Mardin Meslek Yüksekokulu

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Browsing Mardin Meslek Yüksekokulu by Subject "1D-LBP"

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    Article
    Local Pattern Transformation-Based convolutional neural network for sleep stage scoring
    (Elsevier Sci Ltd, 2023) Zan, Hasan; Yıldız, Abdulnasır
    Sleep stage scoring is essential for the diagnosis and treatment of sleep disorders. However, manual sleep scoring is a tedious, time-consuming, and subjective task. Therefore, this paper proposes a novel framework based on local pattern transformation (LPT) methods and convolutional neural networks for automatic sleep stage scoring. Unlike in previous works in other fields, these methods were not employed for manual feature extraction, which requires expert knowledge and the pipeline behind it might bias results. The transformed signals were directly fed into a CNN model (called EpochNet) that can accept multiple successive epochs. The model learns features from multiple input epochs and considers inter-epoch context during classification. To evaluate and validate the effectiveness of the proposed approach, we conducted several experiments on the Sleep-EDF dataset. Four LPT methods, including One-dimensional Local Binary Pattern (1D-LBP), Local Neighbor Descriptive Pattern (LNDP), Local Gradient Pattern (LGP), and Local Neighbor Gradient Pattern (LNGP), and different polysomnography (PSG) signals were analyzed as sequence length (number of input epochs) increased from one to five. 1D-LBP and LNDP achieved similar performances, outperforming other LPT methods that are less sensitive to local variations. The best performance was achieved when an input sequence containing five epochs of PSG signals transformed by 1D-LBP was employed. The best accuracy, F1 score, and Kohen's kappa coefficient were 0.848, 0.782, and 0.790, respectively. The results showed that our approach can achieve comparable performance to other state-of-the-art methods while occupying fewer computing resources because of the compact size of EpochNet.
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    Conference Object
    Sleep arousal detection using one dimensional local binary pattern-based convolutional neural network
    (IEEE, 2021) Zan, Hasan; Yıldız, Abdulnasır
    Sleep 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.