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.

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.

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.

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.

In recent years, the analysis of the photoplethys-mographic (PPG) pulse waveforms has attracted much research focus. However, the considered signals are primarily recorded at the fingertips, which suffer from reduced peripheral perfusion in situations like hypovolemia or sepsis, rendering waveform analysis infeasible. The ear canal is not affected by cardiovascular centralization and could thus prove to be an ideal alternate measurement site for pulse waveform analysis. Therefore, we developed a novel system that allows for highly accurate photoplethysmographic measurements in the ear canal. We conducted a measurement study in order to assess the signal-to-noise ratio of our developed system Hereby, we achieved a mean SNR of 40.65 dB. Hence, we could show that our system allows for highly accurate PPG recordings in the ear canal facilitating sophisticated pulse waveform analysis. Furthermore, we demonstrated that the pulse decomposition analysis is also applicable to in-ear PPG recordings.