Cuff-based Blood Pressure Measurement Research Articles

O84 BLOOD PRESSURE MEASUREMENT VIA IMPEDANCE CARDIOGRAPHY: AN ARTIFICIAL INTELLIGENCE BASED PROOF-OF-CONCEPT

Objective: Accurate blood pressure (BP) measurement is crucial for the diagnosis, risk assessment, treatment decision-making, and monitoring of cardiovascular diseases. Unfortunately, the gold-standard, cuff-based BP measurement suffer from inaccuracies and discomfort. As a result, continuous, cuff-less, and non-invasive BP assessment methods are merging as promising alternatives. This study is the first to comprehensively assess impedance cardiography (ICG) for Machine Learning enabled BP measurement. Methods: We analysed ICG data from 71 young and healthy adults. Nine different Machine Learning algorithms were evaluated for their BP estimation performance. Accuracy measures comprised mean difference, standard deviation, Limits of Agreement, mean absolute error, and root mean squared error. Additionally, the B-Score was calculated to evaluate the “true”, relative performance of the best-performing model. Results: The Multi-Linear Regression model showed the highest performance (mean difference = -0.01, r = 0.82) for systolic BP. The Support Vector Regressor model achieved the best results (mean difference = 0.15, r = 0.51) for diastolic BP. Both models exhibited positive B-Scores, indicative of their predictive capability. All tested models’ estimations correlated with both systolic and diastolic reference BP (r > 0.72 / r > 0.34, p < 0.001). Conclusion: The study highlights the potential of ICG based Machine Learning algorithms for accurately estimating arterial BP. Such algorithms can provide accurate and continuous BP data. This offers the possibility of enhancing patient care, timely diagnostics, and treatment decision-making. Our approach is especially promising for post-surgical and intensive care environments.

O58 CONTINUOUS MEASUREMENT OF BLOOD PRESSURE DURING NIGHT - INSIGHTS INTO THE GENESIS OF THE NOCTURNAL BLOOD PRESSURE FLUCTUATIONS

Background and Objective: Nighttime blood pressure (BP) is a strongly predictive marker for overall cardiovascular risk. Traditional cuff-based BP measurements methods provide only discrete BP data. Short-term BP fluctuations are not detectable with such methods. Therefore, we applied two independent and continuously measuring methods with the aim to characterize systolic nocturnal blood pressure fluctuations (NBPF) related to sleep apnoea or periodic leg movements (PLM). Methods: BP fluctuations were analysed in patients with suspected sleep disturbances. The Portapres™ device (Penaz method) and a system based on the measurement of the pulse transit time (SOMNOscreen®) were used to measure the BP in addition to a polysomnography performed under the conditions of a clinical sleep laboratory. The calculation of the BP by the SOMNOscreen® was performed using a BP-pulse wave velocity (PWV) transfer function. Results: We saw BP fluctuations which occurred in connection with obstructive apnoeic events as well as in connection with PLM. Both methods recorded the same apnoea related NBPF. The amplitudes of these NBPF accounted for 24.7 mmHg and 22.8 mmHg (Portapres™ and SOMNOscreen®, respectively, 11 patients, number of events: n=695). PLM related PB fluctuations (in 5 of 11 patients, n=317) amounted to 18.4 mmHg (Portapres™ and 16.9 mmHg (SOMNOscreen®). Most of the patients did not show BP dipping when comparing awake with sleep time periods and they had a moderate to severe apnoea/hypopnoea index. Conclusions: The study demonstrates a strong relation between apnoeic events/PLM and NPBF. The NBPF were similarly detected by two different BP measuring methods suggesting that the more indirect method of BP determination using the PWV provides a reliable measurement of NBPF. The results suggest that OSA and PLM related NBPF strongly contribute to non- or reverse dipping of patients with these sleep disorders.

