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Contact-free sensing gained much traction in the past decade. While remote monitoring of some parameters (heart rate) is approaching clinical levels of precision, others remain challenging (blood pressure). We investigated the feasibility of estimating blood pressure (BP) via pulse transit time (PTT) in a novel remote single-site manner, using a modified RGB camera. A narrow-band triple band-pass filter allowed us to measure the PTT between different skin layers, harvesting information from green and near-infrared wavelengths. The filter minimizes the inter-channel influence and band overlap, however, some overlap remains within the filter bands. We further resolve this using a color-channel model and a novel channel-separation method. Using the proposed setup and algorithm, we obtained multi-wavelength (MW) PTTs in an experiment inducing BP changes to 9 subjects. The results showed good absolute Pearson's correlation coefficient between both MW PTT and systolic BP (R = 0.61, p = 0.08) as well as diastolic BP (R = 0.54, p = 0.05), pointing to feasibility of the proposed novel remote MW BP estimation via PTT. This was further confirmed in a leave-one-subject-out experiment, where a simple Random Forest regression model achieved mean absolute errors of 3.59 and 2.63 mmHg for systolic and diastolic BP respectively. [ABSTRACT FROM AUTHOR]

Objective: This study extracts atherosclerosis indices from six channels of Cunkou and discusses the data distribution, aims to explore the connection between cardiology of modern medicine (MM) and traditional Chinese medicine (TCM) pulse diagnosis. Methods: We use a device capable of simultaneously collecting the pulse signals of the Cun, Guan and Chi to test the population participating in routine physical examinations. Firstly, we collected pressure pulse waves from six channels of Cunkou of 1045 healthy subjects (578 men and 467 women, average age=37.99±16.02 years). Secondly, we extracted the two most common arteriosclerosis indices pulse transit time (PTT) and Augmentation index (AIx) from six-channel pulse waves. Lastly, T-test and correlation test were taken to analyze the differences and relevance of the atherosclerosis indices extracted from six channels of Cunkou. Results: When analyzing AIx and PTT at different wrist, it was found that AIx of left wrist is significantly higher than that of right wrist (P0.05). In addition, regardless of gender, when the specific channel is not considered, AIx and PTT of each wrist are significantly correlated with age (P

Abstract Arterial pulse wave velocity (PWV) is recognized as a convenient method to assess peripheral vascular stiffness. This study explored the clinical characteristics of hand PWV (hPWV) and hand pulse transit time (hPTT) in healthy adults (sixty males = 42.4 ± 13.9 yrs; sixty-four females = 42.8 ± 13.9 yrs) voluntarily participated in this study. The arterial pulse waveform and the anatomical distance from the radial styloid process to the tip of the middle finger of both hands were recorded in the sitting position. The hPWV was calculated as the traversed distance divided by hPTT between those two points. Male subjects showed significantly greater hPWV, systolic blood pressure, and pulse pressure than age-matched female subjects, while the hPTT was not significantly different between genders. Multiple linear regression analysis showed that gender is a common determinant of hPWV and hPTT, and that age and heart rate (HR) were negatively correlated with hPWV and hPTT, respectively. We conclude that male subjects have greater hPWV than female subjects. Ageing is associated with decreased hPWV, while increased HR is associated with a smaller hPTT. The hPWV and hPTT might be used as non-invasive indices to characterise the ageing and arterial stiffness of peripheral blood vessels.

Arterial pulse wave velocity (PWV) is recognized as a convenient method to assess peripheral vascular stiffness. This study explored the clinical characteristics of hand PWV (hPWV) and hand pulse transit time (hPTT) in healthy adults (sixty males = 42.4 ± 13.9 yrs; sixty-four females = 42.8 ± 13.9 yrs) voluntarily participated in this study. The arterial pulse waveform and the anatomical distance from the radial styloid process to the tip of the middle finger of both hands were recorded in the sitting position. The hPWV was calculated as the traversed distance divided by hPTT between those two points. Male subjects showed significantly greater hPWV, systolic blood pressure, and pulse pressure than age-matched female subjects, while the hPTT was not significantly different between genders. Multiple linear regression analysis showed that gender is a common determinant of hPWV and hPTT, and that age and heart rate (HR) were negatively correlated with hPWV and hPTT, respectively. We conclude that male subjects have greater hPWV than female subjects. Ageing is associated with decreased hPWV, while increased HR is associated with a smaller hPTT. The hPWV and hPTT might be used as non-invasive indices to characterise the ageing and arterial stiffness of peripheral blood vessels. [ABSTRACT FROM AUTHOR]

Introduction: Sleep is the physiological state of the body where proper morphology and duration are indispensable for human functions throughout both, physical and mental spheres. Disordered breathing during sleep impairs its morphology and results in major disorders in any age group. Adverse effects of Obstructive Sleep Apnea Syndrome in children and poor availability of centers offering children's polysomnography call for a reliable and easily accessible screening method. Aim: The aim of the study were to evaluate the usefulness of pulse transit time in the diagnostics of disordered sleep breathing in children and to attempt to employ the parameter in screening tests. Pulse transit time is a physiological parameter determining the time needed for the pulse wave to travel between two measurement points. Material and methods: Enrolled in the retrospective study were 153 patients (100 boys and 53 girls) suspected of obstructive sleep apnea syndrome who underwent polysomnography at I. Mościcki ENT Hospital in Chorzów. Results: Statistically significant relations between apnea/hypopnea index and pulse transit time were observed in both, individual age groups and all of the patients. Pulse transit time results proved a negative correlation with apnea/hypopnea index values commonly accepted as a parameter concluding the polysomnography procedures. Conclusions: The results of the study indicate that pulse transit time measurements may find application in screening tests of sleep-disordered breathing in children. [ABSTRACT FROM AUTHOR]

In this study, we proposed a cuff‐less blood pressure measurement using a medical tape sensor with an embedded hetero‐core optical fiber. The tape sensor pasted on the skin can directly detect pulse wave signals with a simple measuring device. Pulse transit time (PTT) was obtained from the time difference between the two pulse waveforms at the neck and foot. Systolic blood pressure (SBP) was estimated from PTT and the regression equation. The result shows that the maximum prediction error was 3.8 mmHg in five healthy young subjects. The SBP data were evaluated using a Bland‐Altman method to assess the agreement of SBP data estimated by the proposed method with those measured by the cuff‐based electronic sphygmomanometer. The mean ± 1.96 standard deviation of the estimated SBP against the reference was −1.3 ± 5.1 mmHg. The results show that the proposed noninvasive blood pressure measurement method can measure blood pressure, and its accuracy is comparable to that of the cuff‐based electronic sphygmomanometer. [ABSTRACT FROM AUTHOR]

Comprehending the regulatory mechanisms influencing blood pressure control is pivotal for continuous monitoring of this parameter. Implementing a personalized machine learning model, utilizing data-driven features, presents an opportunity to facilitate tracking blood pressure fluctuations in various conditions. In this work, data-driven photoplethysmograph features extracted from the brachial and digital arteries of 28 healthy subjects were used to feed a random forest classifier in an attempt to develop a system capable of tracking blood pressure. We evaluated the behavior of this latter classifier according to the different sizes of the training set and degrees of personalization used. Aggregated accuracy, precision, recall, and F1-score were equal to 95.1%, 95.2%, 95%, and 95.4% when 30% of a target subject’s pulse waveforms were combined with five randomly selected source subjects available in the dataset. Experimental findings illustrated that incorporating a pre-training stage with data from different subjects made it viable to discern morphological distinctions in beat-to-beat pulse waveforms under conditions of cognitive or physical workload.

