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1. Identity Authentication of OSA Patients Using Microwave Doppler radar and Machine Learning Classifiers

   https://doi.org/10.1109/RWS45077.2020.9049983  

   Islam, Shekh M; Rahman, Ashikur; Yavari, Ehsan; Baboli, Meheran; Boric-Lubecke, Olga; Lubecke, Victor M. (January 2020, 2020 IEEE Radio and Wireless Symposium (RWS))

   null (Ed.)

   Non-contact home-based sleep monitoring will bring a paradigm shift to diagnosis and treatment of Obstructive Sleep Apnea (OSA) as it can facilitate easier access to specialized care in order to reach a much boarder set of patients. However, current remote unattended sleep studies are mostly contact sensor based and test results are sometimes falsified by sleep-critical job holders (driver, airline pilots) due to fear of potential job loss. In this work, we investigated identity authentication of patients with OSA symptoms based on extracting respiratory features (peak power spectral density, packing density and linear envelop error) from radar captured paradoxical breathing patterns in a small-scale clinical sleep study integrating three different machine learning classifiers (Support Vector Machine (SVM), K-nearest neighbor (KNN), Random forest). The proposed OSA-based authentication method was tested and validated for five OSA patients with 93.75% accuracy using KNN classifier which outperformed other classifiers. 

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2. Radar Monitoring in Sleep Medicine

   https://doi.org/10.1109/TELSIKS57806.2023.10316075  

   Lubecke, Victor; Borić-Lubecke, Olga; Ishrak, Mohammad Shadman; Wu, Teresa; Cai, Fulin (October 2023, IEEE)

   An overnight sleep study can provide vital health diagnostics yet typically involves applying and monitoring multiple body-contact sensors, which can interfere with sleep and require cumbersome manual data analysis. Doppler radar technology has been demonstrated to provide a non-invasive means of measuring vital signs through clothing and bedding, including respiratory rate, heart rate, motion activity, body position, and tidal respiratory volume. This paper examines the potential for applying physiological radar to assess sleep apnea and intervention strategies. 

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3. What Radio Waves Tell Us about Sleep

   He, H; Li, C; Ganglberger, W; Gallagher, K; Hristov, R; Ouroutzoglou, M; Sun, H; Sun, J; Westover, B; Katabi, D (May 2024, arXivorg)

   The ability to assess sleep at home, capture sleep stages, and detect the occurrence of apnea (without on-body sensors) simply by analyzing the radio waves bouncing off people's bodies while they sleep is quite powerful. Such a capability would allow for longitudinal data collection in patients' homes, informing our understanding of sleep and its interaction with various diseases and their therapeutic responses, both in clinical trials and routine care. In this article, we develop an advanced machine learning algorithm for passively monitoring sleep and nocturnal breathing from radio waves reflected off people while asleep. Validation results in comparison with the gold standard (i.e., polysomnography) (n=849) demonstrate that the model captures the sleep hypnogram (with an accuracy of 81% for 30-second epochs categorized into Wake, Light Sleep, Deep Sleep, or REM), detects sleep apnea (AUROC = 0.88), and measures the patient's Apnea-Hypopnea Index (ICC=0.95; 95% CI = [0.93, 0.97]). Notably, the model exhibits equitable performance across race, sex, and age. Moreover, the model uncovers informative interactions between sleep stages and a range of diseases including neurological, psychiatric, cardiovascular, and immunological disorders. These findings not only hold promise for clinical practice and interventional trials but also underscore the significance of sleep as a fundamental component in understanding and managing various diseases. 

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4. Doppler radar remote sensing of respiratory function

   https://doi.org/10.3389/fphys.2023.1130478  

   Ishrak, Mohammad Shadman; Cai, Fulin; Islam, Shekh_Md Mahmudul; Borić-Lubecke, Olga; Wu, Teresa; Lubecke, Victor M (April 2023, Frontiers in Physiology)

   Doppler radar remote sensing of torso kinematics can provide an indirect measure of cardiopulmonary function. Motion at the human body surface due to heart and lung activity has been successfully used to characterize such measures as respiratory rate and depth, obstructive sleep apnea, and even the identity of an individual subject. For a sedentary subject, Doppler radar can track the periodic motion of the portion of the body moving as a result of the respiratory cycle as distinct from other extraneous motions that may occur, to provide a spatial temporal displacement pattern that can be combined with a mathematical model to indirectly assess quantities such as tidal volume, and paradoxical breathing. Furthermore, it has been demonstrated that even healthy respiratory function results in distinct motion patterns between individuals that vary as a function of relative time and depth measures over the body surface during the inhalation/exhalation cycle. Potentially, the biomechanics that results in different measurements between individuals can be further exploited to recognize pathology related to lung ventilation heterogeneity and other respiratory diagnostics. 

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5. 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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