Detecting temporal lobe seizures in ultra long-term subcutaneous EEG using algorithm-based data reduction

Line S. Remvig, Jonas Duun-Henriksen, Franz Fürbass, Manfred Hartmann, Pedro Viana, Anne Mette Kappel Overby, Sigge Weisdorf, Mark P. Richardson, Sándor Beniczky, Troels W. Kjaer

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)86-93
Number of pages8
JournalClinical Neurophysiology
Publication statusPublished - 2022

Research Field

  • Medical Signal Analysis


  • Seizure detection


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