Blood Pressure Monitoring System Research Articles

Novel metrics for tracking blood pressure changes incontinuous cuffless blood pressure estimations

Recent studies revealed the importance of tracking continuous blood pressure (BP) changes in monitoring and controlling hypertension and diagnosing cardiovascular diseases. However, current evaluation protocols utilize distance measures as primary metrics, which cannot properly evaluate the ability of the estimation model to track BP changes. This paper proposes a comprehensive evaluation framework which evaluates the distance and trend similarity metrics, and the composite metric of both between the reference and estimated BPs. The results of applying both widely used conventional metrics and the new proposed metrics for BP estimations are demonstrated in an example of comparing the reference with a set of different BP estimations. Then, the metrics are applied to BP estimations obtained using state-of-the-art (SOTA) algorithms. It is shown that even though SOTA algorithms have a low mean and standard deviation of absolute difference, they are not capable of tracking short-term blood pressure changes. Additionally, the proposed metrics are normalized metrics and range from -1 to 1, making them intuitively interpretable, similar to well-known correlation coefficients. Therefore, we suggest that the proposed evaluation framework should be used regularly in evaluating continuous BP monitoring systems.

IMPLEMENTASI INTERNET OF THINGS PADA SISTEM PEMANTAUAN TEKANAN DARAH LANSIA

The expanding elderly population demands special attention, including health monitoring. Blood pressure is an important health indicator for the elderly and should be checked on a regular basis to avoid complications. According to the World Health Organization (WHO), around 1.13 billion people worldwide were diagnosed with hypertension in 2015, which equates to one in every three people. Because of the difficulties in getting to hospitals, the elderly do not constantly monitor their blood pressure. Even while measuring at home, people may neglect to document the results. This paper presents the development of an IoT-based blood pressure monitoring system for the elderly that utilizes the MPX5700AP sensor and can display and store measurements via Blynk and Telegram. Data and information collection, requirement identification, system design, device assembly, implementation, system testing, and assessment are all part of the approach. The test results indicate that the system can precisely detect blood pressure. Thus, this technology can assist the elderly by automatically capturing blood pressure measurements.

Performance Evaluation of Deep Learning Architectures for Blood Pressure Estimation Using Photoplethysmography

High blood pressure (BP) monitoring Blood pressure (BP) is one of the common cardiovascular diseases and therefore the early high blood pressure (hypertension) detection, management, and prevention are mandatory. One promising method of continuous, non-invasive blood pressure estimation is photoplethysmography (PPG). In this study, a novel method was proposed to introduce the AlexNet framework into the time-frequency domain for classification of BP levels based on PPG signals. The study was conducted using the publicly available Figshare dataset which offers PPG signals, and the blood pressure labels against them. Data balancing techniques were used to alleviate class imbalances. Preprocessing and Feature Extraction of PPG Signals. The PPG signals were preprocessed with noise filtering and signals were then transformed from 1D-time to image to facilitate robust feature extraction. The proposed classification model, based on AlexNet showed the best result, with 98.89% accuracy, recall, and precision, and 99.44% specificity. This model outperformed alternative models (VGG16, DenseNet, ResNet50, GoogleNet) for classifying BP levels into the JNC 7 report standard categories normotension, prehypertension and hypertension. This study has two primary contributions. Initially, it demonstrates the efficacy of AlexNet model to extract meaningful features from PPG signals by its hierarchical convolutional and max-pooling layers thereby enabling accurate classification of BP levels. This study underscores the potential of deep learning and PPG signals for developing a highly accurate and truly non-invasive BP monitoring system. In the second aspect, the study offers a systematic assessment and comparison of the proposed over other well-known deep-learning networks, presenting the effectiveness of the AlexNet-based one. These results are of critical importance in the development of novel non-invasive BP monitoring modalities and optimization of cardiovascular health managements and personalized health cares.

Automated image transcription for perinatal blood pressure monitoring using mobile health technology.

