Long-term Blood Pressure Monitoring Research Articles

Fair non-contact blood pressure estimation using imaging photoplethysmography.

Hypertension is typically manifested as a latent symptom that requires detection through specialized equipment. This poses an inconvenience for individuals who need to undergo long-term blood pressure monitoring in their daily lives. Therefore, there is a need for a portable, non-contact method for estimating blood pressure. However, current non-contact blood pressure estimation methods often rely on relatively narrow datasets, lacking a broad range of blood pressure distributions. Additionally, their applicability is confined to controlled experimental environments. This study proposes a non-contact blood pressure estimation method suitable for various life scenarios, encompassing multiple age groups, diverse ethnicities, and individuals with different skin tones. The aim is to enhance the practicality and accuracy of existing non-contact blood pressure estimation methods. The research extracts the imaging photoplethysmogram (IPPG) signal from facial videos and processes the signal through four layers of filtering operations to obtain an IPPG signal reflecting pulse wave variations. A CNN+BiLSTM+GRU network structure is constructed to improve the accuracy of current non-contact blood pressure estimation methods. In comparison to existing approaches, the mean absolute error (MAE) for systolic blood pressure (SBP) and diastolic blood pressure (DBP) is reduced by 13.6% and 16.4%, respectively.

Monitoring blood pressure and cardiac function without positioning via a deep learning-assisted strain sensor array.

Continuous and reliable monitoring of blood pressure and cardiac function is of great importance for diagnosing and preventing cardiovascular diseases. However, existing cardiovascular monitoring approaches are bulky and costly, limiting their wide applications for early diagnosis. Here, we developed an intelligent blood pressure and cardiac function monitoring system based on a conformal and flexible strain sensor array and deep learning neural networks. The sensor has a variety of advantages, including high sensitivity, high linearity, fast response and recovery, and high isotropy. Experiments and simulation synergistically verified that the sensor array can acquire high-precise and feature-rich pulse waves from the wrist without precise positioning. By combining high-quality pulse waves with a well-trained deep learning model, we can monitor blood pressure and cardiac function parameters. As a proof of concept, we further constructed an intelligent wearable system for real-time and long-term monitoring of blood pressure and cardiac function, which may contribute to personalized health management, precise and early diagnosis, and remote treatment.

FEASIBILITY AND MEASUREMENT STABILITY OF SMARTWATCH-BASED CUFFLESS BLOOD PRESSURE MONITORING

Objective: Cuffless wearable devices are currently being developed for long-term monitoring of blood pressure (BP) in patients with hypertension and in apparently healthy people. This study evaluated the feasibility and measurement stability of smartwatch-based cuffless BP monitoring in real-world conditions. Design and method: Users of the first smartwatch-based cuffless BP monitor approved in Korea (Samsung Galaxy Watch) were invited to upload their data from using the device for 4 weeks post calibration. Results: A total of 760 participants (mean age 43.7±11.9, 80.3% men) provided 35,797 BP readings (average monitoring 22±4 days [SD]; average readings 47±42 per participant [median 36]). Each participant obtained 1.5±1.3 readings/day and 19.7% of the participants obtained measurements every day. BP showed considerable variability, mainly depending on the day and time of the measurement. There was a trend towards higher BP levels on Mondays than on other days of the week and on workdays than in weekends. BP readings taken between 00:00 and 04:00 tended to be the lowest, whereas those between 12:00 and 16:00 the highest. The average pre-post calibration error for systolic BP (difference in 7-day BP before and after calibration), was 6.8±5.6 mmHg, and was increased with higher systolic BP levels before calibration. Conclusions: Smartwatch-based cuffless BP monitoring is feasible for out-of-office monitoring in the real-world setting. The stability of BP measurement post calibration and the standardization and optimal time interval for recalibration need further investigation.

