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      Noncontact Sleep Study by Multi-Modal Sensor Fusion

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          Abstract

          Polysomnography (PSG) is considered as the gold standard for determining sleep stages, but due to the obtrusiveness of its sensor attachments, sleep stage classification algorithms using noninvasive sensors have been developed throughout the years. However, the previous studies have not yet been proven reliable. In addition, most of the products are designed for healthy customers rather than for patients with sleep disorder. We present a novel approach to classify sleep stages via low cost and noncontact multi-modal sensor fusion, which extracts sleep-related vital signals from radar signals and a sound-based context-awareness technique. This work is uniquely designed based on the PSG data of sleep disorder patients, which were received and certified by professionals at Hanyang University Hospital. The proposed algorithm further incorporates medical/statistical knowledge to determine personal-adjusted thresholds and devise post-processing. The efficiency of the proposed algorithm is highlighted by contrasting sleep stage classification performance between single sensor and sensor-fusion algorithms. To validate the possibility of commercializing this work, the classification results of this algorithm were compared with the commercialized sleep monitoring device, ResMed S+. The proposed algorithm was investigated with random patients following PSG examination, and results show a promising novel approach for determining sleep stages in a low cost and unobtrusive manner.

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          Most cited references29

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          Range Correlation and<tex>\(I/ Q\)</tex>Performance Benefits in Single-Chip Silicon Doppler Radars for Noncontact Cardiopulmonary Monitoring

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            Neurobiology of REM and NREM sleep.

            This paper presents an overview of the current knowledge of the neurophysiology and cellular pharmacology of sleep mechanisms. It is written from the perspective that recent years have seen a remarkable development of knowledge about sleep mechanisms, due to the capability of current cellular neurophysiological, pharmacological and molecular techniques to provide focused, detailed, and replicable studies that have enriched and informed the knowledge of sleep phenomenology and pathology derived from electroencephalographic (EEG) analysis. This chapter has a cellular and neurophysiological/neuropharmacological focus, with an emphasis on rapid eye movement (REM) sleep mechanisms and non-REM (NREM) sleep phenomena attributable to adenosine. The survey of neuronal and neurotransmitter-related brainstem mechanisms of REM includes monoamines, acetylcholine, the reticular formation, a new emphasis on GABAergic mechanisms and a discussion of the role of orexin/hypcretin in diurnal consolidation of REM sleep. The focus of the NREM sleep discussion is on the basal forebrain and adenosine as a mediator of homeostatic control. Control is through basal forebrain extracellular adenosine accumulation during wakefulness and inhibition of wakefulness-active neurons. Over longer periods of sleep loss, there is a second mechanism of homeostatic control through transcriptional modification. Adenosine acting at the A1 receptor produces an up-regulation of A1 receptors, which increases inhibition for a given level of adenosine, effectively increasing the gain of the sleep homeostat. This second mechanism likely occurs in widespread cortical areas as well as in the basal forebrain. Finally, the results of a new series of experimental paradigms in rodents to measure the neurocognitive effects of sleep loss and sleep interruption (modeling sleep apnea) provide animal model data congruent with those in humans.
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              Normative EMG values during REM sleep for the diagnosis of REM sleep behavior disorder.

              Correct diagnosis of rapid eye movement sleep behavior disorder (RBD) is important because it can be the first manifestation of a neurodegenerative disease, it may lead to serious injury, and it is a well-treatable disorder. We evaluated the electromyographic (EMG) activity in the Sleep Innsbruck Barcelona (SINBAR) montage (mentalis, flexor digitorum superficialis, extensor digitorum brevis) and other muscles to obtain normative values for the correct diagnosis of RBD for clinical practice. Two university hospital sleep disorder centers. Thirty RBD patients (15 idiopathic [iRBD], 15 with Parkinson disease [PD]) and 30 matched controls recruited from patients with effectively treated sleep related breathing disorders. Not applicable. Participants underwent video-polysomnography, including registration of 11 body muscles. Tonic, phasic, and "any" (any type of EMG activity, irrespective of whether it consisted of tonic, phasic or a combination of both) EMG activity was blindly quantified for each muscle. When choosing a specificity of 100%, the 3-sec miniepoch cutoff for a diagnosis of RBD was 18% for "any" EMG activity in the mentalis muscle (area under the curve [AUC] 0.990). Discriminative power was higher in upper limb (100% specificity, AUC 0.987-9.997) than in lower limb muscles (100% specificity, AUC 0.813-0.852). The combination of "any" EMG activity in the mentalis muscle with both phasic flexor digitorum superficialis muscles yielded a cutoff of 32% (AUC 0.998) for patients with iRBD and with PD-RBD. For the diagnosis of iRBD and RBD associated with PD, we recommend a polysomnographic montage quantifying "any" (any type of EMG activity, irrespective of whether it consisted of tonic, phasic or a combination of both) EMG activity in the mentalis muscle and phasic EMG activity in the right and left flexor digitorum superficialis muscles in the upper limbs with a cutoff of 32%, when using 3-sec miniepochs.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                21 July 2017
                July 2017
                : 17
                : 7
                : 1685
                Affiliations
                [1 ]Department of Electronics and Computer Engineering, Hanyang University, Seoul 04763, Korea; kuyoungninezero@ 123456hanyang.ac.kr (K.C.); sentel103@ 123456naver.com (K.S.)
                [2 ]Intelligence Lab, LG Electronics Woomyon Research and Development Campus, Seoul 06763, Korea; ksoo.shin@ 123456lge.com (K.S.); jinho.sohn@ 123456lge.com (J.S.)
                [3 ]Department of Otorhinolaryngology-Head and Neck Surgery, College of Medicine, Hanyang University, Seoul 04763, Korea
                Author notes
                [* ]Correspondence: shcho@ 123456hanyang.ac.kr (S.H.C.); jchang@ 123456hanyang.ac.kr (J.-H.C.); Tel.: +82-2-2290-8583 (S.H.C.); +82-2-2220-0355 (J.-H.C.)
                Article
                sensors-17-01685
                10.3390/s17071685
                5539697
                28753994
                4efbb502-44f2-4c0a-ac0c-13f428a5a28a
                © 2017 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 28 June 2017
                : 20 July 2017
                Categories
                Article

                Biomedical engineering
                radar,vital signal,sleep stage,medical device,sensor fusion,microphone
                Biomedical engineering
                radar, vital signal, sleep stage, medical device, sensor fusion, microphone

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