Introduction: Coronary heart disease (CAD) is the leading cause of morbidity and mortality worldwide, with India being the top cause. The Asian subcontinent accounts for 20% of the world's population, but 60% of the cardiovascular disease burden is due to genetic predisposition and environmental factors. Arterial stiffness increases with aging, accelerated by cardiovascular risk factors like hypertension and diabetes. Endothelial dysfunction in coronary arteries affects arterial stiffness and potentially reversible in atherogenesis. Pressure wave velocity (PWV) is a useful index for assessing endothelial status and large artery stiffness. Large artery stiffening can increase systolic blood pressure, left ventricular afterload, and left ventricular hypertrophy, potentially limiting coronary perfusion. Material and Methods: A prospective cross-sectional study was conducted at Sri Jayadeva Institute of Cardiovascular Sciences and Research Centre in Bangalore, India, from 1/6/2018 to 31/01/2019. Thirty patients with triple vessel disease and 30 age-matched controls underwent angiography at the institute. Echocardiography and Doppler evaluation assessed heart carotid pulse wave velocity and transit time. The study included patients with triple vessel disease and normal epicardial coronary arteries. The study excluded patients over 70 years old, having a heart rate above 100, having severe LV dysfunction below 30%, having chronic kidney disease, or having coronary artery stenosis. Demographic details, family history, medical history, clinical presentation, diagnostic tests, and blood tests were collected from patients. The study used descriptive and inferential statistical analysis, with continuous measurements presented as Mean ± SD and categorical measurements as Number (%). Results: The study involved 30 cases and 30 controls, with the majority of patients aged 50-70 years and controls aged 40-60 years. The gender distribution was predominantly males. Diagnosis distribution was similar between the two groups, with patients with EA, UA, or TMT positive being more likely to undergo angiography. BMI (kg/m2) distribution was similar between the two groups, with the majority of patients still overweight or obese but having normal BMIs. Pulse transit time (PTT) was significantly lower in the triple vessel disease case group compared to the control group with normal coronary angiography. Pulse wave velocity (PWV) increased in the case group compared to the control group. In contrast to the control group, 66% of the patients in the case group showed some kind of LV dysfunction due to more patients with myocardial infarction. Pulse wave velocity was significantly lower in the case group, with 20% and 63% shorter. LMCA distribution was also similar between the two groups, with patients with LMCA having lower pulse transit time (20%) and (63% shorter) compared to the control group. The study highlights the importance of assessing patients' angiography and LMCA distribution in determining the appropriate treatment for patients with LV dysfunction. Conclusion: Aortic PWV predicts future cardiovascular risk by accounting for known risk variables. It could be a valuable biomarker for intermediate risk, but randomized controlled studies are needed to prove its therapeutic utility before implementation. [ABSTRACT FROM AUTHOR]

Blood pressure (BP) is an essential vital sign related to many severe diseases, such as heart failure, kidney failure. Frequent BP detection can provide doctors more information to treat the disease. However, conventional at-home BP detection devices require completely blocking blood flow, which can lead to discomfort and disruption of normal activity when users want to perform frequent assessments. So a convenient solution should reduce the trouble of detecting BP in the daily life. In this work, we have designed and evaluated a smartphone-based BP detection application named WIB. WIB utilizes the smartphone’s acoustic sensors to obtain the chest motion caused by the heart beating and the smartphone’s camera to capture the pulse at the fingertip. We have recruited 30 volunteers who come from the author’s institution, to carry out comprehensive evaluations of WIB. We perform BP perturbation experiments to obtain different blood pressure data from these volunteers. The experiment results show that the average of Pearson correlation coefficient across all volunteers of the blood pressure estimation is 0.42–0.74 (α = 0.6, σ = 0.12), the average of RMSE across all volunteers is 4.2–8.8 mmHg (α = 5.8, σ = 1.8). [ABSTRACT FROM AUTHOR]

The influence of cuff inflations on night-time measurements during 24 h ambulatory blood pressure (BP) measurements is unknown. We investigated the potential effect of cuff inflations on sleep parameters using measurements taken simultaneously with a cuffless device using pulse-transit-time (PTT). On the first day of measurement, standard cuff-based 24 h BP and cuffless measurements were simultaneously performed on the right and left arms (CUFF/PTT-D). In this experiment, 1–2 days after the first measurement, the cuffless device was worn alone (PTT-D). Only data from the cuffless device were analyzed. The following mean sleep parameters were analyzed: mean systolic and diastolic BP, arousals, sleep efficiency, total arousals, arousal per hour, and desaturations. In total, 21 individuals were prospectively enrolled. The mean (SD) age was 47 (±15) years, and 57% were female. The mean systolic asleep BP during CUFF/PTT-D and during PTT-D were 131 (±21) and 131 (±26) mmHg, respectively. The mean diastolic asleep BP values during CUFF/PTT-D and during PTT-D were 80 (±14) and 84 (±14) mmHg, respectively (p = 0.860, p = 0.100, respectively). Systolic and diastolic asleep mean difference was 0.1 (±18.0) and −3.6 (±9.8) mmHg, respectively. There were significantly more total arousals during PTT-D (p = 0.042). There were no significant differences seen in sleep efficiency (p = 0.339) or desaturations (p = 0.896) between the two measurement periods. We could not show any significant impact from cuff inflations during sleep, as documented by PTT-D measurements. [ABSTRACT FROM AUTHOR]

