72 research outputs found
Photoplethysmography-Based Biomedical Signal Processing
In this dissertation, photoplethysmography-based biomedical signal processing methods are developed and analyzed. The developed methods solve problems concerning the estimation of the heart rate during physical activity and the monitoring of cardiovascular health. For the estimation of heart rate during physical activity, two methods are presented that are very accurate in estimating the instantaneous heart rate at the wrist and, at the same time, are computationally efficient so that they can easily be integrated into wearables. In the context of cardiovascular health monitoring, a method for the detection of atrial fibrillation using the video camera of a smartphone is proposed that achieves a high detection rate of atrial fibrillation (AF) on a clinical pre-study data set. Further monitoring of cardiovascular parameters includes the estimation of blood pressure (BP), pulse wave velocity (PWV), and vascular age index (VAI), for which an approach is presented that requires only a single photoplethysmographic (PPG) signal.
Heart rate estimation during physical activity using PPG signals constitutes an important research focus of this thesis. In this work, two computationally efficient algorithms are presented that estimate the heart rate from two PPG signals using a three axis accelerometer. In the first approach, adaptive filters are applied to estimate motion artifacts that severely deteriorate the signal quality. The non-stationary relationship between the measured acceleration signals and the artifacts is modeled as a linear system. The outputs of the adaptive filters are combined to further enhance the signal quality and a constrained heart rate tracker follows the most probable high energy continuous line in the spectral domain. The second approach is modest in computational complexity and very fast in execution compared to existing approaches. It combines correlation-based fundamental frequency indicating functions and spectral combination to enhance the correlated useful signal and suppress uncorrelated noise. Additional harmonic noise damping further reduces the impact of strong motion artifacts and a spectral tracking procedure uses a linear least squares prediction. Both approaches are modest in computational complexity and especially the second approach is very fast in execution, as it is shown on a widely used benchmark data set and compared to state-of-the-art methods.
The second research focus and a further major contribution of this thesis lies in the monitoring of the cardiovascular health with a single PPG signal. Two methods are presented, one for detection of AF and one for the estimation of BP, PWV, and VAI. The first method is able to detect AF based on a smartphone filming the finger placed on the video camera. The algorithm transforms the video into a PPG signal and extracts features which are then used to discriminate between AF and normal sinus rhythm (NSR). Perfect detection of AF is already achieved on a data set of 326 measurements (including 20 with AF) that were taken at a clinical pre-study using an appropriate pair of features whereby a decision is formed through a simple linear decision equation. The second method aims at estimating cardiovascular parameters from a single PPG signal without the conventional use of an additional electrocardiogram (ECG). The proposed method extracts a large number of features from the PPG signal and its first and second order difference series, and reconstructs missing features by the use of matrix completion. The estimation of cardiovascular parameters is based on a nonlinear support vector regression (SVR) estimator and compared to single channel PPG based estimators using a linear regression model and a pulse arrival time (PAT) based method. If the training data set contains the person for whom the cardiovascular parameters are to be determined, the proposed method can provide an accurate estimate without further calibration.
