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- W2912276634 abstract "This study mainly focuses on automatic sleep stage classification based on polysomnographic sleep recordings obtained from obstructive sleep apnea subjects. Various studies have so far classified sleep stages based on EEG recordings obtained from normal subjects. Because obstructive sleep apnea subjects’ sleep is often interrupted throughout the night, accurate scoring of their sleep disorders is important for diagnosis. The signals for automatic sleep stages classification were selected in accordance with American Academy of Sleep Medicine criteria. Feature vectors consisting of these signal power values for the automatic sleep stage classification were calculated as inputs of ANN ( Artificial Neural Networks). We re-ordered the feature vector table obtained from signals via the algorithm developed to increase the success of the ANN. In this study, training and testing success of ANN were determined by using 10-fold cross-validation. In the study of automatic sleep stage scoring implemented by ANN, the correct recognition rate of Wakefulness, REM (Rapid Eye Movement), NREM1(Non REM1), NREM2, NREM3 were found as 95%, 93%, 91%, 86% and 92%, respectively. The findings suggest that training and test success of automatic sleep stage classification are better compared to the other studies in the literature." @default.
- W2912276634 created "2019-02-21" @default.
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- W2912276634 date "2019-01-27" @default.
- W2912276634 modified "2023-09-23" @default.
- W2912276634 title "DALGACIK DÖNÜŞÜMÜ İLE YAPAY SİNİR AĞLARI KULLANILARAK UYKU EVRELERİNİN OTOMATİK SINIFLANDIRILMASI" @default.
- W2912276634 doi "https://doi.org/10.28948/ngumuh.516809" @default.
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