Pulse arrival time as a surrogate of blood pressure

Non-invasive pulse arrival time is associated with cardiac index in pediatric heart transplant patients with normal ejection fraction

Objective.Cardiac Index (CI) is a key physiologic parameter to ensure end organ perfusion in the pediatric intensive care unit (PICU). Determination of CI requires invasive cardiac measurements and is not routinely done at the PICU bedside. To date, there is no gold standard non-invasive means to determine CI. This study aims to use a novel non-invasive methodology, based on routine continuous physiologic data, called Pulse Arrival Time (PAT) as a surrogate for CI in patients with normal Ejection Fraction (EF).Approach.Electrocardiogram (ECG) and photoplethysmogram (PPG) signals were collected from beside monitors at a sampling frequency of 250 samples per second. Continuous PAT, derived from the ECG and PPG waveforms was averaged per patient. Pearson's correlation coefficient was calculated between PAT and CI, PAT and heart rate (HR), and PAT and EF.Main Results.Twenty patients underwent right heart cardiac catheterization. The mean age of patients was 11.7 ± 5.4 years old, ranging from 11 months old to 19 years old, the median age was 13.4 years old. HR in this cohort was 93.8 ± 17.0 beats per minute. The average EF was 54.4 ± 9.6%. The average CI was 3.51 ± 0.72 l min-1m-2, with ranging from 2.6 to 4.77 l min-1m-2. The average PAT was 0.31 ± 0.12 s. Pearson correlation analysis showed a positive correlation between PAT and CI (0.57,p< 0.01). Pearson correlation between HR and CI, and correlation between EF and CI was 0.22 (p= 0.35) and 0.03 (p= 0.23) respectively. The correlation between PAT, when indexed by HR (i.e. PAT × HR), and CI minimally improved to 0.58 (p< 0.01).Significance.This pilot study demonstrates that PAT may serve as a valuable surrogate marker for CI at the bedside, as a non-invasive and continuous modality in the PICU. The use of PAT in clinical practice remains to be thoroughly investigated.

Non-invasive pulse arrival time as a surrogate for oscillometric systolic blood pressure changes during non-pharmacological intervention

Background.Non-invasive continuous blood pressure (BP) monitoring is of longstanding interest in various cardiovascular scenarios. In this context, pulse arrival time (PAT), i.e., a surrogate parameter for systolic BP (change), became very popular recently, especially in the context of cuffless BP measurement and dedicated lifestyle interventions. Nevertheless, there is also understandable doubt on its reliability in uncontrolled and mobile settings.Objective.The aim of this work is therefore the investigation whether PAT follows oscillometric systolic BP readings during moderate interventions by physical or mental activity using a medical grade handheld device for non-invasive PAT assessment.Approach.A study was conducted featuring an experimental group performing a physical and a mental task, and a control group. Oscillometric BP and PAT were assessed at baseline and after each intervention. Interventions were selected randomly but then performed sequentially in a counterbalanced order. Multivariate analyses of variance were used to test within-subject and between-subject effects for the dependent variables, followed by univariate analyses for post-hoc testing. Furthermore, correlation analysis was performed to assess the association of intervention effects between BP and PAT.Mainresults.The study included 51 subjects (31 females). Multivariate analysis of variances showed that effects in BP, heart rate, PAT and pulse wave parameters were consistent and significantly different between experimental and control groups. After physical activity, heart rate and systolic BP increased significantly whereas PAT decreased significantly. Mental activity leads to a decrease in systolic BP at stable heart rate. Pulse wave parameters follow accordingly by an increase of PAT and mainly unchanged pulse wave analysis features due to constant heart rate. Finally, also the control group behaviour was accurately registered by the PAT method compared to oscillometric cuff. Correlation analyses revealed significant negative associations between changes of systolic BP and changes of PAT from baseline to the physical task (-0.33 [-0.63, 0.01],p< 0.048), and from physical to mental task (-0.51 [-0.77, -0.14],p= 0.001), but not for baseline to mental task (-0.12 [-0,43,0,20],p= 0.50) in the experimental group.Significance.PAT and the used digital, handheld device proved to register changes in BP and heart rate reliably compared to oscillometric measurements during intervention. Therefore, it might add benefit to future mobile health solutions to support BP management by tracking relative, not absolute, BP changes during non-pharmacological interventions.

Wearable Ring-Shaped Biomedical Device for Physiological Monitoring through Finger-Based Acquisition of Electrocardiographic, Photoplethysmographic, and Galvanic Skin Response Signals: Design and Preliminary Measurements