This paper introduces a novel approach to address the challenges associated with transferring blood pressure (BP) data obtained from oscillometric devices used in self-measured BP monitoring systems to integrate this data into medical health records or a proxy database accessible by clinicians, particularly in low literacy populations. To this end, we developed an automated image transcription technique to effectively transcribe readings from BP devices, ultimately enhancing the accessibility and usability of BP data for monitoring and managing BP during pregnancy and the postpartum period, particularly in low-resource settings and low-literate populations. In the designed study, the photos of the BP devices were captured as part of perinatal mobile health (mHealth) monitoring programs, conducted in four studies across two countries. The Guatemala Set 1 and Guatemala Set 2 datasets include the data captured by a cohort of 49 lay midwives from 1697 and 584 pregnant women carrying singletons in the second and third trimesters in rural Guatemala during routine screening. Additionally, we designed an mHealth system in Georgia for postpartum women to monitor and report their BP at home with 23 and 49 African American participants contributing to the Georgia I3 and Georgia IMPROVE projects, respectively. We developed a deep learning-based model which operates in two steps: LCD localization using the You Only Look Once (YOLO) object detection model and digit recognition using a convolutional neural network-based model capable of recognizing multiple digits. We applied color correction and thresholding techniques to minimize the impact of reflection and artifacts. Three experiments were conducted based on the devices used for training the digit recognition model. Overall, our results demonstrate that the device-specific model with transfer learning and the device independent model outperformed the device-specific model without transfer learning. The mean absolute error (MAE) of image transcription on held-out test datasets using the device-independent digit recognition were 1.2 and 0.8 mmHg for systolic and diastolic BP in the Georgia IMPROVE and 0.9 and 0.5 mmHg in Guatemala Set 2 datasets. The MAE, far below the FDA recommendation of 5 mmHg, makes the proposed automatic image transcription model suitable for general use when used with appropriate low-error BP devices.

Comparison of the ClearSight™ finger cuff monitor versus invasive arterial blood pressure measurement in elective cardiac surgery patients: a prospective observational study.

To determine the acceptability of the ClearSight™ system (Edwards Lifesciences Corp., Irvine, CA, USA) for continuous blood pressure monitoring during elective cardiac surgery compared with arterial catheterization. We enrolled 30 patients undergoing elective cardiac surgery in a prospective observational study. Blood pressure measurements were recorded every 10 sec intraoperatively. We determined agreement based on the Association for the Advancement of Medical Instrumentation (AAMI) recommendations. Statistical analysis included fixed bias (difference of measurements between methods), percentage error (accuracy between ClearSight measurement and expected measurement from arterial line), and interchangeability (ability to substitute ClearSight monitor without effecting overall outcome of analysis). We used a paired samples t test to compare the time required for placing each monitor. We found fixed bias in the differences between the ClearSight monitor and invasive arterial blood pressure measurement in systolic blood pressure (SBP; mean difference, 8.7; P < 0.001) and diastolic blood pressure (DBP; mean difference, -2.2; P < 0.001), but not in mean arterial pressure (MAP; mean difference, -0.5; P < 0.001). Bland-Altman plots showed that the means of the limits of agreement were greater than 5mm Hg for SBP, DBP, and MAP. The percentage errors for SBP, DBP, and MAP were lower than the cutoff we calculated from the invasive arterial blood pressure measurements. Average interchangeability rates were 38% for SBP, 50% for DBP, and 50% for MAP. Placement of the ClearSight finger cuff was significantly faster compared with arterial catheterization (mean [standard deviation], 1.7 [0.6] min vs 5.6 [4.1] min; P < 0.001). In this prospective observational study, we did not find the ClearSight system to be an acceptable substitute for invasive arterial blood pressure measurement in elective cardiac surgery patients according to AAMI guidelines. Nevertheless, based on statistical standards, there is evidence to suggest otherwise. ClinicalTrials.gov ( NCT05825937 ); first submitted 11 April 2023.

