A Wearable Functional Near-Infrared Spectroscopy (fNIRS) System for Obstructive Sleep Apnea Assessment

Obstructive sleep apnea (OSA), one of the most common sleep-related breathing disorders, contributes as a potentially life-threatening disease. In this paper, a wearable functional near-infrared spectroscopy (fNIRS) system for OSA monitoring is proposed. As a non-invasive system that can monitor oxy...

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Main Authors: Xude Huang (Author), Jinbu Tang (Author), Jingchun Luo (Author), Feng Shu (Author), Chen Chen (Author), Wei Chen (Author)
Format: Book
Published: IEEE, 2023-01-01T00:00:00Z.
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100 1 0 |a Xude Huang  |e author 
700 1 0 |a Jinbu Tang  |e author 
700 1 0 |a Jingchun Luo  |e author 
700 1 0 |a Feng Shu  |e author 
700 1 0 |a Chen Chen  |e author 
700 1 0 |a Wei Chen  |e author 
245 0 0 |a A Wearable Functional Near-Infrared Spectroscopy (fNIRS) System for Obstructive Sleep Apnea Assessment 
260 |b IEEE,   |c 2023-01-01T00:00:00Z. 
500 |a 1558-0210 
500 |a 10.1109/TNSRE.2023.3260303 
520 |a Obstructive sleep apnea (OSA), one of the most common sleep-related breathing disorders, contributes as a potentially life-threatening disease. In this paper, a wearable functional near-infrared spectroscopy (fNIRS) system for OSA monitoring is proposed. As a non-invasive system that can monitor oxygenation and cerebral hemodynamics, the proposed system is dedicated to mapping the pathogenic characteristics of OSA to dynamic changes in blood oxygen concentration and to constructing an automatic approach for assessing OSA. An algorithm including feature extraction, feature selection, and classification is proposed to signals. Permutation entropy(PE), for quantitative measuring the complexity of time series, is firstly involved to characterize the features of the physiological signals. Subsequently, the principal component analysis (PCA) for feature dimensionality reduction and support vector machine (SVM) algorithm for OSA classification are applied. The proposed method has been validated on a dataset that collected by the wearable system. It includes 40 subjects and composes of normal, and various severity cessation of breathing (e.g., mild, moderate, and severe). Experimental results exhibit that the proposed system can effectively distinguish OSA and non-OSA subjects, with an accuracy of 91.89%. The proposed system is expected to pave the novel perspective for OSA assessment in terms of cerebral hemodynamics. 
546 |a EN 
690 |a OSA 
690 |a fNIRS 
690 |a PE 
690 |a PCA 
690 |a SVM 
690 |a Medical technology 
690 |a R855-855.5 
690 |a Therapeutics. Pharmacology 
690 |a RM1-950 
655 7 |a article  |2 local 
786 0 |n IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 31, Pp 1837-1846 (2023) 
787 0 |n https://ieeexplore.ieee.org/document/10078794/ 
787 0 |n https://doaj.org/toc/1558-0210 
856 4 1 |u https://doaj.org/article/80eab5ea394349e09e76b46a222fecd8  |z Connect to this object online.