Wearable health devices (WHDs) are rapidly gaining ground in the biomedical field due to their ability to monitor the individual physiological state in everyday life scenarios, while providing a comfortable wear experience. This study introduces a novel wearable biomedical device capable of synchronously acquiring electrocardiographic (ECG), photoplethysmographic (PPG), galvanic skin response (GSR) and motion signals. The device has been specifically designed to be worn on a finger, enabling the acquisition of all biosignals directly on the fingertips, offering the significant advantage of being very comfortable and easy to be employed by the users. The simultaneous acquisition of different biosignals allows the extraction of important physiological indices, such as heart rate (HR) and its variability (HRV), pulse arrival time (PAT), GSR level, blood oxygenation level (SpO2), and respiratory rate, as well as motion detection, enabling the assessment of physiological states, together with the detection of potential physical and mental stress conditions. Preliminary measurements have been conducted on healthy subjects using a measurement protocol consisting of resting states (i.e., SUPINE and SIT) alternated with physiological stress conditions (i.e., STAND and WALK). Statistical analyses have been carried out among the distributions of the physiological indices extracted in time, frequency, and information domains, evaluated under different physiological conditions. The results of our analyses demonstrate the capability of the device to detect changes between rest and stress conditions, thereby encouraging its use for assessing individuals' physiological state. Furthermore, the possibility of performing synchronous acquisitions of PPG and ECG signals has allowed us to compare HRV and pulse rate variability (PRV) indices, so as to corroborate the reliability of PRV analysis under stationary physical conditions. Finally, the study confirms the already known limitations of wearable devices during physical activities, suggesting the use of algorithms for motion artifact correction.

Design of a Remote Multiparametric Tool to Assess Mental Well-Being and Distress in Young People (mHealth Methods in Mental Health Research Project): Protocol for an Observational Study

BACKGROUND

Mental health conditions have become a substantial cause of disability worldwide, resulting in economic burden and strain on the public health system. Incorporating cognitive and physiological biomarkers using noninvasive sensors combined with self-reported questionnaires can provide a more accurate characterization of the individual's well-being. Biomarkers such as heart rate variability or those extracted from the electrodermal activity signal are commonly considered as indices of autonomic nervous system functioning, providing objective indicators of stress response. A model combining a set of these biomarkers can constitute a comprehensive tool to remotely assess mental well-being and distress.

OBJECTIVE

This study aims to design and validate a remote multiparametric tool, including physiological and cognitive variables, to objectively assess mental well-being and distress.

METHODS

This ongoing observational study pursues to enroll 60 young participants (aged 18-34 years) in 3 groups, including participants with high mental well-being, participants with mild to moderate psychological distress, and participants diagnosed with depression or anxiety disorder. The inclusion and exclusion criteria are being evaluated through a web-based questionnaire, and for those with a mental health condition, the criteria are identified by psychologists. The assessment consists of collecting mental health self-reported measures and physiological data during a baseline state, the Stroop Color and Word Test as a stress-inducing stage, and a final recovery period. Several variables related to heart rate variability, pulse arrival time, breathing, electrodermal activity, and peripheral temperature are collected using medical and wearable devices. A second assessment is carried out after 1 month. The assessment tool will be developed using self-reported questionnaires assessing well-being (short version of Warwick-Edinburgh Mental Well-being Scale), anxiety (Generalized Anxiety Disorder-7), and depression (Patient Health Questionnaire-9) as the reference. We will perform correlation and principal component analysis to reduce the number of variables, followed by the calculation of multiple regression models. Test-retest reliability, known-group validity, and predictive validity will be assessed.

RESULTS

Participant recruitment is being carried out on a university campus and in mental health services. Recruitment commenced in October 2022 and is expected to be completed by June 2024. As of July 2023, we have recruited 41 participants. Most participants correspond to the group with mild to moderate psychological distress (n=20, 49%), followed by the high mental well-being group (n=13, 32%) and those diagnosed with a mental health condition (n=8, 20%). Data preprocessing is currently ongoing, and publication of the first results is expected by September 2024.

CONCLUSIONS

This study will establish an initial framework for a comprehensive mental health assessment tool, taking measurements from sophisticated devices, with the goal of progressing toward a remotely accessible and objectively measured approach that maintains an acceptable level of accuracy in clinical practice and epidemiological studies.

TRIAL REGISTRATION

OSF Registries N3GCH; https://doi.org/10.17605/OSF.IO/N3GCH.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/51298.

Increasing accuracy of pulse arrival time estimation in low frequency recordings

Objective.Wearable devices that measure vital signals using photoplethysmography are becoming more commonplace. To reduce battery consumption, computational complexity, memory footprint or transmission bandwidth, companies of commercial wearable technologies are often looking to minimize the sampling frequency of the measured vital signals. One such vital signal of interest is the pulse arrival time (PAT), which is an indicator of blood pressure. To leverage this non-invasive and non-intrusive measurement data for use in clinical decision making, the accuracy of obtained PAT-parameters needs to increase in lower sampling frequency recordings. The aim of this paper is to develop a new strategy to estimate PAT at sampling frequencies up to 25 Hertz.Approach.The method applies template matching to leverage the random nature of sampling time and expected change in the PAT.Main results.The algorithm was tested on a publicly available dataset from 22 healthy volunteers, under sitting, walking and running conditions. The method significantly reduces both the mean and the standard deviation of the error when going to lower sampling frequencies by an average of 16.6% and 20.2%, respectively. Looking only at the sitting position, this reduction is even larger, increasing to an average of 22.2% and 48.8%, respectively.Significance.This new method shows promise in allowing more accurate estimation of PAT even in lower frequency recordings.