Adequate oxygen in red blood cells carrying through the body to the heart and brain is important to maintain life. For those patients requiring blood, blood transfusion is a common procedure in which donated blood or blood components are given through an intravenous line. However, detecting the need for blood transfusion is time-consuming and sometimes not easily diagnosed, such as internal bleeding. This study considered physiological signals such as electrocardiogram (ECG), photoplethysmogram (PPG), blood pressure, oxygen saturation (SpO2), and respiration, and proposed the machine learning model to detect the need for blood transfusion accurately. For the model, this study extracted 14 features from the physiological signals and used an ensemble approach combining extreme gradient boosting and random forest. The model was evaluated by a stratified five-fold crossvalidation: the detection accuracy and area under the receiver operating characteristics were 92.7% and 0.977, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes to remotely estimate a human subject's blood pressure using a millimeter-wave radar system. High blood pressure is a critical health threat that can lead to diseases including heart attacks, strokes, kidney disease, and vision loss. The commonest method of measuring blood pressure is based on a cuff that is contact-based, non-continuous, and cumbersome to wear. Continuous remote monitoring of blood pressure can facilitate early detection and treatment of heart disease. This paper investigates the possibility of using millimeter-wave frequency-modulated continuous-wave radar to measure the heart blood pressure by means of pulse wave velocity (PWV). PWV is known to be highly correlated with blood pressure, which can be measured by pulse transit time. We measured PWV using a two-millimeter wave radar focused on the subject's chest and wrist. The measured time delay provided the PWV given the length from the chest to the wrist. In addition, we analyzed the measured radar signal from the wrist because the shape of the pulse wave purveyed information on blood pressure. We investigated the area under the curve (AUC) as a feature and found that AUC is strongly correlated with blood pressure. In the experiment, five human subjects were measured 50 times each after performing different activities intended to influence blood pressure. We used artificial neural networks to estimate systolic blood pressure (SBP) and diastolic blood pressure (SBP) with both PWV and AUC as inputs. The resulting root mean square errors of estimated blood pressure were 3.33 mmHg for SBP and 3.14 mmHg for DBP. [ABSTRACT FROM AUTHOR]

Blood pressure (BP) surrogates, such as pulse transit time (PTT) or pulse arrival time (PAT), have been intensively explored with the goal of achieving cuffless, continuous, and accurate BP inference. In order to estimate BP, a one-point calibration strategy between PAT and BP is typically used. Recent research focuses on advanced calibration procedures exploiting the cuff inflation process to improve calibration robustness by active and controlled modulation of peripheral PAT, as measured via plethysmograph (PPG) and electrocardiogram (ECG) combination. Such methods require a detailed understanding of the mechanisms behind the vasculature's response to cuff inflation; for this, a model has recently been developed to infer the PAT-BP calibration from measured cuff-induced vasculature changes. The model, while promising, is still preliminary and only partially validated; in-depth analysis and further developments are still needed. Therefore, this work aims to improve our understanding of the cuff-vasculature interaction in this model; we seek to define potential opportunities and to highlight which aspects may require further study. We compare model behaviors with clinical data samples based on a set of observable characteristics relevant for BP inference and calibration. It is found that the observed behaviors are qualitatively well represented with the current simulation model and complexity, with limitations regarding the prediction of the onset of the distal arm dynamics and behavior changes at high cuff pressures. Additionally, a sensitivity analysis of the model's parameter space is conducted to show the factors that influence the characteristics of its observable outputs. It was revealed that easily controllable experimental variables, such as lateral cuff length and inflation rate, have a significant impact on cuff-induced vasculature changes. An interesting dependency between systemic BP and cuff-induced distal PTT change is also found, revealing opportunities for improved methods for BP surrogate calibration. However, validation via patient data shows that this relation does not hold for all patients, indicating required model improvements to be validated in follow up studies. These results provide promising directions to improve the calibration process featuring cuff inflation towards accurate and robust non-invasive blood pressure estimation. [ABSTRACT FROM AUTHOR]

The relationship between sleep dynamics and blood pressure (BP) changes is well established. Moreover, sleep efficiency and wakefulness during sleep (WASO) events have a significant impact on BP dipping. Despite this knowledge, there is limited research on the measurement of sleep dynamics and continuous blood pressure (CBP). This study aims to explore the relationship between sleep efficiency and cardiovascular function indicators such as pulse transit time (PTT), as a biomarker of CBP, and heart rate variability (HRV), measured using wearable sensors. The results of the study conducted on 20 participants at the UConn Health Sleep Disorders Center suggest a strong linear relationship between sleep efficiency and changes in PTT (r2 = 0.8515) and HRV during sleep (r2 = 5886). The findings of this study contribute to our understanding of the relationship between sleep dynamics, CBP, and cardiovascular health. [ABSTRACT FROM AUTHOR]

The increasing incidence of cardiovascular diseases (CVDs) is reflected in additional costs for healthcare systems all over the world. To date, pulse transit time (PTT) is considered a key index of cardiovascular health status and for diagnosis of CVDs. In this context, the present study focuses on a novel image analysis-based method for PTT estimation through the application of equivalent time sampling. The method, which post-processes color Doppler videos, was tested on two different setups: a Doppler flow phantom set in pulsatile mode and an in-house arterial simulator. In the former, the Doppler shift was due to the echogenic properties of the blood mimicking fluid only, since the phantom vessels are non-compliant. In the latter, the Doppler signal relied on the wall movement of compliant vessels in which a fluid with low echogenic properties was pumped. Therefore, the two setups allowed the measurement of the flow average velocity (FAV) and the pulse wave velocity (PWV), respectively. Data were collected through an ultrasound diagnostic system equipped with a phased array probe. Experimental outcomes confirm that the proposed method can represent an alternative tool for the local measurement of both FAV in non-compliant vessels and PWV in compliant vessels filled with low echogenic fluids. [ABSTRACT FROM AUTHOR]

We perform a novel comparative analysis between optically and mechanically derived pulse transit time (PTT) that is universally employed technique for cuffless blood pressure (BP) estimation. For data collection two inline photoplethysmogram (PPG) sensors were mounted at the distal and proximal phalanxes of the index finger of each subject and top each PPG sensor fixture a finger ballistocardiogram (BPP) sensors were clamped. The clamped stacking of the BPP sensors over the PPG sensors provided vertical aligned acquisition of the blood flow waveform through the radial artery for both sensors. The analysis of variance (ANOVA) between PTT derived from the BPP and PPG sensors resulted in a statistically significant difference at $p < 0.05$ . The PTT derived from the BPP sensors showed higher values,17.8 milliseconds on average, than the PTT derived from the PPG sensors. Higher accuracy PTT values will improve the estimation of cuffless BP and thus has the potential to revolutionize the technology.