Digital Twin in Managing Hypertension Among People With Type 2 Diabetes: 1-Year Randomized Controlled Trial

BackgroundDigital twin (DT)-guided lifestyle changes induce type 2 diabetes (T2D) remission but effects on hypertension (HTN) in this population are unknown. ObjectivesThe purpose of this study was to assess effects of DT vs standard of care (SC) on blood pressure (BP), anti-HTN medication, HTN remission, and microalbuminuria in participants with T2D. MethodsThis is a secondary analysis of a randomized controlled trial in India of 319 participants with T2D. Participants were randomized to DT group (N = 233), which used artificial intelligence-enabled DT technology, or SC group (N = 86). A Home Blood Pressure Monitoring system guided anti-HTN medication adjustments. BP, anti-HTN medications, HTN remission rates, and microalbuminuria were compared between groups. ResultsAmong the 319 participants, 44 in DT and 15 in SC group were on anti-HTN medications, totaling 59 (18.4%) participants. DT group achieved significant reductions in systolic blood pressure (−7.6 vs −3.2 mm Hg; P < 0.007) and diastolic blood pressure (−4.3 vs −2.2 mm Hg; P = 0.046) after 1 year compared with SC group. 68.2% of DT group remained off anti-HTN medications compared to none in SC group. Among participants with HTN, DT subgroup achieved higher rates of normotension (40.9% vs 6.7%; P = 0.0009) and HTN remission (50% vs 0%; P < 0.0001) than SC subgroup. DT group had a higher rate of achieving normoalbuminuria (92.4% vs 83.1%; P = 0.018) at 1 year compared with SC group. ConclusionsArtificial intelligence -enabled DT technology is more effective than SC in reducing BP and anti-HTN medications and inducing HTN remission and normoalbuminuria in participants with HTN and T2D. (A Novel WholeBody Digital Twin Enabled Precision Treatment for Reversing Diabetes; CTRI/2020/08/027072)

Real-time monitoring system for blood pressure monitoring based on internet of things

Real-time monitoring system for blood pressure monitoring based on internet of things

Revolutionizing Chronic Heart Disease Management: The Role of IoT-Based Ambulatory Blood Pressure Monitoring System.

Chronic heart disease (CHD) is a widespread and persistent health challenge that demands immediate attention. Early detection and accurate diagnosis are essential for effective treatment and management of this condition. To overcome this difficulty, we created a state-of-the-art IoT-Based Ambulatory Blood Pressure Monitoring System that provides real-time blood pressure readings, systolic, diastolic, and pulse rates at predefined intervals. This unique technology comes with a module that forecasts CHD's early warning score. Various machine learning algorithms employed comprise Naïve Bayes, K-Nearest Neighbors (K-NN), random forest, decision tree, and Support Vector Machine (SVM). Using Naïve Bayes, the proposed model has achieved an impressive 99.44% accuracy in predicting blood pressure, a vital aspect of real-time intensive care for CHD. This IoT-based ambulatory blood pressure monitoring (IABPM) system will provide some advancement in the field of healthcare. The system overcomes the limitations of earlier BP monitoring devices, significantly reduces healthcare costs, and efficiently detects irregularities in chronic heart diseases. By implementing this system, we can take a significant step forward in improving patient outcomes and reducing the global burden of CHD. The system's advanced features provide an accurate and reliable diagnosis that is essential for treating and managing CHD. Overall, this IoT-based ambulatory blood pressure monitoring system is an important tool for the early identification and treatment of CHD in the field of healthcare.

Investigating the Sources of Variability in Blood Pressure Monitoring Systems

Accurate and reliable blood pressure measurement is critical for diagnosing and managing hypertension properly. Based on a consensus document announced in the Journal of Hypertension, this is so much so that a 5.0 mm Hg BP measurement error can result in incorrect hypertension classification in 84 million individuals worldwide. Understanding how BP measurement goes wrong and how to tackle them can improve the diagnosis and management of hypertension. Unfortunately, BP measurement is problematically performed in clinical practice, accumulating errors that inappropriately alter management decisions in 20% to 45% of cases. In this study, the sources of variability from the aspects of the blood pressure monitoring system were attempted to be scrutinized. The variability increases with additive properties such as pressure gauge inaccuracy, cuff tightness, and cloth over-measurement. An artificial plastic arm was created to measure the pressure with multiple monitors. The study concluded that each monitor had its inherent characteristics in increasing monitor pressures concerning the actual pressure, and further, the increases in cuff loosening distance and the cloth-over layers might cause more significant variability, as hypothesized in this study. The study could more clearly verify the reason for blood pressure measurement variability.

