Reliable, remote measurement of respiration rate is still an unmet need in clinical and home settings. Although the predictive power of respiratory rate for a patient's health status is well-known, this vital sign is often measured inaccurately or not at all. In this paper we propose a camera-based monitoring system to reliably measure respiration rate without any body contact. A computationally efficient algorithm to extract raw breathing signals from the video stream has been developed and implemented. Additionally, a camera offers an easy access to motion information in the analyzed scenes, which significantly improves subsequent breath-to-breath classification. The performance of the sensor system was evaluated using data acquired with healthy volunteers, as well as with a mechanical phantom, under laboratory conditions covering a large range of challenging measurement situations.

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.

In the analysis of fingertip photoplethysmograms (PPG), the Pulse Decomposition Analysis (PDA) has emerged as a powerful tool for the extraction of physiologically relevant information from the morphology of single digital volume pulse (DVP) cycles. In previously published works on the PDA, many different models are suggested. In this work, we conducted a data driven approach to address the question of which model to choose for the PDA. For this purpose, we compiled an extensive dataset of 7805 single DVP pulses that comprises most expectable pulse morphologies and conducted PDA simulations with four different basis functions types and a meaningful range of model orders. We then performed model selection based on the Corrected Akaike Information Criterion (AICc) with the aim of identifying the PDA models that provided the best fit. As a result, we found that a PDA model based on the linear superposition of three scaled Gamma basis functions was selected as the best fitting model in 28.1% of all pulses. The second highest relative selection frequency of 14.4% was achieved by fitting two Rayleigh functions. Consequently, we recommend to consider the employment of this PDA model in further work on the PDA.

Abstract:In recent years, the analysis of the photoplethysmographic (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.