Zan, HasanYıldız, Abdulnasır2024-04-242024-04-242021Zan, H. ve Yıldız, A. (2021). Sleep arousal detection using one dimensional local binary pattern-based convolutional neural network. 2021 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021 - Proceedings.9781665436038https://doi.org/10.1109/INISTA52262.2021.9548369https://hdl.handle.net/11468/23512Kocaeli University;Kocaeli University Technopark2021 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021 -- 25 August 2021 through 27 August 2021 -- -- 172175Sleep arousal is defined as a shift from deep sleep to light sleep or complete awakening. Arousals cause sleep deprivation by fragmenting sleep, and ultimately, many health problems. Arousals can be induced by well-studied apneas and hypopneas or other sleep orders such as hypoventilation, bruxism, respiratory effort-related arousals. Thus, detection of less-studied non-apnea/hypopnea arousals is important for diagnosis and treatment of sleep disorders. Traditionally, polysomnography (PSG) test that is recording and inspecting overnight physiological signals is used for sleep studies. In this work, a novel method based on one dimensional local binary pattern (1D-LBP) and convolutional neural network (CNN) for automatic arousal detection from polysomnography recordings is proposed. 25 recordings from PhysioNet Challenge 2018 PSG dataset are used for experiments. Each signal in PSG recordings is transformed to a new signal using 1D-LBP, and then segmented using 10-s-long sliding window. The segments are fed to a CNN model formed by stacking 25 layers for classification of non-apnea/hypopnea arousal regions from non-arousal regions. Area under precision-recall curve (AUPRC) and area under receiver operating characteristic curve (AUROC) metrics are used for performance measurement. Experimental results reflect that the proposed method shows a great promise and obtains an AUPRC of 0.934 and an AUROC of 0.866.eninfo:eu-repo/semantics/closedAccess1d-LbpCnnConvolutional neural networkDeep learningOne dimensional local binary patternSleep arousalConference Object2-s2.0-8511668575110.1109/INISTA52262.2021.9548369N/A