An Intelligent Non-Invasive Blood Pressure Monitoring System Based on a Novel Polyvinylidene Fluoride Piezoelectric Thin Film.

Hypertension is a common cause of cardiovascular diseases, closely associated with the high mortality and disability rates of cardiovascular diseases such as stroke and coronary heart disease. Therefore, developing a comfortable and sustainable device for monitoring human pulse signals holds practical significance for the prevention and treatment of hypertension and cardiovascular diseases. PVDF flexible pressure sensors possess the characteristics of high sensitivity, good flexibility, and strong biocompatibility, thereby demonstrating extensive application potential in areas such as health monitoring, wearable devices, and electronic skins. This paper focuses on the development of a modified piezoelectric polymer and its application in an intelligent blood pressure monitoring system, demonstrating its outstanding performance and feasibility through a series of experiments. This research provides innovative material choices for the development of intelligent medical devices and offers beneficial guidance for the design and application of future intelligent health monitoring systems.

UWB-enabled Sensing for Fast and Effortless Blood Pressure Monitoring

Blood Pressure (BP) is a critical vital sign to assess cardiovascular health. However, existing cuff-based and wearable-based BP measurement methods require direct contact between the user's skin and the device, resulting in poor user experience and limited engagement for regular daily monitoring of BP. In this paper, we propose a contactless approach using Ultra-WideBand (UWB) signals for regular daily BP monitoring. To remove components of the received signals that are not related to the pulse waves, we propose two methods that utilize peak detection and principal component analysis to identify aliased and deformed parts. Furthermore, to extract BP-related features and improve the accuracy of BP prediction, particularly for hypertensive users, we construct a deep learning model that extracts features of pulse waves at different scales and identifies the different effects of features on BP. We build the corresponding BP monitoring system named RF-BP and conduct extensive experiments on both a public dataset and a self-built dataset. The experimental results show that RF-BP can accurately predict the BP of users and provide alerts for users with hypertension. Over the self-built dataset, the mean absolute error (MAE) and standard deviation (SD) for SBP are 6.5 mmHg and 6.1 mmHg, and the MAE and SD for DBP are 4.7 mmHg and 4.9 mmHg.

NEW HOME BLOOD PRESSURE TELEMONITORING SYSTEM IN AN AGED COMMUNITY: PROTOCOL OF THE DIGITAL CARDIOVASCULAR PREVENTION FEASIBILITY STUDY

Objective: We sought to (i) determine the precise feasibility of a new telemonitoring-based healthcare system in a community of aged individuals and (ii) clarify the clinical implications of blood pressure variabilities in a time series until the end of life for the management of hypertension in elderly patients. Design and method: We have devised a simple, easy-to-use blood-pressure monitoring system and automated telemonitoring system, i.e., the DICAP (DIgital Cardiovascular Prevention) system, that obtains all of the different blood pressure values measured in a time series in different settings in community-dwelling elderly individuals in their homes and other facilities. Results: The feasibility of the DICAP system was confirmed by the successful serial transmission of the home blood pressure data of the community-dwelling hypertensive patients including those who were not familiar with digital technology and those who resided at different community locations. Among the first-recruited 221 subjects, 92.3% have been monitoring their blood pressure with the DICAP &gt;12 times/week. Conclusions: The newly developed DICAP system using a cellular blood-pressure monitoring device suggests the possibility of being feasible and acceptable for the management of hypertension among elderly community-living individuals.

