data reduction - Remvig, Line S.; Duun-Henriksen, Jonas; Fürbass, Franz; Hartmann, Manfred; Viana, Pedro F.; Kappel Overby, Anne Mette; Weisdorf, Sigge; Richardson, Mark P.; Beniczky, Sándor; Kjaer, Troels W. (Clinical neurophysiology, 2022)
•Ultra long-term subcutaneous EEG offers a novel option for the recording of electrographic epileptic seizures in everyday life.•A semi-automatic seizure detection process is proposed to limit the time spent on review to periods of potential seizure activity.•The algorithm of the semi-automatic detection process had a sensitivity of 86% and a false detection rate of 2.4 per 24 hours. Ultra long-term monitoring with subcutaneous EEG (sqEEG) offers objective outpatient recording of electrographic seizures as an alternative to self-reported epileptic seizure diaries. This methodology requires an algorithm-based automatic seizure detection to indicate periods of potential seizure activity to reduce the time spent on visual review. The objective of this study was to evaluate the performance of a sqEEG-based automatic seizure detection algorithm. A multicenter cohort of subjects using sqEEG were analyzed, including nine people with epilepsy (PWE) and 12 healthy subjects, recording a total of 965 days. The automatic seizure detections of a deep-neural-network algorithm were compared to annotations from three human experts. Data reduction ratios were 99.6% in PWE and 99.9% in the control group. The cross-PWE sensitivity was 86% (median 80%, range 69–100% when PWE were evaluated individually), and the corresponding median false detection rate was 2.4 detections per 24 hours (range: 2.0–13.0). Our findings demonstrated that step one in a sqEEG-based semi-automatic seizure detection/review process can be performed with high sensitivity and clinically applicable specificity. Ultra long-term sqEEG bears the potential of improving objective seizure quantification.