#2118 Monitoring hemodynamics in dialysis: on the feasibility of semi-continuous and multifrequency thoracic bioimpedance measurements by a wearable device

Abstract Background and Aims Hemodialysis patients face a high hemodynamic variability in blood pressure and fluid status, which contributes to their cardiovascular mortality. Currently, intradialytic hemodynamic monitoring has to rely on cuff-based blood pressure measurements. During the interdialytic interval, no monitoring system is applied. An interesting alternative to monitor hemodynamics continuously can be found in the bioimpedance technique, and more specifically in bioimpedance measurements of the thoracic segment. Repeated single-point measurements of thoracic bioimpedance at single (low)-frequency are strongly related to fluid changes during hemodialysis. Extension to semi-continuous measurements may provide longitudinal details in the time pattern of the bioimpedance signal, and multifrequency measurements may add in-depth information on the distribution between intra- and extracellular fluid. This study aims to investigate the feasibility of semi-continuous multifrequency thoracic bioimpedance measurements by a wearable device in hemodialysis patients. Method Thoracic bioimpedance was recorded semi-continuously (i.e. every ten minutes) at nine frequencies (8–160 kHz) in 68 patients during two consecutive hemodialysis sessions, complemented by an interdialytic single-point measurement at home. The wearable bioimpedance device was provided by imec, The Netherlands (Fig. 1). A linear mixed model was built to integrate all measurements at frequencies 8 and 160 kHz, incorporating the different dialysis sessions up until 240 minutes after the start of dialysis as well as the home measurement that was performed. Results On average, the thoracic resistance signals increased during the first hemodialysis session, decreased during the interdialytic interval, and increased again during the second hemodialysis session, at all frequencies. The average intradialytic increase was larger at 8 kHz (∆ 32.6 Ω during session 1 and ∆ 10 Ω during session 2) compared to 160 kHz (∆ 29.5 Ω during session 1 and ∆ 5.1 Ω during session 2). By measuring semi-continuously and at multiple frequencies, a different time pattern became clear within and between frequencies. The resistance at 8 kHz followed a linear time pattern, whereas the evolution of the resistance at 160 kHz showed a significant quadratic trend in the first dialysis session (p < 0.0001). During the first interdialytic interval (from the end of the first dialysis session towards the home measurement), the decrease in resistance was more distinct at 8 kHz (∆ −2.27 Ω) compared to a small increase of 0.84 Ω in 160 kHz. This finding reveals a certain inertia in the higher frequencies, mirroring the changes in intracellular volume. Finally, the statistical model could create individual predicting profiles over time, including as well the intradialytic as the interdialytic interval (Fig. 2). Conclusion In this study, we showed that it is feasible to perform semi-continuous and multifrequency bioimpedance measurements by a wearable device during hemodialysis. Semi-continuous and multifrequency measurements provided a broader, and respectively profounder knowledge on the trend of the bioimpedance signal during fluid changes compared to single-point and single-frequency measurements. Measuring thoracic bioimpedance semi-continuously and with a multifrequency current is a major step forward in the understanding of fluid dynamics in hemodialysis patients, by which the road is paved towards remote fluid monitoring and the prevention of hemodynamic instabilities.

Noninvasive Cuffless Blood Pressure Estimation With Dendritic Neural Regression.

Blood pressure (BP) is one of the most important indicators of health. BP that is too high or too low causes varying degrees of diseases, such as renal impairment, cerebrovascular incidents, and cardiovascular diseases. Since traditional cuff-based BP measurement techniques have the drawbacks of patient discomfort and the impossibility of continuous BP monitoring, noninvasive cuffless continuous BP measurement has become a popular topic. The common noninvasive approach uses machine-learning (ML) algorithms to estimate BP by using the features extracted from simultaneous photoplethysmogram (PPG) and electrocardiogram (ECG) signals, such as the pulse transit time and pulse wave velocity. This study investigates the BP estimation performance of the novel dendritic neural regression (DNR) method proposed by us. Unlike conventional neural networks, DNR utilizes the multiplication operator as the excitation function in each dendritic branch, inspired by biological neuron phenomena, and can effectively capture nonlinear relationships between distinct input features. In addition, AMSGrad is used as the optimization algorithm to further enhance the dendritic neural model's performance. The experimental results show that by being fed a combination of the raw features extracted from the ECG and PPG signals and the components of the BP mathematical models, DNR can increase the accuracy of systolic BP, diastolic BP, and mean arterial pressure estimation significantly, which are superior to the state-of-the-art ML techniques. According to the British Hypertension Society protocol, DNR achieves a grade of A for the long-term BP estimation. Considering its architectural simplicity and powerful performance, the proposed method can be regarded as a reliable tool for estimating long-term continuous BP in a noninvasive cuffless way.

