An armband wearable device for overnight and cuff-less blood pressure measurement

A framework for integrating artificial intelligence for clinical care with continuous therapeutic monitoring

The complex relationships between continuously monitored health signals and therapeutic regimens can be modelled via machine learning. However, the clinical implementation of the models will require changes to clinical workflows. Here we outline ClinAIOps ('clinical artificial-intelligence operations'), a framework that integrates continuous therapeutic monitoring and the development of artificial intelligence (AI) for clinical care. ClinAIOps leverages three feedback loops to enable the patient to make treatment adjustments using AI outputs, the clinician to oversee patient progress with AI assistance, and the AI developer to receive continuous feedback from both the patient and the clinician. We lay out the central challenges and opportunities in the deployment of ClinAIOps by means of examples of its application in the management of blood pressure, diabetes and Parkinson's disease. By enabling more frequent and accurate measurements of a patient's health and more timely adjustments to their treatment, ClinAIOps may substantially improve patient outcomes.

DNN-BP: a novel framework for cuffless blood pressure measurement from optimal PPG features using deep learning model

Continuous blood pressure (BP) provides essential information for monitoring one's health condition. However, BP is currently monitored using uncomfortable cuff-based devices, which does not support continuous BP monitoring. This paper aims to introduce a blood pressure monitoring algorithm based on only photoplethysmography (PPG) signals using the deep neural network (DNN). The PPG signals are obtained from 125 unique subjects with 218 records and filtered using signal processing algorithms to reduce the effects of noise, such as baseline wandering, and motion artifacts. The proposed algorithm is based on pulse wave analysis of PPG signals, extracted various domain features from PPG signals, and mapped them to BP values. Four feature selection methods are applied and yielded four feature subsets. Therefore, an ensemble feature selection technique is proposed to obtain the optimal feature set based on major voting scores from four feature subsets. DNN models, along with the ensemble feature selection technique, outperformed in estimating the systolic blood pressure (SBP) and diastolic blood pressure (DBP) compared to previously reported approaches that rely only on the PPG signal. The coefficient of determination ( R 2 ) and mean absolute error (MAE) of the proposed algorithm are 0.962 and 2.480 mmHg, respectively, for SBP and 0.955 and 1.499 mmHg, respectively, for DBP. The proposed approach meets the Advancement of Medical Instrumentation standard for SBP and DBP estimations. Additionally, according to the British Hypertension Society standard, the results attained Grade A for both SBP and DBP estimations. It concludes that BP can be estimated more accurately using the optimal feature set and DNN models. The proposed algorithm has the potential ability to facilitate mobile healthcare devices to monitor continuous BP.

Wearable cuffless blood pressure monitoring devices: a commentary

Hypertension is a growing public challenge as a leading risk factor for cardiovascular disease and all-cause mortality. Reducing overall cardiovascular risk through early screening, initiation of treatment and ongoing monitoring remains a priority in the comprehensive management of hypertension and its complications. Community nurses are ideally positioned to play a crucial role in the early detection of hypertension and providing support for its management. Wearable cuffless devices have the potential for continuous remote blood pressure monitoring. However, there is not enough literature on the validity and usability of wearable cuffless blood pressure devices to justify their use in clinical practice. This commentary critically appraises a systematic review designed to assess the validity, features and clinical usability of wearable cuffless devices, and expands on its findings and their relevance to community nursing and future research.

A Physical Model-Based Approach to One-Point Calibration of Pulse Transit Time to Blood Pressure

OBJECTIVE

To develop a novel physical model-based approach to enable 1-point calibration of pulse transit time (PTT) to blood pressure (BP).

METHODS

The proposed PTT-BP calibration model is derived by combining the Bramwell-Hill equation and a phenomenological model of the arterial compliance (AC) curve. By imposing a physiologically plausible constraint on the skewness of AC at positive and negative transmural pressures, the number of tunable parameters in the PTT-BP calibration model reduces to 1. Hence, as opposed to most existing PTT-BP calibration models requiring multiple (≥2) PTT-BP measurements to personalize, the PTT-BP calibration model can be personalized to an individual subject using a single PTT-BP measurement pair. Equipped with the physically relevant PTT-AC and AC-BP relationships, the proposed approach may serve as a universal means to calibrate PTT to BP over a wide BP range. The validity and proof-of-concept of the proposed approach were evaluated using PTT and BP measurements collected from 22 healthy young volunteers undergoing large BP changes.

RESULTS

The proposed approach modestly yet significantly outperformed an empiric linear PTT-BP calibration with a group-average slope and subject-specific intercept in terms of bias (5.5 mmHg vs 6.4 mmHg), precision (8.4 mmHg vs 9.4 mmHg), mean absolute error (7.8 mmHg vs 8.8 mmHg), and root-mean-squared error (8.7 mmHg vs 10.3 mmHg, all in the case of diastolic BP).

CONCLUSION

We demonstrated the preliminary proof-of-concept of an innovative physical model-based approach to one-point PTT-BP calibration.

SIGNIFICANCE

The proposed physical model-based approach has the potential to enable more accurate and convenient calibration of PTT to BP.

European Society of Hypertension recommendations for the validation of cuffless blood pressure measuring devices: European Society of Hypertension Working Group on Blood Pressure Monitoring and Cardiovascular Variability

BACKGROUND

There is intense effort to develop cuffless blood pressure (BP) measuring devices, and several are already on the market claiming that they provide accurate measurements. These devices are heterogeneous in measurement principle, intended use, functions, and calibration, and have special accuracy issues requiring different validation than classic cuff BP monitors. To date, there are no generally accepted protocols for their validation to ensure adequate accuracy for clinical use.

OBJECTIVE

This statement by the European Society of Hypertension (ESH) Working Group on BP Monitoring and Cardiovascular Variability recommends procedures for validating intermittent cuffless BP devices (providing measurements every >30 sec and usually 30-60 min, or upon user initiation), which are most common.

VALIDATION PROCEDURES

Six validation tests are defined for evaluating different aspects of intermittent cuffless devices: static test (absolute BP accuracy); device position test (hydrostatic pressure effect robustness); treatment test (BP decrease accuracy); awake/asleep test (BP change accuracy); exercise test (BP increase accuracy); and recalibration test (cuff calibration stability over time). Not all these tests are required for a given device. The necessary tests depend on whether the device requires individual user calibration, measures automatically or manually, and takes measurements in more than one position.

CONCLUSION

The validation of cuffless BP devices is complex and needs to be tailored according to their functions and calibration. These ESH recommendations present specific, clinically meaningful, and pragmatic validation procedures for different types of intermittent cuffless devices to ensure that only accurate devices will be used in the evaluation and management of hypertension.

Meta-Analysis of Pulse Transition Features in Non-Invasive Blood Pressure Estimation Systems: Bridging Physiology and Engineering Perspectives

The pulse transition features (PTFs), including pulse arrival time (PAT) and pulse transition time (PTT), hold significant importance in estimating non-invasive blood pressure (NIBP). However, the literature showcases considerable variations in terms of PTFs' correlation with blood pressure (BP), accuracy in NIBP estimation, and the comprehension of the relationship between PTFs and BP. This inconsistency is exemplified by the wide-ranging correlations reported across studies investigating the same feature. Furthermore, investigations comparing PAT and PTT have yielded conflicting outcomes. Additionally, PTFs have been derived from various bio-signals, capturing distinct characteristic points like the pulse's foot and peak. To address these inconsistencies, this study meticulously reviews a selection of such research endeavors while aligning them with the biological intricacies of blood pressure and the human cardiovascular system (CVS). Each study underwent evaluation, considering the specific signal acquisition locale and the corresponding recording procedure. Moreover, a comprehensive meta-analysis was conducted, yielding multiple conclusions that could significantly enhance the design and accuracy of NIBP systems. Grounded in these dual aspects, the study systematically examines PTFs in correlation with the specific study conditions and the underlying factors influencing the CVS. This approach serves as a valuable resource for researchers aiming to optimize the design of BP recording experiments, bio-signal acquisition systems, and the fine-tuning of feature engineering methodologies, ultimately advancing PTF-based NIBP estimation.

