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Stochastic Signal Processing Techniques for Reconstruction of Multilayered Tissue Profiles Using UWB Radar

Civek, Burak Cevat
http://rave.ohiolink.edu/etdc/view?acc_num=osu1629688169327339

Abstract Details

2021, Doctor of Philosophy, Ohio State University, Electrical and Computer Engineering.
Sensors that can reliably assess physiology in the clinic and home environment are poised to revolutionize research and practice in the management of chronic diseases such as heart failure or pulmonary edema. Ultrawideband (UWB) radar sensors provide a viable and unobtrusive alternative to traditional sensor modalities for physiological sensing. In principle, a UWB radar system transmits a short-duration pulse and records the backscattered signal composed of reflections from the target object. In the human body, each tissue exhibits distinct dielectric properties, i.e., permittivity and conductivity, causing impedance mismatches at the interfaces and creating multiple reflection points for the impinging transmitted pulse. Therefore, a rich backscattered signal, which is strongly affected by the dielectric properties, is observed and can be processed to make inferences about the tissue composition underneath the skin. To this end, in this thesis, we investigate the problem of monitoring the tissue composition in the thoracic cavity using UWB radar sensors and present stochastic signal processing techniques to recover characteristic properties of the target tissues. We model the target tissue profile as a multilayered structure composed of planar homogeneous layers and work on a one-dimensional forward model simulating the electromagnetic (EM) wave propagation in layered media. We first tackle the problem from an indirect approach and aim to estimate the reflectivity profile, which is a function of characteristic properties of the target tissues, from the measured radar signal. We model the reflectivity profile as a sparse sequence in time-domain and pose the problem as a sparse blind deconvolution (BD) problem, where we simultaneously estimate the transmitted radar waveform as well to allow self-calibration. We study the problem under a Bayesian setting and present novel Markov Chain Monte Carlo (MCMC) methods, which incorporate the Normal-Inverse-Gamma prior to model sparsity. The constructed Gibbs samplers achieve enhanced sampling performance by eliminating the computational burden created by the discrete nature of the Bernoulli-Gaussian prior, which is conventionally used for modeling sparse sequences in the Bayesian BD literature. We then switch to a direct approach and explicitly recover the characteristic properties, including permittivity, conductivity, and thickness, of the target tissues illuminated by the radar sensor. Following the Bayesian setting, we present a comprehensive and adaptive MCMC sampling mechanism that achieves enhanced sampling efficiency compared to conventional sampling schemes. We provide marginal posterior density estimations of the unknowns and calculate credibility intervals along with the point estimations. We also derive theoretical lower bounds to quantify the best possible estimator performance in terms of the mean squared error. Finally, we approach the problem from a detection perspective and aim to detect the existence of anomalous reflections in the reflectivity profile instead of explicitly estimating the characteristic properties. We consider different Binary Hypothesis Testing (BHT) formulations associated with the anomaly detection and change detection approaches, and present Generalized Likelihood Ratio Test (GLRT) and Bayes Factor methods for each problem formulation. We derive theoretical lower/upper bounds on the error probabilities of the presented detectors and provide comparisons with the actual performance of the detectors.
Emre Ertin (Advisor)
Kiryung Lee (Committee Member)
Joel Johnson (Committee Member)
200 p.
Computer Engineering; Electrical Engineering
Bayesian Inverse Methods; UWB Radar; Multilayered Tissue Profiles; MCMC Methods

Recommended Citations

Citations

  • Civek, B. C. (2021). Stochastic Signal Processing Techniques for Reconstruction of Multilayered Tissue Profiles Using UWB Radar [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1629688169327339

    APA Style (7th edition)

  • Civek, Burak. Stochastic Signal Processing Techniques for Reconstruction of Multilayered Tissue Profiles Using UWB Radar. 2021. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1629688169327339.

    MLA Style (8th edition)

  • Civek, Burak. "Stochastic Signal Processing Techniques for Reconstruction of Multilayered Tissue Profiles Using UWB Radar." Doctoral dissertation, Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1629688169327339

    Chicago Manual of Style (17th edition)

osu1629688169327339
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This open access ETD is published by The Ohio State University and OhioLINK.