CAMERA-BASED REMOTE PHOTOPLETHYSMOGRAPHY TO PREDICT BLOOD PRESSURE IN CLINIC PATIENTS WITH CARDIOVASCULAR DISEASE

Objective: Remote photoplethysmography (rPPG) uses a camera to capture peripheral blood flow. It remains to be seen if rPPG can accurately predict blood pressure (BP). Most studies have included healthy, normotensive subjects. This is the first investigation of rPPG to predict BP for patients with cardiovascular disease (CVD). Design and method: 132 patient visits were completed in a cardiology clinic between March and December, 2022 (Table 1). Patients were seated while a digital camera recorded two 2.5 minute videos of the face and palm. BP was measured by an oscillometric cuff from the left arm at the beginning, midpoint, and end of the session. Continuous heart rate and rhythm were measured by a single-lead ECG from electrodes on the right and left wrists. Continuous PPG was measured by a contact sensor on the left index finger. Videos were cropped and spatially-averaged to extract the green channel, which serves as a proxy for the rPPG signal. Pulse segmentation and selection were used to identify five high-quality consecutive beats. Raw rPPG signal was analyzed with its first and second derivatives into a one-dimensional convolutional neural network. The network is trained to predict systolic and diastolic BP by minimizing the mean squared error between the predicted video-based contactless BP and cuff-based BP measurement. A 5-fold subject independent cross-validation was performed. Results: There is modest correlation between rPPG-predicted and measured systolic BP (r = 0.47, mean error -0.47 ± 15.5 mm Hg), but weaker correlation for diastolic BP (r = 0.16, mean error -0.80 ± 8.1 mm Hg). Conclusions: rPPG shows potential for BP prediction in real-world patients with CVD. Future work will leverage larger data driven machine learning methods to improve the rPPG signal quality and examine the impact on BP measurements. Robust training data, including extreme values of blood pressure and a variety of patient demographics, are needed to improve these models. Major limitations include lack of guideline-directed protocols for rPPG validation for BP prediction and inability to assess temporal correlation between rPPG signal and BP.

S-58-3: HOW COULD CONTINUOUS CUFFLESS AUTOMATED BLOOD PRESSURE MEASUREMENT DEVICES CONTRIBUTE TO HYPERTENSION MANAGEMENT?

Evidence that hypertension is the most importat global risk factor for cardiovascular morbidity and mortality, and demonstration of the cardiovascular protection offered by antihypertensive treatment, have been based on upper-arm cuff blood pressure (BP) measurements. This approach is recommended by current hypertension guidelines for BP measurement in clinical practice, as well as for validation of novel BP measuring devices. However, the spot or intermittent BP measurements in static conditions allowed by arm-cuff devices are unable to identify and track the continuous and rapid BP changes characterizing daily life. Moreover, cuff-based BP measurements are exposed to errors related to cuff size, shape and positioning, and cuff inflation may itself induce an alerting response and produce discomfort in the users during daily activities and in particular during sleep, which may modify the measured BP levels. To face these issues, use of continuous recordings techniques, not based on arm-cuff inflation, would be required. This is possible through intra-arterial recordings, which however are not feasible in daily practice because of their invasiveness. An alternative solution is represented by photoplethysmographic volume-clamp method for finger BP monitoring which, however, is difficult to implement in clinical practice because of its cost and complexity. Technological progress is now offering an increasingly large and heterogeneous series of cuffless BP monitors, which estimate BP based on sensors, signal processing and algorithms embedded in wearable devices, smartphones or pocket devices. These cuffless devices might allow for comfortable and continuous BP recordings, detecting rapid BP changes during day and night sleep, thus offering information on both short, mid and long term BP variability. Cuffless devices might also be useful for continuous non invasive BP monitoring in different clinical settings, including ICUs, during anaesthesia, in patients with arrhythmias, hypotension/syncope, and other transient conditions affecting BP levels and variability. Moreover, cuffless BP measurement devices connected to telemonitoring facilities or smartphones apps may improve patients empowerment, adherence to treatment and long-term BP control. In spite of all these potential advantages, however, cuffless BP devices have to face specific accuracy issues, which would need refinement of specific validation protocols aimed at testing the need of individual calibration, the stability of measurements after calibration and the device actual ability to track BP changes before they might be recommended for clinical use.

A Review of Noninvasive Methodologies to Estimate the Blood Pressure Waveform.

