A review of machine learning techniques in photoplethysmography for the non-invasive cuff-less measurement of blood pressure

Hypertension or high blood pressure is a leading cause of death throughout the world and a critical factor for increasing the risk of serious diseases, including cardiovascular diseases such as stroke and heart failure. Blood pressure is a primary vital sign that must be monitored regularly for the early detection, prevention and treatment of cardiovascular diseases. Traditional blood pressure measurement techniques are either invasive or cuff-based, which are impractical, intermittent, and uncomfortable for patients. Over the past few decades, several indirect approaches using photoplethysmogram (PPG) have been investigated, namely, pulse transit time, pulse wave velocity, pulse arrival time and pulse wave analysis, in an effort to utilise PPG for estimating blood pressure. Recent advancements in signal processing techniques, including machine learning and artificial intelligence, have also opened up exciting new horizons for PPG-based cuff less and continuous monitoring of blood pressure. Such a device will have a significant and transformative impact in monitoring patients’ vital signs, especially those at risk of cardiovascular disease. This paper provides a comprehensive review for non-invasive cuff-less blood pressure estimation using the PPG approach along with their challenges and limitations

Advanced signal processing methods in dynamic contrast enhanced magnetic resonance imaging

Tato dizertační práce představuje metodu zobrazování perfúze magnetickou rezonancí, jež je výkonným nástrojem v diagnostice, především v onkologii. Po ukončení sběru časové sekvence T1-váhovaných obrazů zaznamenávajících distribuci kontrastní látky v těle začíná fáze zpracování dat, která je předmětem této dizertace. Je zde představen teoretický základ fyziologických modelů a modelů akvizice pomocí magnetické rezonance a celý řetězec potřebný k vytvoření obrazů odhadu parametrů perfúze a mikrocirkulace v tkáni. Tato dizertační práce je souborem uveřejněných prací autora přispívajícím k rozvoji metodologie perfúzního zobrazování a zmíněného potřebného teoretického rozboru.This dissertation describes quantitative dynamic contrast enhanced magnetic resonance imaging (DCE-MRI), which is a powerful tool in diagnostics, mainly in oncology. After a time series of T1-weighted images recording contrast-agent distribution in the body has been acquired, data processing phase follows. It is presented step by step in this dissertation. The theoretical background in physiological and MRI-acquisition modeling is described together with the estimation process leading to parametric maps describing perfusion and microcirculation properties of the investigated tissue on a voxel-by-voxel basis. The dissertation is divided into this theoretical analysis and a set of publications representing particular contributions of the author to DCE-MRI.

Mathematical Modeling of Arterial Blood Pressure Using Photo-Plethysmography Signal in Breath-hold Maneuver

Recent research has shown that each apnea episode results in a significant rise in the beat-to-beat blood pressure and by a drop to the pre-episode levels when patient resumes normal breathing. While the physiological implications of these repetitive and significant oscillations are still unknown, it is of interest to quantify them. Since current array of instruments deployed for polysomnography studies does not include beat-to-beat measurement of blood pressure, but includes oximetry, it is both of clinical interest to estimate the magnitude of BP oscillations from the photoplethysmography (PPG) signal that is readily available from sleep lab oximeters. We have investigated a new method for continuous estimation of systolic (SBP), diastolic (DBP), and mean (MBP) blood pressure waveforms from PPG. Peaks and troughs of PPG waveform are used as input to a 5th order autoregressive moving average model to construct estimates of SBP, DBP, and MBP waveforms. Since breath hold maneuvers are shown to simulate apnea episodes faithfully, we evaluated the performance of the proposed method in 7 subjects (4 F; 32+-4 yrs., BMI 24.57+-3.87 kg/m2) in supine position doing 5 breath maneuvers with 90s of normal breathing between them. The modeling error ranges were (all units are in mmHg) -0.88+-4.87 to -2.19+-5.73 (SBP); 0.29+-2.39 to -0.97+-3.83 (DBP); and -0.42+-2.64 to -1.17+-3.82 (MBP). The cross validation error ranges were 0.28+-6.45 to -1.74+-6.55 (SBP); 0.09+-3.37 to -0.97+-3.67 (DBP); and 0.33+-4.34 to -0.87+-4.42 (MBP). The level of estimation error in, as measured by the root mean squared of the model residuals, was less than 7 mmHgComment: 4 pages, published in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC

