Cuffless Blood Pressure Measurement

Controversy in Hypertension: Pro-Side of the Argument Using Artificial Intelligence for Hypertension Diagnosis and Management

Hypertension presents the largest modifiable public health challenge due to its high prevalence, its intimate relationship to cardiovascular diseases, and its complex pathogenesis and pathophysiology. Low awareness of blood pressure elevation and suboptimal hypertension diagnosis serve as the major hurdles in effective hypertension management. Advances in artificial intelligence in hypertension have permitted the integrative analysis of large data sets including omics, clinical (with novel sensor and wearable technologies), health-related, social, behavioral, and environmental sources, and hold transformative potential in achieving large-scale, data-driven approaches toward personalized diagnosis, treatment, and long-term management. However, although the emerging artificial intelligence science may advance the concept of precision hypertension in discovery, drug targeting and development, patient care, and management, its clinical adoption at scale today is lacking. Recognizing that clinical implementation of artificial intelligence-based solutions need evidence generation, this opinion statement examines a clinician-centric perspective of the state-of-art in using artificial intelligence in the management of hypertension and puts forward recommendations toward equitable precision hypertension care.

Cuffless Blood Pressure Measurement: Where Do We Actually Stand?

Cuffless blood pressure (BP) measurement offers considerable potential for clinical practice but is a challenging technological field. Many are investigating pulse wave analysis with or without pulse arrival time in which machine learning is applied to pulsatile waveforms obtained with mobile devices (eg, wristbands, smartphones) to estimate BP. These methods generally require individual user calibration with cuff BP measurements or demographics (eg, age, sex). This calibration makes it difficult to evaluate the method's accuracy, and many studies claiming accuracy used inadequate testing procedures. Yet, publications and regulatory-cleared devices continue to rise, seemingly implying technological advancements. An update is provided on the flurry of activity in cuffless BP technologies over the last 2 to 3 years, covering the clinical need, the latest devices, recent publications based on pulse wave analysis and pulse arrival time, progress in developing validation standards for cuffless BP devices, and recent publications on other cuffless BP measurement principles. Despite the high volume of research and development, to date, there is no compelling evidence that pulse wave analysis and pulse arrival time can provide significant added value in BP measurement accuracy beyond the cuff BP or demographic data for calibration. Thus, it is reasonable to at least be skeptical of published and future studies on pulse wave analysis and pulse arrival time for cuffless BP measurement with uncertain testing procedures. It is important to focus on establishing robust validation standards for cuffless BP devices requiring individual user calibration and also pursuing cuffless and calibration-free BP measurement methodologies going forward.

Intra-operative haemodynamic monitoring and management of adults having noncardiac surgery: A statement from the European Society of Anaesthesiology and Intensive Care

This article was developed by a diverse group of 25 international experts from the European Society of Anaesthesiology and Intensive Care (ESAIC), who formulated recommendations on intra-operative haemodynamic monitoring and management of adults having noncardiac surgery based on a review of the current evidence. We recommend basing intra-operative arterial pressure management on mean arterial pressure and keeping intra-operative mean arterial pressure above 60 mmHg. We further recommend identifying the underlying causes of intra-operative hypotension and addressing them appropriately. We suggest pragmatically treating bradycardia or tachycardia when it leads to profound hypotension or likely results in reduced cardiac output, oxygen delivery or organ perfusion. We suggest monitoring stroke volume or cardiac output in patients with high baseline risk for complications or in patients having high-risk surgery to assess the haemodynamic status and the haemodynamic response to therapeutic interventions. However, we recommend not routinely maximising stroke volume or cardiac output in patients having noncardiac surgery. Instead, we suggest defining stroke volume and cardiac output targets individually for each patient considering the clinical situation and clinical and metabolic signs of tissue perfusion and oxygenation. We recommend not giving fluids simply because a patient is fluid responsive but only if there are clinical or metabolic signs of hypovolaemia or tissue hypoperfusion. We suggest monitoring and optimising the depth of anaesthesia to titrate doses of anaesthetic drugs and reduce their side effects.