Estimation of aortic stiffness by finger photoplethysmography using enhanced pulse wave analysis and machine learning

Introduction

Aortic stiffness plays a critical role in the evolution of cardiovascular diseases, but the assessment requires specialized equipment. Photoplethysmography (PPG) and single-lead electrocardiogram (ECG) are readily available in healthcare and wearable devices. We studied whether a brief PPG registration, alone or in combination with single-lead ECG, could be used to reliably estimate aortic stiffness.

Methods

A proof-of-concept study with simultaneous high-resolution index finger recordings of infrared PPG, single-lead ECG, and finger blood pressure (Finapres) was performed in 33 participants [median age 44 (range 21-66) years, 19 men] and repeated within 2 weeks. Carotid-femoral pulse wave velocity (cfPWV; two-site tonometry with SphygmoCor) was used as a reference. A brachial single-cuff oscillometric device assessed aortic pulse wave velocity (aoPWV; Arteriograph) for further comparisons. We extracted 136 established PPG waveform features and engineered 13 new with improved coupling to the finger blood pressure curve. Height-normalized pulse arrival time (NPAT) was derived using ECG. Machine learning methods were used to develop prediction models.

Results

The best PPG-based models predicted cfPWV and aoPWV well (root-mean-square errors of 0.70 and 0.52 m/s, respectively), with minor improvements by adding NPAT. Repeatability and agreement were on par with the reference equipment. A new PPG feature, an amplitude ratio from the early phase of the waveform, was most important in modelling, showing strong correlations with cfPWV and aoPWV (r = -0.81 and -0.75, respectively, both P < 0.001).

Conclusion

Using new features and machine learning methods, a brief finger PPG registration can estimate aortic stiffness without requiring additional information on age, anthropometry, or blood pressure. Repeatability and agreement were comparable to those obtained using non-invasive reference equipment. Provided further validation, this readily available simple method could improve cardiovascular risk evaluation, treatment, and prognosis.

Strengths-weaknesses-opportunities-threats analysis of artificial intelligence in anesthesiology and perioperative medicine

The use of artificial intelligence (AI) and machine learning (ML) in anesthesiology and perioperative medicine is quickly becoming a mainstay of clinical practice. Anesthesiology is a data-rich medical specialty that integrates multitudes of patient-specific information. Perioperative medicine is ripe for applications of AI and ML to facilitate data synthesis for precision medicine and predictive assessments. Examples of emergent AI models include those that assist in assessing depth and modulating control of anesthetic delivery, event and risk prediction, ultrasound guidance, pain management, and operating room logistics. AI and ML support analyzing integrated perioperative data at scale and can assess patterns to deliver optimal patient-specific care. By exploring the benefits and limitations of this technology, we provide a basis of considerations for evaluating the adoption of AI models into various anesthesiology workflows. This analysis of AI and ML in anesthesiology and perioperative medicine explores the current landscape to understand better the strengths, weaknesses, opportunities, and threats (SWOT) these tools offer.

Graphene-based Two-Stage Enhancement Pressure Sensor for Subtle Mechanical Force Monitoring

The development of pressure sensors with high sensitivity and a low detection limit for subtle mechanical force monitoring and the understanding of the sensing mechanism behind subtle mechanical force monitoring are of great significance for intelligent technology. Here, we proposed a graphene-based two-stage enhancement pressure sensor (GTEPS), and we analyzed the difference between subtle mechanical force monitoring and conventional mechanical force monitoring. The GTEPS exhibited a high sensitivity of 62.2 kPa-1 and a low detection limit of 0.1 Pa. Leveraging its excellent performance, the GTEPS was successfully applied in various subtle mechanical force monitoring applications, including acoustic wave detection, voice-print recognition, and pulse wave monitoring. In acoustic wave detection, the GTEPS achieved a 100% recognition accuracy for six words. In voiceprint recognition, the sensor exhibited accurate identification of distinct voiceprints among individuals. Furthermore, in pulse wave monitoring, GTEPS demonstrated effective detection of pulse waves. By combination of the pulse wave signals with electrocardiogram (ECG) signals, it enabled the assessment of blood pressure. These results demonstrate the excellent performance of GTEPS and highlight its great potential for subtle mechanical force monitoring and its various applications. The current results indicate that GTEPS shows great potential for applications in subtle mechanical force monitoring.

Effect of physiological and pharmacological stress on heart rate, blood pressure, and echocardiographic measurements in healthy Warmblood horses

BACKGROUND

Echocardiographic measurements are important prognostic indicators but might be influenced by heart rate and blood pressure. This is particularly important when comparing repeated examinations.

HYPOTHESIS

To determine the effect of physiological stress at mildly increased heart rates and pharmacological challenge using IV administration of N-butylscopolammonium bromide and metamizol sodium on heart rate, blood pressure, and echocardiographic measurements.

ANIMALS

Twenty healthy Warmblood horses.

METHODS

Randomized crossover study. Horses were examined echocardiographically by 2-dimensional, M-mode, pulsed wave (PW) Doppler, and PW tissue Doppler imaging with simultaneous ECG recording and noninvasive blood pressure measurements during rest, physiological stress, and pharmacological challenge. Cardiac dimensions and functions were measured by a blinded observer. Data were analyzed using repeated-measures analysis of variance.

