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1. Obstructive Sleep Apnea (OSA) Events Classification by Effective Radar Cross Section (ERCS) Method Using Microwave Doppler Radar and Machine Learning Classifier

   https://doi.org/10.1109/IMBIoC47321.2020.9385028  

   Snigdha, Farjana ; Islam, Shekh M ; Boric-Lubecke, Olga ; Lubecke, Victor ( December 2020 , 2020 IEEE MTT-S International Microwave Biomedical Conference (IMBioC))

   null (Ed.)

   In-home sleep monitoring system using Microwave Doppler radar is gaining attention as it is unobtrusive and noncontact form of measurement. Most of the reported results in literature focused on utilizing radar-reflected signal amplitude to recognize Obstructive sleep apnea (OSA) events which requires iterative analysis and cannot recommend about sleep positions also (supine, prone and side). In this paper, we propose a new, robust and automated ERCS-based (Effective Radar Cross section) method for classifying OSA events (normal, apnea and hypopnea) by integrating radar system in a clinical setup. In our prior attempt, ERCS has been proven versatile method to recognize different sleep postures. We also employed two different machine learning classifiers (K-nearest neighbor (KNN) and Support Vector machine (SVM) to recognize OSA events from radar captured ERCS and breathing rate measurement from five different patients' clinical study. SVM with quadratic kernel outperformed with other classifiers with an accuracy of 96.7 % for recognizing different OSA events. The proposed system has several potential applications in healthcare, continuous monitoring and security/surveillance applications. 

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2. Identity Authentication in Two-Subject Environments Using Microwave Doppler Radar and Machine Learning Classifiers

   https://doi.org/10.1109/TMTT.2022.3197413  

   Islam, Shekh M. ; Boric-Lubecke, Olga ; Lubecke, Victor M. ( August 2022 , IEEE Transactions on Microwave Theory and Techniques)

   Identity authentication based on Doppler radar respiration sensing is gaining attention as it requires neither contact nor line of sight and does not give rise to privacy concerns associated with video imaging. Prior research demonstrating the recognition of individuals has been limited to isolated single subject scenarios. When two equidistant subjects are present, identification is more challenging due to the interference of respiration motion patterns in the reflected radar signal. In this research, respiratory signature separation techniques are functionally combined with machine learning (ML) classifiers for reliable subject identity authentication. An improved version of the dynamic segmentation algorithm (peak search and triangulation) was proposed, which can extract distinguishable airflow profile-related features (exhale area, inhale area, inhale/exhale speed, and breathing depth) for medium-scale experiments of 20 different participants to examine the feasibility of extraction of an individual’s respiratory features from a combined mixture of motions for subjects. Independent component analysis with the joint approximation of diagonalization of eigenmatrices (ICA-JADE) algorithm was employed to isolate individual respiratory signatures from combined mixtures of breathing patterns. The extracted hyperfeature sets were then evaluated by integrating two different popular ML classifiers, k-nearest neighbor (KNN) and support vector machine (SVM), for subject authentication. Accuracies of 97.5% for two-subject experiments and 98.33% for single-subject experiments were achieved, which supersedes the performance of prior reported methods. The proposed identity authentication approach has several potential applications, including security/surveillance, the Internet-of- Things (IoT) applications, virtual reality, and health monitoring. 

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3. Structure and severity of pharyngeal obstruction determine oral appliance efficacy in sleep apnoea

   https://doi.org/10.1113/JP278164  

   Marques, Melania ; Genta, Pedro R. ; Azarbarzin, Ali ; Taranto‐Montemurro, Luigi ; Messineo, Ludovico ; Hess, Lauren B. ; Demko, Gail ; White, David P. ; Sands, Scott A. ; Wellman, Andrew ( October 2019 , The Journal of Physiology)

   Some patients with obstructive sleep apnoea (OSA) respond well to oral appliance therapy, whereas others do not for reasons that are unclear.

   In the present study, we used gold‐standard measurements to demonstrate that patients with a posteriorly‐located tongue (natural sleep endoscopy) exhibit a preferential improvement in collapsibility (lowered critical closing pressure) with oral appliances.

   We also show that patients with both posteriorly‐located tongue and less severe collapsibility (predicted responder phenotype) exhibit greater improvements in severity of obstructive sleep apnoea (i.e. reduction in event frequency by 83%, in contrast to 48% in predicted non‐responders).

   The present study suggests that the structure and severity of pharyngeal obstruction determine the phenotype of sleep apnoea patients who benefit maximally from oral appliance efficacy.