UTransBPNet for cuffless and calibration-free blood pressure estimation under dynamic conditions

Accurate cuffless blood pressure (BP) estimation remains challenging, particularly under dynamic conditions with significant intra-individual BP variations. This study introduces UTransBPNet, a novel, calibration-free model for cuffless BP estimation. It integrates a squeeze-and-excitation-enhanced Unet architecture for short-range feature extraction with a transformer and cross attention module to capture long-range dependencies from high-resolution, multi-channel physiological signals, further refined through an optimized fine-tuning scheme. Comprehensive validations were conducted across multiple dynamic datasets-Dataset_Drink, Dataset_Exercise, and Dataset_MIMIC-in both scenario-specific and cross-scenario settings. Results demonstrate that UTransBPNet outperformed existing models in tracking BP variations under dynamic conditions, achieving individual Pearson's correlation coefficients of 0.61 ± 0.17 and 0.62 ± 0.13 for systolic BP (SBP) and diastolic BP (DBP) in Dataset_Drink, 0.82 ± 0.11 and 0.72 ± 0.18 in Dataset_Exercise, and low mean absolute differences (MADs) of 4.38 and 2.25 mmHg in Dataset_MIMIC. The analysis also highlights the impact of dataset characteristics on model performance, such as distribution shift, distribution imbalance and individual BP variability, highlighting the need for well-curated data to ensure generalizability. This study advances the development of robust, cuffless BP estimation models for real-world applications.

Machine learning models based on FEM simulation of hoop mode vibrations to enable ultrasonic cuffless measurement of blood pressure

Blood pressure (BP) is one of the vital physiological parameters, and its measurement is done routinely for almost all patients who visit hospitals. Cuffless BP measurement has been of great research interest over the last few years. In this paper, we aim to establish a method for cuffless measurement of BP using ultrasound. In this method, the arterial wall is pushed with an acoustic radiation force impulse (ARFI). After the completion of the ARFI pulse, the artery undergoes impulsive unloading which stimulates a hoop mode vibration. We designed two machine learning (ML) models which make it possible to estimate the internal pressure of the artery using ultrasonically measurable parameters. To generate the training data for the ML models, we did extensive finite element method (FEM) eigen frequency simulations for different tubes under pressure by sweeping through a range of values for inner lumen diameter (ILD), tube density (TD), elastic modulus, internal pressure (IP), tube length, and Poisson's ratio. Through image processing applied on images of different eigen modes supported for each simulated case, we identified its hoop mode frequency (HMF). Two different ML models were designed based on the simulated data. One is a four-parameter model (FPM) that takes tube thickness (TT), TD, ILD, and HMF as the inputs and gives out IP as output. Second is a three-parameter model (TPM) that takes TT, ILD, and HMF as inputs and IP as output. The accuracy of these models was assessed using simulated data, and their performance was confirmed through experimental verification on two arterial phantoms across a range of pressure values. The first prediction model (FPM) exhibited a mean absolute percentage error (MAPE) of 5.63% for the simulated data and 3.68% for the experimental data. The second prediction model (TPM) demonstrated a MAPE of 6.5% for simulated data and 8.73% for experimental data. We were able to create machine learning models that can measure pressure within an elastic tube through ultrasonically measurable parameters and verified their performance to be adequate for BP measurement applications. This work establishes a pathway for cuffless, continuous, real-time, and non-invasive measurement of BP using ultrasound.

Importance of Considering Temporal Variations in Pulse Wave Velocity for Accurate Blood Pressure Prediction

PURPOSE

Continuous, cuffless blood pressure (BP) monitoring devices based on measuring pulse wave velocity (PWV) or pulse transit time (PTT) are emerging but are often plagued by large prediction errors. A key issue is that these techniques typically rely on a single PWV value, assuming a linear response and small arterial wall deformations. However, arterial response to BP is inherently nonlinear, with PWV varying over time [PWV(t)] by up to 50% during a cardiac cycle. This study evaluates the impact of assuming a single PWV on BP prediction accuracy.