All proposed algorithms are applied to real data that we have either recorded ourselves in our biomedical laboratory, that have been recorded by a clinical research partner, or that are freely available as benchmark data sets
Proposals for a practical calibration method for mechanical torque measurement on the wind turbine drive train under test on a test bench
The mechanical torque input into the wind turbine drive train is a very useful measurement for tests performed on a test bench. To ensure the accuracy and the reliability, an accurate calibration of the torque measurement must be carried out and repeated within a certain period of time. However, owing to the high torque level and large structure size, such a calibration is both expensive and time consuming. To overcome this challenge, a new calibration method is proposed here. The method is based on the electrical power measurement, where a high level of accuracy is much easier to achieve. With the help of a special test process, a relationship between the torque-measuring signal and the electrical power can be established. The process comprises two tests with the drive train running in different operating modes. The calibration is possible by carrying out the same test process on several different torque levels. Detailed uncertainty analysis of the method is presented, whereby the uncertainty can be calculated by means of matrix operation and also numerically. As a demonstration, the implementation of the method on a test bench drive train that contains two 5-MW motors in tandem with the motors operating in a back-to-back configuration is also presented. Finally, some variations on the method and possible ways of achieving better accuracy are discussed. © 2020 The Authors. Wind Energy published by John Wiley & Sons Lt
Expression of CD24 in Human Bone Marrow-Derived Mesenchymal Stromal Cells Is Regulated by TGF β
Human bone marrow-derived stromal cells (hBMSCs) derived from the adult organism hold great promise for diverse settings in regenerative medicine. Therefore a more complete understanding of hBMSC biology to fully exploit the cells’ potential for clinical settings is important. The protein CD24 has been reported to be involved in a diverse range of processes such as cancer, adaptive immunity, inflammation, and autoimmune diseases in other cell types. Its expression in hBMSCs, which has not yet been analyzed, may add an important aspect in the understanding of hBMSC biology. The present study therefore analyzes the expression, regulation, and functional implication of the surface protein CD24 in hBMSCs. Methods used are stimulation studies with TGF beta as well as shRNA-mediated knockdown and overexpression of CD24 followed by microarray, immunocytochemistry, and flow cytometric analyses. To our knowledge, we demonstrate for the first time that the expression of CD24 is an inherent property of hBMSCs. Importantly, the data links the upregulation of CD24 to the adoption of a myofibroblast-like gene expression pattern in hBMSCs. We demonstrate that CD24 is an important modulator in transforming growth factor beta 3 (TGFβ3) signaling with a reciprocal regulatory relationship between these two proteins
Estimating acoustic speech features in low signal-to-noise ratios using a statistical framework
Accurate estimation of acoustic speech features from noisy speech and from different speakers is an ongoing problem in speech processing. Many methods have been proposed to estimate acoustic features but errors increase as signal-to-noise ratios fall. This work proposes a robust statistical framework to estimate an acoustic speech vector (comprising voicing, fundamental frequency and spectral envelope) from an intermediate feature that is extracted from a noisy time-domain speech signal. The initial approach is accurate in clean conditions but deteriorates in noise and with changing speaker. Adaptation methods are then developed to adjust the acoustic models to the noise conditions and speaker. Evaluations are carried out in stationary and nonstationary noises and at SNRs from -5dB to clean conditions. Comparison with conventional methods of estimating fundamental frequency, voicing and spectral envelope reveals the proposed framework to have lowest errors in all conditions tested
Entwurf und Aufbau eines miniaturisierten Präzisionsverstärkers zur Ableitung neuronaler Signale
Zusammenfassung:
Am Institut für Elektromechanische Konstruktionen (EMK) der Technischen Universität Darmstadt (TUD) wird in Zusammenarbeit mit dem Deutschen Primatenzentrum (DPZ) in Göttingen eine Telemetrieeinheit zur Ableitung neuronaler Signale entwickelt. Neuronale Signale sind die Aktivitäten von Gehirnzellen. Diese Signale können mit Hilfe von Elektroden abgeleitet und als elektrische Spannungen gemessen werden.
Ziel dieser Diplomarbeit ist es, einen miniaturisierten Präzisionsverstärker zur Ableitung sechs paralleler neuronaler Signale zu entwickeln und als Muster aufzubauen.
Aus den am Markt verfügbaren Produkten und Angaben des Deutschen Primatenzentrums werden Anforderungen für einen solchen Verstärker abgeleitet. Die zentralen Herausforderungen sind die geforderte maximale Platinengröße von 1,8cm x 2,8cm und der minimale Eingangspegel von 50µV. Dieser erfordert einen sehr rauscharmen Verstärker. Es wird detailliert untersucht, wie sich der Eingangswiderstand des Präzisionsverstärkers auf die Signalqualität auswirkt. Eine weitere Analyse betrachtet das Quantisierungsrauschen bei einer anschließenden AD-Wandlung.
Es zeigt sich, dass aufgrund des großen Messbereichs über drei Dekaden das relative Quantisierungsrauschen bei Verwendung eines variablen Verstärkers auf unter 0,4 Prozent verringert werden kann.
Im Anschluss an die theoretische Entwicklung folgt der Aufbau eines Musters. Hierzu wird mit einer neuen Fertigungsmethode am Institut eine Platine gefertigt. Eine Charakterisierung des Verstärkers bildet den Abschluss der Arbeit. Mit Hilfe des realisierten Präzisionsverstärkers ist es möglich, sechs Kanäle gleichzeitig mit jeweils einer Abtastrate von 46,3kHz abzutasten. Für jeden einzelnen Kanal kann ein Verstärkungsfaktor von 100 bis 102.400 eingestellt werden
Publisher’s Note The Scenario documents were taken from the OASIS WSRP Website 5 October 2002.