RESULTS

Mean heart rate and arterial blood pressure were significantly higher during physiological stress (46 ± 2 bpm, 93 ± 16 mm Hg) and pharmacological challenge (62 ± 13 bpm, 107 ± 17 mm Hg) compared with rest (34 ± 3 bpm, 86 ± 12 mm Hg; P < .05). Compared with rest, physiological stress resulted in increased left atrial fractional area change (34.3 ± 7.5 vs 27.3 ± 5.1%; P = .01) and left ventricular late diastolic radial wall motion velocity (13 ± 3 vs 10 ± 2 cm/s; P = .01) but had no significant effect on most other echocardiographic variables. Compared with rest, pharmacological challenge led to significantly decreased left atrial and diastolic ventricular dimensions (left ventricular internal diameter: 10.3 ± 0.9 vs 10.7 ± 0.8 cm; P = .01), increased aortic and pulmonary diameters, and ventricular wall thickness.

CONCLUSIONS AND CLINICAL IMPORTANCE

Physiological stress at mildly increased heart rates significantly enhanced atrial pump function. Larger heart rate and blood pressure increases during pharmacological challenge resulted in altered cardiac dimensions. This should be taken into account when evaluating echocardiographic measurements at increased heart rates.

Synchronized wearables for the detection of haemodynamic states via electrocardiography and multispectral photoplethysmography

Cardiovascular health is typically monitored by measuring blood pressure. Here we describe a wireless on-skin system consisting of synchronized sensors for chest electrocardiography and peripheral multispectral photoplethysmography for the continuous monitoring of metrics related to vascular resistance, cardiac output and blood-pressure regulation. We used data from the sensors to train a support-vector-machine model for the classification of haemodynamic states (resulting from exposure to heat or cold, physical exercise, breath holding, performing the Valsalva manoeuvre or from vasopressor administration during post-operative hypotension) that independently affect blood pressure, cardiac output and vascular resistance. The model classified the haemodynamic states on the basis of an unseen subset of sensor data for 10 healthy individuals, 20 patients with hypertension undergoing haemodynamic stimuli and 15 patients recovering from cardiac surgery, with an average precision of 0.878 and an overall area under the receiver operating characteristic curve of 0.958. The multinodal sensor system may provide clinically actionable insights into haemodynamic states for use in the management of cardiovascular disease.

Temporal complexity in photoplethysmography and its influence on blood pressure

Objective: The temporal complexity of photoplethysmography (PPG) provides valuable information about blood pressure (BP). In this study, we aim to interpret the stochastic PPG patterns with a model-based simulation, which may help optimize the BP estimation algorithms. Methods: The classic four-element Windkessel model is adapted in this study to incorporate BP-dependent compliance profiles. Simulations are performed to generate PPG responses to pulse and continuous stimuli at various timescales, aiming to mimic sudden or gradual hemodynamic changes observed in real-life scenarios. To quantify the temporal complexity of PPG, we utilize the Higuchi fractal dimension (HFD) and autocorrelation function (ACF). These measures provide insights into the intricate temporal patterns exhibited by PPG. To validate the simulation results, continuous recordings of BP, PPG, and stroke volume from 40 healthy subjects were used. Results: Pulse simulations showed that central vascular compliance variation during a cardiac cycle, peripheral resistance, and cardiac output (CO) collectively contributed to the time delay, amplitude overshoot, and phase shift of PPG responses. Continuous simulations showed that the PPG complexity could be generated by random stimuli, which were subsequently influenced by the autocorrelation patterns of the stimuli. Importantly, the relationship between complexity and hemodynamics as predicted by our model aligned well with the experimental analysis. HFD and ACF had significant contributions to BP, displaying stability even in the presence of high CO fluctuations. In contrast, morphological features exhibited reduced contribution in unstable hemodynamic conditions. Conclusion: Temporal complexity patterns are essential to single-site PPG-based BP estimation. Understanding the physiological implications of these patterns can aid in the development of algorithms with clear interpretability and optimal structures.

Continuous Blood Pressure Monitoring in Patients Having Surgery: A Narrative Review

Hypotension can occur before, during, and after surgery and is associated with postoperative complications. Anesthesiologists should thus avoid profound and prolonged hypotension. A crucial part of avoiding hypotension is accurate and tight blood pressure monitoring. In this narrative review, we briefly describe methods for continuous blood pressure monitoring, discuss current evidence for continuous blood pressure monitoring in patients having surgery to reduce perioperative hypotension, and expand on future directions and innovations in this field. In summary, continuous blood pressure monitoring with arterial catheters or noninvasive sensors enables clinicians to detect and treat hypotension immediately. Furthermore, advanced hemodynamic monitoring technologies and artificial intelligence-in combination with continuous blood pressure monitoring-may help clinicians identify underlying causes of hypotension or even predict hypotension before it occurs.

