Classification of sleep-wake is necessary for the diagnosis and treatment of sleep disorders, and EEG is normally used to assess sleep quality. Manual scoring is time-consuming and requires a sleep expert. Therefore, automatic sleep classification is essential. To accomplish this, features are extracted from the time domain, frequency domain, wavelet domain, and also from non-linear dynamics. In this study, a novel Jaya Optimization based hyper-Parameter and feature Selection (JOPS) algorithm is proposed to select optimal feature subset as well as hyper-parameters of the classifier such as KNN and SVM, simultaneously. JOPS is self-adaptive that automatically adapts to the population size. The proposed JPOS yielded the accuracy of 94.99% and 94.85% using KNN and SVM, respectively. JPOS algorithm is compared with genetic algorithm and differential evaluation-based feature selection algorithm. Finally, a decision support system is created to graphically visualize the sleep-wake state which will be beneficial to clinical staffs. Furthermore, the proposed JOPS can not only be used in sleep-wake classification but could be applied in other classification problems.
- Sleep-Wake cycle
- Hyper-parameter tuning
- Feature Selection
- JOPS algorithm
- Decision Support System
A. A.-M. Bulbul and M. Abdul Awal—Equal Contributions.
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In 10-fold cross validation, one-fold is used for testing and nine other folds are used for training and repeated ten times so that each fold i.e. whole dataset is tested.
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This work is a part of the work supported by Khulna University Research Cell (KURC).
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Bulbul, A.AM., Abdul Awal, M., Debjit, K. (2020). EEG Based Sleep-Wake Classification Using JOPS Algorithm. In: Mahmud, M., Vassanelli, S., Kaiser, M.S., Zhong, N. (eds) Brain Informatics. BI 2020. Lecture Notes in Computer Science(), vol 12241. Springer, Cham. https://doi.org/10.1007/978-3-030-59277-6_33
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