METHOD

Using a Fluid-structure Interaction (FSI) testbed, we simulate the radial and common carotid arteries with the Holzapfel-Gasser-Ogden (HGO) constitutive model to capture nonlinear arterial behavior under a pulsatile physiological blood flow. Pressure data from FSI simulation are used as the ground truth, while inner area A(t) and two PWV values, at diastole and systole, serve as inputs to BP prediction models. Two models are tested: one using a single PWV value, emulating existing PWV-based BP prediction methods; another using the two PWV values to account for PWV(t).

RESULTS

The single-PWV BP model produced prediction errors of 17.44 mmHg and 6.57 mmHg for the radial and carotid arteries, respectively. The model incorporating two PWV values reduced these errors by 90.6% and 96.8%, respectively.

CONCLUSION

Relying on a single PWV in BP prediction models can lead to significant errors. To improve BP accuracy, future efforts should focus on incorporating PWV(t), or at least both diastolic and systolic PWV values, into these models.

Cuffless Blood Pressure Measurement Devices-International Perspectives on Accuracy and Clinical Use: A Narrative Review

Importance

Hypertension is a primary modifiable risk factor for cardiovascular death and disability. Accurate blood pressure (BP) measurement is essential for the diagnosis and treatment of hypertension. Conventional BP measurement with cuff devices is recommended but difficult for patients to perform due to inconvenience, discomfort, and challenges with appropriate cuff sizing and measurement protocols. The emergence of cuffless BP devices provides an opportunity to address many of these problems, including inconvenience, patient comfort, positional requirements, and continuous measurement.

Observations

Cuffless BP measurement devices are appealing to patients and clinicians, but validation of these technologies is essential before they can be deployed for clinical use. Key issues that remain include accuracy with risk of undertreatment or overtreatment, equitable access for low- and middle-income countries and minoritized populations, data privacy concerns, and how the devices will be deployed in clinical practice.

Conclusions

Clinicians and patients should only use validated BP cuff devices until cuffless BP measurement devices are appropriately tested and validated.

Exploring the limitations of blood pressure estimation using the photoplethysmography signal

Objetive.Hypertension, a leading contributor to cardiovascular morbidity, underscores the need for accurate and continuous blood pressure (BP) monitoring. Photoplethysmography (PPG) emerges as a promising approach for continuous BP monitoring. However, the precision of BP estimates derived from PPG signals has been the subject of ongoing debate, requiring a comprehensive evaluation of their efficacy. This paper aims to provide the potentials and limitations regarding BP estimation from single-site PPG signals.Approach.We developed a calibration-based Siamese ResNet model for BP estimation. We compared the use of normalized PPG (N-PPG) against the normalized invasive arterial BP (N-IABP) signals as input. N-IABP signals, while not directly presenting systolic (SBP) and diastolic (DBP) BP values, are expected to offer more precise estimations than PPG since it is a direct pressure sensor inside the body. Thus, if N-IABP poses challenges in BP estimation, predicting BP from PPG signals might be even more challenging.Main results.Our evaluation, conducted using the AAMI and BHS standards on the VitalDB dataset, revealed that inference using N-IABP signals meet with AAMI standards for both SBP and DBP, with errors of1.29±6.33mmHg for systolic pressure and1.17±5.78for diastolic pressure. In contrast, N-PPG based inference exhibited inferior performance than N-IABP, presenting1.49±11.82mmHg and0.89±7.27mmHg for systolic and diastolic pressure respectively in their best setup.Significance.Our findings establish a critical benchmark for PPG performance, providing realistic expectations for its BP estimation capabilities. We concluded that while PPG signals contain BP-correlated information, they may not suffice for accurate prediction.

Hypertension Canada Statement on the Use of Cuffless Blood Pressure Monitoring Devices in Clinical Practice

BACKGROUND

Cuffless blood pressure (BP) devices are an emerging technology marketed as providing frequent, nonintrusive and reliable BP measurements. With the increasing interest in these devices, it is important for Hypertension Canada to provide a statement regarding the current place of cuffless BP measurements in hypertension management.

METHODS

An overview of the technology in cuffless BP devices, the potential with this technology and the challenges related to determining the accuracy of these devices.