Nocturnal Blood Pressure Estimation from Sleep Plethysmography Using Machine Learning

BACKGROUND

Elevated nocturnal blood pressure (BP) is a risk factor for cardiovascular disease (CVD) and mortality. Cuffless BP assessment aided by machine learning could be a desirable alternative to traditional cuff-based methods for monitoring BP during sleep. We describe a machine-learning-based algorithm for predicting nocturnal BP using single-channel fingertip plethysmography (PPG) in healthy adults.

METHODS

Sixty-eight healthy adults with no apparent sleep or CVD (53% male), with a median (IQR) age of 29 (23-46 years), underwent overnight polysomnography (PSG) with fingertip PPG and ambulatory blood pressure monitoring (ABPM). Features based on pulse morphology were extracted from the PPG waveforms. Random forest models were used to predict night-time systolic blood pressure (SBP) and diastolic blood pressure (DBP).

RESULTS

Our model achieved the highest out-of-sample performance with a window length of 7 s across window lengths explored (60 s, 30 s, 15 s, 7 s, and 3 s). The mean absolute error (MAE ± STD) was 5.72 ± 4.51 mmHg for SBP and 4.52 ± 3.60 mmHg for DBP. Similarly, the root mean square error (RMSE ± STD) was 6.47 ± 1.88 mmHg for SBP and 4.62 ± 1.17 mmHg for DBP. The mean correlation coefficient between measured and predicted values was 0.87 for SBP and 0.86 for DBP. Based on Shapley additive explanation (SHAP) values, the most important PPG waveform feature was the stiffness index, a marker that reflects the change in arterial stiffness.

CONCLUSION

Our results highlight the potential of machine learning-based nocturnal BP prediction using single-channel fingertip PPG in healthy adults. The accuracy of the predictions demonstrated that our cuffless method was able to capture the dynamic and complex relationship between PPG waveform characteristics and BP during sleep, which may provide a scalable, convenient, economical, and non-invasive means to continuously monitor blood pressure.

Blood Pressure Measurement Based on the Camera and Inertial Measurement Unit of a Smartphone: Instrument Validation Study

Background

Even though several mobile apps that can measure blood pressure have been developed, the data about the accuracy of these apps are limited.

Objective

We assessed the accuracy of AlwaysBP (test) in blood pressure measurement compared with the standard, cuff-based, manual method of brachial blood pressure measurement (reference).

Methods

AlwaysBP is a smartphone software that estimates systolic blood pressure (SBP) and diastolic blood pressure (DBP) based on pulse transit time (PTT). PTT was calculated with a finger photoplethysmogram and seismocardiogram using, respectively, the camera and inertial measurement unit sensor of a commercially available smartphone. After calculating PTT, SBP and DBP were estimated via the Bramwell-Hill and Moens-Korteweg equations. A calibration process was carried out 3 times for each participant to determine the input parameters of the equations. This study was conducted from March to August 2021 at Chungnam National University Sejong Hospital with 87 participants aged between 19 and 70 years who met specific conditions. The primary analysis aimed to evaluate the accuracy of the test method compared with the reference method for the entire study population. The secondary analysis was performed to confirm the stability of the test method for up to 4 weeks in 15 participants. At enrollment, gender, arm circumference, and blood pressure distribution were considered according to current guidelines.

Results

Among the 87 study participants, 45 (52%) individuals were male, and the average age was 35.6 (SD 10.4) years. Hypertension was diagnosed in 14 (16%) participants before this study. The mean test and reference SBPs were 120.0 (SD 18.8) and 118.7 (SD 20.2) mm Hg, respectively (difference: mean 1.2, SD 7.1 mm Hg). The absolute differences between the test and reference SBPs were <5, <10, and <15 mm Hg in 57.5% (150/261), 84.3% (220/261 ), and 94.6% (247/261) of measurements. The mean test and reference DBPs were 80.1 (SD 12.6) and 81.1 (SD 14.4) mm Hg, respectively (difference: mean -1.0, SD 6.0 mm Hg). The absolute differences between the test and reference DBPs were <5, <10, and <15 mm Hg in 75.5% (197/261), 93.9% (245/261), and 97.3% (254/261) of measurements, respectively. The secondary analysis showed that after 4 weeks, the differences between SBP and DBP were 0.1 (SD 8.8) and -2.4 (SD 7.6) mm Hg, respectively.

Conclusions

AlwaysBP exhibited acceptable accuracy in SBP and DBP measurement compared with the standard measurement method, according to the Association for the Advancement of Medical Instrumentation/European Society of Hypertension/International Organization for Standardization protocol criteria. However, further validation studies with a specific validation protocol designed for cuffless blood pressure measuring devices are required to assess clinical accuracy. This technology can be easily applied in everyday life and may improve the general population's awareness of hypertension, thus helping to control it.

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

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

A Hemodynamic Pulse Wave Simulator Designed for Calibration of Local Pulse Wave Velocities Measurement for Cuffless Techniques

OBJECTIVE

Devices for cuffless blood pressure (BP) measurement have become increasingly widespread in recent years. Non-invasive continuous BP monitor (BPM) devices can diagnose potential hypertensive patients at an early stage; however, these cuffless BPMs require more reliable pulse wave simulation equipment and verification methods. Therefore, we propose a device to simulate human pulse wave signals that can test the accuracy of cuffless BPM devices using pulse wave velocity (PWV).

METHODS

We design and develop a simulator capable of simulating human pulse waves comprising an electromechanical system to simulate the circulatory system and an arm model-embedded arterial phantom. These parts form a pulse wave simulator with hemodynamic characteristics. We use a cuffless device for measuring local PWV as the device under test to measure the PWV of the pulse wave simulator. We then use a hemodynamic model to fit the cuffless BPM and pulse wave simulator results; this model can rapidly calibrate the cuffless BPM's hemodynamic measurement performance.

RESULTS

We first used multiple linear regression (MLR) to generate a cuffless BPM calibration model and then investigated differences between the measured PWV with and without MLR model calibration. The mean absolute error of the studied cuffless BPM without the MLR model is 0.77 m/s, which improves to 0.06 m/s when using the model for calibration. The measurement error of the cuffless BPM at BPs of 100-180 mmHg is 1.7-5.99 mmHg before calibration, which decreases to 0.14-0.48 mmHg after calibration.

CONCLUSION

This study proposes a design of a pulse wave simulator based on hemodynamic characteristics and provides a standard performance verification method for cuffless BPMs that requires only MLR modeling on the cuffless BPM and pulse wave simulator. The pulse wave simulator proposed in this study can be used to quantitively assess the performance of cuffless BPMs. The proposed pulse wave simulator is suitable for mass production for the verification of cuffless BPMs. As cuffless BPMs become increasingly widespread, this study can provide performance testing standards for cuffless devices.

Challenges and Limitation Analysis of an IoT-Dependent System for Deployment in Smart Healthcare Using Communication Standards Features

The use of IoT technology is rapidly increasing in healthcare development and smart healthcare system for fitness programs, monitoring, data analysis, etc. To improve the efficiency of monitoring, various studies have been conducted in this field to achieve improved precision. The architecture proposed herein is based on IoT integrated with a cloud system in which power absorption and accuracy are major concerns. We discuss and analyze development in this domain to improve the performance of IoT systems related to health care. Standards of communication for IoT data transmission and reception can help to understand the exact power absorption in different devices to achieve improved performance for healthcare development. We also systematically analyze the use of IoT in healthcare systems using cloud features, as well as the performance and limitations of IoT in this field. Furthermore, we discuss the design of an IoT system for efficient monitoring of various healthcare issues in elderly people and limitations of an existing system in terms of resources, power absorption and security when implemented in different devices as per requirements. Blood pressure and heartbeat monitoring in pregnant women are examples of high-intensity applications of NB-IoT (narrowband IoT), technology that supports widespread communication with a very low data cost and minimum processing complexity and battery lifespan. This article also focuses on analysis of the performance of narrowband IoT in terms of delay and throughput using single- and multinode approaches. We performed analysis using the message queuing telemetry transport protocol (MQTTP), which was found to be efficient compared to the limited application protocol (LAP) in sending information from sensors.

