A system identification approach to non-invasive central cardiovascular monitoring

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2008.Includes bibliographical references (leaves 180-187).This thesis presents a new system identification approach to non-invasive central cardiovascular monitoring problem. For this objective, this thesis will develop and analyze blind system identification and input signal reconstruction algorithms for a class of 2-channel IIR and Wiener systems. In particular, this thesis will present blind identifiability conditions for a class of 2-channel IIR and Wiener wave propagation systems and develop the associated blind identification algorithms. It will be shown that the blind identifiability conditions can be achieved in many real-world applications by appropriate selection of channel lengths, sensor locations, and sampling frequency which are the specifications that the system design can exploit for blind identifiability In addition, this thesis will develop a novel input signal reconstruction algorithm that is applicable to general class of multi-channel IIR and Wiener systems. Furthermore, this thesis will rigorously analyze and evaluate three analytic measures for determining the system order and other key parameters of the black-box dynamics as well as for quantifying the quality of the identified gray-box dynamics, without any direct use of unknown input signal: persistent excitation, model identifiability and asymptotic variance. The blind identification and input signal reconstruction algorithms will first be applied to 2-sensor central cardiovascular monitoring problem using two distinct peripheral blood pressure measurements, where the cardiovascular wave propagation dynamics is blindly identified and the aortic blood pressure and flow signals are reconstructed by exploiting black-box and physics-based gray-box model structures of the cardiovascular system.(cont.) The validity of the 2-sensor central cardiovascular monitoring methodology will be illustrated by experimental data from swine subjects and simulation data from a full-scale human cardiovascular simulator across diverse physiologic conditions. The 2-sensor central cardiovascular monitoring methodology will then be extended to address noninvasive, 1-sensor cardiovascular monitoring problem, where the specific challenges involved are 1) identifying the cardiovascular wave propagation dynamics and reconstructing the aortic blood pressure signal by exploiting the measurement from a single peripheral sensor, and 2) identifying the scale for calibrating the blood pressure signal. In order to address these challenges, this thesis will propose a heuristics-based system order estimation algorithm and a model-based blood pressure calibration algorithm, which will be combined with the blind identification of the cardiovascular wave propagation dynamics to realize the non-invasive 1-sensor central cardiovascular monitoring. The non-invasive 1-sensor central cardiovascular monitoring methodology will be illustrated by experimental data from swine subjects, simulation data from a full-scale human cardiovascular simulator, and experimental data from human subjects across diverse physiologic conditions.by Jin-Oh Hahn.Ph.D

AI for time-resolved imaging: from fluorescence lifetime to single-pixel time of flight

Time-resolved imaging is a field of optics which measures the arrival time of light on the camera. This thesis looks at two time-resolved imaging modalities: fluorescence lifetime imaging and time-of-flight measurement for depth imaging and ranging. Both of these applications require temporal accuracy on the order of pico- or nanosecond (10−12 − 10−9s) scales. This demands special camera technology and optics that can sample light-intensity extremely quickly, much faster than an ordinary video camera. However, such detectors can be very expensive compared to regular cameras while offering lower image quality. Further, information of interest is often hidden (encoded) in the raw temporal data. Therefore, computational imaging algorithms are used to enhance, analyse and extract information from time-resolved images. "A picture is worth a thousand words". This describes a fundamental blessing and curse of image analysis: images contain extreme amounts of data. Consequently, it is very difficult to design algorithms that encompass all the possible pixel permutations and combinations that can encode this information. Fortunately, the rise of AI and machine learning (ML) allow us to instead create algorithms in a data-driven way. This thesis demonstrates the application of ML to time-resolved imaging tasks, ranging from parameter estimation in noisy data and decoding of overlapping information, through super-resolution, to inferring 3D information from 1D (temporal) data