Cuffless Blood Pressure Estimation Using Dual Physiological Signal and Its Morphological Features

Prehypertension is difficult to diagnose early because of its hidden nature. Long-term monitoring of blood pressure (BP) can help in the early detection and timely treatment of this condition. This study proposes an innovative and efficient BP detection platform that combines portable electrocardiography (ECG) and photoplethysmogram (PPG) signals simultaneous acquisition equipment and BP detection algorithm to obtain real-time BP values conveniently and accurately for a long time. In this study, nine kinds of feature parameters and classification algorithm are used to build multiple linear regression (MLR) models. It not only adopts the Multi-Parameter Intelligent Monitoring in Intensive Care Units (MIMIC-II) database to train and validate the model but also uses self-developed equipment for acquisition and verification in long-term health monitoring. According to the experimental results, the mean absolute error (MAE) and standard deviation (SD) of systolic BP (SBP) have estimated values of 4.46 and 3.20 mmHg, respectively, and simultaneously, the MAE and SD of diastolic BP (DBP) are 4.20 and 3.28 mmHg, respectively. Moreover, both SBP and DBP experimental results conform to the Advancement of Medical Instrumentation BP standard. The proposed BP acquisition platform is proven to be capable of easily acquiring ECG and PPG signals with the proposed sensor device, and the MLR algorithm can also effectively and accurately monitor BP values for a long time.

A Multi-Parameter Fusion Method for Cuffless Continuous Blood Pressure Estimation Based on Electrocardiogram and Photoplethysmogram

Blood pressure (BP) is an essential physiological indicator to identify and determine health status. Compared with the isolated BP measurement conducted by traditional cuff approaches, cuffless BP monitoring can reflect the dynamic changes in BP values and is more helpful to evaluate the effectiveness of BP control. In this paper, we designed a wearable device for continuous physiological signal acquisition. Based on the collected electrocardiogram (ECG) and photoplethysmogram (PPG), we proposed a multi-parameter fusion method for noninvasive BP estimation. An amount of 25 features were extracted from processed waveforms and Gaussian copula mutual information (MI) was introduced to reduce feature redundancy. After feature selection, random forest (RF) was trained to realize systolic BP (SBP) and diastolic BP (DBP) estimation. Moreover, we used the records in public MIMIC-III as the training set and private data as the testing set to avoid data leakage. The mean absolute error (MAE) and standard deviation (STD) for SBP and DBP were reduced from 9.12 ± 9.83 mmHg and 8.31 ± 9.23 mmHg to 7.93 ± 9.12 mmHg and 7.63 ± 8.61 mmHg by feature selection. After calibration, the MAE was further reduced to 5.21 mmHg and 4.15 mmHg. The result showed that MI has great potential in feature selection during BP prediction and the proposed multi-parameter fusion method can be used for long-term BP monitoring.

Upper-Arm Photoplethysmographic Sensor with One-Time Calibration for Long-Term Blood Pressure Monitoring.

Wearable cuffless photoplethysmographic blood pressure monitors have garnered widespread attention in recent years; however, the long-term performance values of these devices are questionable. Most cuffless blood pressure monitors require initial baseline calibration and regular recalibrations with a cuffed blood pressure monitor to ensure accurate blood pressure estimation, and their estimation accuracy may vary over time if left uncalibrated. Therefore, this study assessed the accuracy and long-term performance of an upper-arm, cuffless photoplethysmographic blood pressure monitor according to the ISO 81060-2 standard. This device was based on a nonlinear machine-learning model architecture with a fine-tuning optimized method. The blood pressure measurement protocol followed a validation procedure according to the standard, with an additional four weekly blood pressure measurements over a 1-month period, to assess the long-term performance values of the upper-arm, cuffless photoplethysmographic blood pressure monitor. The results showed that the photoplethysmographic signals obtained from the upper arm had better qualities when compared with those measured from the wrist. When compared with the cuffed blood pressure monitor, the means ± standard deviations of the difference in BP at week 1 (baseline) were -1.36 ± 7.24 and -2.11 ± 5.71 mmHg for systolic and diastolic blood pressure, respectively, which met the first criterion of ≤5 ± ≤8.0 mmHg and met the second criterion of a systolic blood pressure ≤ 6.89 mmHg and a diastolic blood pressure ≤ 6.84 mmHg. The differences in the uncalibrated blood pressure values between the test and reference blood pressure monitors measured from week 2 to week 5 remained stable and met both criteria 1 and 2 of the ISO 81060-2 standard. The upper-arm, cuffless photoplethysmographic blood pressure monitor in this study generated high-quality photoplethysmographic signals with satisfactory accuracy at both initial calibration and 1-month follow-ups. This device could be a convenient and practical tool to continuously measure blood pressure over long periods of time.