Continuous cuffless blood pressure monitoring using photoplethysmography-based PPG2BP-net for high intrasubject blood pressure variations

Continuous, comfortable, convenient (C3), and accurate blood pressure (BP) measurement and monitoring are needed for early diagnosis of various cardiovascular diseases. To supplement the limited C3 BP measurement of existing cuff-based BP technologies, though they may achieve reliable accuracy, cuffless BP measurement technologies, such as pulse transit/arrival time, pulse wave analysis, and image processing, have been studied to obtain C3 BP measurement. One of the recent cuffless BP measurement technologies, innovative machine-learning and artificial intelligence-based technologies that can estimate BP by extracting BP-related features from photoplethysmography (PPG)-based waveforms have attracted interdisciplinary attention of the medical and computer scientists owing to their handiness and effectiveness for both C3 and accurate, i.e., C3A, BP measurement. However, C3A BP measurement remains still unattainable because the accuracy of the existing PPG-based BP methods was not sufficiently justified for subject-independent and highly varying BP, which is a typical case in practice. To circumvent this issue, a novel convolutional neural network(CNN)- and calibration-based model (PPG2BP-Net) was designed by using a comparative paired one-dimensional CNN structure to estimate highly varying intrasubject BP. To this end, approximately [Formula: see text], [Formula: see text], and [Formula: see text] of 4185 cleaned, independent subjects from 25,779 surgical cases were used for training, validating, and testing the proposed PPG2BP-Net, respectively and exclusively (i.e., subject-independent modelling). For quantifying the intrasubject BP variation from an initial calibration BP, a novel 'standard deviation of subject-calibration centring (SDS)' metric is proposed wherein high SDS represents high intrasubject BP variation from the calibration BP and vice versa. PPG2BP-Net achieved accurately estimated systolic and diastolic BP values despite high intrasubject variability. In 629-subject data acquired after 20 minutes following the A-line (arterial line) insertion, low error mean and standard deviation of [Formula: see text] and [Formula: see text] for highly varying A-line systolic and diastolic BP values, respectively, where their SDSs are 15.375 and 8.745. This study moves one step forward in developing the C3A cuffless BP estimation devices that enable the push and agile pull services.

Assessment of a Calibration-Free Method of Cuffless Blood Pressure Measurement: A Pilot Study

This study proposes a low-cost, high-sensitivity sensor of beat-to-beat local pulse wave velocity (PWV), to be used in a cuffless blood pressure monitor (BPM).

OBJECTIVE

We design an adaptive algorithm to detect the feature of the pulse wave, making it possible for two sensors to measure the local PWV in the radial artery at a short distance. Unlike the cuffless BPM that needs to use a regression model for calibration.

METHOD

We encapsulate the piezoelectric sensor material in a cavity and design an analog front-end circuit. This study used color ultrasound imaging equipment to measure radial arterial parameters, including the diameter and wall thickness, to aid the estimation of blood pressure (BP) using the Moens-Korteweg (MK) equation of hemodynamics.

RESULTS

We compared the blood pressure estimated by the MK equation with the reference BP measured using an aneroid sphygmomanometer in a test group of 32 people, resulting in a mean difference of systolic BP of -0.63 mmHg, and a standard deviation of ±5.14 mmHg, a mean difference of mean arterial pressure (MAP) of 0.97 mmHg, with a standard deviation of ±3.54 mmHg, and a mean difference of diastolic BP of -1.14 mmHg, with a standard deviation of ±4.08 mmHg. This study has verified its compliance with ISO 81060-2.

CONCLUSIONS

A new type of wearable continuous calibration-free BPM can replace the situation that requires the use of traditional ambulatory BPM and reduce patient discomfort.

CLINICAL IMPACT

In this study can provide long-term continuous blood pressure monitoring in the hospital.

Blood Pressure Cuff Inflation Briefly Increases Female Adolescents' Restlessness During Sleep on the First But Not Second Night of Ambulatory Blood Pressure Monitoring

OBJECTIVE

Ambulatory blood pressure monitoring (ABPM) increases restlessness during adults' sleep in laboratory settings, but there is little evidence of an association among adolescents or in naturalistic environments. This study examined activity levels before and after blood pressure cuff inflation during sleep to determine whether and for how long ABPM increased restlessness during sleep in healthy adolescents.

METHODS

Two hundred thirty-four healthy adolescents (mean age = 15.72 [1.30] years; 54% female; 57% Black) completed two consecutive nights of hourly ABPM and wrist-worn actigraphy. Activity counts during sleep, averaged across 5-minute bins, were compared in the 20 minutes before and after blood pressure cuff inflation using a four-level mixed model (bins within hours within nights within participants). Interactions of bin with night, sex, and race were examined. Covariates included age, sex, and race.

RESULTS

Activity counts in the 5-minute bin immediately after cuff inflation were 10% to 14% higher than all other bins before ( p < .001) and after ( p < .001) cuff inflation. This effect differed by night and sex, as activity levels during 5-minute post-cuff inflation were elevated only on night 1 ( p values < .001) and only in female participants ( p values < .001). Effects did not differ by race.

CONCLUSIONS

Cuff inflation during ABPM briefly increased adolescent female participants' restlessness during sleep. Habituation occurred after one night, so two nights of ABPM may minimize impact on sleep. If only one night of ABPM is feasible, excluding 5 minutes of actigraphy data after each cuff inflation may accommodate the impact of ABPM on restlessness during sleep.

Real-Time Cuffless Continuous Blood Pressure Estimation Using 1D Squeeze U-Net Model: A Progress toward mHealth

Measuring continuous blood pressure (BP) in real time by using a mobile health (mHealth) application would open a new door in the advancement of the healthcare system. This study aimed to propose a real-time method and system for measuring BP without using a cuff from a digital artery. An energy-efficient real-time smartphone-application-friendly one-dimensional (1D) Squeeze U-net model is proposed to estimate systolic and diastolic BP values, using only raw photoplethysmogram (PPG) signal. The proposed real-time cuffless BP prediction method was assessed for accuracy, reliability, and potential usefulness in the hypertensive assessment of 100 individuals in two publicly available datasets: Multiparameter Intelligent Monitoring in Intensive Care (MIMIC-I) and Medical Information Mart for Intensive Care (MIMIC-III) waveform database. The proposed model was used to build an android application to measure BP at home. This proposed deep-learning model performs best in terms of systolic BP, diastolic BP, and mean arterial pressure, with a mean absolute error of 4.42, 2.25, and 2.56 mmHg and standard deviation of 4.78, 2.98, and 3.21 mmHg, respectively. The results meet the grade A performance requirements of the British Hypertension Society and satisfy the AAMI error range. The result suggests that only using a short-time PPG signal is sufficient to obtain accurate BP measurements in real time. It is a novel approach for real-time cuffless BP estimation by implementing an mHealth application and can measure BP at home and assess hypertension.

IoT-Based COVID-19 Diagnosing and Monitoring Systems: A Survey

To date, the novel Coronavirus (SARS-CoV-2) has infected millions and has caused the deaths of thousands of people around the world. At the moment, five antibodies, two from China, two from the U.S., and one from the UK, have already been widely utilized and numerous vaccines are under the trail process. In order to reach herd immunity, around 70% of the population would need to be inoculated. It may take several years to hinder the spread of SARS-CoV-2. Governments and concerned authorities have taken stringent measurements such as enforcing partial, complete, or smart lockdowns, building temporary medical facilities, advocating social distancing, and mandating masks in public as well as setting up awareness campaigns. Furthermore, there have been massive efforts in various research areas and a wide variety of tools, technologies and techniques have been explored and developed to combat the war against this pandemic. Interestingly, machine learning (ML) algorithms and internet of Things (IoTs) technology are the pioneers in this race. Up till now, several real-time and intelligent IoT-based COVID-19 diagnosing, and monitoring systems have been proposed to tackle the pandemic. In this article we have analyzed a wide range of IoTs technologies which can be used in diagnosing and monitoring the infected individuals and hotspot areas. Furthermore, we identify the challenges and also provide our vision about the future research on COVID-19.