Abstract Airflow limitation in patients with obstructive sleep apnea (OSA) leads to arousal, increased sympathetic nervous system activity, and elevated blood pressure, which causes a decrease in pulse transit time (PTT). The present study aims to evaluate the effect of CPAP therapy on PTT in patients with moderate to severe OSA. This was a cross‐sectional study. Split‐night polysomnography (PSG) study was performed for each participant with apnea‐hypopnea index (AHI) ≥ 15 before and during CPAP therapy. The PTT was calculated as the time interval between the R wave of the electrocardiogram and the following arrival point in fingertip photoplethysmography. PTT drop was defined as a fall in the PTT curve of ≥15 ms lasting at least for 3 s and at most for 30 s. PTT drop index was defined as the number of drops in PTT that occur per hour of sleep. A total of 30 patients were included. PTT significantly increased, and PTT drop index significantly decreased during CPAP therapy (P

Wearable medical devices (WMDs) for healthcare applications have become ubiquitous, allowing remote, at-home, and real-time chronic monitoring that have significantly decongested clinics. These WMDs permitted the monitoring of several physiological parameters, such as heart and respiration rates, SPO2, temperature, and energy expenditure during activities of daily living (ADLs) or fitness activities. While the measurement of these parameters has become common, full noninvasive, unobtrusive, and real-time blood pressure (BP) monitoring remains elusive owing to BP's complex dynamics. To bring this into fruition, several works have been conducted combining different biosignals to indirectly extract BP by using PTT. Unlike previous works, we considered PTT variability by averaging it over discrete durations to account for BP variability for a more accurate estimation. PTTs were obtained using electrocardiograph (ECG) and reflective photoplethysmograph (rPPG) signals extracted by a wearable device attached to a single site on the upper arm. Our results show a significant correlation between average PTT and the BP measured using auscultation in a trial study. The developed system has potential for chronic, noninvasive, and cuff-less blood pressure monitors (BPMs) for localized and single-site implementations. Meanwhile, real-time data from the wearable device may be accessed via a remote desktop or a mobile phone application. [ABSTRACT FROM AUTHOR]

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 (SBPIBP, DBPIBP, and MAPIBP, respectively) and to detect SBPIBP 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 SBPIBP was determined by area under the curve (reported as AUC, sensitivity, specificity). Results: Significant correlations between 1/PTT and SBPIBP were found for PES (r = 0.64) and PCS (r = 0.55) (p < 0.01), as well as MAPIBP/DBPIBP for PES (r = 0.6/0.55) and PCS (r = 0.5/0.45) (p < 0.05). A 7% decrease in 1/PTTPES predicted a 30% SBPIBP decrease (0.82, 0.76, 0.76), while a 5.6% increase predicted a 30% SBPIBP increase (0.75, 0.7, 0.68). A 6.6% decrease in 1/PTTPCS detected a 30% SBPIBP decrease (0.81, 0.72, 0.8), while a 4.8% 1/PTTPCS increase detected a 30% SBPIBP 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 SBPIBP. Thus, PES/PCS as a novel sensor technology may augment intraoperative hemodynamic monitoring during major surgery. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Telemedicine is a word that the world has been hearing about since the early days of the telephonic technology used to exchange information for delivering medical care. Medical practitioners need the holistic information of the patients to treat them effectively. Telemedicine is relatively limited compared to direct appointments; hence a prototype is needed to create an environment that is identical to the direct appointments where the practitioners can see the patient and their physiological data. A novel prototype with state-of-the-art software and hardware is developed for establishing a holistic telemedicine environment in this work. The designed system measures the skin temperature, SpO2, pulse rate, heart rate, breath rate, and Non-invasive Blood Pressure (NIBP). The SpO2 of the system is measured with Beer-Lambert's law, and the NIBP is measured using a single synchronous ECG and PPG sensor. Quality video conferencing is provided between the patient and the practitioners. [ABSTRACT FROM AUTHOR]

Blood pressure (BP) surrogates, such as pulse transit time (PTT) or pulse arrival time (PAT), have been intensively explored with the goal of achieving cuffless, continuous, and accurate BP inference. In order to estimate BP, a one-point calibration strategy between PAT and BP is typically used. Recent research focuses on advanced calibration procedures exploiting the cuff inflation process to improve calibration robustness by active and controlled modulation of peripheral PAT, as measured via plethysmograph (PPG) and electrocardiogram (ECG) combination. Such methods require a detailed understanding of the mechanisms behind the vasculature’s response to cuff inflation; for this, a model has recently been developed to infer the PAT-BP calibration from measured cuff-induced vasculature changes. The model, while promising, is still preliminary and only partially validated; in-depth analysis and further developments are still needed. Therefore, this work aims to improve our understanding of the cuff-vasculature interaction in this model; we seek to define potential opportunities and to highlight which aspects may require further study. We compare model behaviors with clinical data samples based on a set of observable characteristics relevant for BP inference and calibration. It is found that the observed behaviors are qualitatively well represented with the current simulation model and complexity, with limitations regarding the prediction of the onset of the distal arm dynamics and behavior changes at high cuff pressures. Additionally, a sensitivity analysis of the model’s parameter space is conducted to show the factors that influence the characteristics of its observable outputs. It was revealed that easily controllable experimental variables, such as lateral cuff length and inflation rate, have a significant impact on cuff-induced vasculature changes. An interesting dependency between systemic BP and cuff-induced distal PTT change is also found, revealing opportunities for improved methods for BP surrogate calibration. However, validation via patient data shows that this relation does not hold for all patients, indicating required model improvements to be validated in follow up studies. These results provide promising directions to improve the calibration process featuring cuff inflation towards accurate and robust non-invasive blood pressure estimation.

Research has shown that pulse transit time (PTT), which is the time delay between the electrocardiogram (ECG) signal and the signal from a photoplethysmogram (PPG) sensor, can be used to estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP) without the need for a cuff. However, the LED of the PPG sensor requires the precise adjustment of both light intensity and light absorption rates according to the contact status of the light-receiving element. This results in the need for regular calibration. In this study, we propose a cuffless blood pressure monitor that measures real-time blood pressure using a microphone instead of a PPG sensor. The blood pulse wave is measured in the radial artery of the wrist using a microphone that can directly measure the sound generated by a body rather than sending energy inside the body and receiving a returning signal. Our blood pressure monitor uses the PTT between the R-peak of the ECG signal and two feature points of the blood pulse wave in the radial artery of the wrist. ECG electrodes and circuits were fabricated, and a commercial microelectromechanical system (MEMS) microphone was used as the microphone to measure blood pulses. The peak points of the blood pulse from the microphone were clear, so the estimated SBP and DBP could be obtained from each ECG pulse in real time, and the resulting estimations were similar to those made by a commercial cuff blood pressure monitor. Since neither the ECG electrodes nor the microphone requires calibration over time, the real-time cuffless blood pressure monitor does not require calibration. Using the developed device, blood pressure was measured three times daily for five days, and the mean absolute error (MAE) and standard deviation (SD) of the SBP and DBP were found to be 2.72 ± 3.42 mmHg and 2.29 ± 3.53 mmHg, respectively. As a preliminary study for proof-of-concept, these results were obtained from one subject. The next step will be a pilot study on a large number of subjects. [ABSTRACT FROM AUTHOR]

