Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2022
Detecting temporal lobe seizures in ultra long-term subcutaneous EEG using algorithm-based data reduction.
СтатьяМногоцентровое исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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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
Аннотация
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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).
CONCLUSIONS: 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.
SIGNIFICANCE: Ultra long-term sqEEG bears the potential of improving objective seizure quantification.
METHODS: 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.
RESULTS: 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).
CONCLUSIONS: 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.
SIGNIFICANCE: Ultra long-term sqEEG bears the potential of improving objective seizure quantification.