Cuffless Blood Pressure Monitoring: Academic Insights and Perspectives Analysis

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

The B-Score is a novel metric for measuring the true performance of blood pressure estimation models

We aimed to develop and test a novel metric for the relative performance of blood pressure estimation systems (B-Score). The B-Score sets absolute blood pressure estimation model performance in contrast to the dataset the model is tested upon. We calculate the B-Score based on inter- and intrapersonal variabilities within the dataset. To test the B-Score for reliable results and desired properties, we designed generic datasets with differing inter- and intrapersonal blood pressure variability. We then tested the B-Score’s real-world functionality with a small, published dataset and the largest available blood pressure dataset (MIMIC IV). The B-Score demonstrated reliable and desired properties. The real-world test provided allowed the direct comparison of different datasets and revealed insights hidden from absolute performance measures. The B-Score is a functional, novel, and easy to interpret measure of relative blood pressure estimation system performance. It is easily calculated for any dataset and enables the direct comparison of various systems tested on different datasets. We created a metric for direct blood pressure estimation system performance. The B-Score allows researchers to detect promising trends quickly and reliably in the scientific literature. It further allows researchers and engineers to quickly assess and compare performances of various systems and algorithms, even when tested on different datasets.

Wearable cuffless blood pressure monitoring devices: a systematic review and meta-analysis

Aims

High blood pressure (BP) is the commonest modifiable cardiovascular risk factor, yet its monitoring remains problematic. Wearable cuffless BP devices offer potential solutions; however, little is known about their validity and utility. We aimed to systematically review the validity, features and clinical use of wearable cuffless BP devices.

Methods and results

We searched MEDLINE, Embase, IEEE Xplore and the Cochrane Database till December 2019 for studies that reported validating cuffless BP devices. We extracted information about study characteristics, device features, validation processes, and clinical applications. Devices were classified according to their functions and features. We defined devices with a mean systolic BP (SBP) and diastolic BP (DBP) biases of <5 mmHg as valid as a consensus. Our definition of validity did not include assessment of device measurement precision, which is assessed by standard deviation of the mean difference-a critical component of ISO protocol validation criteria. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies version 2 tool. A random-effects model meta-analysis was performed to summarise the mean biases for SBP and DBP across studies. Of the 430 studies identified, 16 studies (15 devices, 974 participants) were selected. The majority of devices (81.3%) used photoplethysmography to estimate BP against a reference device; other technologies included tonometry, auscultation and electrocardiogram. In addition to BP and heart rate, some devices also measured night-time BP (n = 5), sleep monitoring (n = 3), oxygen saturation (n = 3), temperature (n = 2) and electrocardiogram (n = 3). Eight devices showed mean biases of <5 mmHg for SBP and DBP compared with a reference device and three devices were commercially available. The meta-analysis showed no statistically significant differences between the wearable and reference devices for SBP (pooled mean difference = 3.42 mmHg, 95% CI: -2.17, 9.01, I² 95.4%) and DBP (pooled mean = 1.16 mmHg, 95% CI: -1.26, 3.58, I² 87.1%).

Conclusion

Several cuffless BP devices are currently available using different technologies, offering the potential for continuous BP monitoring. The variation in standards and validation protocols limited the comparability of findings across studies and the identification of the most accurate device. Challenges such as validation using standard protocols and in real-life settings must be overcome before they can be recommended for uptake into clinical practice.

A Data-Driven Model with Feedback Calibration Embedded Blood Pressure Estimator Using Reflective Photoplethysmography

Ambulatory blood pressure (BP) monitoring (ABPM) is vital for screening cardiovascular activity. The American College of Cardiology/American Heart Association guideline for the prevention, detection, evaluation, and management of BP in adults recommends measuring BP outside the office setting using daytime ABPM. The recommendation to use night-day BP measurements to confirm hypertension is consistent with the recommendation of several other guidelines. In recent studies, ABPM was used to measure BP at regular intervals, and it reduces the effect of the environment on BP. Out-of-office measurements are highly recommended by almost all hypertension organizations. However, traditional ABPM devices based on the oscillometric technique usually interrupt sleep. For all-day ABPM purposes, a photoplethysmography (PPG)-based wrist-type device has been developed as a convenient tool. This optical, noninvasive device estimates BP using morphological characteristics from PPG waveforms. As measurement can be affected by multiple variables, calibration is necessary to ensure that the calculated BP values are accurate. However, few studies focused on adaptive calibration. A novel adaptive calibration model, which is data-driven and embedded in a wearable device, was proposed. The features from a 15 s PPG waveform and personal information were input for estimation of BP values and our data-driven calibration model. The model had a feedback calibration process using the exponential Gaussian process regression method to calibrate BP values and avoid inter- and intra-subject variability, ensuring accuracy in long-term ABPM. The estimation error of BP (ΔBP = actual BP-estimated BP) of systolic BP was -0.1776 ± 4.7361 mmHg; ≤15 mmHg, 99.225%, and of diastolic BP was -0.3846 ± 6.3688 mmHg; ≤15 mmHg, 98.191%. The success rate was improved, and the results corresponded to the Association for the Advancement of Medical Instrumentation standard and British Hypertension Society Grading criteria for medical regulation. Using machine learning with a feedback calibration model could be used to assess ABPM for clinical purposes.

A Comparison of Wearable Tonometry, Photoplethysmography, and Electrocardiography for Cuffless Measurement of Blood Pressure in an Ambulatory Setting

OBJECTIVE

While non-invasive, cuffless blood pressure (BP) measurement has demonstrated relevancy in controlled environments, ambulatory measurement is important for hypertension diagnosis and control. We present both in-lab and ambulatory BP estimation results from a diverse cohort of participants.

METHODS

Participants (N=1125, aged 21-85, 49.2% female, multiple hypertensive categories) had BP measured in-lab over a 24-hour period with a subset also receiving ambulatory measurements. Radial tonometry, photoplethysmography (PPG), electrocardiography (ECG), and accelerometry signals were collected simultaneously with auscultatory or oscillometric references for systolic (SBP) and diastolic blood pressure (DBP). Predictive models to estimate BP using a variety of sensor-based feature groups were evaluated against challenging baselines.

RESULTS

Despite limited availability, tonometry-derived features showed superior performance compared to other feature groups and baselines, yielding prediction errors of 0.329.8 mmHg SBP and 0.547.7 mmHg DBP in-lab, and 0.868.7 mmHg SBP and 0.755.9 mmHg DBP for 24-hour averages. SBP error standard deviation (SD) was reduced in normotensive (in-lab: 8.1 mmHg, 24-hr: 7.2 mmHg) and younger (in-lab: 7.8 mmHg, 24-hr: 6.7 mmHg) subpopulations. SBP SD was further reduced 1520% when constrained to the calibration posture alone.

CONCLUSION

Performance for normotensive and younger participants was superior to the general population across all feature groups. Reference type, posture relative to calibration, and controlled vs. ambulatory setting all impacted BP errors.

SIGNIFICANCE

Results highlight the need for demographically diverse populations and challenging evaluation settings for BP estimation studies. We present the first public dataset of ambulatory tonometry and cuffless BP over a 24-hour period to aid in future cardiovascular research.

Blood Pressure Estimation by Photoplethysmogram Decomposition into Hyperbolic Secant Waves

Photoplethysmographic (PPG) pulses contain information about cardiovascular parameters. In particular, blood pressure can be estimated using PPG pulse decomposition analysis, which assumes that a PPG pulse is composed of the original heart ejection blood wave and its reflections in arterial branchings. Among pulse decomposition wave functions that have been studied in the literature, Gaussian waves are the most successful ones. However, a more adequate pulse decomposition function could be found to improve blood pressure estimates. In this paper, we propose pulse decomposition analysis using hyperbolic secant (sech) waves and compare results with corresponding Gaussian wave decomposition. We analyze how the parameters of each of the two types of decomposition waves correlate with blood pressure. For this analysis, continuous blood pressure data and PPG data were acquired from ten healthy volunteers. The blood pressure of volunteers was varied by asking them to hold their breath for up to 60 s. The results suggested sech wave decomposition had higher accuracy in estimating blood pressure than the Gaussian function. Thus, sech wave decomposition should be considered as a more robust alternative to Gaussian wave pulse decomposition for blood pressure estimation models.