Unlike traditional pulse transit time (PTT), continuous PTT (CPTT) can be used to calculate PTT from all samples within the cardiac cycle. It has the potential to be utilized for continuous blood pressure (BP) estimation. This study evaluated the feasibility of CPTT as a non‐invasive consecutive blood pressure estimation method in 20 volunteers. The CPTT was calculated with a time delay in all discrete samples of photoplethysmograms measured at two different body sites. BP was then calculated with a regression equation. For comparative evaluation, BP based on PTT was also estimated. Continuous blood pressure was measured using a non‐invasive volume clamp BP monitoring device. Four types of BP measurement, systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP), and pulse pressure (PP), were estimated using PTT and CPTT. Correlation coefficients and root‐mean‐squared‐error (RMSE) were used for evaluating BP estimation performance. For estimating SBP, DBP, PP, and MAP, PTT‐based BP estimation showed correlations of.407,.373,.410, and.286, respectively, and CPTT‐based BP estimation showed correlations of.436,.446,.506, and.097, respectively. With PTT‐based estimation, the RMSE between the estimated BP and the baseline BP was 5.44 ± 1.56 mmHg for SBP, 3.14 ± 0.46 mmHg for DBP, 3.66 ± 0.70 mmHg for MAP, and 3.73 ± 1.31 mmHg for PP. The estimated BP using CPTT showed RMSE of 5.36 ± 1.39 mmHg for SBP, 3.02 ± 0.49 mmHg for SBP, 3.44 ± 0.63 mmHg for MAP, and 3.91 ± 1.41 mmHg for PP. We present an innovative application of a non‐invasive blood pressure (BP) estimation method based on continuous pulse transit time (CPTT). Conventional pulse transit time (PTT)‐based BP estimation uses a PTT value per pulse that does not reflect the continuous nature of blood flow. We propose an alternative method to estimate BP using PTT measured continuously and we validated its feasibility. CPTT‐based BP estimation performance was equal to or higher than that of conventional PTT. [ABSTRACT FROM AUTHOR]

Hypertension diagnosis is one of the most common and important procedures in everyday clinical practice. Its applicability depends on correct and comparable measurements. Cuff-based measurement paradigms have dominated ambulatory blood pressure (BP) measurements for multiple decades. Cuffless and non-invasive methods may offer various advantages, such as a continuous and undisturbing measurement character. This review presents a conceptual overview of recent advances in the field of cuffless measurement paradigms and possible future developments which would enable cuffless beat–to–beat BP estimation paradigms to become clinically viable. It was refrained from a direct comparison between most studies and focussed on a conceptual merger of the ideas and conclusions presented in landmark scientific literature. There are two main approaches to cuffless beat–to–beat BP estimation represented in the scientific literature: First, models based on the physiological understanding of the cardiovascular system, mostly reliant on the pulse wave velocity combined with additional parameters. Second, models based on Deep Learning techniques, which have already shown great performance in various other medical fields. This review wants to present the advantages and limitations of each approach. Following this, the conceptional idea of unifying the benefits of physiological understanding and Deep Learning techniques for beat–to–beat BP estimation is presented. This could lead to a generalised and uniform solution for cuffless beat–to–beat BP estimations. This would not only make them an attractive clinical complement or even alternative to conventional cuff-based measurement paradigms but would substantially change how we think about BP as a fundamental marker of cardiovascular medicine. This concept review wants to highlight the current state of non-invasive cuffless continuous blood pressure estimation. Cuffless blood pressure measurement devices usually rely on pulse wave velocity. Pulse wave velocity is mostly calculated via measuring pulse arrival time. Using pulse transit time instead of pulse arrival time showed improved results. Additional biomarkers like heart rate, photoplethysmogram intensity ratio or heart rate power spectrum ratio can be used to improve measurement precision. For cuffless and cuff-based devices intended for 24-hour BP measurements, a more refined validation protocol is required. The ESH assesses the measurement accuracy of cuffless devices as unclear and does not recommend hypertension diagnosis based on cuffless devices. Machine Learning and Deep Learning applications are a powerful tool to generate complex algorithms, which can be used to estimate blood pressure. Selecting biomarkers like pulse wave velocity, heart rate, etc. as input features for Deep Learning systems would be a very promising approach to measure blood pressure more precise. [ABSTRACT FROM AUTHOR]

The influence of cuff inflations on night-time measurements during 24 h ambulatory blood pressure (BP) measurements is unknown. We investigated the potential effect of cuff inflations on sleep parameters using measurements taken simultaneously with a cuffless device using pulse-transit-time (PTT). On the first day of measurement, standard cuff-based 24 h BP and cuffless measurements were simultaneously performed on the right and left arms (CUFF/PTT-D). In this experiment, 1–2 days after the first measurement, the cuffless device was worn alone (PTT-D). Only data from the cuffless device were analyzed. The following mean sleep parameters were analyzed: mean systolic and diastolic BP, arousals, sleep efficiency, total arousals, arousal per hour, and desaturations. In total, 21 individuals were prospectively enrolled. The mean (SD) age was 47 (±15) years, and 57% were female. The mean systolic asleep BP during CUFF/PTT-D and during PTT-D were 131 (±21) and 131 (±26) mmHg, respectively. The mean diastolic asleep BP values during CUFF/PTT-D and during PTT-D were 80 (±14) and 84 (±14) mmHg, respectively (p = 0.860, p = 0.100, respectively). Systolic and diastolic asleep mean difference was 0.1 (±18.0) and −3.6 (±9.8) mmHg, respectively. There were significantly more total arousals during PTT-D (p = 0.042). There were no significant differences seen in sleep efficiency (p = 0.339) or desaturations (p = 0.896) between the two measurement periods. We could not show any significant impact from cuff inflations during sleep, as documented by PTT-D measurements.

This study presents a wearable neckband device for continuous cuffless blood pressure (BP) monitoring, where reliable neck-based BP measurement is achieved by utilizing the electrocardiogram (ECG) and impedance plethysmogram (IPG). Optimal composition and position of sensing electrodes around the neck were decided with consideration for physiological characteristics of the human body, and the core parameter of IPG magnitude was experimentally selected to improve the BP estimation accuracy. For its small-featured noises-tolerant detection capability, a customized readout integrated circuit (ROIC) was developed, which includes two kinds of analog front-ends (AFEs) for biopotential and bio-impedance. Both AFEs are proposed to include an attenuator-assisted hybrid DC-servo loop (DSL) structure, achieving precise pulse transit time (PTT) calculation by lowering the high-pass filtering cutoff frequency and also stable signal acquisition by widening the offset-cancellation range. Real-time PTT monitoring and BP estimation are wirelessly conducted, and its continuous operation time with 350-mAh battery capacity is longer than 5 hours. Efficacy of the proposed neckband BP device was evaluated for 8 healthy subjects under the physical stress condition, where the mean correlation coefficient and the standard deviation were achieved as 0.8508 and 4.17mmHg respectively.