Multi-Dimensional Feature Combination Method for Continuous Blood Pressure Measurement Based on Wrist PPG Sensor.

The cuff-less blood pressure (BP) monitoring method based on photoplethysmo- gram (PPG) makes it possible for long-term BP monitoring to prevent and treat cardiovascular and cerebrovascular events. In this paper, a portable BP prediction system based on feature combination and artificial neural network (ANN) is implemented. The robustness of the model is improved from three aspects. Firstly, an adaptive peak extraction algorithm was used to improve the accuracy of peaks and troughs detection. Secondly, multi-dimensional features were extracted and fused, including three groups of PPG-based features and one group of demographics-based features. Finally, a two-layer feedforward artificial neural networks algorithm was used for regression. Thirty-three subjects distributed in the three BP groups were recruited. The proposed method passed the European Society of Hypertension International Protocol revision 2010 (ESP-IP2). Experimental results show that the proposed method exhibits good accuracy for a diverse population with an estimation error of -0.07 ± 4.47 mmHg for SBP and 0.00 ± 3.61 mmHg for DBP. Moreover, the model tracked the BP of two subjects for half a month, laying the foundation work for daily BP monitoring. This work will contribute to the long-term wellness management and rehabilitation process, enabling timely detection and improvement of the user's physical health.

Noninvasive, Wearable Multi Biosensors for Continuous, Long-term Monitoring of Blood Pressure via Internet of Things Applications

Continuous blood pressure (BP) monitoring through various physical stress tests is important for the prediction of hypertension and cardiovascular mortality. This study proposes a multimodal wearable biosensor device for continuous BP monitoring through various tests during exercise and internet of things (IoT) applications. We evaluated three pulse transit time (PTT)-BP estimation models to identify the best mode for predicting BP response to exercise. Moreover, the proposed BP monitoring system was designed to connect to the cloud server for health data processing and management. The efficacy of the proposed device was validated by monitoring the natural variability of BP in various cycling tests. Thus, BP estimation based on the impedance plethysmography waveform and arterial impedance offers superiority compared to other conventional PTT estimation methods. These results suggest the feasibility of the multimodal biosensor device for continuous, long-term BP monitoring in remote diagnosis and management with IoT-based healthcare applications.

Bio-Impedance Sensor for Real-Time Artery Diameter Waveform Assessment.

The real-time artery diameter waveform assessment during cardio cycle can allow the measurement of beat-to-beat pressure change and the long-term blood pressure monitoring. The aim of this study is to develop a self-calibrated bio-impedance-based sensor, which can provide regular measurement of the blood-pressure-dependence time variable parameters such as the artery diameter waveform and the elasticity. This paper proposes an algorithm based on analytical models which need prior geometrical and physiological patient parameters for more appropriate electrode system selection and hence location to provide accurate blood pressure measurement. As a result of this study, the red cell orientation effect contribution was estimated and removed from the bio-impedance signal obtained from the artery to keep monitoring the diameter waveform correspondence to the change of blood pressure.

Unobtrusive, Cuffless Blood Pressure Monitoring Using a Soft Polymer Sensor Array With Flexible Hybrid Electronics