A Wearable Tele-Health System towards Monitoring COVID-19 and Chronic Diseases

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic since early 2020. The coronavirus disease 2019 (COVID-19) has already caused more than three million deaths worldwide and affected people's physical and mental health. COVID-19 patients with mild symptoms are generally required to self-isolate and monitor for symptoms at least for 14 days in the case the disease turns towards severe complications. In this work, we overviewed the impact of COVID-19 on the patients' general health with a focus on their cardiovascular, respiratory and mental health, and investigated several existing patient monitoring systems. We addressed the limitations of these systems and proposed a wearable telehealth solution for monitoring a set of physiological parameters that are critical for COVID-19 patients such as body temperature, heart rate, heart rate variability, blood oxygen saturation, respiratory rate, blood pressure, and cough. This physiological information can be further combined to potentially estimate the lung function using artificial intelligence (AI) and sensor fusion techniques. The prototype, which includes the hardware and a smartphone app, showed promising results with performance comparable to or better than similar commercial devices, thus potentially making the proposed system an ideal wearable solution for long-term monitoring of COVID-19 patients and other chronic diseases.

Cuffless blood pressure monitoring from a wristband with calibration-free algorithms for sensing location based on bio-impedance sensor array and autoencoder

Continuous monitoring of blood pressure (BP) is essential for the prediction and the prevention of cardiovascular diseases. Cuffless BP methods based on non-invasive sensors integrated into wearable devices can translate blood pulsatile activity into continuous BP data. However, local blood pulsatile sensors from wearable devices suffer from inaccurate pulsatile activity measurement based on superficial capillaries, large form-factor devices and BP variation with sensor location which degrade the accuracy of BP estimation and the device wearability. This study presents a cuffless BP monitoring method based on a novel bio-impedance (Bio-Z) sensor array built in a flexible wristband with small-form factor that provides a robust blood pulsatile sensing and BP estimation without calibration methods for the sensing location. We use a convolutional neural network (CNN) autoencoder that reconstructs an accurate estimate of the arterial pulse signal independent of sensing location from a group of six Bio-Z sensors within the sensor array. We rely on an Adaptive Boosting regression model which maps the features of the estimated arterial pulse signal to systolic and diastolic BP readings. BP was accurately estimated with average error and correlation coefficient of 0.5 ± 5.0 mmHg and 0.80 for diastolic BP, and 0.2 ± 6.5 mmHg and 0.79 for systolic BP, respectively.

Pulse arrival time as a surrogate of blood pressure

Various models have been proposed for the estimation of blood pressure (BP) from pulse transit time (PTT). PTT is defined as the time delay of the pressure wave, produced by left ventricular contraction, measured between a proximal and a distal site along the arterial tree. Most researchers, when they measure the time difference between the peak of the R-wave in the electrocardiogram signal (corresponding to left ventricular depolarisation) and a fiducial point in the photoplethysmogram waveform (as measured by a pulse oximeter attached to the fingertip), describe this erroneously as the PTT. In fact, this is the pulse arrival time (PAT), which includes not only PTT, but also the time delay between the electrical depolarisation of the heart's left ventricle and the opening of the aortic valve, known as pre-ejection period (PEP). PEP has been suggested to present a significant limitation to BP estimation using PAT. This work investigates the impact of PEP on PAT, leading to a discussion on the best models for BP estimation using PAT or PTT. We conducted a clinical study involving 30 healthy volunteers (53.3% female, 30.9 ± 9.35 years old, with a body mass index of 22.7 ± 3.2 kg/m[Formula: see text]). Each session lasted on average 27.9 ± 0.6 min and BP was varied by an infusion of phenylephrine (a medication that causes venous and arterial vasoconstriction). We introduced new processing steps for the analysis of PAT and PEP signals. Various population-based models (Poon, Gesche and Fung) and a posteriori models (inverse linear, inverse squared and logarithm) for estimation of BP from PTT or PAT were evaluated. Across the cohort, PEP was found to increase by 5.5 ms ± 4.5 ms from its baseline value. Variations in PTT were significantly larger in amplitude, - 16.8 ms ± 7.5 ms. We suggest, therefore, that for infusions of phenylephrine, the contribution of PEP on PAT can be neglected. All population-based models produced large BP estimation errors, suggesting that they are insufficient for modelling the complex pathways relating changes in PTT or PAT to changes in BP. Although PAT is inversely correlated with systolic blood pressure (SBP), the gradient of this relationship varies significantly from individual to individual, from - 2946 to - 470.64 mmHg/s in our dataset. For the a posteriori inverse squared model, the root mean squared errors (RMSE) for systolic and diastolic blood pressure (DBP) estimation from PAT were 5.49 mmHg and 3.82 mmHg, respectively. The RMSEs for SBP and DBP estimation by PTT were 4.51 mmHg and 3.53 mmHg, respectively. These models take into account individual calibration curves required for accurate blood pressure estimation. The best performing population-based model (Poon) reported error values around double that of the a posteriori inverse squared model, and so the use of population-based models is not justified.

Motion Artifact Resilient Cuff-Less Blood Pressure Monitoring Using a Fusion of Multi-Dimensional Seismocardiograms

Blood pressure (BP) monitoring is critical to raise awareness of hypertension and hypotension, yet the commonly used techniques require the person staying still along with a cuff around the arm. Some cuff-less approaches have been researched, but all hinder the person from moving around. To address the challenge, we propose using a fusion of accelerometers to achieve motion artifact resilient blood pressure monitoring. Such technique is accomplished with the motion artifact removal process and feature extraction from multi-dimensional seismocardiograms. The efficacy of our BP monitoring models is validated in 19 young healthy adults. Both the diastolic and systolic BP monitoring models fulfill the AAMI standard and British Hypertension Society protocol. For sitting still BP monitoring, the mean and standard deviation of diastolic and systolic difference errors (DE) are 0.09 ± 4.10 and -0.25 ± 5.45 mmHg; moreover, the mean absolute difference errors (MADE) are 3.62 and 4.73 mmHg. In walking motions, the DE are 1.15 ±4.47 mmHg for diastolic BP and -0.38 ± 6.67 for systolic BP; furthermore, the MADE are 3.36 and 5.07 mmHg, respectively. The motion artifact resilient cuff-less BP monitoring reveals the potential of portable BP monitoring in healthcare environments.

Piezoelectric Sensor for the Monitoring of Arterial Pulse Wave: Detection of Arrhythmia Occurring in PAC/PVC Patients

Previous studies have found that the non-invasive blood pressure measurement method based on the oscillometric method is inaccurate when an arrhythmia occurs. Therefore, we propose a high-sensitivity pulse sensor that can measure the hemodynamic characteristics of the pulse wave and then estimate the blood pressure. When an arrhythmia occurs, the hemodynamics of the pulse wave are abnormal and change the morphology of the pulse wave. Our proposed sensor can measure the occurrence of ectopic beats from the radial artery, and the detection algorithm can reduce the error of blood pressure estimation caused by the distortion of ectopic beats that occurs when the pulse wave is measured. In this study, we tested patients with premature atrial contraction (PAC) or premature ventricular contraction (PVC) and analyzed the morphology of the pulse waves when the sensor detected the ectopic beats. We discuss the advantages of using the Moens–Korteweg equation to estimate the blood pressure of patients with arrhythmia, which is different from the oscillometric method. Our research provides a possible arrhythmia detection method for wearable devices and can accurately estimate blood pressure in a non-invasive way during an arrhythmia.

