Reinhold Orglmeister

Cardiovascular diseases (CVDs) are one of the leading members of non-communicable diseases. An early diagnosis is essential for effective treatment, to reduce hospitalization time and health care costs. Nowadays, an exercise stress test on an ergometer is used to identify CVDs. To improve the accuracy of diagnostics, the hemodynamic status and parameters of a person can be investigated. For hemodynamic management, thoracic electrical bioimpedance has recently been used. This technique offers beat-to-beat stroke volume calculation but suffers from an artifact-sensitive signal that makes such measurements difficult during movement. We propose a new method based on a gated recurrent unit (GRU) neural network and the ECG signal to improve the measurement of bioimpedance signals, reduce artifacts and calculate hemodynamic parameters. We conducted a study with 23 subjects. The new approach is compared to ensemble averaging, scaled Fourier linear combiner, adaptive filter, and simple neural networks. The GRU neural network performs better with single artifact events than shallow neural networks (mean error −0.0244, mean square error 0.0181 for normalized stroke volume). The GRU network is superior to other algorithms using time-correlated data for the exercise stress test.

Compensated shock and hypovolaemia are frequent conditions that remain clinically undetected and can quickly cause deterioration of perioperative and critically ill patients. Automated, accurate and non-invasive detection methods are needed to avoid such critical situations. In this experimental study, we aimed to create a prediction model for stroke volume index (SVI) decrease based on electrical cardiometry (EC) measurements. Transthoracic echo served as reference for SVI assessment (SVI-TTE). In 30 healthy male volunteers, central hypovolaemia was simulated using a lower body negative pressure (LBNP) chamber. A machine-learning algorithm based on variables of EC was designed. During LBNP, SVI-TTE declined consecutively, whereas the vital signs (arterial pressures and heart rate) remained within normal ranges. Compared to heart rate (AUC: 0.83 (95% CI: 0.73–0.87)) and systolic arterial pressure (AUC: 0.82 (95% CI: 0.74–0.85)), a model integrating EC variables (AUC: 0.91 (0.83–0.94)) showed a superior ability to predict a decrease in SVI-TTE ≥ 20% (p = 0.013 compared to heart rate, and p = 0.002 compared to systolic blood pressure). Simulated central hypovolaemia was related to a substantial decline in SVI-TTE but only minor changes in vital signs. A model of EC variables based on machine-learning algorithms showed high predictive power to detect a relevant decrease in SVI and may provide an automated, non-invasive method to indicate hypovolaemia and compensated shock.

Cardiovascular diseases are the main cause of death worldwide, with sleep disordered breathing being a further aggravating factor. Respiratory illnesses are the third leading cause of death amongst the noncommunicable diseases. The current COVID-19 pandemic, however, also highlights the impact of communicable respiratory syndromes. In the clinical routine, prolonged postanesthetic respiratory instability worsens the patient outcome. Even though early and continuous, long-term cardiorespiratory monitoring has been proposed or even proven to be beneficial in several situations, implementations thereof are sparse. We employed our recently presented, multimodal patch stethoscope to estimate Einthoven electrocardiogram (ECG) Lead I and II from a single 55 mm ECG lead. Using the stethoscope and ECG subsystems, the pre-ejection period (PEP) and left ventricular ejection time (LVET) were estimated. ECG-derived respiration techniques were used in conjunction with a novel, phonocardiogram-derived respiration approach to extract respiratory parameters. Medical-grade references were the SOMNOmedics SOMNO HD^TM and Osypka ICON-Core^TM. In a study including 10 healthy subjects, we analyzed the performances in the supine, lateral, and prone position. Einthoven I and II estimations yielded correlations exceeding 0.97. LVET and PEP estimation errors were 10% and 21%, respectively. Respiratory rates were estimated with mean absolute errors below 1.2 bpm, and the respiratory signal yielded a correlation of 0.66. We conclude that the estimation of ECG, PEP, LVET, and respiratory parameters is feasible using a wearable, multimodal acquisition device and encourage further research in multimodal signal fusion for respiratory signal estimation.