RESULTS

Cuffless BP monitoring is an emerging field where various technologies are applied to measure BP without the use of a brachial cuff. None of the devices currently sold have been validated in static and dynamic conditions using a recognized validation standard. Important issues persist in regard to the accuracy and the place of these devices in clinical practice. Current data only support using validated cuff-based devices for the diagnosis and management of hypertension. Presently, readings from cuffless devices that are used for diagnosis or clinical management need to be confirmed using measurements obtained from a clinically validated BP device.

CONCLUSIONS

Cuffless BP devices are a developing technology designed to track BP in most daily life activities. However, many steps remain before they should be used in clinical practice.

Development and Internal Validation of an AI-Enabled Cuff-less, Non-invasive Continuous Blood Pressure Monitor Across All Classes of Hypertension

BACKGROUND

Non-invasive, continuous blood pressure monitoring technologies require additional validation beyond standard cuff-based methods. This study evaluates a non-invasive, multiparametric wearable cuffless blood pressure (BP) diagnostic monitor across all hypertension classes with diverse subjects.

METHODS

A prospective, multicenter study assessed Nanowear's SimpleSense-BP performance, including induced and natural BP changes, significant BP variations (Systolic BP (SBP) ≥ ± 15 mm Hg and Diastolic BP (DBP) ≥ ± 10 mm Hg), and reference input value validity over 4 weeks.

RESULTS

303 subjects (18-83 yrs; 50.16% Female) participated in algorithmic development and validation (Normal - 35%, Prehypertensive - 24%, Stage 1 - 24%, Stage 2 - 17%). 54 subjects were tested for induced change performance, 149 exhibited significant changes, and 91 validated reference value duration.

CONCLUSIONS

The study clinically validated a continuous, AI-based BP diagnostic monitor using non-invasive wearable data. Further testing on diverse populations and external validation are recommended. The protocol was inspired by ISO 81060-2 and IEEE 1708:2019 standards.

Blood pressure measurement and nocturnal dipping patterns are heavily affected by body posture through changes in hydrostatic pressure between the arm and the heart

Nocturnal blood pressure (BP) shows the highest predictive power for cardiovascular events. However, there is a poor reproducibility of personalized dipping patterns in single individuals. We hypothesize that changes in body position during sleep cause variations in hydrostatic pressure,leading to incorrect BP values and dipping classifications. 26 subjects aged 18-30 years, as well as 25 participants aged 50 years and older underwent ambulatory BP measurements on the left arm, as well as determination of the hydrostatic pressure difference between the cuff and heart level during BP measurement. We observed that the BP measurement cuff was above the heart level (negative hydrostatic pressure) mostly through the night. Laying on the right side revealed the largest hydrostatic pressure difference and maximum incorrect BP measurement, with a mean of -9.61 mmHg during sleep. Correcting for hydrostatic pressure led to reclassification of nocturnal hypertension in 14 subjects (27.5%). Dipping patterns changed in 19 participants (37.3%). In total, 25 subjects (49.0%) changed either their nocturnal hypertension and/or their dipping classification. Our findings underscore the importance of accounting for hydrostatic pressure in ambulatory BP monitoring. Changes in body posture during sleep provide a plausible reason for the variability seen in nocturnal dipping patterns. Further research should focus on incorporating hydrostatic pressure compensation mechanisms in 24-h BP measurement. Limiting the noticeable effect of hydrostatic pressure differences could greatly improve hypertension diagnosis, classification, and treatment monitoring.

Multi-point sensing organic light-emitting diode display based mobile cardiovascular monitor

Cardiovascular diseases are the major cause of death globally and require ubiquitous monitoring due to their asymptomatic yet modifiable nature. Photoplethysmography is an effective optical sensing technique for non-invasive health monitoring. However, its reliance on the current relatively large and rigid inorganic semiconductor-based light-emitting diodes and silicon photodiodes hampers high-resolution integration thus restricts a sensing from single measurement point. So, it limits detectable biomarkers to monitor cardiovascular diseases in a ubiquitous manner. In order to facilitate, here we report a single smartphone type multi-functional cardiovascular health monitor based on the massive array of organic photodiodes integrated into the most user interactive display device. Therefore, we achieved: 1) multi-point concurrent photoplethysmography and high-resolution dynamic image sensing, and 2) user-interactive sensing within the large display area. These advancements enabled new functions, including high-accuracy screening for cardiovascular diseases, blood pressure monitoring from both fingers, monitoring of finger blood vessels and flow dynamics, and single-device-based biofeedback. Applied machine learning enhanced diagnostic accuracy, with pilot studies showing results comparable to medical-grade devices. As a result, we believe smartphones harnessing the sensor organic light-emitting diode display could evolve into mobile health monitors and digital therapeutics thus revolutionizing diagnostic and treatment.