Pulse arrival time is the time elapsed between the R-wave of electrocardiogram and systolic peak in peripheral pulse obtained by any of the plethysmographic methods. Similarly, differential pulse arrival time, also known as pulse transit time, is the time elapsed between systolic peaks of proximal and distal peripheral pulse recordings in an extremity. Distance between the proximal and distal site in the extremity (in meters) divided by differential pulse arrival time (in seconds) gives arterial pulse wave velocity in the limb segment. Differential pulse arrival time has been used to discriminate between an aortic or arterial block from generalized atherosclerosis in aortic and arterial occlusive diseases for nearly four decades. All along there have been efforts to monitor beat-to-beat blood pressure with the help of these time intervals and other pulse parameters. Encouraging correlation has been observed with that obtained by Finapres. Recently pulse arrival time has been explored for the prompt detection of sudden hypertensive episodes during laryngeal microsurgery, for detection of mental stress, monitoring of baroreflex sensitivity, and real-time monitoring of blood pressure. This paper briefly describes the measurement technique of pulse arrival time and an overview of its clinical applications.

Pulse transit time (PTT) and blood pressure (BP) are widely used to quantify arterial characteristics. Arm position influences arterial BP and peripheral PTT. This study aims to quantify the relationship between PTT changes with internal vascular pressure variations induced by the arm moving. With left arm at horizontal position as reference and the right arm moving from 90 to 45, 0, −45, and −90° respectively, PTT difference was calculated by the difference of the pulse foot between right arm and left arm within the same heartbeat. The change in the BP was calculated from the gravitational effect with the measured arm length. Our results showed that the change in PTT with arm elevating is more obvious than that with arm lowering, indicating the different relationship between PTT changes due to the internal BP changes. This can help in understanding the inherent physiological/pathological mechanism of cardiovascular system.

Recently, many researches have been conducted to estimate blood pressure using ECG(Electrocardiogram) and PPG(Photoplentysmography) signals. In this paper, we designed and implemented a mobile system to monitor blood pressure in real time by using 1-D convolutional neural networks. The proposed model consists of deep 11 layers which can learn to extract various features of ECG and PPG signals. The simulation results show that the more the number of convolutional kernels the learned neural network has, the more detailed characteristics of ECG and PPG signals resulted in better performance with reduced mean square error compared to linear regression model. With receiving measurement signals from wearable ECG and PPG sensor devices attached to the body, the developed system receives measurement data transmitted through Bluetooth communication from the devices, estimates systolic and diastolic blood pressure values using a learned model and displays its graph in real time. [ABSTRACT FROM AUTHOR]

Pulse photoplethysmography (PPG) is a simple and economical technique for obtaining cardiovascular information. In fact, PPG has become a very popular technology among wearable devices. However, the PPG signal is well-known to be very vulnerable to artifacts, and a good quality signal cannot be expected for most of the time in daily life. The percentage of time that a given measurement can be estimated (e.g., pulse rate) is denoted coverage (C), and it is highly dependent on the subject activity and on the configuration of the sensor, location, and stability of contact. This work aims to quantify the coverage of PPG sensors, using the simultaneously recorded electrocardiogram as a reference, with the PPG recorded at different places in the body and under different stress conditions. While many previous works analyzed the feasibility of PPG as a surrogate for heart rate variability analysis, there exists no previous work studying coverage to derive other cardiovascular indices. We report the coverage not only for estimating pulse rate (PR) but also for estimating pulse arrival time (PAT) and pulse amplitude variability (PAV). Three different datasets are analyzed for this purpose, consisting of a tilt-table test, an acute emotional stress test, and a heat stress test. The datasets include 19, 120, and 51 subjects, respectively, with PPG at the finger and at the forehead for the first two datasets and at the earlobe, in addition, for the latter. C ranges from 70% to 90% for estimating PR. Regarding the estimation of PAT, C ranges from 50% to 90%, and this is very dependent on the PPG sensor location, PPG quality, and the fiducial point (FP) chosen for the delineation of PPG. In fact, the delineation of the FP is critical in time for estimating derived series such as PAT due to the small dynamic range of these series. For the estimation of PAV, the C rates are between 70% and 90%. In general, lower C rates have been obtained for the PPG at the forehead. No difference in C has been observed between using PPG at the finger or at the earlobe. Then, the benefits of using either will depend on the application. However, different C rates are obtained using the same PPG signal, depending on the FP chosen for delineation. Lower C is reported when using the apex point of the PPG instead of the maximum flow velocity or the basal point, with a difference from 1% to even 10%. For further studies, each setup should first be analyzed and validated, taking the results and guidelines presented in this work into account, to study the feasibility of its recording devices with respect to each specific application. [ABSTRACT FROM AUTHOR]

Airflow limitation in patients with obstructive sleep apnea (OSA) leads to arousal, increased sympathetic nervous system activity, and elevated blood pressure, which causes a decrease in pulse transit time (PTT). The present study aims to evaluate the effect of CPAP therapy on PTT in patients with moderate to severe OSA. This was a cross‐sectional study. Split‐night polysomnography (PSG) study was performed for each participant with apnea‐hypopnea index (AHI) ≥ 15 before and during CPAP therapy. The PTT was calculated as the time interval between the R wave of the electrocardiogram and the following arrival point in fingertip photoplethysmography. PTT drop was defined as a fall in the PTT curve of ≥15 ms lasting at least for 3 s and at most for 30 s. PTT drop index was defined as the number of drops in PTT that occur per hour of sleep. A total of 30 patients were included. PTT significantly increased, and PTT drop index significantly decreased during CPAP therapy (P < 0.001). PTT was significantly correlated to sleep efficiency (rs = −0.376, P = 0.049) and oxygen desaturation index (ODI) (rs = −0.428, P = 0.018). PTT drop index was strongly correlated to AHI (rs = 0.802, P < 0.001), respiratory disturbance index (RDI) (rs = 0.807, P < 0.001), ODI (rs = 0.693, P < 0.001), arousal index (rs = 0.807, P < 0.001), and periodic leg movement (PLM) index (rs = 0.400, P = 0.035). Overall, the findings from this study indicated that the PTT drop index is a non‐invasive and useful marker for evaluating the severity of OSA and the effectiveness of treatment in patients with moderate to severe OSA. [ABSTRACT FROM AUTHOR]

