Spelling suggestions: "subject:"cardiovascular dynamics"" "subject:"ardiovascular dynamics""
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The left ventricle, aortic valve, and arterial tree - a fresh engineering perspectiveMcGregor, Brian January 1996 (has links)
No description available.
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Cuffless Blood Pressure Estimation Using Cardiovascular DynamicsSamimi, Hamed 06 July 2023 (has links)
Blood pressure (BP) monitoring is an important tool for management of hypertension, which is a significant risk for cardiovascular disease and premature death. Since cuff-based BP measurement can be uncomfortable and does not provide continuous readings, several cuffless methods that are typically based on within-beat information or on the pulse transit time (PTT) have recently been investigated. This work proposes a novel cuffless BP estimation approach that mainly uses the information from cardiovascular dynamics of photoplethysmogram (PPG) waveforms.
This work is divided into three parts. The first part proposes a calibration-free approach that uses dynamic changes in the pulse waveform. Results from 200 patients showed that the method achieved grade B, in terms of accuracy, for diastolic blood pressure (DBP) based on the British Hypertension Society (BHS) standard and complied with the accuracy requirements of the Association for Advancement of Medical Instrumentation/European Society of Hypertension/International Organization for Standardization (AAMI/ESH/ISO) standard. The second part presents a method based on calibrated cardiovascular dynamics, achieved through a mathematical model that relates reflective PTT (R-PTT) to BP. Results from 30 patients showed a mean error (ME) of 0.58 mmHg, standard deviation of the error (SDE) of 8.13 mmHg, and a mean absolute error (MAE) of 4.93 mmHg for DBP and an ME of 2.52 mmHg, SDE of 12.28 mmHg, and an MAE of 8.82 mmHg for systolic blood pressure (SBP). The third part proposes a calibration-free method that combines morphology features and dynamic changes of the pulse waveform over short intervals. In this method a neural network was trained on 200 patients and tested on never-seen data from 25 other patients and provided an ME of -0.31 mmHg, SDE of 4.89 mmHg, and MAE of 3.32 mmHg for DBP and an ME of -4.02 mmHg, SDE of 10.40 mmHg, and MAE of 7.41 mmHg for SBP. Overall, the results show that cardiovascular dynamics may contribute useful information for cuffless estimation of BP.
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Coupling analysis of transient cardiovascular dynamicsMüller, Andreas 09 March 2016 (has links)
Die Untersuchung kausaler Zusammenhänge in komplexen dynamischen Systemen spielt in der Wissenschaft eine immer wichtigere Rolle. Ziel dieses aktuellen, interdisziplinären Forschungsbereiches ist ein grundlegendes, tiefes Verständnis der vorherrschenden Prozesse und deren Wechselwirkungen in solchen Systemen. Die Untersuchung von Zeitreihen mithilfe moderner Kopplungsanalysemethoden liefert dabei Möglichkeiten zur Modellierung der betreffenden Systeme und somit bessere Vorhersagemethoden und fortgeschrittene Interpretationsmöglichkeiten der Ergebnisse. In der vorliegenden Arbeit werden zunächst einige existierende Kopplungsmaße mit ihren jeweiligen Anwendungsgebieten vorgestellt. Eine Gemeinsamkeit dieser Maße liegt in der Voraussetzung stationärer Zeitreihen, um die Anwendbarkeit zu gewährleisten. Daher wird im Verlauf der Dissertation eine Möglichkeit zur Erweiterung solcher Maße vorgestellt, die eine Kopplungsanalyse mit einer sehr hohen Zeitauflösung und somit auch die Untersuchung nichtstationärer, transienter Ereignisse ermöglicht. Die Erweiterung basiert auf der Verwendung von Ensembles von Messreihen und der Schätzung der jeweiligen Maße über das Ensemble anstatt über die Zeit. Dies ermöglicht eine Zeitauflösung bei der Analyse in der Größenordnung der Abtastrate des ursprünglichen Signals, die nur von der Art der verwendeten Kopplungsmaße abhängt. Der Ensemble-Ansatz wird auf verschiedene Kopplungsmaße angewandt. Zunächst werden die Methoden ausführlich an verschiedenen theoretischen Modellen und unter verschiedenen Bedingungen getestet. Anschließend erfolgt eine zeitaufgelöste Kopplungsanalyse kardiovaskulärer Zeitreihen, die während transienter Ereignisse aufgenommen wurden. Die Ergebnisse dieser Analyse bestätigen zum einen aktuelle Studienresultate, liefern aber auch neue Erkenntnisse, die es in Zukunft ermöglichen können, Modelle des Herz-Kreislauf-Systems zu erweitern und zu verbessern. / The analysis of causal relationships in complex dynamic systems plays a more and more important role in various scientific fields. The aim of this current, interdisciplinary field of research is a fundamental, deep understanding of predominant processes and their interactions in such systems. The study of time series using modern coupling analysis tools allows the modelling of the respective systems and thus better prediction methods and advanced interpretation possibilities for the results. In this work, initially some existing coupling measures and their fields of application are introduced. One trait these measures have in common is the requirement of stationary time series to ensure their applicability. Therefore, in the course of this thesis a possibility to extend these measures is presented, which allows a coupling analysis with a high temporal resolution and thus also the analysis of transient, nonstationary events. The extension is based on the use of ensembles of time series and the calculation of the respective measures across these ensembles instead of across time. This allows for a temporal resolution of the same order of magnitude as the sampling rate in the original signal. The resolution only depends on the kind of coupling analysis method employed. The ensemble extension is applied to different coupling measures. To begin with, the regarded tools are tested on various theoretical models and under different conditions. This is followed by a coupling analysis of cardiovascular time series recorded during transient events. The results on the one hand confirm topical study outcomes and on the other hand deliver new insights, which will allow to extend and improve cardiovascular system models in the future.
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