Accurate estimation of blood pressure (BP) waveforms is critical for ensuring the safety and proper care of patients in intensive care units (ICUs) and for intraoperative hemodynamic monitoring. Normal cuff-based BP measurements can only provide systolic blood pressure (SBP) and diastolic blood pressure (DBP). Alternatively, the BP waveform can be used to estimate a variety of other physiological parameters and provides additional information about the patient’s health. As a result, various techniques are being proposed for accurately estimating the BP waveforms. The purpose of this review is to summarize the current state of knowledge regarding the BP waveform, three methodologies (pressure-based, ultrasound-based, and deep-learning-based) used in noninvasive BP waveform estimation research and the feasibility of employing these strategies at home as well as in ICUs. Additionally, this article will discuss the physical concepts underlying both invasive and noninvasive BP waveform measurements. We will review historical BP waveform measurements, standard clinical procedures, and more recent innovations in noninvasive BP waveform monitoring. Although the technique has not been validated, it is expected that precise, noninvasive BP waveform estimation will be available in the near future due to its enormous potential.

Smartphones and Video Cameras: Future Methods for Blood Pressure Measurement.

Regular blood pressure (BP) monitoring enables earlier detection of hypertension and reduces cardiovascular disease. Cuff-based BP measurements require equipment that is inconvenient for some individuals and deters regular home-based monitoring. Since smartphones contain sensors such as video cameras that detect arterial pulsations, they could also be used to assess cardiovascular health. Researchers have developed a variety of image processing and machine learning techniques for predicting BP via smartphone or video camera. This review highlights research behind smartphone and video camera methods for measuring BP. These methods may in future be used at home or in clinics, but must be tested over a larger range of BP and lighting conditions. The review concludes with a discussion of the advantages of the various techniques, their potential clinical applications, and future directions and challenges. Video cameras may potentially measure multiple cardiovascular metrics including and beyond BP, reducing the risk of cardiovascular disease.

An investigation of the individualized, two-point calibration method for cuffless blood pressure estimation using pulse arrival time: an historical perspective using the Casio BP-100 digital watch.

The use of wearable cuffless blood pressure (BP) devices is becoming commercially prevalent with little published validation information. Most devices rely, at least in part, on the relationship between pulse arrival time (PAT) and BP, a theoretical fundamental relationship that was first commercially exploited in 1993 with the release of the Casio BP-100 digital watch. This study explored the PAT method of BP estimation in a commercial device where it first began, the Casio BP-100 (Model No. 900) digital watch, which employs an individualized, two-point calibration method. Device accuracy was determined by comparison to a conventional cuff-based BP device measurements. Twenty participants (11 female, 9 male) had BP measured using both devices at rest, during a 5-minute isometric hand-grip exercise and at 1-minute post-exercise. Due to bidirectional scatter of BP estimation by the BP-100 device, there was no significant difference between the reference device and the BP-100. The devices showed poor correlation for both systolic BP (SBP) (R=0.36, p=0.13) and diastolic BP (DBP) (R=0.044, p=0.37). However, on average the watch was able to provide correct directional changes in SBP but not DBP with exercise. Despite being an industry first, the Casio BP-100 watch employed a method that gives a great chance of accuracy: a two point, individualized calibration method - more detailed than calibration methods in more modern devices. The watch, on average across a cohort, provided some information on BP directional change but was uncorrelated with cuff-based BP measurement. If the utility of beat-by-beat BP estimation is to be utilized, limitations of this method need to be addressed.

Pilot study of comparability of a smartphone blood pressure monitoring algorithm to conventional cuff-based blood pressure measurements