Journal of Telecommunications and Information Technology, 2001, nr 3

kwartalni

Theory and Application of Autoproducts

Acoustics is a branch of physics largely governed by linear field equations. Linearity carries with it the implication that only the frequencies broadcast by acoustic sources can be measured in the surrounding acoustic medium. However, nonlinearities introduced not in the physical world, but in the mathematical and signal processing realm, have the potential to change frequency content. In this dissertation, nonlinear mathematical constructions termed ‘autoproducts’ are created which have the potential to shift frequencies from the measured, in-band frequencies to other higher or lower frequencies which may no longer be in-band. These out-of-band autoproduct fields did not physically propagate in the environment, and yet, this research has found that autoproducts can nonetheless mimic genuine out-of-band fields in a number of different acoustic environments. Approximately half of this dissertation addresses the theory of autoproducts. More specifically, mathematical analyses and simple acoustic models are used to uncover the reasons for how this frequency-shifting behavior works, and what its limitations are. It is found that there are no inherent limitations on the frequencies considered, and that in single-path environments, like plane or spherical waves, autoproducts mimic out-of-band fields in all or nearly all circumstances, respectively. However, in multipath environments, the mimicry of out-of-band fields by autoproducts is no longer so complete. Though, with bandwidth averaging techniques, it is found that the difference in time-of-arrivals of multiple paths is an important parameter: if it is larger than the inverse of the bandwidth available for averaging, then autoproducts can succeed in mimicking out-of-band fields. Other theoretical considerations include the effects of diffraction behind barriers and the effects of strong refraction. Strengths and limitations of autoproducts are assessed with a variety of simple acoustic models, and conclusions are drawn as to the predicted capabilities of autoproduct-based techniques. The other half of this dissertation covers applications of autoproducts. More specifically, it focuses on the use of autoproducts to perform physics-based source localization, especially for applications in the shallow ocean. Existing techniques are well-known to be very sensitive to uncertainties in the acoustic environment (e.g. the sound speed), especially at high frequencies (nominally greater than 1 kHz in the shallow ocean). Through the use of autoproducts, measured fields at high frequency can be shifted to much lower frequencies, where they can be processed with much more robustness to environmental uncertainties. In one of the main results of this dissertation, it is shown that a remote acoustic source broadcasting sound between 11 and 33 kHz in a 106-meter-deep, downward refracting sound channel could be localized using measurements from a sparse array located 3 km away. The data from the method suggest that autoproduct-based source localization can make physics-based array signal processing robust at arbitrarily high frequencies – a novel and important contribution to existing literature. Overall, by developing the theory for, and exploring applications of, these nonlinear mathematical constructions, the extent to which autoproducts are fundamentally limited is assessed, and new signal processing techniques are developed which have the potential to significantly improve the robustness of source localization algorithms for uncertain multipath environments. Through this study, significant portions of the necessary theoretical foundation have been laid, which will aid in the further development of robust, autoproduct-based signal processing techniques.PHDApplied PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145865/1/bworthma_1.pd

Compressed Sensing Beyond the IID and Static Domains: Theory, Algorithms and Applications

Sparsity is a ubiquitous feature of many real world signals such as natural images and neural spiking activities. Conventional compressed sensing utilizes sparsity to recover low dimensional signal structures in high ambient dimensions using few measurements, where i.i.d measurements are at disposal. However real world scenarios typically exhibit non i.i.d and dynamic structures and are confined by physical constraints, preventing applicability of the theoretical guarantees of compressed sensing and limiting its applications. In this thesis we develop new theory, algorithms and applications for non i.i.d and dynamic compressed sensing by considering such constraints. In the first part of this thesis we derive new optimal sampling-complexity tradeoffs for two commonly used processes used to model dependent temporal structures: the autoregressive processes and self-exciting generalized linear models. Our theoretical results successfully recovered the temporal dependencies in neural activities, financial data and traffic data. Next, we develop a new framework for studying temporal dynamics by introducing compressible state-space models, which simultaneously utilize spatial and temporal sparsity. We develop a fast algorithm for optimal inference on such models and prove its optimal recovery guarantees. Our algorithm shows significant improvement in detecting sparse events in biological applications such as spindle detection and calcium deconvolution. Finally, we develop a sparse Poisson image reconstruction technique and the first compressive two-photon microscope which uses lines of excitation across the sample at multiple angles. We recovered diffraction-limited images from relatively few incoherently multiplexed measurements, at a rate of 1.5 billion voxels per second