Heart rate variability enhances the accuracy of non-invasive continuous blood pressure estimation under blood loss

To propose a new method for real-time monitoring of blood pressure of blood loss (BPBL), this article combines pulse transit time (PTT) and heart rate variability (HRV) as input parameters to build a model for BPBL estimation. In this article, effective parameters such as PTT, R-R interval (RRI), and HRV were extracted and used to establish the blood pressure (BP) estimation. Three BP estimation models were created: the PTT model, the RRI model, and the HRV model, and they were divided into an experimental group and a control group. Finally, the effects of the different estimation models on the accuracy of BPBL were evaluated using the experimental results. The result showed that both the RRI model and the HRV model have a good improvement effect on the prediction accuracy of BPBL, and the HRV model has the highest prediction accuracy than the PTT model and the RRI model. The correlation coefficients between the actual systolic BP (SBP) and diastolic BP (DBP) and the estimated SBP and DBP of the HRV model were 0.9580 and 0.9749, respectively, and the root-mean-square error of the HRV model for both SBP and DBP were 7.59 and 6.56 mmHg, respectively. The results suggest that the accuracy of the BPBL estimated by the HRV models is better than that of the PTT model, which means that HRV seems to be more effective in improving the accuracy of BP estimation compared with RRI. These results in this article provide a new idea for other researchers in the field of BPBL estimation research.

Characterization of the vascular system using overnight wearable-based pulse arrival time and ambulatory blood pressure: A pilot study

Pulse arrival time (PAT) has been broadly investigated for its potential for cuffless blood pressure (BP) estimation and ease of measurement by wearable devices. It is also of great significance to explore whether PAT conveys complementary information to BP for vascular health assessment. In this paper, the differences between the 24-h ambulatory BP and wearable-based PAT were compared among 12 young normotensives and 15 elderly hypertensives in terms of the mean and coefficients of variation (CoVs). The correlations of the nocturnal normalized PAT (n-PAT) and BP with two arterial stiffness-related parameters (i.e., the intrinsic elastic modulus E₀ and the vascular modulation factor α) estimated by a proposed model-based method were also compared. The results showed that the inter-subject variances of the nocturnal average n-PAT were significantly different between the hypertensives and the normotensives (P < 0.001), and the intra-subject CoVs of the nocturnal n-PAT were also significantly different between the two groups (P < 0.05). However, these findings were not shown in the nocturnal BP. The correlation coefficient between the nocturnal average n-PAT and ln(E₀) is larger than that with the nocturnal BP, i.e., 0.91 vs. 0.56. Furthermore, the result also revealed that all the hypertensives receiving antihypertensive medications did not achieve the optimal control of the nocturnal BP while presented diverse arterial stiffness indicated by the nocturnal average n-PAT and ln(E₀). It is concluded that wearable-based PAT contains complementary information about the vascular system to the ambulatory BP, which may be useful for designing effective antihypertensive treatments.

A deep transfer learning approach for wearable sleep stage classification with photoplethysmography

Unobtrusive home sleep monitoring using wrist-worn wearable photoplethysmography (PPG) could open the way for better sleep disorder screening and health monitoring. However, PPG is rarely included in large sleep studies with gold-standard sleep annotation from polysomnography. Therefore, training data-intensive state-of-the-art deep neural networks is challenging. In this work a deep recurrent neural network is first trained using a large sleep data set with electrocardiogram (ECG) data (292 participants, 584 recordings) to perform 4-class sleep stage classification (wake, rapid-eye-movement, N1/N2, and N3). A small part of its weights is adapted to a smaller, newer PPG data set (60 healthy participants, 101 recordings) through three variations of transfer learning. Best results (Cohen's kappa of 0.65 ± 0.11, accuracy of 76.36 ± 7.57%) were achieved with the domain and decision combined transfer learning strategy, significantly outperforming the PPG-trained and ECG-trained baselines. This performance for PPG-based 4-class sleep stage classification is unprecedented in literature, bringing home sleep stage monitoring closer to clinical use. The work demonstrates the merit of transfer learning in developing reliable methods for new sensor technologies by reusing similar, older non-wearable data sets. Further study should evaluate our approach in patients with sleep disorders such as insomnia and sleep apnoea.

Accurate Blood Pressure Estimation During Activities of Daily Living: A Wearable Cuffless Solution

The objective is to develop a cuffless method that accurately estimates blood pressure (BP) during activities of daily living. User-specific nonlinear autoregressive models with exogenous inputs (NARX) are implemented using artificial neural networks to estimate the BP waveforms from electrocardiography and photoplethysmography signals. To broaden the range of BP in the training data, subjects followed a short procedure consisting of sitting, standing, walking, Valsalva maneuvers, and static handgrip exercises. The procedure was performed before and after a six-hour testing phase wherein five participants went about their normal daily living activities. Data were further collected at a four-month time point for two participants and again at six months for one of the two. The performance of three different NARX models was compared with three pulse arrival time (PAT) models. The NARX models demonstrate superior accuracy and correlation with "ground truth" systolic and diastolic BP measures compared to the PAT models and a clear advantage in estimating the large range of BP. Preliminary results show that the NARX models can accurately estimate BP even months apart from the training. Preliminary testing suggests that it is robust against variabilities due to sensor placement. This establishes a method for cuffless BP estimation during activities of daily living that can be used for continuous monitoring and acute hypotension and hypertension detection.

Learning and non-learning algorithms for cuffless blood pressure measurement: a review

The machine learning approach has gained a significant attention in the healthcare sector because of the prospect of developing new techniques for medical devices and handling the critical database of chronic diseases. The learning approach has potential to analyze complex medical data, disease diagnosis, and patient monitoring system, and to monitor e-health record. Non-invasive cuffless blood pressure (CLBP) measurement secured a significant position in the patient monitoring system. From a few recent decades, the importance of cuffless technology has been perceived towards continuous monitoring of blood pressure (BP) and supplementary efforts have been made towards its continuous monitoring. However, the optimal method that measures BP unambiguously and continuously has not yet emerged along with issues like calibration time, accuracy and long-term estimation of BP with miniaturizing hardware. The present study provides an insight into several learning algorithms along with their feature selection models. Various challenges and future improvements towards the current state of machine learning in healthcare industries are discussed in the present review. The bottom line of this study is to provide a comprehensive perspective of the machine learning approach of CLBP for the generation of highly precise predictive models for continuous BP measurement.

Comprehensive pregnancy monitoring with a network of wireless, soft, and flexible sensors in high- and low-resource health settings

Vital signs monitoring is a fundamental component of ensuring the health and safety of women and newborns during pregnancy, labor, and childbirth. This monitoring is often the first step in early detection of pregnancy abnormalities, providing an opportunity for prompt, effective intervention to prevent maternal and neonatal morbidity and mortality. Contemporary pregnancy monitoring systems require numerous devices wired to large base units; at least five separate devices with distinct user interfaces are commonly used to detect uterine contractility, maternal blood oxygenation, temperature, heart rate, blood pressure, and fetal heart rate. Current monitoring technologies are expensive and complex with implementation challenges in low-resource settings where maternal morbidity and mortality is the greatest. We present an integrated monitoring platform leveraging advanced flexible electronics, wireless connectivity, and compatibility with a wide range of low-cost mobile devices. Three flexible, soft, and low-profile sensors offer comprehensive vital signs monitoring for both women and fetuses with time-synchronized operation, including advanced parameters such as continuous cuffless blood pressure, electrohysterography-derived uterine monitoring, and automated body position classification. Successful field trials of pregnant women between 25 and 41 wk of gestation in both high-resource settings (n = 91) and low-resource settings (n = 485) demonstrate the system's performance, usability, and safety.

Blood Pressure Monitoring System Using a Two-Channel Ballistocardiogram and Convolutional Neural Networks

Hypertension is a chronic disease that kills 7.6 million people worldwide annually. A continuous blood pressure monitoring system is required to accurately diagnose hypertension. Here, a chair-shaped ballistocardiogram (BCG)-based blood pressure estimation system was developed with no sensors attached to users. Two experimental sessions were conducted with 30 subjects. In the first session, two-channel BCG and blood pressure data were recorded for each subject. In the second session, the two-channel BCG and blood pressure data were recorded after running on a treadmill and then resting on the newly developed system. The empirical mode decomposition algorithm was used to remove noise in the two-channel BCG, and the instantaneous phase was calculated by applying a Hilbert transform to the first intrinsic mode functions. After training a convolutional neural network regression model that predicts the systolic and diastolic blood pressures (SBP and DBP) from the two-channel BCG phase, the results of the first session (rest) and second session (recovery) were compared. The results confirmed that the proposed model accurately estimates the rapidly rising blood pressure in the recovery state. Results from the rest sessions satisfied the Association for the Advancement of Medical Instrumentation (AAMI) international standards. The standard deviation of the SBP results in the recovery session exceeded 0.7.