The state-of-the-art systems for blood pressure (BP) monitoring often use cuffed-based rigid sensory systems, bulky electronics, multiple measurement sites, and intermittent measurement. Here, we introduce a new type of epidermal electronics that were specifically designed for cuffless BP-monitoring system, named multiPANI. The proposed fully integrated wearable device is equipped with a wireless-flexible hybrid electronics, a personalized flexible sensor array, and encapsulated with a thin biocompatible elastomer layer for soft-conformal contact with the complex skin surface structure. In addition, a unique capability of multiPANI to monitor multiple local pulse wave velocity (PWV) enabling us to develop a novel hybrid BP estimation algorithm that combine a selective-local PWV and high-signal-to-noise-ratio-based pulse wave analysis (PWA) for robust-cuffless BP-monitoring system. Experimental results indicate that the proposed hybrid selective PWV-PWA BP model outperform the typical single-PWV BP model by ~30%, with an acceptable root-mean-square-error performance of 3.94 ± 0.5 and 1.88 ± 0.9 mmHg, for systolic BP and diastolic BP respectively. Collectively, this study demonstrated a unique solution of fully integrated epidermal electronics and hybrid BP estimation algorithm for a robust, cuffless, and long-term BP monitoring.

Homecare-Oriented Intelligent Long-Term Monitoring of Blood Pressure Using Electrocardiogram Signals

Long-term blood pressure (BP) monitoring is a widely used approach in a homecare intelligent system. However, BP is usually measured using cuff-based devices with tedious operation in practice, which may not be cost effective for continuous BP tracking. In this article, we propose a novel attention-based multitask network with a weighting scheme for BP estimation by analyzing and modeling single lead electrocardiogram (ECG) signals. Experimental results demonstrate that the proposed method could achieve mean error of systolic blood pressure, diastolic blood pressure, and mean arterial pressure estimation in levels of 0.18 $\pm$ 10.83, 1.24 $\pm$ 5.90, and 0.84 $\pm$ 6.47 mmHg, respectively. In comparison to other cutting-edge methods using ECG signals, the proposed method shows superior BP estimation performance. By integrating with a wearable/portable ECG monitoring device, the proposed model can be deployed to an embedded system or remote healthcare intelligent system to provide long-term BP monitoring service, which would help to reduce the incidence of malignant events happened in hypertensive population.

Continuous Blood Pressure Estimation From Electrocardiogram and Photoplethysmogram During Arrhythmias.

ObjectiveContinuous blood pressure (BP) provides valuable information for the disease management of patients with arrhythmias. The traditional intra-arterial method is too invasive for routine healthcare settings, whereas cuff-based devices are inferior in reliability and comfortable for long-term BP monitoring during arrhythmias. The study aimed to investigate an indirect method for continuous and cuff-less BP estimation based on electrocardiogram (ECG) and photoplethysmogram (PPG) signals during arrhythmias and to test its reliability for the determination of BP using invasive BP (IBP) as reference.MethodsThirty-five clinically stable patients (15 with ventricular arrhythmias and 20 with supraventricular arrhythmias) who had undergone radiofrequency ablation were enrolled in this study. Their ECG, PPG, and femoral arterial IBP signals were simultaneously recorded with a multi-parameter monitoring system. Fifteen features that have the potential ability in indicating beat-to-beat BP changes during arrhythmias were extracted from the ECG and PPG signals. Four machine learning algorithms, decision tree regression (DTR), support vector machine regression (SVR), adaptive boosting regression (AdaboostR), and random forest regression (RFR), were then implemented to develop the BP models.ResultsThe results showed that the mean value ± standard deviation of root mean square error for the estimated systolic BP (SBP), diastolic BP (DBP) with the RFR model against the reference in all patients were 5.87 ± 3.13 and 3.52 ± 1.38 mmHg, respectively, which achieved the best performance among all the models. Furthermore, the mean error ± standard deviation of error between the estimated SBP and DBP with the RFR model against the reference in all patients were −0.04 ± 6.11 and 0.11 ± 3.62 mmHg, respectively, which complied with the Association for the Advancement of Medical Instrumentation and the British Hypertension Society (Grade A) standards.ConclusionThe results indicated that the utilization of ECG and PPG signals has the potential to enable cuff-less and continuous BP estimation in an indirect way for patients with arrhythmias.