GENDER DIFFERENCES IN ARTERIAL STIFFNESS IN YOUNG PATIENTS WITH PRIMARY HYPERTENSION

Objective: Influence of sex hormones on regulation of arterial stiffness (AS) include different mechanisms where changes in extracellular matrix, smooth and endothelial cells, and influence on oxidative stress and chronic inflammation are highlighted. In women after puberty AS decreases what confirm selective and fluctuating effect of sex hormones, primarly estrogen. In older men, hypogonadism is associated with incresed AS, while testosterone replacement reduce pulse wave velocity (PWV). Our aim was to analyze difference in central hemodynamic parameters between men and women with stage 1 hypertension. Design and method: This study included 154 patients with the primary hypertnsion (69.5% women, mean age 38.5). In all of them we collected different data and antroplogical paramaters using medical records. Parameters of AS were measured with the office tonometric system (SphygmoCor) and 24h ambulatory blood pressure monitoring system using Mobil-o-Graph and Arteriograph, but also estimated PWV was calculated using validated equation (ePWV). Results: There were no statistically significant gender differences in PWV values measured in the office oCor, measured during 24 hours (using Arteriograph and Mobil-O-Graph), and ePWV. There were no statistically significant gender differences in the values of 24 hours systolic and diastolic central aortic pressure (CAP) (Arteriograph or Mobil-O-Graph). Women had significant higher office CAP Men, on average, had statistically significantly higher amplified pulse pressure (APP) values than women (144.20 vs. 128.94, t = 3.275, p &lt; 0.01), while women had statistically significantly higher office augumentation index (AiX) values (t = -6.118, p &lt; 0.001), as well 24 hours (Mobil- O-Graph (t = -5.940, p &lt; 0.001;Arteriograph (t = -5.580, p &lt; 0.001). Conclusions: Possible explanation of no statistically significant differences in PWV values between men and women is average age of our group. A possible gender difference in AiX can be consequence of the increased reflection of pulse waves, which is increased in women. Longitudinal study should be conducted with monitoring of patients at longer time intervals in order to determine the further hormonal influence on structure of the arteries and for the assesment of total CV risk.

Validation of the Meditech ABPM-06 24-hour blood pressure monitoring system in a pediatric population according to International Organization for Standardization Protocol 81060-2:2018.

Ambulatory blood pressure monitoring (ABPM) devices play a crucial role in diagnosing hypertension, not only in adults but also in pediatric patients. ABPM-06, the latest oscillometric device from Meditech Ltd. (Budapest, Hungary), is the focus of this study. The objective was to validate the ABPM-06 device using the International Organization for Standardization (ISO) 81060-2 : 2018 standard. A total of 86 healthy patients, consisting of 34 males and 52 females, aged between 3 and 17 years, were enrolled in this study. During the ambulatory phase, a total of 50 patients were enrolled, with 35 patients falling within the 3- to 12-year-old age range and 15 patients aged between 12 and 17 years. Additionally, for the dynamic test, 36 patients were selected, comprising of 10 individuals aged 3-12 years and 26 patients aged 12-17 years. These patients were recruited from the outpatient clinics of the Department of Pediatrics at Albert Szent-Györgyi University in Szeged, Hungary. The validation process involved utilizing the same-arm sequence protocol, both in resting positions and during stress testing. The ABPM-06 performed well in both clinical and ambulatory validations. In terms of validation criterion 1, the mean ± SD of the differences between the test device and reference blood pressure readings was -1.3 ± 3.5 mmHg for systolic and -0.1 ± 2.3 mmHg for diastolic, in children under the age of 12 years. For those over the age of 12 years, the mean ± SD of the differences was -2.8 ± 4.6 mmHg for systolic and -0.5 ± 2.7 mmHg for diastolic. Regarding the ambulatory validation, for children under 12 years old, the mean ± SD of the differences was -1.3 ± 3.5 mmHg for systolic and -0.1 ± 2.3 mmHg for diastolic. In the age group above 12 years, the mean ± SD of the differences was -2.8 ± 4.6 mmHg for systolic and -0.5 ± 2.7 mmHg for diastolic. Both tests successfully met the established criteria regarding the mean and SD values of the differences between the device readings and the observed SBP and DBP measurements. The ABPM-06 oscillometric device fully adheres to the ISO 81060-2 : 2018 standard requirements for ABPM determination in the pediatric population (ages 3-17 years). Consequently, this ABPM device proves to be suitable for effectively managing hypertension in children and adolescents.