Abstract Background/Introduction There is a growing market for smartphone applications (apps), offering medical assessments such as blood pressure measurements (BPM). These apps have the potential to improve blood pressure (BP) control by making BPM broadly and easily accessible. Yet, to be suitable for clinical and diagnostic purposes, BPM measured with smartphone apps need to be comparable to conventional BPM. Purpose We sought to compare a novel photoplethysmographic BPM algorithm used in a smartphone app to conventional cuff-based BPM. Methods We included consecutive patients with an indication for ambulatory BPM. Office blood pressure measurements (OBPM) were taken with an oscillometric cuff-based device (Welch Allyn SureBP). The algorithm of the smartphone app detects the pulse wave in finger capillaries using the phone's camera and estimates BP based on the form of the pulse wave. Before estimating a BP value, the algorithm performs a quality assessment to automatically reject recordings with insufficient quality. On the first day (D1), we took 6 OBPM alternating with 5 smartphone BPM (TestBP). On the second day (D2), 4 OBPM alternating with 3 TestBP were measured. TestBP calibrated based on the first OBPM of D1. Each TestBP was then compared to its RefBP (defined as mean of the previous and following OBPM). Results 50 patients were included in the study, resulting in 50 TestBP values on D1 and 33 on D2. There was no difference at the 5% significance level between the TestBP and RefBP distributions on both days, and for both systolic and diastolic pressures. The mean ± standard deviation (SD) of the differences between TestBP and RefBP was 0.7±9.4 / 1.0±4.5 mmHg on D1 and 2.6±8.2 / 1.3±4.1 mmHg on D2 for systolic/diastolic values, respectively. The number of TestBP measurements within 5, 10 and 15 mmHg from RefBP are shown in Table 1. Bland-Altman plots depicting the agreement between TestBP and RefBP are shown in Figure 1. Conclusion This smartphone algorithm shows comparable values to oscillometric cuff-based especially diastolic values. Its differences between TestBP – RefBP have a good stability 1 day after calibration. Before clinical use, this algorithm needs to undergo formal validation against a reference BP method accepted by international standards (auscultatory or invasive methods). Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): Centre Suisse d'Electronique et de Microtechnique Table 1Figure 1

Cuffless blood pressure estimation based on haemodynamic principles: progress towards mobile healthcare

BackgroundAlthough cuff-sphygmomanometry is used worldwide in medical and healthcare fields, it is a fact that the use of an occlusive cuff to obtain blood pressure (BP) is troublesome and inconvenient. There have therefore been on-going efforts to devise methods that do not require the use of a cuff, almost all being based on the measurement of pulse wave velocity or pulse transit time, but so far few significant developments have been made, especially regarding measurement accuracy. We have previously reported a smartphone-based cuffless method using a linear multiple regression calibration model comprising of BP obtained with a cuff-sphygmomanometer as an objective variable and modified normalized pulse volume (mNPV: a measure of vasoconstrictive activity in a finger) and pulse rate (PR) as explanatory variables. This requires a number of subjects to construct a calibration model and thus is largely dependent on the accuracy due to the model. To address these drawbacks, we report here a new cuffless method to surpass considerably the results of our previous study as well as earlier works.MethodsWith this method we can estimate BP, with much higher accuracy, using mNPV and PR, both also obtained from a smartphone-derived photoplethysmogram. The subject firstly performs a cuff-based BP measurement in parallel with the acquisition of mNPV and PR from a smartphone. These parameters are set as initial values (BPc0, mNPV0 and PR0; initial calibration procedure). Then, the estimated BP (BPe) can be calculated from the relation: “BPe = (BPc0·PR·mNPV)/(PR0·mNPV0)”, which is derived from the so-called haemodynamic Ohm’s law. To validate this method, preliminary experiments using 13 volunteers were carried out to compare results from the new method with those from the cuff-sphygmomanometry, used as a reference.ResultsAltogether 299 paired data sets were analyzed: A good agreement was found between the cuff-based and the estimated BP values, with correlation coefficients of 0.968 for systolic BP (SBP), 0.934 for mean BP (MBP) and 0.844 for diastolic BP (DBP). Bland-Altman analyses for the BPe (SBPe, MBPe, DBPe) and the BPc (SBPc, MBPc, DBPc) values also supported these comparison results. Mean absolute differences between the BPe and the BPc values in total subjects were less than 5 mmHg. Fairly good tracking availability in terms of time series data of the BPc against the corresponding BPe values was also confirmed in each subject during the study periods (1–2 weeks for 12 subjects and about 4 months for one subject).DiscussionThe present study reported the successful development of the new cuffless BP estimation method, given as the status of a trial stage of investigation. This method could easily be used with various smartphones, smart watches, and finger-based devices, and it appears to have significant potential as a convenient substitute for conventional cuff-sphygmomanometers as well as for practical application to mobile healthcare.

The effect of arterial stiffness on cuff-based blood pressure measurement

Cuff-based blood pressure measurement is the gold standard method for blood pressure monitoring. However, the wall of the sclerotic arteries (stiffen or thicken) generates extra resistance to the cuff pressure in the cuff-based blood pressure measurement, which tends to overestimate the blood pressure. This paper uses a collapse model to study the arterial deformation under the cuff pressure in the cuff-based blood pressure measurement, which gives the effects of arterial stiffness on measured systolic and diastolic blood pressure by cuff-based method, respectively. The results have general utility for future work in improving the accuracy of cuff-based BP measurement.

