Klinische Neurophysiologie 2020; 51(03): 118-131
DOI: 10.1055/a-1169-4254
CME-Fortbildung

Automatische Erkennung von epilepsietypischen Potenzialen und Anfällen im EEG

Automatic Detection of Epileptiform Potentials and Seizures in the EEG
Christoph Baumgartner
,
Sebastian Hafner
,
Johannes P. Koren

Die Elektroenzephalografie (EEG) ist der wichtigste apparative Eckpfeiler in der Diagnostik und Therapieführung bei Epilepsien. Die visuelle EEG-Befundung stellt dabei nach wie vor den Goldstandard dar. Automatische computerunterstützte Methoden zur Detektion und Quantifizierung von interiktalen epilepsietypischen Potenzialen und Anfällen unterstützen eine zeitsparende, objektive, rasch und jederzeit verfügbare quantitative EEG-Befundung.

Abstract

Automatic computer-based algorithms for the detection of epileptiform potentials and seizure patterns on EEG facilitate a time-saving, objective method of quantitative EEG interpretation which is available 7/24. For the automatic detection of interictal epileptiform potentials sensitivities range from 65 to 99% with false positive detections of 0,09 to 13,4 per minute. Recent studies documented equal or even better performance of automatic spike detection programs compared with experienced human EEG readers. The seizure detection problem – one of the major problems in clinical epileptology – consists of the fact that the majority of focal onset seizures with impaired awareness and of seizures arising out of sleep occur unnoticed by patients and their caregivers. Automatic seizure detection systems could facilitate objective seizure documentation and thus help to solve the seizure detection problem. Furthermore, seizure detection systems may help to prevent seizure-related injuries and sudden unexpected death in epilepsy (SUDEP), and could be an integral part of novel, seizure-triggered on-demand therapies in epilepsy. During long-term video-EEG monitoring seizure detection systems could improve patient safety, provide a time-saving objective and reproducible analysis of seizure patterns and facilitate automatic computer-based patient testing during seizures. Sensitivities of seizure detection systems range from 75 to 90% with extratemporal seizures being more difficult to detect than temporal seizures. The false positive alarm rate ranges from 0,1 to 5 per hour. Finally, machine learning algorithms, especially deep learning approaches, open a new highly promising era in automatic spike and seizure detection.



Publication History

Publication Date:
29 September 2020 (online)

© Georg Thieme Verlag KG
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