Objective: Pulse transit time (PTT) is a popular indicator of blood pressure (BP) changes. However, the relationship between PTT and BP is somehow individual dependent, resulting in the inaccuracy of PTT-based BP estimation. Confounding factors, e.g., heart rate (HR), of PTT and BP could be the primary cause. In this study we attempt to explore the impact of HR as a window to look at the influence of confounding factors on the relationship between PTT and BP., Approach: We investigated the relationship between PTT and systolic BP (SBP) at different HR levels by introducing the heterogeneous treatment effects (HTE) as a quantitative indicator. Compared to the average HR calculated using traditional indicators (e. g. regression coefficient, correlation coefficient), the HTE calculation method can compute the relationship between PTT and SBP at different HR levels, and reduce the influence of confounding factors., Main Results: We analyzed the HTE of PTT and SBP of 47 subjects who are resting healthy young people with varying levels of HR. The results showed that the strength of the HTE of PTT and SBP varied with HR, indicating that the strength of the causal relationship between PTT and SBP is subject to HR levels. Whereas the correlation between SBP and PTT was individual dependent; either the strength or the direction of the correlation can vary with HR. We further investigated the group in which PTT and SBP exhibited a negative correlation, and found that about 50% of the subjects showed enhanced strength of HTE in with an increase in HR and the remaining showed the opposite., Significance: This study means that HR needs to be considered when PTT is used as an indicator of SBP., (© 2024 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Mental stress triggers positive inotropic and chronotropic effects as well as peripheral vasoconstriction. This alters the pulse arrival time (PAT), the duration between electrical excitation of the ventricles and arrival of the pulse wave in the periphery. We conducted a study to examine PAT during five rest blocks and under mental stress utilizing the Mannheim Multicomponent Stress Test. Electrocardiograms as well as finger and earlobe photoplethysmograms were recorded. PAT was calculated for over 135,000 heartbeats from 42 healthy volunteers as the time duration between the R peak in the electrocardiogram and the following pulse onset in the respective photoplethysmogram. To identify the effect of mental stress, block-wise PAT means were statistically analyzed with repeated measures ANOVA. The analyses showed significant differences between the block means for both PAT measures (p < 0.001). Post-hoc tests revealed significantly reduced PAT during the stress block compared to all rest blocks for both PAT measures (p < 0.001). We found no significant differences between the rest blocks. Our results support that PAT is a sensitive vital parameter for the detection of mental stress in healthy volunteers. This holds true for both measurement positions, the finger and the earlobe.

Commonly used blood pressure measurement devices have noticeable limitations in accuracy, measuring time, comfort or safety. To overcome these limitations, we developed and tested a surrogate-based, non-invasive blood pressure measurement method using an RGB-camera. Our proposed method employs the relation between the pulse transit time (PTT) and blood pressure. Two remote photoplethysmography (rPPG) signals at different distances from the heart are extracted to calculate the temporal delay of the pulse wave. In order to establish the correlation between the PTT values and the blood pressure, a regression model is trained and evaluated. Tests were performed with five subjects, where each subject was recorded fifteen times for 30 seconds. Since the physiological parameters of the cardiac system are different for each person, an individual calibration is required to obtain the systolic and diastolic blood pressure from the PTT values. The calibration results are limited by the small number of samples and the accuracy of the reference system. However, our results show a strong correlation between the PTT values and the blood pressure and we obtained a mean error of 0.18 +/- 5.50 mmHg for the diastolic blood pressure and 0.01 +/- 7.71 mmHg for the systolic pressure, respectively.

Pulse photoplethysmography (PPG) is a simple and economical technique for obtaining cardiovascular information. In fact, PPG has become a very popular technology among wearable devices. However, the PPG signal is well-known to be very vulnerable to artifacts, and a good quality signal cannot be expected for most of the time in daily life. The percentage of time that a given measurement can be estimated (e.g., pulse rate) is denoted coverage (C), and it is highly dependent on the subject activity and on the configuration of the sensor, location, and stability of contact. This work aims to quantify the coverage of PPG sensors, using the simultaneously recorded electrocardiogram as a reference, with the PPG recorded at different places in the body and under different stress conditions. While many previous works analyzed the feasibility of PPG as a surrogate for heart rate variability analysis, there exists no previous work studying coverage to derive other cardiovascular indices. We report the coverage not only for estimating pulse rate (PR) but also for estimating pulse arrival time (PAT) and pulse amplitude variability (PAV). Three different datasets are analyzed for this purpose, consisting of a tilt-table test, an acute emotional stress test, and a heat stress test. The datasets include 19, 120, and 51 subjects, respectively, with PPG at the finger and at the forehead for the first two datasets and at the earlobe, in addition, for the latter. C ranges from 70% to 90% for estimating PR. Regarding the estimation of PAT, C ranges from 50% to 90%, and this is very dependent on the PPG sensor location, PPG quality, and the fiducial point (FP) chosen for the delineation of PPG. In fact, the delineation of the FP is critical in time for estimating derived series such as PAT due to the small dynamic range of these series. For the estimation of PAV, the C rates are between 70% and 90%. In general, lower C rates have been obtained for the PPG at the forehead. No difference in C has been observed between using PPG at the finger or at the earlobe. Then, the benefits of using either will depend on the application. However, different C rates are obtained using the same PPG signal, depending on the FP chosen for delineation. Lower C is reported when using the apex point of the PPG instead of the maximum flow velocity or the basal point, with a difference from 1% to even 10%. For further studies, each setup should first be analyzed and validated, taking the results and guidelines presented in this work into account, to study the feasibility of its recording devices with respect to each specific application.

This paper proposes to remotely estimate a human subject’s blood pressure using a millimeter-wave radar system. High blood pressure is a critical health threat that can lead to diseases including heart attacks, strokes, kidney disease, and vision loss. The commonest method of measuring blood pressure is based on a cuff that is contact-based, non-continuous, and cumbersome to wear. Continuous remote monitoring of blood pressure can facilitate early detection and treatment of heart disease. This paper investigates the possibility of using millimeter-wave frequency-modulated continuous-wave radar to measure the heart blood pressure by means of pulse wave velocity (PWV). PWV is known to be highly correlated with blood pressure, which can be measured by pulse transit time. We measured PWV using a two-millimeter wave radar focused on the subject’s chest and wrist. The measured time delay provided the PWV given the length from the chest to the wrist. In addition, we analyzed the measured radar signal from the wrist because the shape of the pulse wave purveyed information on blood pressure. We investigated the area under the curve (AUC) as a feature and found that AUC is strongly correlated with blood pressure. In the experiment, five human subjects were measured 50 times each after performing different activities intended to influence blood pressure. We used artificial neural networks to estimate systolic blood pressure (SBP) and diastolic blood pressure (SBP) with both PWV and AUC as inputs. The resulting root mean square errors of estimated blood pressure were 3.33 mmHg for SBP and 3.14 mmHg for DBP.