Automatic detection of ectopic beats has become a thoroughly researched topic, with literature providing manifold proposals typically incorporating morphological analysis of the electrocardiogram (ECG). Although being well understood, its utilization is often neglected, especially in practical monitoring situations like online evaluation of signals acquired in wearable sensors. Continuous blood pressure estimation based on pulse wave velocity considerations is a prominent example, which depends on careful fiducial point extraction and is therefore seriously affected during periods of increased occurring extrasystoles. In the scope of this work, a novel ectopic beat discriminator with low computational complexity has been developed, which takes advantage of multimodal features derived from ECG and pulse wave relating measurements, thereby providing additional information on the underlying cardiac activity. Moreover, the blood pressure estimations’ vulnerability towards ectopic beats is closely examined on records drawn from the Physionet database as well as signals recorded in a small field study conducted in a geriatric facility for the elderly. It turns out that a reliable extrasystole identification is essential to unsupervised blood pressure estimation, having a significant impact on the overall accuracy. The proposed method further convinces by its applicability to battery driven hardware systems with limited processing power and is a favorable choice when access to multimodal signal features is given anyway.

Zusammenfassung Es wurden Untersuchungen hinsichtlich der Eignung und der praktischen Relevanz von codierten Sendesignalen als Anregungsimpulse für die Ultraschallprüfung von Betonbauteilen durchgeführt. Anhand von synthetischen Daten werden die theoretischen Vorteile der codierten Impulssequenzen gezeigt. Im Anschluss werden Barker- und Golay-codierte Sendesignale an einer Betonstufenplatte mit einem Größtkorn von 16 mm getestet. Es zeigt sich das große Potenzial dieses Verfahrens, insbesondere bei Verwendung von Golay-Sequenzen als Anregung für die Ultraschallprüfköpfe. Es ergibt sich ein deutlich verbessertes Signal-Stör-Verhältnis im Vergleich zu den herkömmlichen bipolaren Rechteckimpulsen.

An ever increasing number of research is examining the question to what extent physiological information beyond the blood oxygen saturation could be drawn from the photoplethysmogram. One important approach to elicit that information from the photoplethysmogram is the analysis of its waveform. One prominent example for the value of photoplethysmographic waveform analysis in cardiovascular monitoring that has emerged is hemodynamic compensation assessment in the peri-operative setting or trauma situations, as digital pulse waveform dynamically changes with alterations in vascular tone or pulse wave velocity. In this work, we present an algorithm based on modern machine learning techniques that automatically finds individual digital volume pulses in photoplethysmographic signals and sorts them into one of the pulse classes defined by Dawber et al. We evaluate our approach based on two major datasets – a measurement study that we conducted ourselves as well as data from the PhysioNet MIMIC II database. As the results are satisfying we could demonstrate the capabilities of classification algorithms in the automated assessment of the digital volume pulse waveform measured by photoplethysmographic devices.

Being able to accurately monitor blood pressure in a reliable, truly non-invasive manner is a highly sought after goal within the biomedical community. In this paper we propose and assess a system, methodology and algorithm for unobtrusively obtaining true pulse transit time data from readily accessible peripheral locations, such as the hand, using a highly synchronous body-sensor-network encompassing an electrocardiogram- and dual mode photoplethysmogram sensor node. The results suggest the feasibility of acquiring such data, which strongly correlates with the recorded reference blood pressure, and can therefore be further employed to track changes thereof.

Unobtrusive medical instrumentation is a key in continuous patient monitoring. To increase compliance, multi-functional sensor concepts and measurement sites different from gold-standards are used. In this work, we aim to combine both approaches. We focus on minimally spaced electrode positions with high signal correlations to gold-standards. We present twofold experimental data from six and eleven healthy volunteers and provide chest positions with individual correlations up to 0.83 ± 0.06 for ECG and 0.73 ± 0.28 for the respiratory frequency. Using a performance index, we assess positions with correlations up to 0.77 ± 0.12 for ECG and 0.65 ± 0.35 for the respiratory frequency with 24 mm electrode distance.

Compressed Sensing has recently been proposed for efficient data compression of multi-lead electrocardiogram recordings within ambulatory patient monitoring applications, e.g. wireless body sensor networks. However, current approaches only focus on signal reconstruction and do not consider the efficient compression of signal ensembles. In this work, we propose the utilization of a compressive multiplexing architecture that facilitates an efficient implementation of hardware compressed sensing for multi-lead ECG signals. For the reconstruction of ECG signal ensembles, we employ an greedy algorithm that exploits their joint sparsity structure. Our simulative study shows promising results which motivate further research in the field of compressive multiplexing for the acquisition multi-lead ECG signals.