Chronic insomnia can significantly impair an individual's quality of life leading to a high societal cost. Unfortunately, limited automated tools exist that can assist clinicians in the timely detection of insomni...
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Chronic insomnia can significantly impair an individual's quality of life leading to a high societal cost. Unfortunately, limited automated tools exist that can assist clinicians in the timely detection of insomnia. In this paper, we propose a two stage approach to automatically detect insomnia from an overnight EEG recording. In the first stage we trained a sleep stage scoring model and an epoch-level insomnia detection model. Both models are deep neural network (DNN)- based which are fed by a set of temporal and spectral features derived from 2 EEG channels. In the second stage we computed two subject-level feature sets. One is computed using the output of the sleep stage scoring model and consists of the sleep stage ratios, the stage pair ratios and the stage transition ratios. The second feature set is derived from the output of the epoch-level insomnia detection model and represents the ratio of detected insomniac epochs in each stage and their average posterior probability. These features are then used to train a final binary classifier to classify each subject as control, i.e., with no sleep complaints, or insomniac. We compared 5 different binary classifiers, namely the linear discriminant analysis (LDA), the classification and regression trees (CART) and the support vector machine (SVM) with linear, Gaussian and sigmoid kernels. The system was evaluated against data collected from 115 participants, 61 control and 54 with insomnia, and achieved F1 score, sensitivity and specificity of 0.88, 84% and 91% respectively.
QIIME 2 is a completely re-engineered microbiome bioinformatics platform based on the popular QIIME platform, which it has replaced. QIIME 2 facilitates comprehensive and fully reproducible microbiome data science, im...
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Artificial neural networks (ANNs), while exceptionally useful for classification, are vulnerable to misdirection. Small amounts of noise can significantly affect their ability to correctly complete a task. Instead of ...
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In this paper, we investigate the performance of a dual-hop block fading cognitive radio network with underlay spectrum sharing over independent but not necessarily identically distributed (i.n.i.d.) Nakagami-m fading...
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Search engines have become an integral part of student learning activities. This study aims to see how the use of search engines by students, especially by students of the Library and Information Science program, Facu...
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STEM-Inc is a 3-year NSF ITEST project designed as an after-school program targeting 7th and 8th grade students from traditionally underrepresented groups in Anaheim, California. This project created a simulated techn...
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The formation of resonant photonic structures in porous silicon leverages the benefit of high surface area for improved molecular capture that is characteristic of porous materials with the advantage of high detection...
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Image magnification is one of the branches in digital image processing that is often required in various applications such as in the field of medicine, multimedia, and in satellite imagery. As technology grows, more a...
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