Abstract
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.
Originalsprache | Englisch |
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Seiten (von - bis) | 86-93 |
Seitenumfang | 8 |
Fachzeitschrift | Clinical Neurophysiology |
Volume | 142 |
DOIs | |
Publikationsstatus | Veröffentlicht - 2022 |
Research Field
- Medical Signal Analysis
Schlagwörter
- Seizure detection
- UNEEG