Because these were Microsoft Word documents, the version number was incremented and shown in page header. The actual date and version number can be found in the Revision Notice for each of the separate requirements documents. The six documents were converted to Adobe Acrobat Portable Document Format (PDF) and combined into a single document (file). These were subsequently indexed using bookmarks. The original documents were marked Confidential. This is likely an artifact of earlier versions of the documents. These were subsequently posted on the OASIS Website and are available under license from OASIS. Following the im+m editorial policy, no changes, such as removing the Confidential marking or correcting spelling errors, are made to original documents. OASIS WSIA Technical Committee Business Scenario Document Information Sharing between Portal Servers Version 1.1Business Scenario Document Version: 1.11..
Photoplethysmography-Based Biomedical Signal Processing
In this dissertation, photoplethysmography-based biomedical signal processing methods are developed and analyzed. The developed methods solve problems concerning the estimation of the heart rate during physical activity and the monitoring of cardiovascular health. For the estimation of heart rate during physical activity, two methods are presented that are very accurate in estimating the instantaneous heart rate at the wrist and, at the same time, are computationally efficient so that they can easily be integrated into wearables. In the context of cardiovascular health monitoring, a method for the detection of atrial fibrillation using the video camera of a smartphone is proposed that achieves a high detection rate of atrial fibrillation (AF) on a clinical pre-study data set. Further monitoring of cardiovascular parameters includes the estimation of blood pressure (BP), pulse wave velocity (PWV), and vascular age index (VAI), for which an approach is presented that requires only a single photoplethysmographic (PPG) signal.
Heart rate estimation during physical activity using PPG signals constitutes an important research focus of this thesis. In this work, two computationally efficient algorithms are presented that estimate the heart rate from two PPG signals using a three axis accelerometer. In the first approach, adaptive filters are applied to estimate motion artifacts that severely deteriorate the signal quality. The non-stationary relationship between the measured acceleration signals and the artifacts is modeled as a linear system. The outputs of the adaptive filters are combined to further enhance the signal quality and a constrained heart rate tracker follows the most probable high energy continuous line in the spectral domain. The second approach is modest in computational complexity and very fast in execution compared to existing approaches. It combines correlation-based fundamental frequency indicating functions and spectral combination to enhance the correlated useful signal and suppress uncorrelated noise. Additional harmonic noise damping further reduces the impact of strong motion artifacts and a spectral tracking procedure uses a linear least squares prediction. Both approaches are modest in computational complexity and especially the second approach is very fast in execution, as it is shown on a widely used benchmark data set and compared to state-of-the-art methods.
The second research focus and a further major contribution of this thesis lies in the monitoring of the cardiovascular health with a single PPG signal. Two methods are presented, one for detection of AF and one for the estimation of BP, PWV, and VAI. The first method is able to detect AF based on a smartphone filming the finger placed on the video camera. The algorithm transforms the video into a PPG signal and extracts features which are then used to discriminate between AF and normal sinus rhythm (NSR). Perfect detection of AF is already achieved on a data set of 326 measurements (including 20 with AF) that were taken at a clinical pre-study using an appropriate pair of features whereby a decision is formed through a simple linear decision equation. The second method aims at estimating cardiovascular parameters from a single PPG signal without the conventional use of an additional electrocardiogram (ECG). The proposed method extracts a large number of features from the PPG signal and its first and second order difference series, and reconstructs missing features by the use of matrix completion. The estimation of cardiovascular parameters is based on a nonlinear support vector regression (SVR) estimator and compared to single channel PPG based estimators using a linear regression model and a pulse arrival time (PAT) based method. If the training data set contains the person for whom the cardiovascular parameters are to be determined, the proposed method can provide an accurate estimate without further calibration.
