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.

Gold-standards for biosignal acquisition require body-spanning sensor positioning which is contradictory to the high integration of modern wearable medical monitors. In applications where obtrusiveness can decrease accuracy, as in sleep monitoring, compact sensor configurations are not only a matter of convenience. To acquire respiratory signals, most systems rely on nasal cannula pressure sensors or inductance plethysmography. Another well-established method is the impedance pneumography, where we aim to contribute to the field of short distance electrode configurations. Evaluating distances down to 8 cm we report linear correlations above 0.85 with respect to a pneumotachometer reference. We estimate the respiratory rate with an error below 0.2 bpm. Inspiratory and expiratory phase detection is possible with an error below 2.5 %. Using a first order polynomial model we estimated the respiratory flow with a relative error of down to 19 % at 8 cm. We conclude that short distance impedance pneumography is feasible and rough flow and volume estimates are possible using linear models. Further research regarding shorter distances and calibration is of great interest.

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.

Stethoscope auscultation is a diagnostic method widely used by medical professionals. With the introduction of digital stethoscopes, auscultation sound analysis has been objectified, which led to an increased interest in the field. Until today, however, no standard to assess the acoustical properties of stethoscopes is available. Some approaches use phantoms mimicking the properties of human soft tissue. In most cases, however, the properties of the phantoms have not been analyzed with respect to environmental variables. In our work, we propose a stethoscope characterization system for the frequency range between 50 Hz and 2.5 kHz with a small financial footprint. We analyzed its frequency behavior over temperature, time and position on the phantom and derived quantitative recommendations for environmental variables. Finally, the frequency response of a commercial digital stethoscope was characterized at different pressure levels. We conclude that the presented system is capable to stably and reproducibly assess the transfer function of digital stethoscopes. We hope that future stethoscope designs will be characterized with respect to their acoustical properties.

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.