Calibrating CHIME, A New Radio Interferometer to Probe Dark Energy

The Canadian Hydrogen Intensity Mapping Experiment (CHIME) is a transit interferometer currently being built at the Dominion Radio Astrophysical Observatory (DRAO) in Penticton, BC, Canada. We will use CHIME to map neutral hydrogen in the frequency range 400 -- 800\,MHz over half of the sky, producing a measurement of baryon acoustic oscillations (BAO) at redshifts between 0.8 -- 2.5 to probe dark energy. We have deployed a pathfinder version of CHIME that will yield constraints on the BAO power spectrum and provide a test-bed for our calibration scheme. I will discuss the CHIME calibration requirements and describe instrumentation we are developing to meet these requirements

Intersection SPaT Estimation by means of Single-Source Connected Vehicle Data

The file attached to this record is the author's final peer reviewed version.Current traffic management systems in urban networks require real-time estimation of the traffic states. With the development of in-vehicle and communication technologies, connected vehicle data has emerged as a new data source for traffic measurement and estimation. In this work, a machine learning-based methodology for signal phase and timing information (SPaT) which is highly valuable for many applications such as green light optimal advisory systems and real-time vehicle navigation is proposed. The proposed methodology utilizes data from connected vehicles travelling within urban signalized links to estimate the queue tail location, vehicle accumulation, and subsequently, link outflow. Based on the produced high-resolution outflow estimates and data from crossing connected vehicles, SPaT information is estimated via correlation analysis and a machine learning approach. The main contribution is that the single-source proposed approach relies merely on connected vehicle data and requires neither prior information such as intersection cycle time nor data from other sources such as conventional traffic measuring tools. A sample four-leg intersection where each link comprises different number of lanes and experiences different traffic condition is considered as a testbed. The validation of the developed approach has been undertaken by comparing the produced estimates with realistic micro-simulation results as ground truth, and the achieved simulation results are promising even at low penetration rates of connected vehicles

Precision measurement of the neutrino velocity with the ICARUS detector in the CNGS beam

During May 2012, the CERN-CNGS neutrino beam has been operated for two weeks for a total of 1.8 10^17 pot in bunched mode, with a 3 ns narrow width proton beam bunches, separated by 100 ns. This tightly bunched beam structure allows a very accurate time of flight measurement of neutrinos from CERN to LNGS on an event-by-event basis. Both the ICARUS-T600 PMT-DAQ and the CERN-LNGS timing synchronization have been substantially improved for this campaign, taking ad-vantage of additional independent GPS receivers, both at CERN and LNGS as well as of the deployment of the "White Rabbit" protocol both at CERN and LNGS. The ICARUS-T600 detector has collected 25 beam-associated events; the corresponding time of flight has been accurately evaluated, using all different time synchronization paths. The measured neutrino time of flight is compatible with the arrival of all events with speed equivalent to the one of light: the difference between the expected value based on the speed of light and the measured value is tof_c - tof_nu = (0.10 \pm 0.67stat. \pm 2.39syst.) ns. This result is in agreement with the value previously reported by the ICARUS collaboration, tof_c - tof_nu = (0.3 \pm 4.9stat. \pm 9.0syst.) ns, but with improved statistical and systematic errors.Comment: 21 pages, 13 figures, 1 tabl

Arterial spin labelling magnetic resonance imaging of the brain: techniques and development

This thesis centres on the development of arterial spin labelling (ASL) MRI, a non-invasive technique to image cerebral perfusion. In the first chapter I explain the principles of cerebral blood flow (CBF) quantification using ASL beginning with the original implementation through to the most recent advances. I proceed to describe the established theory behind the key additional MRI contrast mechanisms and techniques that underpin the novel experiments described in this thesis (T2 and T1 relaxation, diffusion imaging and half-Fourier acquisition and reconstruction). In Chapter 2 I describe work undertaken to sample the transverse relaxation of the ASL perfusion-weighted and control images acquired with and without vascular crusher gradients at a range of post-labelling delay times and tagging durations, to estimate the intra-vascular, intra-cellular and extra-cellular distribution of labelled water in the rat cortex. The results provide evidence for rapid exchange of labelled water into the intra-cellular space relative to the transit-time through the vascular bed, and provide a more solid foundation for CBF quantification using ASL techniques. In Chapter 3 the performance of image de-noising techniques for reducing errors in ASL CBF and arterial transit time estimates is investigated. I show that noise reduction methods can suppress random and systematic errors, improving both the precision and accuracy of CBF measurements and the precision of transit time maps. In Chapter 4 I present the first in-vivo demonstration of Hadamard-encoded continuous ASL (H-CASL); an efficient method of imaging small volumes of labelled blood water in the brain at multiple post labelling delay times. I present evidence that H-CASL is viable for in-vivo application and can improve the precision of δa estimation in 2/3 of the imaging time required for standard multi post labelling delay continuous ASL