A review of machine learning methods for non-invasive blood pressure estimation

Blood pressure is a very important clinical measurement, offering valuable insights into the hemodynamic status of patients. Regular monitoring is crucial for early detection, prevention, and treatment of conditions like hypotension and hypertension, both of which increasing morbidity for a wide variety of reasons. This monitoring can be done either invasively or non-invasively and intermittently vs. continuously. An invasive method is considered the gold standard and provides continuous measurement, but it carries higher risks of complications such as infection, bleeding, and thrombosis. Non-invasive techniques, in contrast, reduce these risks and can provide intermittent or continuous blood pressure readings. This review explores modern machine learning-based non-invasive methods for blood pressure estimation, discussing their advantages, limitations, and clinical relevance.

A method for blood pressure hydrostatic pressure correction using wearable inertial sensors and deep learning

Cuffless noninvasive blood pressure (BP) measurement could enable early unobtrusive detection of abnormal BP patterns, but when the sensor is placed on a location away from heart level (such as the arm), its accuracy is compromised by variations in the position of the sensor relative to heart level; such positional variations produce hydrostatic pressure changes that can cause swings in tens of mmHg in the measured BP if uncorrected. A standard method to correct for changes in hydrostatic pressure makes use of a bulky fluid-filled tube connecting heart level to the sensor. Here, we present an alternative method to correct for variations in hydrostatic pressure using unobtrusive wearable inertial sensors. This method, called IMU-Track, analyzes motion information with a deep learning model; for sensors placed on the arm, IMU-Track calculates parameterized arm-pose coordinates, which are then used to correct the measured BP. We demonstrated IMU-Track for BP measurements derived from pulse transit time, acquired using electrocardiography and finger photoplethysmography, with validation data collected across 20 participants. Across these participants, for the hand heights of 25 cm below or above the heart, mean absolute errors were reduced for systolic BP from 13.5 ± 1.1 and 9.6 ± 1.1 to 5.9 ± 0.7 and 5.9 ± 0.5 mmHg, respectively, and were reduced for diastolic BP from 15.0 ± 1.0 and 11.5 ± 1.5 to 6.8 ± 0.5 and 7.8 ± 0.8, respectively. On a commercial smartphone, the arm-tracking inference time was ~134 ms, sufficiently fast for real-time hydrostatic pressure correction. This method for correcting hydrostatic pressure may enable accurate passive cuffless BP monitors placed at positions away from heart level that accommodate everyday movements.

Sleep as a window of cardiometabolic health: The potential of digital sleep and circadian biomarkers

Digital biomarkers are quantifiable and objective indicators of a person's physiological function, behavioral state or treatment response, that can be captured using connected sensor technologies such as wearable devices and mobile apps. We envision that continuous and 24-h monitoring of the underlying physiological and behavioral processes through digital biomarkers can enhance early diagnostics, disease management, and self-care of cardiometabolic diseases. Cardiometabolic diseases, which include a combination of cardiovascular and metabolic disorders, represent an emerging global health threat. The prevention potential of cardiometabolic diseases is around 80%, indicating a promising role for interventions in the lifestyle and/or the environmental context. Disruption of sleep and circadian rhythms are increasingly recognized as risk factors for cardiometabolic disease. Digital biomarkers can be used to measure around the clock, that is, day and night, to quantify not only sleep patterns but also diurnal fluctuations of certain biomarkers and processes. In this way, digital biomarkers can support the delivery of optimal timed medical care. Night-time cardiometabolic patterns, such as blood pressure dipping, are predictive of cardiometabolic health outcomes. In addition, the sleep period provides an opportunity for digital cardiometabolic health monitoring with relatively low influence of artifacts, such as physical activity and eating. Digital biomarkers that utilize sleep as a window of health can be used during daily life to enable early diagnosis of cardiometabolic diseases, facilitate remote patient monitoring, and support self-management in people with cardiometabolic diseases. This review describes the influence of sleep and circadian rhythms on cardiometabolic disease and highlights the state-of-the-art sleep and circadian digital biomarkers which could be of benefit in the prevention of cardiometabolic disease.