All proposed algorithms are applied to real data that we have either recorded ourselves in our biomedical laboratory, that have been recorded by a clinical research partner, or that are freely available as benchmark data sets
Konfigurierbare Prozessorsysteme zur hardwareunterstĂĽtzten Simulation von Agentensystemen auf der Basis von Globalen Zellularen Automaten
In dieser Arbeit werden verschiedene Hardwarearchitekturen für das GCA-Modell (engl.: Global Cellular Automata, GCA) entwickelt, bewertet und für die Simulation von Multi-Agenten- Systemen optimiert. Das GCA-Modell besteht aus einer Menge von Zellen, die ihren Zustand synchron-parallel abhängig von den Zuständen der Nachbarzellen ändern. Damit ist es ein massiv-paralleles Berechnungsmodell, bei dem, im Gegensatz zum CA-Modell, die Nachbarschaft nicht fest und lokal, sondern global und variabel ist. Das GCA-Modell eignet sich gut für die Umsetzung von Multi-Agenten-Systemen, da u. a. auch mit einfachen Zellregeln komplexes Verhalten simuliert werden kann und die Zellregel unabhängig von der Anzahl der Prozessoren ist. Die Programmierung kann einfach gehalten werden, da keine komplexen Synchronisationskonstrukte verwendet werden müssen. Es wird die unterschiedliche Leistungsfähigkeit verschiedener Architekturen dargestellt und aufgezeigt, in welcher Art und Weise diese weiter optimiert werden können bzw. wurden. Die Auswertung der Architekturen erfolgt auf verschiedenen FPGAs (Field Programmable Gate Array) mit unterschiedlichen Testdaten. Obwohl die in dieser Arbeit gezeigten Architekturen allgemein einsetzbar sind, liegt der Fokus auf der beschleunigten Simulation von Multi-Agenten-Systemen. Zunächst wurde eine allgemeine Hardwarearchitektur für das GCA-Modell entwickelt und dabei verschiedene Verbindungsnetzwerke untersucht und optimiert. Als Verbindungsnetzwerke wurden das Busnetzwerk mit zwei verschiedenen Arbitrierungsmöglichkeiten, das Omeganetzwerk mit unterschiedlichen Optimierungen und das Ringnetzwerk ebenfalls mit einer Optimierung, um die Zugriffe zu beschleunigen, realisiert und ausgewertet. Um die Simulation von Multi-Agenten-Anwendungen weiter zu beschleunigen, wurde eine Architektur mit Hashfunktionen implementiert, bei der leere Zellen von der Berechung ausgeschlossen werden. Diese Architektur erweist sich als sehr leistungsfähig, obwohl die Skalierbarkeit stark begrenzt ist und die maximale Taktfrequenz eher gering ist. In einer weiteren speicheroptimierten Architektur wurde der gleiche Ansatz zugrunde gelegt, aber die Skalierbarkeit verbessert. Durch die nun höhere maximale Taktfrequenz und den einfacheren Aufbau der Architektur waren weitere Beschleunigungen möglich. Die allgemeine Hardwarearchitektur eignet sich für die Berechnung von Multi-Agenten-Systemen, die sehr viele Agenten beinhalten. Multi-Agenten-Systeme mit weniger Agenten, dafür aber sehr großen Agentenwelten, können dafür sehr gut auf der Hardwarearchitektur mit Hashfunktionen simuliert werden, da hier die Größe der Agentenwelt irrelevant ist und lediglich die Anzahl der Agenten durch die Speichergröße limitiert ist. Die speicheroptimierte Hardwarearchitektur zeichnet sich durch eine sehr hohe Simulationsgeschwindigkeit aus. Da der Begriff des Agenten in der Literatur unterschiedlichste Verwendung findet, erfolgt zuerst eine Definition des Agenten, wie er in dieser Arbeit verstanden wird. Für die Darstellung der Agentenwelten sowie für die Konfiguration der FPGAs ist ein Simulationsprogramm (AgentSim) in Java entwickelt worden. Die Hauptfunktionen des Simulationsprogramms sind die Darstellung, Auswertung, Fehleranalyse, Programmierung und Simulation verschiedenster Agentenwelten. Die Anwendungen für Multi-Agenten-Systeme sind sehr vielfältig und erstrecken sich u. a. über Gebiete der Biologie, Soziologie, Verkehrsphysik, Evakuierungssimulation, Computergraphik, Filmtechnik sowie Wirtschaftssimulation
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