The cuffless SOMNOtouch NIBP device shows poor agreement with a validated oscillometric device during 24-h ambulatory blood pressure monitoring.

Repeated cuff‐based blood pressure (BP) measurements may cause discomfort resulting in stress and erroneous recording values. SOMNOtouch NIBP is an alternative cuff‐less BP measurement device that calculates changes in BP based on changes in pulse transit time (PTT) and a software algorithm. The device is calibrated with a single upper arm cuff‐based BP measurement. We tested the device against a validated 24‐h ambulatory BP monitoring (ABPM) device using both the previous (SomBP1) and the current software algorithm (SomBP2). In this study, 51 patients (mean age ± SD 61.5 ± 13.0 years) with essential hypertension underwent simultaneous 24‐h ABPM with the SOMNOtouch NIBP on the left arm and a standard cuff‐based oscillometric device on the right arm (OscBP). We found that mean daytime systolic BP (SBP) with OscBP was 140.8 ± 19.7 compared to 148.0 ± 25.2 (P = .008) and 146.9 ± 26.0 mmHg (P = .034) for SomBP1 and SomBP2, respectively. Nighttime SBP with OscBP was 129.5 ± 21.1 compared with 146.1 ± 25.8 (P < .0001) and 141.1 ± 27.4 mmHg (P = .001) for SomBP1 and SomBP2, respectively. Ninety‐five% limits of agreement between OscBP and SomBP1 were ± 36.6 mmHg for daytime and ± 42.6 mmHg for nighttime SBP, respectively. Agreements were not improved with SomBP2. For SBP, a nocturnal dipping pattern was found in 33% of the study patients when measured with OscBP but only in 2% and 20% with SomBP1 and ‐2, respectively. This study demonstrates that BP values obtained with the cuff‐less PTT‐based SOMNOtouch device should be interpreted with caution as these may differ substantially from what would be obtained from a validated cuff‐based BP device.

Estimating Blood Pressure from the Photoplethysmogram Signal and Demographic Features Using Machine Learning Techniques

Hypertension is a potentially unsafe health ailment, which can be indicated directly from the blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; however, cuff-based BP measurements are discrete and uncomfortable to the user. To address this need, a cuff-less, continuous, and noninvasive BP measurement system is proposed using the photoplethysmograph (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo preprocessing and feature extraction steps. Time, frequency, and time-frequency domain features were extracted from the PPG and their derivative signals. Feature selection techniques were used to reduce the computational complexity and to decrease the chance of over-fitting the ML algorithms. The features were then used to train and evaluate ML algorithms. The best regression models were selected for systolic BP (SBP) and diastolic BP (DBP) estimation individually. Gaussian process regression (GPR) along with the ReliefF feature selection algorithm outperforms other algorithms in estimating SBP and DBP with a root mean square error (RMSE) of 6.74 and 3.59, respectively. This ML model can be implemented in hardware systems to continuously monitor BP and avoid any critical health conditions due to sudden changes.

Feasibility of a New Cuffless Device for Ambulatory Blood Pressure Measurement in Patients With Hypertension: Mixed Methods Study.

BackgroundFrequent home blood pressure (BP) measurements result in a better estimation of the true BP. However, traditional cuff-based BP measurements are troublesome for patients.ObjectiveThis study aimed to evaluate the feasibility of a cuffless device for ambulatory systolic blood pressure (SBP) measurement.MethodsThis was a mixed method feasibility study in patients with hypertension. Performance of ambulatory SBPs with the device was analyzed quantitatively by intrauser reproducibility and comparability to a classic home BP monitor. Correct use by the patients was checked with video, and user-friendliness was assessed using a validated questionnaire, the System Usability Scale (SUS). Patient experiences were assessed using qualitative interviews.ResultsA total of 1020 SBP measurements were performed using the Checkme monitor in 11 patients with hypertension. Duplicate SBPs showed a high intrauser correlation (R=0.86, P<.001). SBPs measured by the Checkme monitor did not correlate well with those of the different home monitors (R=0.47, P=.007). However, the mean SBPs measured by the Checkme and home monitors over the 3-week follow-up were strongly correlated (R=0.75, P=.008). In addition, 36.4% (n=4) of the participants performed the Checkme measurements without any mistakes. The mean SUS score was 86.4 (SD 8.3). The most important facilitator was the ease of using the Checkme monitor. Most important barriers included the absence of diastolic BP and the incidental difficulties in obtaining an SBP result.ConclusionsGiven the good intrauser reproducibility, user-friendliness, and patient experience, all of which facilitate patients to perform frequent measurements, cuffless BP monitoring may change the way patients measure their BP at home in the context of ambulant hypertension management.