Effects of Pulse Transit Time and Pulse Arrival Time on Cuff-less Blood Pressure Estimation: A Comparison Study with Multiple Experimental Interventions

Pulse transit time (PTT) has shown a correlation with blood pressure (BP), and it is considered as a potential marker for cuff-less BP estimation. However, pulse arrival time (PAT) including pre-ejection period (PEP) has been utilized more widely because of its convenience to acquisition and calculation. In spite of this, whether PAT can surrogate PTT has been a controversial topic for many years. In this study, we designed an experiment on 55 subjects with multiple interventions, those may cause the changes in BP and PEP. We analyzed the linear and nonlinear correlations between BP and PTT/PAT, and also assessed the performances of PTT-based and PAT-based models on tracking the BP variation. Five typical BP estimation models were used for comparison. We found that PEP could change rapidly in response to the interventions related with physical stress. Although PTT had a better linear correlation with BP, most of the PAT-based models showed more accuracy than PTT-based models in all of the interventions, especially for the calibrated models. It is suggested that PAT has the potential to predict BP, and the inclusion of PEP in the measurement of PAT is necessary.

Wrist ballistocardiography and invasively recorded blood pressure in healthy volunteers during reclining bike exercise

Objective: Ballistocardiogram (BCG) features are of interest in wearable cardiovascular monitoring of cardiac performance. We assess feasibility of wrist acceleration BCG during exercise for estimating pulse transit time (PTT), enabling broader cardiovascular response studies during acute exercise and improved monitoring in individuals at risk for cardiovascular disease (CVD). We also examine the relationship between PTT, blood pressure (BP), and stroke volume (SV) during exercise and posture interventions. Methods: 25 participants underwent a bike exercise protocol with four incremental workloads (0 W, 50 W, 100 W, and 150 W) in supine and semirecumbent postures. BCG, invasive radial artery BP, tonometry, photoplethysmography (PPG) and echocardiography were recorded. Ensemble averages of BCG signals determined aortic valve opening (AVO) timings, combined with peripheral pulse wave arrival times to calculate PTT. We tested for significance using Wilcoxon signed-rank test. Results: BCG was successfully recorded at the wrist during exercise. PTT exhibited a moderate negative correlation with systolic BP (ρ_Sup = -0.65, ρ_SR = -0.57, ρ_All = -0.54). PTT differences between supine and semirecumbent conditions were significant at 0 W and 50 W (p < 0.001), less at 100 W (p = 0.0135) and 150 W (p = 0.031). SBP and DBP were lower in semirecumbent posture (p < 0.01), while HR was slightly higher. Echocardiography confirmed association of BCG features with AVO and indicated a positive relationship between BCG amplitude and SV (ρ = 0.74). Significance: Wrist BCG may allow convenient PTT and possibly SV tracking during exercise, enabling studies of cardiovascular response to acute exercise and convenient monitoring of cardiovascular performance.

Optimizing the input feature sets and machine learning algorithms for reliable and accurate estimation of continuous, cuffless blood pressure

The advent of mobile devices, wearables and digital healthcare has unleashed a demand for accurate, reliable, and non-interventional ways to measure continuous blood pressure (BP). Many consumer products claim to measure BP with a cuffless device, but their lack of accuracy and reliability limit clinical adoption. Here, we demonstrate how multimodal feature datasets, comprising: (i) pulse arrival time (PAT); (ii) pulse wave morphology (PWM), and (iii) demographic data, can be combined with optimized Machine Learning (ML) algorithms to estimate Systolic BP (SBP), Diastolic BP (DBP) and Mean Arterial Pressure (MAP) within a 5 mmHg bias of the gold standard Intra-Arterial BP, well within the acceptable limits of the IEC/ANSI 80601-2-30 (2018) standard. Furthermore, DBP's calculated using 126 datasets collected from 31 hemodynamically compromised patients had a standard deviation within 8 mmHg, while SBP's and MAP's exceeded these limits. Using ANOVA and Levene's test for error means and standard deviations, we found significant differences in the various ML algorithms but found no significant differences amongst the multimodal feature datasets. Optimized ML algorithms and key multimodal features obtained from larger real-world data (RWD) sets could enable more reliable and accurate estimation of continuous BP in cuffless devices, accelerating wider clinical adoption.

A Portable Multi-Modal Cushion for Continuous Monitoring of a Driver's Vital Signs

With higher levels of automation in vehicles, the need for robust driver monitoring systems increases, since it must be ensured that the driver can intervene at any moment. Drowsiness, stress and alcohol are still the main sources of driver distraction. However, physiological problems such as heart attacks and strokes also exhibit a significant risk for driver safety, especially with respect to the ageing population. In this paper, a portable cushion with four sensor units with multiple measurement modalities is presented. Capacitive electrocardiography, reflective photophlethysmography, magnetic induction measurement and seismocardiography are performed with the embedded sensors. The device can monitor the heart and respiratory rates of a vehicle driver. The promising results of the first proof-of-concept study with twenty participants in a driving simulator not only demonstrate the accuracy of the heart (above 70% of medical-grade heart rate estimations according to IEC 60601-2-27) and respiratory rate measurements (around 30% with errors below 2 BPM), but also that the cushion might be useful to monitor morphological changes in the capacitive electrocardiogram in some cases. The measurements can potentially be used to detect drowsiness and stress and thus the fitness of the driver, since heart rate variability and breathing rate variability can be captured. They are also useful for the early prediction of cardiovascular diseases, one of the main reasons for premature death. The data are publicly available in the UnoVis dataset.

