Computationally efficient time domain detection algorithm for characteristic points in non invasive continuous blood pressure measurements

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.In this paper a computationally efficient algorithm for continuous blood pressure curve segmentation is presented. It uses only methods in the time domain and can distinguish between systolic, diastolic values and values of calibration steps caused by the continuous blood pressure measuring technique or values of other artefacts. The detection of local extremes, necessary for systolic and diastolic points, is performed with smoothed first and second derivations. An adaptive threshold approach sorts out most of the false extremes, not belonging to the valid blood pressure curve. But only the following plateau detection is suitably reliable to detect local extremes which lie within a calibration step

Unterdrückung von Bewegungsartefakten in PPG-Signalen mittels Adaptive-Noise-Canceling

In diesem Beitrag wird die Problematik der bewegungsbedingten Artefaktbehaftung von PPG-Siganlen im Kontext des Langzeitmonitorings untersucht. Dabei wird besonders das Potential einer Signalrekonstruktion basierend auf adaptiver Filterung mittels Beschleunigungssignalen anhand verschiedener Bewegungsmuster evaluiert und quantifiziert. Ziel ist es, die Anwendbarkeit für kontinuierliches Monitoring abzuschätze

Automatic validation and quality based readjustment of manually scored EEG arousal

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.A knowledge of arousals during sleep is important to attain a deeper understanding regarding cardiovascular diseases. Manual scoring is time consuming and not always accurate. Automatic approaches are even worse inter alia due to inaccurate learning data. This paper presents an algorithm to improve the accuracy of manually scored data. Also a measure of quality is introduced to judge the automatically estimated results.EC/FP6/018474-2/EU/Dynamic analysis of physiological Networks/Daphne

Automatic analysis of systolic, diastolic and mean blood pressure of continuous measurement before, during and after sleep arousals in polysomnographic overnight recordings

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.This paper deals with a detailed examination of sleep arousal events and the corresponding changes of systolic, diastolic and mean blood pressure. Arousals are short awakening events during sleep which do not become noticeable for the sleeping person. But the organism increases vital parameters, e.g. the blood pressure. The recreative sleep is disturbed, and the risk factor for cardiovascular diseases rises significantly. Impact on the continuous measured blood pressure for two arousal groups named spontaneous and non spontaneous arousals will be investigated. Polysomnographic recordings of patients suffering from sleep apnoea and a healthy control group will be examined. Using averaged blood pressure curves and a high time resolution, the courses are investigated in more detail than before. The results show an increasing slope a few seconds before and possible pressure minima a few seconds after the beginning of the arousal.EC/FP6/018474-2/EU/Dynamic analysis of physiological Networks/Daphne

A novel method for motion artifact removal in wearable ppg sensors based on blind source separation

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The recent development of healthcare systems has provided a significant contribution to ambulatory patient monitoring. In that context, signal quality and disturbances induced by noise or motion artifacts play an important role in the field of signal processing tasks. Especially the Photoplethysmogram (PPG) is very liable to movement artifacts which severely hamper the extraction of vital parameters like the heart rate or oxygen saturation. To record patient movements, an innovative sensor system is proposed, which acquires accelerometer data next to the PPG. As in Adaptive Noise Cancelers, we propose to use the acceleration as reference to recover corrupted PPGs by means of the Blind Source Separation. Sophisticated methods of ICA have been used, resulting in a novel approach for artifact suppression in the PPG that has been tested on laboratory datasets

Segmentierungsbasierte Bewegungsschätzung in Ultraschallbildern

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Ein neuer Ansatz zur Verbesserung der Bewegungsschätzung in Ultraschallbildern des menschlichen Herzens wird vorgestellt. Der Ansatz nutzt den Optischen Fluß zur Bewegungsschätzung und bestimmt relevante Bildbereiche über eine Segmentierung auf Basis der Skalierungs-lndex-Methode. Vergleiche der Ergebnisse dieses Ansatzes mit den Ergebnissen konventioneller Ansätze zeigen eine deutliche Verbesserung der Bildqualität und ermöglichen so eine bessere Bewertung der Bewegungen des untersuchten Ultraschallbildes des menschlichen Herzens

Independent Component Analysis and Time-Frequency Masking for Speech Recognition in Multitalker Conditions

When a number of speakers are simultaneously active, for example in meetings or noisy public places, the sources of interest need to be separated from interfering speakers and from each other in order to be robustly recognized. Independent component analysis (ICA) has proven a valuable tool for this purpose. However, ICA outputs can still contain strong residual components of the interfering speakers whenever noise or reverberation is high. In such cases, nonlinear postprocessing can be applied to the ICA outputs, for the purpose of reducing remaining interferences. In order to improve robustness to the artefacts and loss of information caused by this process, recognition can be greatly enhanced by considering the processed speech feature vector as a random variable with time-varying uncertainty, rather than as deterministic. The aim of this paper is to show the potential to improve recognition of multiple overlapping speech signals through nonlinear postprocessing together with uncertainty-based decoding techniques

On-line learning algorithms for extracting respiratory activity from single lead ECGs based on principal component analysis

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.In this paper we present several statistic gradient algorithms from literature to solve the Principal Component Analysis (PCA) problem. We used a linear artificial neural network forming the basis of the implemented algorithms which is a neat way for on-line computation of the PCA expansion. As convergence is a key-aspect of these algorithms and is cru-cial for the usefulness in particular applications, we compared the different learning rules with respect to their suitability in ECG signal processing. Recent studies have shown, that a surrogate respiratory signal can be derived from single-lead ECGs by applying PCA. Since the traditionally applied closed-form computations of PCA are numerically demanding, it seems promising to resort to an adaptive approach when dealing with changing environments like the ECG

Geregelter Meßplatz für Sensoren zur invasiven Blutdruckmessung

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Der im vorliegenden Artikel vorgestellte neuartige Simulator ist ein kompletter Meßplatz für die Prüfung der gesamten Blutdruckmeßkette entsprechend den AAMI-Spezifikationen für alle gängigen medizinischen Drucksensoren. Mit Hilfe einer mikrocontrollergesteuerten Einheit werden gespeicherte technische Solldruckkurven einstellbarer Amplitude und Frequenz sowie physiologische Druckverläufe erzeugt und die Erfassung der Meßdaten vorgenommen. Über eine serielle Schnittstelle können darüber hinaus beliebige Solldruckkurven von und Meßdaten zu einem Personalcomputer übertragen und dort ausgewertet werden. Für möglichst realitätsnahe Messungen wurden zusätzlich ein Spülsystem sowie eine Flußsimulation implementiert

Online learning algorithms for principal component analysis applied on single-lead ECGs

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.This article evaluates several adaptive approaches to solve the principal component analysis (PCA) problem applied on single-lead ECGs. Recent studies have shown that the principal components can indicate morphologically or environmentally induced changes in the ECG signal and can be used to extract other vital information such as respiratory activity. Special interest is focused on the convergence behavior of the selected gradient algorithms, which is a major criterion for the usability of the gained results. As the right choice of learning rates is very data dependant and subject to movement artifacts, a new measurement system was designed, which uses acceleration data to improve the performance of the online algorithms. As the results of PCA seem very promising, we propose to apply a single-channel independent component analysis (SCICA) as a second step, which is investigated in this paper as well