Dual-modality arterial pulse monitoring system for continuous blood pressure measurement.

Accurate and ambulatory measurement of blood pressure (BP) is essential for efficient diagnosis, management and prevention of cardiovascular diseases (CVDs). However, traditional cuff-based BP measurement methods provide only intermittent BP readings and can cause discomfort with the occlusive cuff. Although pulse transit time (PTT) method is promising for cuffless and continuous BP measurement, its pervasive use is restricted by its limited accuracy and requirement of placing sensors on multiple body sites. To tackle these issues, we propose a novel dual-modality arterial pulse monitoring system for continuous blood pressure measurement, which simultaneously records the pressure and photoplethysmography (PPG) signals of radial artery. The obtained signals can be used to generate a pressure-volume curve, from which the elasticity index (EI) and viscosity index (VI) can be extracted. Experiments were carried out among 7 healthy subjects with their PPG, ECG, arterial pressure wave and reference BP collected to examine the effectiveness of the proposed indexes. The results of this study demonstrate that a linear regression model combining EI and VI has significantly higher BP tracking correlation coefficient as compared to the PTT method. This suggests that the proposed system and method can potentially be used for convenient and continuous blood pressure estimation with higher accuracy.

Validity and Usefulness of Wearable Blood Pressure Sensing' for Detection of Inappropriate Short-Term Blood Pressure Variability in the Elderly Impact of Cognitive Function and Stress Response

An increase in short-term blood pressure (BP) variability is a characteristic feature in the elderly. It makes the management of hemodynamics more difficult, because it is frequently seen disturbed baro-reflex function and increased arterial stiffness, leading to isolated systolic hypertension. Large BP variability aggravates hypertensive target organ damage and is an independent risk factor for the cardiovascular (CV) events in elderly hypertensive patients. Therefore, appropriate control in BP is indispensable to manage lifestyle-related diseases and to prevent subsequent CV events. In addition, accumulating recent reports show that excessive BP variability is also associated with a decline in cognitive function and fall in the elderly. In the clinical settings, we usually evaluate their health condition, mainly with single point BP measurement using cuff inflation. However, unfortunately we are not able to find the close changes in BP by the traditional way. Here, we can show our advantageous approach of continuous BP monitoring using newly developing device `wearable BP sensing' without a cuff stress in the elderly. The new device could reflect systolic BP and its detailed changes, in consistent with cuff-based BP measurement. Our new challenge suggests new possibility of its clinical application with high accuracy.

Cuffless Blood Pressure Estimation during Exercise Stress Test

 Abstract—Blood Pressure (BP) measurement without using a cuff is an interesting goal especially during Exercise Stress Test. Conventional methods like Cuff-based blood pressure measurement is not accurate enough due to high motion artifacts. High blood pressure during exercise test will cause pain and discomfort during inflation of cuff as well. Many researches have been conducted and shown Pulse Arrival Time (PAT), can be used in blood pressure estimation. This paper proposes a new calibration method for blood pressure estimation during exercise stress test that increases the quality of estimation. Besides PAT and heart rate, estimated blood pressure of previous stage is added as another input parameter. This technique increases the correlation coefficients between real and estimated blood pressures. We also show that by using ΔPAT, ΔHR instead of PAT and HR quality of estimation improves.

Effect of tissue mechanical properties on cuff-based blood pressure measurements

This paper presents a 3D finite element upper arm model, validated by experiments as well as clinical data, used to study the error introduced in blood pressure measurements due to variability of arm tissue mechanical properties. The model consists of three separate cylindrical parts: soft tissue, bone and brachial artery. The artery volume changes under the cuff are used to represent the cuff pressure oscillations for analyzing blood pressure measurements. These oscillation trends are identical to observed clinical data. Also an upper arm simulator is designed and built for model validation. The model shows that the variation of soft tissue compressibility introduces an error up to 5% in blood pressure measurements. It is also revealed that the variation of the brachial artery and arm tissue stiffness has an insignificant effect on oscillometric blood pressure measurement method.