Intraoperative Beat-to-Beat Pulse Transit Time (PTT) Monitoring via Non-Invasive Piezoelectric/Piezocapacitive Peripheral Sensors Can Predict Changes in Invasively Acquired Blood Pressure in High-Risk Surgical Patients

BACKGROUND

Non-invasive tracking of beat-to-beat pulse transit time (PTT) via piezoelectric/piezocapacitive sensors (PES/PCS) may expand perioperative hemodynamic monitoring. This study evaluated the ability for PTT via PES/PCS to correlate with systolic, diastolic, and mean invasive blood pressure (SBP_IBP, DBP_IBP, and MAP_IBP, respectively) and to detect SBP_IBP fluctuations.

METHODS

PES/PCS and IBP measurements were performed in 20 patients undergoing abdominal, urological, and cardiac surgery. A Pearson's correlation analysis (r) between 1/PTT and IBP was performed. The predictive ability of 1/PTT with changes in SBP_IBP was determined by area under the curve (reported as AUC, sensitivity, specificity).

RESULTS

Significant correlations between 1/PTT and SBP_IBP were found for PES (r = 0.64) and PCS (r = 0.55) (p < 0.01), as well as MAP_IBP/DBP_IBP for PES (r = 0.6/0.55) and PCS (r = 0.5/0.45) (p < 0.05). A 7% decrease in 1/PTT_PES predicted a 30% SBP_IBP decrease (0.82, 0.76, 0.76), while a 5.6% increase predicted a 30% SBP_IBP increase (0.75, 0.7, 0.68). A 6.6% decrease in 1/PTT_PCS detected a 30% SBP_IBP decrease (0.81, 0.72, 0.8), while a 4.8% 1/PTT_PCS increase detected a 30% SBP_IBP increase (0.73, 0.64, 0.68).

CONCLUSIONS

Non-invasive beat-to-beat PTT via PES/PCS demonstrated significant correlations with IBP and detected significant changes in SBP_IBP. Thus, PES/PCS as a novel sensor technology may augment intraoperative hemodynamic monitoring during major surgery.

Wearable Continuous Blood Pressure Monitoring Devices Based on Pulse Wave Transit Time and Pulse Arrival Time: A Review

Continuous blood pressure (BP) monitoring is of great significance for the real-time monitoring and early prevention of cardiovascular diseases. Recently, wearable BP monitoring devices have made great progress in the development of daily BP monitoring because they adapt to long-term and high-comfort wear requirements. However, the research and development of wearable continuous BP monitoring devices still face great challenges such as obvious motion noise and slow dynamic response speeds. The pulse wave transit time method which is combined with photoplethysmography (PPG) waves and electrocardiogram (ECG) waves for continuous BP monitoring has received wide attention due to its advantages in terms of excellent dynamic response characteristics and high accuracy. Here, we review the recent state-of-art wearable continuous BP monitoring devices and related technology based on the pulse wave transit time; their measuring principles, design methods, preparation processes, and properties are analyzed in detail. In addition, the potential development directions and challenges of wearable continuous BP monitoring devices based on the pulse wave transit time method are discussed.

Influence of Photoplethysmogram Signal Quality on Pulse Arrival Time during Polysomnography

Intervals of low-quality photoplethysmogram (PPG) signals might lead to significant inaccuracies in estimation of pulse arrival time (PAT) during polysomnography (PSG) studies. While PSG is considered to be a "gold standard" test for diagnosing obstructive sleep apnea (OSA), it also enables tracking apnea-related nocturnal blood pressure fluctuations correlated with PAT. Since the electrocardiogram (ECG) is recorded synchronously with the PPG during PSG, it makes sense to use the ECG signal for PPG signal-quality assessment. (1) Objective: to develop a PPG signal-quality assessment algorithm for robust PAT estimation, and investigate the influence of signal quality on PAT during various sleep stages and events such as OSA. (2) Approach: the proposed algorithm uses R and T waves from the ECG to determine approximate locations of PPG pulse onsets. The MESA database of 2055 PSG recordings was used for this study. (3) Results: the proportions of high-quality PPG were significantly lower in apnea-related oxygen desaturation (matched-pairs rc = 0.88 and rc = 0.97, compared to OSA and hypopnea, respectively, when p < 0.001) and arousal (rc = 0.93 and rc = 0.98, when p < 0.001) than in apnea events. The significantly large effect size of interquartile ranges of PAT distributions was between low- and high-quality PPG (p < 0.001, rc = 0.98), and regular and irregular pulse waves (p < 0.001, rc = 0.74), whereas a lower quality of the PPG signal was found to be associated with a higher interquartile range of PAT across all subjects. Suggested PPG signal quality-based PAT evaluation reduced deviations (e.g., rc = 0.97, rc = 0.97, rc = 0.99 in hypopnea, oxygen desaturation, and arousal stages, respectively, when p < 0.001) and allowed obtaining statistically larger differences between different sleep stages and events. (4) Significance: the implemented algorithm has the potential to increase the robustness of PAT estimation in PSG studies related to nocturnal blood pressure monitoring.