Noninvasive and Nonocclusive Blood Pressure Monitoring via a Flexible Piezo-Composite Ultrasonic Sensor

Continuous blood pressure monitoring in everyday life is important and necessary to detect and control high blood pressure in advance. While the existing blood pressure monitoring techniques are well suited for applications in current clinical settings, they are inadequate for next-generation wearable long-term monitoring of blood pressure on a daily basis. In this study, a flexible piezo-composite ultrasonic sensor was reported, for the first time, for continuous blood pressure measurement through ultrasonic motion tracking of blood vessel wall. A flexible piezo-composite ultrasonic sensor was designed and fabricated with a layer of PZT-5A/ polydimethylsiloxane (PDMS) anisotropic 1-3 composite and silver nanowire based stretchable electrodes. The material properties and dimensions of the sensor were determined according to the volume fraction of PZT-5A and the material properties of PZT-5A and PDMS. The experimental results illustrated that the flexible sensor possessed adequate bandwidth and sensitivity for blood pressure monitoring. Continuous blood pressure measurement was successfully conducted with the ulnar artery on a volunteer's right arm. Compared with the measurement results using a clinical ultrasound probe and a commercial upper arm blood monitor, the results obtained in this study demonstrated the capability of the proposed flexible sensor to continuously monitor blood pressure waveforms during cardiac cycles. The flexible sensor provides a promising solution for noninvasive, nonocclusive and calibration-free blood pressure monitoring. It has great potential to be integrated into a wearable ultrasonic healthcare sensing system for blood pressure and flow monitoring.

Method of Blood Pressure Measurement in Neonates and Infants: A Systematic Review and Analysis

To determine the recommended blood pressure (BP) measurement methods in neonates after systematically analyzing the literature regarding proper BP cuff size and measurement location and method. A literature search was conducted in MEDLINE, PubMed, Embase, Cochrane Library, and CINAHL from 1946 to 2017 on BP in neonates <3months of age (PROSPERO ID CRD42018092886). Study data were extracted and analyzed with separate analysis of Bland-Altman studies comparing measurement methods. Of 3587 nonduplicate publications identified, 34 were appropriate for inclusion in the analysis. Four studies evaluating BP cuff size support a recommendation for a cuff width to arm circumference ratio of approximately 0.5. Studies investigating measurement location identified the upper arm as the most accurate and least variable location for oscillometric BP measurement. Analysis of studies using Bland-Altman methods for comparison of intra-arterial to oscillometric BP measurement show that the 2 methods correlate best for mean arterial pressure, whereas systolic BP by the oscillometric method tends to overestimate intra-arterial systolic BP. Compared with intra-arterial methods, systolic BP, diastolic BP, and mean arterial pressure by oscillometric methods are less accurate and precise, especially in neonates with a mean arterial pressure <30mm Hg. Proper BP measurement is critical in neonates with naturally lower BP and attention to BP cuff size, location, and method of measurement are essential. With decreasing use of intra-arterial catheters for long-term BP monitoring in neonates, further studies are urgently needed to validate and develop oscillometric methodology with enhanced accuracy.

Safety and effectiveness of lumacaftor-ivacaftor in adults with cystic fibrosis: A single-center Canadian experience

RATIONALE: Lumacaftor-ivacaftor (LUM-IVA) was approved for use in Canada in January 2016. Observational studies have reported a higher incidence of treatment-emergent adverse events (AEs) leading to treatment discontinuation compared to clinical trials. There is still limited data about long-term tolerability and rates of discontinuation in patients who would not have met clinical trial eligibility criteria. OBJECTIVE: To assess the long-term safety and effectiveness of LUM-IVA in a real-world setting. METHODS: We conducted a single-center retrospective cohort study at St. Paul’s Hospital (Vancouver, Canada). We tracked changes in percent-predicted FEV1 (ppFEV1), body mass index (BMI), sweat chloride concentration, blood pressure (BP) and pulmonary exacerbations pre- and post-initiation of LUM-IVA. We noted AEs and treatment discontinuations. RESULTS: Of 22 patients who started on LUM-IVA as part of routine clinical care, 10 (45%) discontinued therapy after a median of 3.3 months. At initiation, median (IQR) ppFEV1 was 40.1% (32.7%, 55.9%). Respiratory-related symptoms were the most common AEs (59%). We observed a statistically significant increase in BP (p = 0.004). Respiratory-related symptoms and increased BP were the most common reasons for treatment discontinuation, including one instance of hypertensive emergency. There was no change in the median rate of ppFEV1 decline or BMI change despite a statistically significant decrease in sweat chloride concentration 3 months’ post-initiation (p < 0.001). CONCLUSION: The rate of LUM-IVA discontinuation in this adult cystic fibrosis (CF) cohort was high and similar to other observational studies, but we also report cases of increased BP leading to treatment discontinuation. We recommend long-term monitoring of blood pressure for patients on LUM-IVA.