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

Coronavirus disease 2019 (COVID-19) has emerged as a pandemic with serious clinical manifestations including death. A pandemic at the large-scale like COVID-19 places extraordinary demands on the world's health systems, dramatically devastates vulnerable populations, and critically threatens the global communities in an unprecedented way. While tremendous efforts at the frontline are placed on detecting the virus, providing treatments and developing vaccines, it is also critically important to examine the technologies and systems for tackling disease emergence, arresting its spread and especially the strategy for diseases prevention. The objective of this article is to review enabling technologies and systems with various application scenarios for handling the COVID-19 crisis. The article will focus specifically on 1) wearable devices suitable for monitoring the populations at risk and those in quarantine, both for evaluating the health status of caregivers and management personnel, and for facilitating triage processes for admission to hospitals; 2) unobtrusive sensing systems for detecting the disease and for monitoring patients with relatively mild symptoms whose clinical situation could suddenly worsen in improvised hospitals; and 3) telehealth technologies for the remote monitoring and diagnosis of COVID-19 and related diseases. Finally, further challenges and opportunities for future directions of development are highlighted.

External temperature sensor assisted a new low power photoplethysmography readout system for accurate measurement of the bio-signs

This study presents an external temperature sensor assisted a new low power, time-interleave, wide dynamic range, and low DC drift photoplethysmography (PPG) signal acquisition system to obtain the accurate measurement of various bio signs in real-time. The designed chip incorporates a 2-bit control programmable transimpedance amplifier (TIA), a high order filter, a 3:8 programmable gain amplifier (PGA) and 2 × 2 organic light-emitting diode (OLED) driver. Temperature sensor is used herein to compensate the adverse effect of low-skin-temperature on the PPG signal quality. The analog front-end circuit is implemented in the integrated chip with chip area of 2008 μm × 1377 μm and fabricated via TSMC T18 process. With the standard 1.8 V, the experimental result shows that the measured current sensing range is 20 nA-100 uA. The measured dynamic range of the designed readout circuit is 80 dB. The estimated signal to noise ratio is 60 dB@1 uA, and the measured input referred noise is 60.2 pA/Hz^½. The total power consumption of the designed chip is 31.32 µW (readout) + 1.62 mW (OLED driver@100% duty cycle). The non-invasive PPG sensor is applied to the wrist artery of the 40 healthy subjects for sensing the pulsation of the blood vessel. The experimental results show that for every 1 °C decrease in mean ambient temperature tends to 0.06 beats/min, 0.125 mmHg and 0.063 mmHg increase in hear rate (HR), systolic (SBP) and diastolic (DBP), respectively. Similarly, for every 1 °C increase in mean ambient temperature tends to 0.13 beats/min, 0.601 mmHg and 0.121 mmHg increase in HR, SBP and DBP, respectively. The measured accuracy and standard error for the HR estimation are 96%, and - 0.022 ± 2.589 beats/minute, respectively. The oxygen stauration (S_pO₂) measurement results shows that the mean absolute percentage error is less than 5%. The resultant errors for the SBP and DBP measurement are - 0.318 ± 5.19 mmHg and - 0.5 ± 1.91 mmHg, respectively.

A Noninvasive Wearable Device for Real-Time Monitoring of Secretion Sweat Pressure by Digital Display

Sweat-based wearable devices have attracted increasing attention by providing abundant physiological information and continuous measurement through noninvasive healthcare monitoring. Sweat pressure generated via sweat glands to the skin surface associated with osmotic effects may help to elucidate such parameters as physiological conditions and psychological factors. This study introduces a wearable device for measuring secretion sweat pressure through noninvasive, continuous monitoring. Secretion pressure is detected by a microfluidic chip that shows the resistance variance from a paired electrode pattern and transfers digital signals to a smartphone for real-time display. A human study demonstrates this measurement with different exercise activities, showing the pressure ranges from 1.3 to 2.5 kPa. This device is user-friendly and applicable to exercise training and personal health care. The convenience and easy-to-wear characteristics of this device may establish a foundation for future research investigating sweat physiology and personal health care.

Cuffless Blood Pressure Estimation for Activities of Daily Living

This work presents a modelling approach to predict the blood pressure (BP) waveform time series during activities of daily living without the use of a traditional pressure cuff. A nonlinear autoregressive model with exogenous inputs (NARX) is implemented using artificial neural networks and trained to predict the BP waveform time series from electrocardiography (ECG) and forehead photoplethysmography (PPG) input signals. To broaden the range of blood pressures present in the training set, a protocol was implemented that included sitting, standing, walking, Valsalva manoeuvers, and static handgrip exercise. A five-minute interval of data in the sitting position at the end of the day was also used for training. The efficacy of the cuffless BP method for continuous BP estimation over 4.67 hours was evaluated on 3 participants for varying training data segments. A mean absolute error of 6.3 and 5.2 mmHg were achieved for systolic BP and diastolic BP estimates, respectively. Including static handgrips and Valsalva manoeuvers in the training dataset leads to better estimation of the higher ranges of BP observed throughout the day. The proposed method shows potential for estimating the range of BP experienced during activities of daily living.Clinical Relevance- Establishes a method for cuffless continuous blood pressure estimation during activities of daily living that can be used for continuous monitoring and acute hypertension detection.

Unobtrusive Blood Pressure Estimation using Personalized Autoregressive Models

Cuffless and continuous blood pressure (BP) measurement using wearable devices is of great clinical value and health monitoring importance. Pulse arrival time (PAT) based technique was considered as one of the most promising methods for this purpose. Considering the dynamic and nonlinear relationship between BP, PAT and other cardiovascular variables, this paper proposes for the first time to use nonlinear autoregressive models with extra inputs (ARX) for BP estimation. The models were first trained by the baseline data of all 25 subjects to determine the model structure and then trained by individual data to obtain the personalized model parameters. To assess the effects of the dynamic and nonlinear factors, the data during water drinking and the first 5 minutes of recovery after drinking were used to validate the four models: linear regression, linear ARX, nonlinear regression and nonlinear ARX. The reference BP, which were measured by Finometer, were increased by 36.7±10.5 mmHg for SBP and 28.4 ±7.7 mmHg for DBP. This BP changes were best modelled by the nonlinear ARX, with Mean ± SD differences of 5.6 ± 8.8 mmHg for SBP and 3.8 ±5.8 mmHg for DBP. The study also showed that nonlinear factor significantly reduced the root mean square error (RSME) by about 50%, i.e., from 20.4 to 10.7 mmHg for SBP and 13.3 to 7.3 mmHg for DBP during drinking. While the effects of dynamic factors were not as significant as nonlinear factors, especially after introducing nonlinear factors.

Continuous blood pressure measurement from one-channel electrocardiogram signal using deep-learning techniques

Continuous blood pressure (BP) measurement is crucial for reliable and timely hypertension detection. State-of-the-art continuous BP measurement methods based on pulse transit time or multiple parameters require simultaneous electrocardiogram (ECG) and photoplethysmogram (PPG) signals. Compared with PPG signals, ECG signals are easy to collect using wearable devices. This study examined a novel continuous BP estimation approach using one-channel ECG signals for unobtrusive BP monitoring. A BP model is developed based on the fusion of a residual network and long short-term memory to obtain the spatial-temporal information of ECG signals. The public multiparameter intelligent monitoring waveform database, which contains ECG, PPG, and invasive BP data of patients in intensive care units, is used to develop and verify the model. Experimental results demonstrated that the proposed approach exhibited an estimation error of 0.07 ± 7.77 mmHg for mean arterial pressure (MAP) and 0.01 ± 6.29 for diastolic BP (DBP), which comply with the Association for the Advancement of Medical Instrumentation standard. According to the British Hypertension Society standards, the results achieved grade A for MAP and DBP estimation and grade B for systolic BP (SBP) estimation. Furthermore, we verified the model with an independent dataset for arrhythmia patients. The experimental results exhibited an estimation error of -0.22 ± 5.82 mmHg, -0.57 ± 4.39 mmHg, and -0.75 ± 5.62 mmHg for SBP, MAP, and DBP measurements, respectively. These results indicate the feasibility of estimating BP by using a one-channel ECG signal, thus enabling continuous BP measurement for ubiquitous health care applications.