Device-guided slow breathing with direct biofeedback of pulse wave velocity - acute effects on pulse arrival time and self-measured blood pressure

BACKGROUND

There isevidence that device-guided slow breathing using biofeedback acutely reduces blood pressure (BP) and pulse wave velocity [i.e. increased pulse arrival time (PAT)].

OBJECTIVES

The objectives of the study presented here were to test whether the results of changes observed in PAT in earlier studies are reproducible over 1 week and how changes in pulse wave velocity/PAT translate into absolute self-measured BP changes.

METHODS

Patients with a systolic BP 130-160 mmHg or treated essential hypertension (21 females/23 males) were trained to perform unattended device-guided slow breathing exercises for 10 min daily over 5 days. Furthermore, they were skilled to perform self-measurement of BP before and after the breathing exercise using a validated upper-arm device.

RESULTS

Office BP at screening [median (1, 3. Q)] was 137 (132, 142)/83 (79, 87) mmHg. We observed a significant ( P  < 0.05) increase in PAT of 5 ms (SD 12.5 ms) on average after 10 min of guided breathing and an additional 1 ms ( P  < 0.05, SD 8 ms) during the following 5 min of spontaneous breathing compared to baseline. PAT before the exercise remained constant over 5 days paralleled by constant self-measured BP before the exercise. Device-guided breathing was associated with a significant reduction of self-measured SBP of 5 mmHg ( P  < 0.01, SD 8 mmHg). Data furthermore demonstrated that these changes were highly reproducible over 1 week.

CONCLUSIONS

Device-guided slow breathing and biofeedback lead to reproducible and favorable changes (increase) in PAT and SBP (decrease).

Association of alternative polysomnographic features with patient outcomes in obstructive sleep apnea: a systematic review

STUDY OBJECTIVES

Polysomnograms (PSGs) collect a plethora of physiologic signals across the night. However, few of these PSG data are incorporated into standard reports, and hence, ultimately, under-utilized in clinical decision making. Recently, there has been substantial interest regarding novel alternative PSG metrics that may help to predict obstructive sleep apnea (OSA)-related outcomes better than standard PSG metrics such as the apnea-hypopnea index. We systematically review the recent literature for studies that examined the use of alternative PSG metrics in the context of OSA and their association with health outcomes.

METHODS

We systematically searched EMBASE, MEDLINE, and the Cochrane Database of Systematic Reviews for studies published between 2000 and 2022 for those that reported alternative metrics derived from PSG in adults and related them to OSA-related outcomes.

RESULTS

Of the 186 initial studies identified by the original search, data from 31 studies were ultimately included in the final analysis. Numerous metrics were identified that were significantly related to a broad range of outcomes. We categorized the outcomes into 2 main subgroups: (1) cardiovascular/metabolic outcomes and mortality and (2) cognitive function- and vigilance-related outcomes. Four general categories of alternative metrics were identified based on signals analyzed: autonomic/hemodynamic metrics, electroencephalographic metrics, oximetric metrics, and respiratory event-related metrics.

CONCLUSIONS

We have summarized the current landscape of literature for alternative PSG metrics relating to risk prediction in OSA. Although promising, further prospective observational studies are needed to verify findings from other cohorts, and to assess the clinical utility of these metrics.

CITATION

Hajipour M, Baumann B, Azarbarzin A, et al. Association of alternative polysomnographic features with patient outcomes in obstructive sleep apnea: a systematic review. J Clin Sleep Med. 2023;19(2):225-242.

Psychosomatic response to acute emotional stress in healthy students

The multidimensionality of the stress response has shown the complexity of this phenomenon and therefore the impossibility of finding a unique biomarker among the physiological variables related to stress. An experimental study was designed and performed to guarantee the correct synchronous and concurrent measure of psychometric tests, biochemical variables and physiological features related to acute emotional stress. The population studied corresponds to a group of 120 university students between 20 and 30 years of age, with healthy habits and without a diagnosis of chronic or psychiatric illnesses. Following the protocol of the experimental pilot, each participant reached a relaxing state and a stress state in two sessions of measurement for equivalent periods. Both states are correctly achieved evidenced by the psychometric test results and the biochemical variables. A Stress Reference Scale is proposed based on these two sets of variables. Then, aiming for a non-invasive and continuous approach, the Acute Stress Model correlated to the previous scale is also proposed, supported only by physiological signals. Preliminary results support the feasibility of measuring/quantifying the stress level. Although the results are limited to the population and stimulus type, the procedure and methodological analysis used for the assessment of acute stress in young people can be extrapolated to other populations and types of stress.

