In-ear photoplethysmography for central pulse waveform analysis in non-invasive hemodynamic monitoring

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

Correlation of arterial blood pressure to synchronous piezo, impedance and photoplethysmographic signal features : Investigating pulse wave features and transit times

In this paper we investigate which pulse wave pick-up technologies are well suited for blood pressure trend estimation. We use custom built hardware to acquire electrocardiographic, applanation-tonometric, photo- and impedance-plethysmographic signals during low intensity workouts. Beat-to-beat features and pulse wave runtimes are correlated to the reference arterial blood pressure. Temporal lag adjustment is performed to determine the latency of feature response. Best results are obtained for systolic arterial blood pressure. These suggest that every subject has a range of well-performing features, but it is not consistent among all. Spearman Rho values reach in excess of 0.8, with their significance being validated by p-values lower than 0.01

Wearable Cardiorespiratory Monitoring Employing a Multimodal Digital Patch Stethoscope: Estimation of ECG, PEP, LVET and Respiration Using a 55 mm Single-Lead ECG and Phonocardiogram

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 HDTM and Osypka ICON-CoreTM. 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.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Detection of a stroke volume decrease by machine-learning algorithms based on thoracic bioimpedance in experimental hypovolaemia

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

Continuous signal quality estimation for robust heart rate extraction from photoplethysmographic signals

This study presents a novel method for estimating the signal quality of photoplethysmographic (PPG) signals. For this purpose a robust classifier is implemented and evaluated by using finger- and inear-PPG. A new procedure is proposed, which uses feature reduction to determine the Mahalanobis distance of the PPG-pulses to a statistical reference model and thus facilitates a robust heart rate extraction. The evaluation of the algorithm is based on a classical binary classification using a manually annotated gold standard. For the finger-PPG a sensitivity of 86 ± 15 % and a specificity of 94 ± 13 % was achieved. Additionally, a novel classification method which is based on a continuous signal quality index is used. Pulse rate estimation errors greater than 5 BPM can be detected with a sensitivity of 91 ± 13 % and a specificity of 91 ± 15 %. Also, a functional correlation between the signal quality index and the standard deviation of the pulse rate error is shown. The proposed classifier can be used for improving the heart rate extration in pulse rate variability analysis or in the area of mobile monitoring for battery saving

Organophosphorus pesticides exhibit compound specific effects in rat precision-cut lung slices (PCLS): mechanisms involved in airway response, cytotoxicity, inflammatory activation and antioxidative defense

Organophosphorus compound pesticides (OP) are widely used in pest control and might be misused for terrorist attacks. Although acetylcholinesterase (AChE) inhibition is the predominant toxic mechanism, OP may induce pneumonia and formation of lung edema after poisoning and during clinical treatment as life-threatening complication. To investigate the underlying mechanisms, rat precision-cut lung slices (PCLS) were exposed to the OP parathion, malathion and their biotransformation products paraoxon and malaoxon (100-2000 µmol/L). Airway response, metabolic activity, release of LDH, cytokine expression and oxidative stress response were analyzed. A concentration-dependent inhibition of airway relaxation was observed after exposure with the oxon but not with the thion-OP. In contrast, cytotoxic effects were observed for both forms in higher concentrations. Increased cytokine expression was observed after exposure to parathion and paraoxon (IL-6, GM-CSF, MIP-1α) and IL-6 expression was dependent on NFκB activation. Intracellular GSH levels were significantly reduced by all four tested OP but an increase in GSSG and HO-1 expression was predominantly observed after malaoxon exposure. Pretreatment with the antioxidant N-acetylcysteine reduced malaoxon but not paraoxon-induced cytotoxicity. PCLS as a 3D lung model system revealed OP-induced effects depending on the particular OP. The experimental data of this study contribute to a better understanding of OP toxicity on cellular targets and may be a possible explanation for the variety of clinical outcomes induced by different OP

Compressed sensing of multi-lead ECG signals by compressive multiplexing

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

Validation System for Digital Stethoscopes

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

Improvement of Stroke Volume Estimation with Bioimpedance Measurement by LSTM Network Approach Based on ECG during Ergometry

Stroke volume (SV) and cardiac output (CO) estimations with non-invasive approaches like thoracic electrical bioimpedance (TEB) measurement become state of the art in clinical practice. Despite the advantages like low costs, low risk of infection and relatively easy application, there are also disadvantages like the sensitivity to movement artifacts and, electrode displacement mistakes. The bioimpedance signal acquired with a tetrapolar measurement has a relatively weak signal strength compared with another common recorded signal, e.g., the electrocardiogram (ECG). For reconstruction and filtering of the dZ/dt signal, different approaches exist like ensemble averaging (EA), scaled fourier linear combiner (SFLC), wavelet denoising and adaptive filter. We propose an artificial neural network with long short-term memory (LSTM) layer for signal reconstruction during ergometry. The LSTM network performs well compared with other algorithms, e.g., with better amplitude (C point) reconstruction. The SV estimation with the LSTM network was at least comparable or even better than the estimation based on SFLC