Video-based beat-by-beat blood pressure monitoring via transfer deep-learning

Currently, learning physiological vital signs such as blood pressure (BP), hemoglobin levels, and oxygen saturation, from Photoplethysmography (PPG) signal, is receiving more attention. Despite successive progress that has been made so far, continuously revealing new aspects characterizes that field as a rich research topic. It includes a diverse number of critical points represented in signal denoising, data cleaning, employed features, feature format, feature selection, feature domain, model structure, problem formulation (regression or classification), and model combinations. It is worth noting that extensive research efforts are devoted to utilizing different variants of machine learning and deep learning models while transfer learning is not fully explored yet. So, in this paper, we are introducing a per-beat rPPG-to-BP mapping scheme based on transfer learning. An interesting representation of a 1-D PPG signal as a 2-D image is proposed for enabling powerful off-the-shelf image-based models through transfer learning. It resolves limitations about training data size due to strict data cleaning. Also, it enhances model generalization by exploiting underlying excellent feature extraction. Moreover, non-uniform data distribution (data skewness) is partially resolved by introducing logarithmic transformation. Furthermore, double cleaning is applied for training contact PPG data and testing rPPG beats as well. The quality of the segmented beats is tested by checking some of the related quality metrics. Hence, the prediction reliability is enhanced by excluding deformed beats. Varying rPPG quality is relaxed by selecting beats during intervals of the highest signal strength. Based on the experimental results, the proposed system outperforms the state-of-the-art systems in the sense of mean absolute error (MAE) and standard deviation (STD). STD for the test data is decreased to 5.4782 and 3.8539 for SBP and DBP, respectively. Also, MAE decreased to 2.3453 and 1.6854 for SBP and DBP, respectively. Moreover, the results for BP estimation from real video reveal that the STD reaches 8.027882 and 6.013052 for SBP and DBP, respectively. Also, MAE for the estimated BP from real videos reaches 7.052803 and 5.616028 for SBP and DBP, respectively.Graphical abstractProposed camera-based blood pressure monitoring system

OVAR-BPnet: A General Pulse Wave Deep Learning Approach for Cuffless Blood Pressure Measurement.

Pulse wave analysis, a non-invasive and cuff-less approach, holds promise for blood pressure (BP) measurement in precision medicine. In recent years, pulse wave learning for BP estimation has undergone extensive scrutiny. However, prevailing methods still encounter challenges in grasping comprehensive features from pulse waves and generalizing these insights for precise BP estimation. In this study, we propose a general pulse wave deep learning (PWDL) approach for BP estimation, introduc-ing the OVAR-BPnet model to powerfully capture intricate pulse wave features and showcasing its effectiveness on multiple types of pulse waves. The approach involves constructing population pulse waves and employing a model comprising an omni-scale convolution subnet, a Vision Transformer subnet, and a multilayer perceptron subnet. This design enables the learning of both single-period and multi-period waveform features from multiple subjects. Additionally, the approach employs a data augmentation strategy to enhance the morphological features of pulse waves and devise a label sequence regularization strategy to strengthen the intrinsic relationship of the subnets' output. Notably, this is the first study to validate the performance of the deep learning approach of BP estimation on three types of pulse waves: photoplethysmography, forehead imaging photoplethysmography, and radial artery pulse pressure waveform. Experiments show that the OVAR-BPnet model has achieved advanced levels in both evaluation indicators and international evaluation criteria, demonstrating its excellent competitiveness and generalizability. The PWDL approach has the potential for widespread application in convenient and continuous BP monitoring systems.

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.