Mobile health technology (WeChat) for the hierarchical management of community hypertension: protocol for a cluster randomized controlled trial.

PurposeThe prevalence of hypertension continues to increase worldwide, raising an urgent need for novel and efficient methods for controlling hypertension. As the Internet and smartphones become more popular, their multiple functions and large user base make mobile health (mHealth) technology a potential tool for hypertension management. We aim to evaluate the use of mHealth technology to improve blood pressure and self-management behavior in people with hypertension and prehypertension.InterventionThe mHealth intervention measures include health education, behavior promotion, group chatting and long-term blood pressure monitoring hierarchically delivered via WeChat application among 242 participants. The frequency, intensity and content of the hierarchical intervention are determined based on the cardiovascular risk stratification of the intervention subjects.Study designThis cluster randomized controlled trial was carried out in two subdistricts in Guangzhou, China, among 492 smartphone users with hypertension or prehypertension, from August 2018 to September 2019. The intervention group received hierarchical intervention through WeChat for six months, while the control group received usual care in the community healthcare center during this period. Indicators are measured at three time points for each group, and a telephone follow-up is planned for two years after the intervention. The primary outcome is systolic blood pressure; secondary outcomes include BMI, CPAT score, improvements in behavior and diet, score of self-efficacy and self-management. Feasibility is evaluated by intervention participation. The cost-effectiveness is evaluated by ICER.ConclusionThis study aims to evaluate the effect of the WeChat-based hierarchical management mode on improving blood pressure and self-management behavior in population with hypertension and prehypertension, based on health-related knowledge, self-efficacy and medication adherence. If successful, the management mode will serve as a feasible, economical and efficient hypertension management mode suitable for the community.Clinical trial identifier: ChiCTR1900023002

Accelerometric patch probe for cuffless blood pressure evaluation from carotid local pulse wave velocity: design, development, and in vivo experimental study

Objective: In this work, we demonstrate an accelerometric patch probe and associated measurement system for local pulse wave velocity (PWV) measurement and its application in cuffless blood pressure (BP) measurement. Approach: The proposed system consists of dual accelerometric patch probe to capture acceleration plethysmography (APG) signals from the carotid artery. The probe was integrated to an application-specific analog front-end circuitry with negligible inter-channel delay and a data acquisition module. Real-time signal processing and local PWV evaluation were performed using custom software. The functionality of the developed system and the relationship between local PWV and reference BP parameters were experimentally validated by multiple in vivo studies on a cohort of 26 subjects. Inter- and intra-subject BP-local pulse transit time (PTT) models were developed and used for cuffless BP measurement. Further, the reliability of the proposed method in long-term BP monitoring was validated by performing a study over a week. Main results: Reliability of the proposed novel approach for local PWV measurement using APG signals has been demonstrated. Measured baseline carotid local PWV values were in the range of 3–4.2 m s−1, with high reproducibility (R = 0.94) and with an inter-beat variation range of 2.61%–15.5%. Mean local PTT versus brachial systolic, diastolic, and mean arterial BP obtained from both sitting and standing posture correlated well with an R-value > 0.8. Beat-by-beat BP parameters and local PWV during the post-exercise recovery of each individual yielded statistically significant intra-subject trends. Cuffless BP estimation with intra-subject BP-local PTT models results in more reliable assessments of BP parameters than inter-subject models. The developed BP prediction models found to be reliable over a period of one week with a root-mean-square error ≤1.7 mmHg. Significance: A non-invasive cost-effective system for continuous monitoring central aortic BP parameters and local arterial stiffness indices.

Multi-Sensor Fusion Approach for Cuff-Less Blood Pressure Measurement.