Multimodal Photoplethysmography-Based Approaches for Improved Detection of Hypertension

Elevated blood pressure (BP) is a major cause of death, yet hypertension commonly goes undetected. Owing to its nature, it is typically asymptomatic until later in its progression when the vessel or organ structure has already been compromised. Therefore, noninvasive and continuous BP measurement methods are needed to ensure appropriate diagnosis and early management before hypertension leads to irreversible complications. Photoplethysmography (PPG) is a noninvasive technology with waveform morphologies similar to that of arterial BP waveforms, therefore attracting interest regarding its usability in BP estimation. In recent years, wearable devices incorporating PPG sensors have been proposed to improve the early diagnosis and management of hypertension. Additionally, the need for improved accuracy and convenience has led to the development of devices that incorporate multiple different biosignals with PPG. Through the addition of modalities such as an electrocardiogram, a final measure of the pulse wave velocity is derived, which has been proved to be inversely correlated to BP and to yield accurate estimations. This paper reviews and summarizes recent studies within the period 2010–2019 that combined PPG with other biosignals and offers perspectives on the strengths and weaknesses of current developments to guide future advancements in BP measurement. Our literature review reveals promising measurement accuracies and we comment on the effective combinations of modalities and success of this technology.

Estimation of the Blood Pressure Waveform using Electrocardiography

This work presents a modelling approach to accurately predict the blood pressure (BP) waveform time series from a single input signal. A nonlinear autoregressive model with exogenous input (NARX) is implemented using artificial neural networks and trained on Electrocardiography (ECG) signals to predict the BP waveform. The efficacy of the model is demonstrated using the MIMIC II database. The proposed method can accurately estimate systolic and diastolic BP. The NARX model together with ECG measurement allows continuous monitoring of BP, enables the estimation of other physiological measurements, such as the cardiac output, and provides more insight on the patient health condition.

Validation and Acceptability of a Cuffless Wrist-Worn Wearable Blood Pressure Monitoring Device Among Users and Health Care Professionals: Mixed Methods Study

BACKGROUND

Blood pressure (BP) is an important modifiable cardiovascular risk factor, yet its long-term monitoring remains problematic. Wearable cuffless devices enable the capture of multiple BP measures during everyday activities and could improve BP monitoring, but little is known about their validity or acceptability.

OBJECTIVE

This study aimed to validate a wrist-worn cuffless wearable BP device (Model T2; TMART Technologies Limited) and assess its acceptability among users and health care professionals.

METHODS

A mixed methods study was conducted to examine the validity and comparability of a wearable cuffless BP device against ambulatory and home devices. BP was measured simultaneously over 24 hours using wearable and ambulatory devices and over 7 days using wearable and home devices. Pearson correlation coefficients compared the degree of association between the measures, and limits of agreement (LOA; Bland-Altman plots) were generated to assess measurement bias. Semistructured interviews were conducted with users and 10 health care professionals to assess acceptability, facilitators, and barriers to using the wearable device. Interviews were audio recorded, transcribed, and analyzed.

RESULTS

A total of 9090 BP measurements were collected from 20 healthy volunteers (mean 20.3 years, SD 5.4; N=10 females). Mean (SD) systolic BP (SBP)/diastolic BP (DBP) measured using the ambulatory (24 hours), home (7 days), and wearable (7 days) devices were 126 (SD 10)/75 (SD 6) mm Hg, 112 (SD 10)/71 (SD 9) mm Hg and 125 (SD 4)/77 (SD 3) mm Hg, respectively. Mean (LOA) biases and precision between the wearable and ambulatory devices over 24 hours were 0.5 (-10.1 to 11.1) mm Hg for SBP and 2.24 (-17.6 to 13.1) mm Hg for DBP. The mean biases (LOA) and precision between the wearable and home device over 7 days were -12.7 (-28.7 to 3.4) mm Hg for SBP and -5.6 (-20.5 to 9.2) mm Hg for DBP. The wearable BP device was well accepted by participants who found the device easy to wear and use. Both participants and health care providers agreed that the wearable cuffless devices were easy to use and that they could be used to improve BP monitoring.

CONCLUSIONS

Wearable BP measures compared well against a gold-standard ambulatory device, indicating potential for this user-friendly method to augment BP management, particularly by enabling long-term monitoring that could improve treatment titration and increase understanding of users' BP response during daily activity and stressors.

Cuff-Less Blood Pressure Monitoring with a 3-Axis Accelerometer

OBJECTIVE

The aim was to propose a cuff-less, cost-efficient, and ultra-convenient blood pressure monitoring technique with a 3-axis accelerometer.

METHODS

The efficacy of the proposed approach was examined in 8 young healthy volunteers undergoing different activities with a 3-axis accelerometer leveled on their upper chest. The 3-dimensional accelerations were exploited to select features for the calculation of systolic pressure (SP) and diastolic pressure (DP); the whole process involved signal processing, feature extraction, linear multivariate regression, and leave-one-out cross validations (LOOCV).

RESULTS

DP and SP could be approximated with the linear combination of the extracted features: the L₂ norm of lateral acceleration for both DP and SP, state variation (defined in the proposed algorithm) of vertical acceleration for SP, and I-J interval (defined in ballistocardiogram) of vertical acceleration for DP. The correlation coefficient (r) of the estimated and the measured DP was 0.97, and for SP, r = 0.96. In LOOCV, our best validated results in difference errors were -0.02±3.82 mmHg for DP and -0.59 ± 7.46 mmHg for SP.

CONCLUSION

Compared to AAMI criteria, the proposed acceleration-based technique fulfilled the requirement. The accelerometer-based technique showed the potential to monitor blood pressure cuff-lessly, cost-efficiently, ultra-conveniently, and to be embedded in a long-term wearable device for clinical usage.

Regulation of Cell Behavior by Hydrostatic Pressure

Hydrostatic pressure (HP) regulates diverse cell behaviors including differentiation, migration, apoptosis, and proliferation. Abnormal HP is associated with pathologies including glaucoma and hypertensive fibrotic remodeling. In this review, recent advances in quantifying and predicting how cells respond to HP across several tissue systems are presented, including tissues of the brain, eye, vasculature and bladder, as well as articular cartilage. Finally, some promising directions on the study of cell behaviors regulated by HP are proposed.

Multimodal Wrist Biosensor for Wearable Cuff-less Blood Pressure Monitoring System

We propose a multimodal biosensor for use in continuous blood pressure (BP) monitoring system. Our proposed novel configuration measures photo-plethysmography (PPG) and impedance plethysmography (IPG) signals simultaneously from the subject wrist. The proposed biosensor system enables a fully non-intrusive system that is cuff-less, also utilize a single measurement site for maximum wearability and convenience of the patients. The efficacy of the proposed technique was evaluated on 10 young healthy subjects. Experimental results indicate that the pulse transit time (PTT)-based features calculated from an IPG peak and PPG maximum second derivative (f14) had a relatively high correlation coefficient (r) to the reference BP, with -0.81 ± 0.08 and -0.78 ± 0.09 for systolic BP (SBP) and diastolic BP (DBP), respectively. Moreover, here we proposed two BP estimation models that utilize six- and one-point calibration models. The six-point model is based on the PTT, whereas the one-point model is based on the combined PTT and radial impedance (Z). Thus, in both models, we observed an adequate root-mean-square-error estimation performance, with 4.20 ± 1.66 and 2.90 ± 0.90 for SBP and DBP, respectively, with the PTT BP model; and 6.86 ± 1.65 and 6.67 ± 1.75 for SBP and DBP, respectively, with the PTT-Z BP model. This study suggests the possibility of estimating a subject's BP from only wrist bio-signals. Thus, the six- and one-point PTT-Z calibration models offer adequate performance for practical applications.