In recent decades, cuffless blood pressure monitoring technology has been a point of research in the field of health monitoring and public media. Based on the web of science database, this paper evaluated the publications in the field from 1990 to 2020 using bibliometric analysis, described the developments in recent years, and presented future research prospects in the field. Through the comparative analysis of keywords, citations, H-index, journals, research institutions, national authors and reviews, this paper identified research hotspots and future research trends in the field of cuffless blood pressure monitoring. From the results of the bibliometric analysis, innovative methods such as machine learning technologies related to pulse transmit time and pulse wave analysis have been widely applied in blood pressure monitoring. The 2091 articles related to cuffless blood pressure monitoring technology were published in 1131 journals. In the future, improving the accuracy of monitoring to meet the international medical blood pressure standards, and achieving portability and miniaturization will remain the development goals of cuffless blood pressure measurement technology. The application of flexible electronics and machine learning strategy in the field will be two major development directions to guide the practical applications of cuffless blood pressure monitoring technology. [ABSTRACT FROM AUTHOR]

The present study aims to analyze the systemic response to auditory stimulation by means of hemodynamic (cephalic and peripheral) and autonomic responses in a broad range of auditory intensities (70.9, 77.9, 84.5, 89.5, 94.5 dBA). This approach could help to understand the possible influence of the autonomic nervous system on the cephalic blood flow. Twenty‐five subjects were exposed to auditory stimulation while electrodermal activity (EDA), photoplethysmography (PPG), electrocardiogram, and functional near‐infrared spectroscopy signals were recorded. Seven trials with 20 individual tones, each for the five intensities, were presented. The results showed a differentiated response to the higher intensity (94.5 dBA) with a decrease in some peripheral signals such as the heart rate (HR), the pulse signal, the pulse transit time (PTT), an increase of the LFnu power in PPG, and at the head level a decrease in oxygenated and total hemoglobin concentration. After the regression of the visual channel activity from the auditory channels, a decrease in deoxyhemoglobin in the auditory cortex was obtained, indicating a likely active response at the highest intensity. Nevertheless, other measures, such as EDA (Phasic and Tonic), and heart rate variability (Frequency and time domain) showed no significant differences between intensities. Altogether, these results suggest a systemic and complex response to high‐intensity auditory stimuli. The results obtained in the decrease of the PTT and the increase in LFnu power of PPG suggest a possible vasoconstriction reflex by a sympathetic control of vascular tone, which could be related to the decrease in blood oxygenation at the head level. [ABSTRACT FROM AUTHOR]

Trauma patients may suffer significant blood loss, and noninvasive methods to diagnose hypovolemia in these patients are needed. Physiologic effects of hypovolemia, aiming to maintain blood pressure, are largely mediated by increased sympathetic nervous activity. Trauma patients may however experience pain, which also increases sympathetic nervous activity, potentially confounding measures of hypovolemia. Elucidating the common and separate effects of the two stimuli on diagnostic methods is therefore important. Lower body negative pressure (LBNP) and cold pressor test (CPT) are experimental models of central hypovolemia and pain, respectively. In the present analysis, we explored the effects of LBNP and CPT on pre‐ejection period and pulse transit time, aiming to further elucidate the potential use of these variables in diagnosing hypovolemia in trauma patients. We exposed healthy volunteers to four experimental sequences with hypovolemia (LBNP 60 mmHg) or normovolemia (LBNP 0 mmHg) and pain (CPT) or no pain (sham) in a 2 × 2 fashion. We calculated pre‐ejection period and pulse transit time from ECG and ascending aortic blood velocity (suprasternal Doppler) and continuous noninvasive arterial pressure waveform (volume‐clamp method). Fourteen subjects were available for the current analyses. This experimental study found that pre‐ejection period increased with hypovolemia and remained unaltered with pain. Pulse transit time was reduced by pain and increased with hypovolemia. Thus, the direction of change in pulse transit time has the potential to distinguish hypovolemia and pain. [ABSTRACT FROM AUTHOR]

Cuffless blood pressure (BP) measurement has become a popular field due to clinical need and technological opportunity. However, no method has been broadly accepted hitherto. The objective of this review is to accelerate progress in the development and application of cuffless BP measurement methods. We begin by describing the principles of conventional BP measurement, outstanding hypertension/hypotension problems that could be addressed with cuffless methods, and recent technological advances, including smartphone proliferation and wearable sensing, that are driving the field. We then present all major cuffless methods under investigation, including their current evidence. Our presentation includes calibrated methods (i.e., pulse transit time, pulse wave analysis, and facial video processing) and uncalibrated methods (i.e., cuffless oscillometry, ultrasound, and volume control). The calibrated methods can offer convenience advantages, whereas the uncalibrated methods do not require periodic cuff device usage or demographic inputs. We conclude by summarizing the field and highlighting potentially useful future research directions. [ABSTRACT FROM AUTHOR]

Pulse arrival time (PAT), Pulse transit time (PTT), and Pulse Wave Velocity (PWV) have all been used as metrics for assessing a number of cardiovascular applications, including arterial stiffness and cuffless blood pressure monitoring. These have been measured using various sensing methods, including electrocardiogram (ECG) with photoplethysmogram (PPG), two PPG sensors, or two Bioimpedance (BioZ) sensors. Our study addresses the mathematical inaccuracies of previous bioimpedance approaches and incorporates PTT weights for the peak-peak (PTTpp), middle-middle (PTTmm), and foot-foot (PTTff) segments of the sensing signal into a single neuron model to determine a more accurate and stable PWV. In addition, we developed a tetherless bioimpedance device and compared our PTT estimation approaches, which yielded PWV across six subjects and two different arteries. Specifically, using our model, we found that the most reliable combination of weights corresponding to PTTpp, PTTmm, and PTTff was (0.260, 0.704, 0.036) for the brachial artery and (0.104, 0.858, 0.038) for radial artery. This model consistently yielded stable values across repetitions, with PWV values of 5.2 m/s, 5.3 m/s, and 5.9 m/s for the brachial artery and values of 5.8 m/s, 6.6 m/s, and 6.5 m/s for the radial artery. This system and model offer the possibility of obtaining higher reliability PTT and PWV values yielding better monitoring of cardiovascular health measures such as blood pressure and arterial stiffness. [ABSTRACT FROM AUTHOR]

Abstract The present study aims to analyze the systemic response to auditory stimulation by means of hemodynamic (cephalic and peripheral) and autonomic responses in a broad range of auditory intensities (70.9, 77.9, 84.5, 89.5, 94.5 dBA). This approach could help to understand the possible influence of the autonomic nervous system on the cephalic blood flow. Twenty‐five subjects were exposed to auditory stimulation while electrodermal activity (EDA), photoplethysmography (PPG), electrocardiogram, and functional near‐infrared spectroscopy signals were recorded. Seven trials with 20 individual tones, each for the five intensities, were presented. The results showed a differentiated response to the higher intensity (94.5 dBA) with a decrease in some peripheral signals such as the heart rate (HR), the pulse signal, the pulse transit time (PTT), an increase of the LFnu power in PPG, and at the head level a decrease in oxygenated and total hemoglobin concentration. After the regression of the visual channel activity from the auditory channels, a decrease in deoxyhemoglobin in the auditory cortex was obtained, indicating a likely active response at the highest intensity. Nevertheless, other measures, such as EDA (Phasic and Tonic), and heart rate variability (Frequency and time domain) showed no significant differences between intensities. Altogether, these results suggest a systemic and complex response to high‐intensity auditory stimuli. The results obtained in the decrease of the PTT and the increase in LFnu power of PPG suggest a possible vasoconstriction reflex by a sympathetic control of vascular tone, which could be related to the decrease in blood oxygenation at the head level.