   A major limitation to the administration of oral appliance therapy for obstructive sleep apnoea (OSA) is that therapeutic responses remain unpredictable. In the present study, we tested the hypotheses that oral appliance therapy (i) reduces pharyngeal collapsibility preferentially in patients with posteriorly‐located tongue and (ii) is most efficacious (reduction in apnoea‐hypopnea index; AHI) in patients with a posteriorly‐located tongue and less‐severe baseline pharyngeal collapsibility. Twenty‐five OSA patients underwent upper airway endoscopy during natural sleep to assess tongue position (type I: vallecula entirely visible; type II: vallecula obscured; type III: vallecula and glottis obscured), as well as obstruction as a result of other pharyngeal structures (e.g. epiglottis). Additional sleep studies with and without oral appliance were performed to measure collapsibility (critical closing pressure; Pcrit) and assess treatment efficacy. Overall, oral appliance therapy reduced Pcrit by 3.9 ± 2.4 cmH₂O (mean ± SD) and AHI by 69 ± 19%. Therapy lowered Pcrit by an additional 2.7 ± 0.9 cmH₂O in patients with posteriorly‐located tongue (types II and III) compared to those without (type I) (P < 0.008). Posteriorly‐located tongue (p = 0.03) and lower collapsibility (p = 0.04) at baseline were significant determinants of (greater‐than‐average) treatment efficacy. Predicted responders (type II and III and Pcrit < 1 cmH₂O) exhibited a greater reduction in the AHI (83 ± 9vs. 48 ± 8% baseline,P < 0.001) and a lower treatment AHI (9 ± 6vs. 32 ± 15 events h^–1,P < 0.001) than predicted non‐responders. The site and severity of pharyngeal collapse combine to determine oral appliance efficacy. Specifically, patients with a posteriorly‐located tongue plus less‐severe collapsibility are the strongest candidates for oral appliance therapy.

    

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4. Patients with obstructive sleep apnea have suppressed levels of soluble cytokine receptors involved in neurodegenerative disease, but normal levels with airways therapy

   https://doi.org/10.1007/s11325-020-02205-y  

   Wang, Ye ; Meagher, Richard B. ; Ambati, Suresh ; Ma, Ping ; Phillips, Bradley G. ( October 2020 , Sleep and Breathing)

   null (Ed.)

   Abstract Purpose Obstructive sleep apnea (OSA) results in systemic intermittent hypoxia. By one model, hypoxic stress signaling in OSA patients alters the levels of inflammatory soluble cytokines TNF and IL6, damages the blood brain barrier, and activates microglial targeting of neuronal cell death to increase the risk of neurodegenerative disorders and other diseases. However, it is not yet clear if OSA significantly alters the levels of the soluble isoforms of TNF receptors TNFR1 and TNFR2 and IL6 receptor (IL6R) and co-receptor gp130, which have the potential to modulate TNF and IL6 signaling. Methods Picogram per milliliter levels of the soluble isoforms of these four cytokine receptors were estimated in OSA patients, in OSA patients receiving airways therapy, and in healthy control subjects. Triplicate samples were examined using Bio-Plex fluorescent bead microfluidic technology. The statistical significance of cytokine data was estimated using the nonparametric Wilcoxon rank-sum test. The clustering of these high-dimensional data was visualized using t -distributed stochastic neighbor embedding (t-SNE). Results OSA patients had significant twofold to sevenfold reductions in the soluble serum isoforms of all four cytokine receptors, gp130, IL6R, TNFR1, and TNFR2, as compared with control individuals ( p  = 1.8 × 10 −13 to 4 × 10 −8 ). Relative to untreated OSA patients, airways therapy of OSA patients had significantly higher levels of gp130 ( p  = 2.8 × 10 −13 ), IL6R ( p  = 1.1 × 10 −9 ), TNFR1 ( p  = 2.5 × 10 −10 ), and TNFR2 ( p  = 5.7 × 10 −9 ), levels indistinguishable from controls ( p  = 0.29 to 0.95). The data for most airway-treated patients clustered with healthy controls, but the data for a few airway-treated patients clustered with apneic patients. Conclusions Patients with OSA have aberrantly low levels of four soluble cytokine receptors associated with neurodegenerative disease, gp130, IL6R, TNFR1, and TNFR2. Most OSA patients receiving airways therapy have receptor levels indistinguishable from healthy controls, suggesting a chronic intermittent hypoxia may be one of the factors contributing to low receptor levels in untreated OSA patients. 

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5. Real-time high-resolution millimeter-wave imaging for in-vivo skin cancer diagnosis

   https://doi.org/10.1038/s41598-022-09047-6  

   Mirbeik, Amir ; Ashinoff, Robin ; Jong, Tannya ; Aued, Allison ; Tavassolian, Negar ( March 2022 , Scientific Reports)

   High-resolution millimeter-wave imaging (HR-MMWI), with its high discrimination contrast and sufficient penetration depth, can potentially provide affordable tissue diagnostic information noninvasively. In this study, we evaluate the application of a real-time system of HR-MMWI for in-vivo skin cancer diagnosis. 136 benign and malignant skin lesions from 71 patients, including melanoma, basal cell carcinoma, squamous cell carcinoma, actinic keratosis, melanocytic nevi, angiokeratoma, dermatofibroma, solar lentigo, and seborrheic keratosis were measured. Lesions were classified using a 3-D principal component analysis followed by five classifiers including linear discriminant analysis (LDA), K-nearest neighbor (KNN) with different K-values, linear and Gaussian support vector machine (LSVM and GSVM) with different margin factors, and multilayer perception (MLP). Our results suggested that the best classification was achieved by using five PCA components followed by MLP with 97% sensitivity and 98% specificity. Our findings establish that real-time millimeter-wave imaging can be used to distinguish malignant tissues from benign skin lesions with high diagnostic accuracy comparable with clinical examination and other methods.

    

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