Ambulatory blood pressure (BP) provides valuable information for cardiovascular risk assessment. The present cuff-based devices are intrusive for long-term BP monitoring, whereas cuff-less BP measurement methods based on pulse transit time or multi-parameter are inferior in robustness and reliability by using electrocardiogram (ECG) and photoplethysmogram signals. This study examined a multi-sensor fusion-based platform and algorithm for systolic BP (SBP), mean arterial pressure (MAP), and diastolic BP (DBP) estimation. The proposed multi-sensor platform was comprised of one ECG sensor and two pulse pressure wave sensors for simultaneous signal collection. After extracting 35 features from the collected signals, a weakly supervised feature selection method was proposed for dimension reduction because the reference oscillometric technique-based BP are intermittent and can be redeemed as coarse-grained labels. BP models were then established using a multi-instance regression algorithm. A total of 85 participants including 17 hypertensive and 12 hypotensive patients were enrolled. Experimental results showed that the proposed approach exhibited good accuracy for diverse population with an estimation error of 1.62±7.76 mmHg for SBP, 1.53±6.03 mmHg for MAP, and 1.49 ± 5.52 for DBP, which complied with the association for the advancement of medical instrumentation standards in BP estimation. Moreover, the estimation accuracy is with random daily fluctuations rather than long-term degradation through a maximum two-month follow-up period indicated good robustness performance. These results suggest that the proposed approach is with high reliability and robustness and thus provides a novel insight for cuff-less BP measurement.

Long-term blood pressure monitoring by office and 24-h ambulatory blood pressure in renal transplant patients: a longitudinal study.

Renal transplant patients have a high prevalence of nocturnal hypertension, and hypertension misclassification by office blood pressure (BP) is quite common in these patients. The potential impact of hypertension misclassification by office BP on hypertension management in this population has never been analysed. We performed a longitudinal study in a cohort of 260 clinically stable renal transplant patients. In all, 785 paired office and 24-h ambulatory blood pressure monitoring (24-hABPM) measurements over a median follow-up of 3.9 years were available in the whole cohort. A total of 74% of patients had nocturnal hypertension (>120/70 mmHg). Average office BP and 24-hABPM remained quite stable over follow-up, as did the prevalence of nocturnal hypertension, which was 77% at the last observation. However, the global agreement between office BP and average 24 h, daytime and night-time BP was unsatisfactory (k-statistics 0.10-0.26). In 193 visits (25% of all visits) where office BP indicated the need of antihypertensive therapy institution or modification (BP >140/90 mmHg), 24-hABPM was actually normal (<130/80 mmHg), while in 94 visits (12%), 24-hABPM was in the hypertensive range while office BP was normal. Overall, in 37% of visits, office BP provided misleading therapeutic indications. Hypertension misclassification by office BP is a common phenomenon in stable renal transplant patients on long-term follow-up. Office BP may lead to inappropriate therapeutic decisions in over one-third of follow-up visits in these patients.

Estimation of Blood Pressure in the Radial Artery Using Strain-Based Pulse Wave and Photoplethysmography Sensors.

Blood pressure (BP) is a crucial indicator of cardiac health and vascular status. This study explores the relationship between radial artery BP and wrist skin strain. A BP estimation method based on the physical model of wrist skin tissues and pulse wave velocity (PWV) is proposed. A photoplethysmography (PPG) sensor and strain gauge are used in this method. The developed strain-based pulse wave sensor consists of a pressing force sensor, which ensures consistent pressing force, and a strain gauge, which measures the cardiac pulsation on the wrist skin. These features enable long-term BP monitoring without incurring the limb compression caused by a cuff. Thus, this method is useful for individuals requiring continuous BP monitoring. In this study, the BP of each participant was measured in three modes (before, during, and after exercise), and the data were compared using a clinically validated sphygmomanometer. The percentage errors of diastolic and systolic BP readings were, respectively, 4.74% and 4.49% before exercise, 6.38% and 6.10% during exercise, and 5.98% and 4.81% after a rest. The errors were compared with a clinically validated sphygmomanometer.