Evaluation of Unsupervised Anomaly Detection Techniques in Labelling Epileptic Seizures on Human EEG

Evaluation of Unsupervised Anomaly Detection Techniques in Labelling Epileptic Seizures on Human EEG

Automated labelling of epileptic seizures on electroencephalograms is an essential interdisciplinary task of diagnostics. Traditional machine learning approaches operate in a supervised fashion requiring complex pre-processing procedures that are usually labour intensive and time-consuming. The bigg...

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Bibliographic Details
Main Authors: Oleg E. Karpov, Matvey S. Khoymov, Vladimir A. Maksimenko, Vadim V. Grubov, Nikita Utyashev, Denis A. Andrikov, Semen A. Kurkin, Alexander E. Hramov
Format: Article
Language:English
Published: MDPI AG 2023-05-01
Series:Applied Sciences
Subjects:
epilepsy
electroencephalogram
machine learning
extreme events
anomaly detection
unsupervised
Online Access:https://www.mdpi.com/2076-3417/13/9/5655

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https://www.mdpi.com/2076-3417/13/9/5655

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