Blood Pressure Monitoring Based on Flexible Encapsulated Sensors

Blood pressure monitoring is a significant concern in the field of healthcare, and the utilization of flexible encapsulated sensors presents a promising solution for achieving noninvasive and comfortable monitoring. This paper presents a study on the flexible encapsulation of MEMS pressure sensors and the development of an enhanced arterial tonometry method for blood pressure measurement, ultimately leading to the realization of a blood pressure monitoring system based on flexible encapsulated sensors. To improve wearer comfort and acquire reliable pulse signals, a flexible encapsulation sensor combining parylene and PDMS materials was fabricated. Additionally, to address the issue of low accuracy in blood pressure measurement, various machine learning algorithms were compared and analyzed, leading to the identification of the random forest model as the optimal regressor. Consequently, a blood pressure monitoring system based on the improved arterial tension method was designed and implemented. The experimental results demonstrate that the proposed system achieved a significant enhancement of 31.4% and 21% in the accuracy of systolic and diastolic blood pressure measurements, respectively, compared with the arterial tension method.

Clinical Validation of Wearable Piezoelectric Blood Pressure Sensor for Continuous Health Monitoring.

Wearable blood pressure sensors have recently attracted attention as healthcare devices for continuous non-invasive arterial pressure (CNAP) monitoring. However, the accuracy of wearable blood pressure (BP) monitoring devices has been controversial due to the low signal quality of sensors, the absence of an accurate transfer function to convert the sensor signals into BP values, and the lack of clinical validation regarding measurement precision. Here, we report a wearable piezoelectric blood pressure sensor (WPBPS), which achieved a high normalized sensitivity (0.062 kPa-1 ), fast response time (23ms) for CNAP monitoring. The transfer function of a linear regression model was designed, offering a simple solution to convert the flexible piezoelectric sensor signals into BP values. In order to verify the measurement accuracy of WPBPS, clinical trials were performed on 35 subjects aged from 20s to 80s after screening. The mean difference between the WPBPS and a commercial sphygmomanometer of 175 BP data pairs was -0.89 ± 6.19mmHg and -0.32 ± 5.28mmHg for systolic blood pressure (SBP) and diastolic blood pressure (DBP), respectively. By building a WPBPS-embedded wristwatch, we demonstrated the potentially promising use of a convenient, portable continuous BP monitoring system for cardiovascular disease diagnosis in this study. This article is protected by copyright. All rights reserved.

Neutrophil and monocyte ratios to high-density lipoprotein cholesterol as biomarkers in non-dipping hypertension

ABSTRACT Objective To investigate the level and significance of neutrophils to high-density lipoprotein cholesterol ratio (NHR) and monocytes to high-density lipoprotein cholesterol ratio (MHR) in patients with non-dipping hypertension. Methods A total of 228 patients were retrospectively enrolled in the study. They were divided into the dipping hypertension group (n = 76), the non-dipping hypertension group (n = 77) and the control group (n = 75) according to 24-h ambulatory blood pressure monitoring system (ABPM) recordings. NHR and MHR were calculated and compared statistically. Receiver operating characteristic (ROC) curve analyses were performed for NHR and MHR. Binary logistic regression analyses were introduced to investigate the independent associations of NHR and MHR with non-dipping hypertension. Results The NHR and MHR were significantly higher in the non-dipping hypertension group compared with the control group (p = .001, p < .001, respectively) and the dipping hypertension group (p = .039, p = .003, respectively). According to ROC curve analyses, NHR>73.35 and MHR>7.54 were regarded as high-risk groups. The area under the curve (AUC) was 0.642 (p < .001) for NHR and 0.718 (p < .001) for MHR. In multivariate analysis, compared with NHR, only MHR was still recognized as a marker for detection of non-dipping hypertension (odds ratio [OR]: 1.208, 95% confidence interval [CI]: 1.076 to 1.356, p = .001). Conclusions Our data indicated that not NHR but MHR as new composite marker of inflammation and lipid metabolism may predict non-dipping hypertension to some extent.