Wearable Blood Pressure Monitoring Devices: Understanding Heterogeneity in Design and Evaluation

OBJECTIVE

Rapid advances in cuffless blood pressure (BP) monitoring have the potential to radically transform clinical care for cardiovascular health. However, due to the large heterogeneity in device design and evaluation, it is difficult to critically and quantitatively evaluate research progress. In this two-part manuscript, we provide a principled way of describing and accounting for heterogeneity in device and study design.

METHODS

We first provide an overview of foundational elements and design principles of three critical aspects: 1) sensors and systems, 2) pre-processing and feature extraction, and 3) BP estimation algorithms. Then, we critically analyze the state-of-the-art methods via a systematic review.

RESULTS

First, we find large heterogeneity in study designs, making fair comparisons extremely challenging. Moreover, many study designs have data leakage and are underpowered. We suggest a first open-contribution BP estimation benchmark for standardization. Next, we observe that BP distribution in the study sample and the time between calibration and test in emerging personalized devices confound BP estimation error. We suggest accounting for these using a convenient metric coined "explained deviation". Finally, we complement this manuscript with a website, https://wearablebp.github.io, containing a bibliography, meta-analysis results, datasets, and benchmarks, providing a timely plaWorm to understand state-of-the-art devices.

CONCLUSION

There is large heterogeneity in device and study design, which should be carefully accounted for when designing, comparing, and contrasting studies.

SIGNIFICANCE

Our findings will allow readers to parse out the heterogeneous literature and move toward promising directions for safer and more reliable devices in clinical practice and beyond.

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.

Innovations in blood pressure measurement and reporting technology: International Society of Hypertension position paper endorsed by the World Hypertension League, European Society of Hypertension, Asian Pacific Society of Hypertension, and Latin American Society of Hypertension

Blood pressure (BP) is a key contributor to the lifetime risk of preclinical organ damage and cardiovascular disease. Traditional clinic-based BP readings are typically measured infrequently and under standardized/resting conditions and therefore do not capture BP values during normal everyday activity. Therefore, current hypertension guidelines emphasize the importance of incorporating out-of-office BP measurement into strategies for hypertension diagnosis and management. However, conventional home and ambulatory BP monitoring devices use the upper-arm cuff oscillometric method and only provide intermittent BP readings under static conditions or in a limited number of situations. New innovations include technologies for BP estimation based on processing of sensor signals supported by artificial intelligence tools, technologies for remote monitoring, reporting and storage of BP data, and technologies for BP data interpretation and patient interaction designed to improve hypertension management ("digital therapeutics"). The number and volume of data relating to new devices/technologies is increasing rapidly and will continue to grow. This International Society of Hypertension position paper describes the new devices/technologies, presents evidence relating to new BP measurement techniques and related indices, highlights standard for the validation of new devices/technologies, discusses the reliability and utility of novel BP monitoring devices, the association of these metrics with clinical outcomes, and the use of digital therapeutics. It also highlights the challenges and evidence gaps that need to be overcome before these new technologies can be considered as a user-friendly and accurate source of novel BP data to inform clinical hypertension management strategies.

