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• W2912461631 abstract "This work addresses the area of a computer-assisted sleep staging using a standard scalp EEG recordings and AASM 2012 scoring rules. We focused on real clinical EEG data containing a large amount of artifacts and/or missing electrodes. The sleep-related features were extracted for 30-seconds segments. Power-in-band features were estimated by a method using Continuous Wavelet Transform (CWT). In addition, entropy, spectral entropy, fractal dimensions and statistical features were used as the input of classifiers. Inter-personal differences and the characteristics of extracted features were evaluated for individual sleep classes. Two expert-in-the-loop strategies and three different classifiers were used to classify data into sleep stages. The results were compared with a fully automated classification and with gold standard expert sleep staging. Due to the proposed improvements the final mean classification sensitivity of expertin-the-loop approach was increased up to 18.4%.The implemented solution allows to classify sleep recordings contaminated by a large amount of the naturally occurring artifacts that are impossible to process by traditional automated classification methods." @default.
• W2912461631 created "2019-02-21" @default.
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• W2912461631 date "2018-12-01" @default.
• W2912461631 modified "2023-10-12" @default.
• W2912461631 title "Expert-in-the-loop Learning for Sleep EEG Data" @default.
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• W2912461631 doi "https://doi.org/10.1109/bibm.2018.8621557" @default.
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