Applied machine learning for blood pressure estimation using a small, real-world electrocardiogram and photoplethysmogram dataset

Applying machine learning techniques to electrocardiography and photoplethysmography signals and their multivariate-derived waveforms is an ongoing effort to estimate non-occlusive blood pressure. Unfortunately, real ambulatory electrocardiography and photoplethysmography waveforms are inevitably affected by motion and noise artifacts, so established machine learning architectures perform poorly when trained on data of the Multiparameter Intelligent Monitoring in Intensive Care II type, a publicly available ICU database. Our study addresses this problem by applying four well-established machine learning methods, i.e., random forest regression, support vector regression, Adaboost regression and artificial neural networks, to a small, self-sampled electrocardiography-photoplethysmography dataset (n = 54) to improve the robustness of machine learning to real-world BP estimates. We evaluated the performance using a selection of optimal feature morphologies of waveforms by using pulse arrival time, morphological and frequency photoplethysmography parameters and heart rate variability as characterization data. On the basis of the root mean square error and mean absolute error, our study showed that support vector regression gave the best performance for blood pressure estimation from noisy data, achieving an mean absolute error of 6.97 mmHg, which meets the level C criteria set by the British Hypertension Society. We demonstrate that ambulatory electrocardiography- photoplethysmography signals acquired by mobile discrete devices can be used to estimate blood pressure.

Accuracy of non-invasive cuffless blood pressure in the intensive care unit: Promises and challenges

Objective

Continuous non-invasive cuffless blood pressure (BP) monitoring may reduce adverse outcomes in hospitalized patients if accuracy is approved. We aimed to investigate accuracy of two different BP prediction models in critically ill intensive care unit (ICU) patients, using a prototype cuffless BP device based on electrocardiogram and photoplethysmography signals. We compared a pulse arrival time (PAT)-based BP model (generalized PAT-based model) derived from a general population cohort to more complex and individualized models (complex individualized models) utilizing other features of the BP sensor signals.

Methods

Patients admitted to an ICU with indication of invasive BP monitoring were included. The first half of each patient's data was used to train a subject-specific machine learning model (complex individualized models). The second half was used to estimate BP and test accuracy of both the generalized PAT-based model and the complex individualized models. A total of 7,327 measurements of 15 s epochs were included in pairwise comparisons across 25 patients.

Results

The generalized PAT-based model achieved a mean absolute error (SD of errors) of 7.6 (7.2) mmHg, 3.3 (3.1) mmHg and 4.6 (4.4) mmHg for systolic BP, diastolic BP and mean arterial pressure (MAP) respectively. Corresponding results for the complex individualized model were 6.5 (6.7) mmHg, 3.1 (3.0) mmHg and 4.0 (4.0) mmHg. Percentage of absolute errors within 10 mmHg for the generalized model were 77.6, 96.2, and 89.6% for systolic BP, diastolic BP and MAP, respectively. Corresponding results for the individualized model were 83.8, 96.2, and 94.2%. Accuracy was significantly improved when comparing the complex individualized models to the generalized PAT-based model in systolic BP and MAP, but not diastolic BP.

Conclusion

A generalized PAT-based model, developed from a different population was not able to accurately track BP changes in critically ill ICU patients. Individually fitted models utilizing other cuffless BP sensor signals significantly improved accuracy, indicating that cuffless BP can be measured non-invasively, but the challenge toward generalizable models remains for future research to resolve.

New Metrics from Polysomnography: Precision Medicine for OSA Interventions

Obstructive sleep apnea (OSA) is a highly preventable disease accompanied by multiple comorbid conditions. Despite the well-established cardiovascular and neurocognitive sequelae with OSA, the optimal metric for assessing the OSA severity and response to therapy remains controversial. Although overnight polysomnography (PSG) is the golden standard for OSA diagnosis, the abundant information is not fully exploited. With the development of deep learning and the era of big data, new metrics derived from PSG have been validated in some OSA consequences and personalized treatment. In this review, these metrics are introduced based on the pathophysiological mechanisms of OSA and new technologies. Emphasis is laid on the advantages and the prognostic value against apnea-hypopnea index. New classification criteria should be established based on these metrics and other clinical characters for precision medicine.

Intra-beat biomarker for accurate continuous non-invasive blood pressure monitoring

Accurate continuous non-invasive blood pressure (CNIBP) monitoring is the holy grail of digital medicine but remains elusive largely due to significant drifts in signal and motion artifacts that necessitate frequent device recalibration. To address these challenges, we developed a unique approach by creating a novel intra-beat biomarker (Diastolic Transit Time, DTT) to achieve highly accurate blood pressure (BP) estimations. We demonstrated our approach’s superior performance, compared to other common signal processing techniques, in eliminating stochastic baseline wander, while maintaining signal integrity and measurement accuracy, even during significant hemodynamic changes. We applied this new algorithm to BP data collected using non-invasive sensors from a diverse cohort of high acuity patients and demonstrated that we could achieve close agreement with the gold standard invasive arterial line BP measurements, for up to 20 min without recalibration. We established our approach's generalizability by successfully applying it to pulse waveforms obtained from various sensors, including photoplethysmography and capacitive-based pressure sensors. Our algorithm also maintained signal integrity, enabling reliable assessments of BP variability. Moreover, our algorithm demonstrated tolerance to both low- and high-frequency motion artifacts during abrupt hand movements and prolonged periods of walking. Thus, our approach shows promise in constituting a necessary advance and can be applied to a wide range of wearable sensors for CNIBP monitoring in the ambulatory and inpatient settings.