Intraoperative Hypotension Prediction: Current Methods, Controversies, and Research Outlook

Intraoperative hypotension prediction has been increasingly emphasized due to its potential clinical value in reducing organ injury and the broad availability of large-scale patient datasets and powerful machine learning tools. Hypotension prediction methods can mitigate low blood pressure exposure time. However, they have yet to be convincingly demonstrated to improve objective outcomes; furthermore, they have recently become controversial. This review presents the current state of intraoperative hypotension prediction and makes recommendations on future research. We begin by overviewing the current hypotension prediction methods, which generally rely on the prevailing mean arterial pressure as one of the important input variables and typically show good sensitivity and specificity but low positive predictive value in forecasting near-term acute hypotensive events. We make specific suggestions on improving the definition of acute hypotensive events and evaluating hypotension prediction methods, along with general proposals on extending the methods to predict reduced blood flow and treatment effects. We present a start of a risk-benefit analysis of hypotension prediction methods in clinical practice. We conclude by coalescing this analysis with the current evidence to offer an outlook on prediction methods for intraoperative hypotension. A shift in research toward tailoring hypotension prediction methods to individual patients and pursuing methods to predict appropriate treatment in response to hypotension appear most promising to improve outcomes.

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.

Economic Impact of Ambulatory Blood Pressure Monitoring Compared With Clinical Blood Pressure Monitoring: A Simulation Model

Background

Ambulatory blood pressure monitoring (ABPM) is considered the gold standard for proper diagnosis of hypertension. Yet, access to ABPM in the U.S. is limited, and the extent of coverage by commercial health plans remains uncertain, potentially limiting access to ABPM among commercially insured patients.

Objective

This study aims to assess the net cost impact of using ABPM in comparison to clinical blood pressure monitoring (CBPM) in the U.S. over a 5-year time period.

Design methods

Using a Markov Model, we estimate the 5-year cumulative cost impact of using ABPM to confirm a prior diagnosis of primary hypertension using CBPM to avoid treatment for white-coat hypertension (WCH) in a hypothetical cohort of 1000 patients from a U.S. healthcare system perspective. The probability and cost inputs for the model were derived from available literature. Base-case model parameters were varied to account for different scenarios.

Results

Base-case results indicate using ABPM instead of CBPM over 5 years saves a total of $348,028, reflecting an average per-person-per-year (PPPY) cost saving of $70. In sensitivity analyses, almost all cases reveal ABPM as a cost-saving approach compared to CBPM, with cost savings reaching up to $228 PPPY in the highest hypertension treatment cost model. Regression results reveal that ABPM was cost-saving compared to CBPM if ABPM annual payment rates are $100 or less and annual hypertension treatment costs are ⩾$300.

Conclusion

The potential cost-savings of using ABPM instead of CBPM found in our simulation model underscores the need for confirmatory research using real-world data to support increased use of ABPM as the standard diagnostic approach for hypertension.

Nonlinear Viscoelastic Modeling of Finger Arteries: Toward Smartphone-Based Blood Pressure Monitoring via the Oscillometric Finger Pressing Method

OBJECTIVE

Oscillometric finger pressing is a smartphone-based blood pressure (BP) monitoring method. Finger photoplethysmography (PPG) oscillations and pressure are measured during a steady increase in finger pressure, and an algorithm computes systolic BP (SP) and diastolic BP (DP) from the measurements. The objective was to assess the impact of finger artery viscoelasticity on the BP computation.

METHODS

Nonlinear viscoelastic models relating transmural pressure (finger BP - applied pressure) to PPG oscillations during finger pressing were developed. The output of each model to a measured transmural pressure input was fitted to measured PPG oscillations from 15 participants. A parametric sensitivity analysis was performed via model simulations to elucidate the viscoelastic effect on the derivative-based BP computation algorithm.

RESULTS

A Wiener viscoelastic model comprising a first-order transfer function followed by a static sigmoidal function fitted the measured PPG oscillations better than an elastic model containing only the static function (median (IQR) error of 30.5% (25.6%-34.0%) vs 50.9% (46.7%-53.7%); p<0.01). In Wiener model simulations, the derivative algorithm underestimated SP, especially with high pulse pressure and low transfer function cutoff frequency (i.e., greater viscoelasticity). The mean of the normalized PPG waveform at the maximum oscillation beat was found to correlate with the cutoff frequency (r = -0.8) and could thus possibly be used to compensate for viscoelasticity.

CONCLUSION

Finger artery viscoelasticity negatively impacts oscillometric BP computation algorithms but can potentially be compensated for using available measurements.

SIGNIFICANCE

These findings may help in converting smartphones into truly cuffless BP monitors for improving hypertension awareness and control.

Examining the challenges of blood pressure estimation via photoplethysmogram

The use of observed wearable sensor data (e.g., photoplethysmograms [PPG]) to infer health measures (e.g., glucose level or blood pressure) is a very active area of research. Such technology can have a significant impact on health screening, chronic disease management and remote monitoring. A common approach is to collect sensor data and corresponding labels from a clinical grade device (e.g., blood pressure cuff) and train deep learning models to map one to the other. Although well intentioned, this approach often ignores a principled analysis of whether the input sensor data have enough information to predict the desired metric. We analyze the task of predicting blood pressure from PPG pulse wave analysis. Our review of the prior work reveals that many papers fall prey to data leakage and unrealistic constraints on the task and preprocessing steps. We propose a set of tools to help determine if the input signal in question (e.g., PPG) is indeed a good predictor of the desired label (e.g., blood pressure). Using our proposed tools, we found that blood pressure prediction using PPG has a high multi-valued mapping factor of 33.2% and low mutual information of 9.8%. In comparison, heart rate prediction using PPG, a well-established task, has a very low multi-valued mapping factor of 0.75% and high mutual information of 87.7%. We argue that these results provide a more realistic representation of the current progress toward the goal of wearable blood pressure measurement via PPG pulse wave analysis. For code, see our project page: https://github.com/lirus7/PPG-BP-Analysis.

PeriOperative Quality Initiative (POQI) international consensus statement on perioperative arterial pressure management

Arterial pressure monitoring and management are mainstays of haemodynamic therapy in patients having surgery. This article presents updated consensus statements and recommendations on perioperative arterial pressure management developed during the 11th POQI PeriOperative Quality Initiative (POQI) consensus conference held in London, UK, on June 4-6, 2023, which included a diverse group of international experts. Based on a modified Delphi approach, we recommend keeping intraoperative mean arterial pressure ≥60 mm Hg in at-risk patients. We further recommend increasing mean arterial pressure targets when venous or compartment pressures are elevated and treating hypotension based on presumed underlying causes. When intraoperative hypertension is treated, we recommend doing so carefully to avoid hypotension. Clinicians should consider continuous intraoperative arterial pressure monitoring as it can help reduce the severity and duration of hypotension compared to intermittent arterial pressure monitoring. Postoperative hypotension is often unrecognised and might be more important than intraoperative hypotension because it is often prolonged and untreated. Future research should focus on identifying patient-specific and organ-specific hypotension harm thresholds and optimal treatment strategies for intraoperative hypotension including choice of vasopressors. Research is also needed to guide monitoring and management strategies for recognising, preventing, and treating postoperative hypotension.

A Direct Assessment of Noninvasive Continuous Blood Pressure Monitoring in the Emergency Department and Intensive Care Unit

INTRODUCTION

Noninvasive continuous blood pressure monitoring has the potential to improve patient treatment in the hospital setting. Such noninvasive devices can be applied earlier in the treatment process to empower nurses and clinicians to react more quickly to patient deterioration with the added benefit of eliminating the risks associated with invasive monitoring. However, emerging technologies must be capable of reproducing current clinical measures for medical decision making.

METHODS

This study aimed to determine the usability and willingness of nurses to implement a noninvasive continuous blood pressure monitoring device. The secondary aim directly compared the systolic blood pressure, diastolic blood pressure, and mean arterial pressure values recorded by the device (VitalStream; CareTaker Medical LLC, Charlottesville, VA) with the "gold standard" brachial cuff and arterial line measures recorded in the emergency department and intensive care unit settings.

RESULTS

VitalStream was similarly received by nurses in the emergency department and intensive care setting, but ultimately had greater promotion from emergency nurses. Despite some statistical similarity between measurement methodologies, all direct comparisons were found to not meet the Association for the Advancement of Medical Instrumentation 2008 and Association for the Advancement of Medical Instrumentation / European Society of Hypertension / International Organization for Standardization 2019 consensus statement criteria for acceptable blood pressure measure differences between the VitalStream and "gold standard" clinical measures. In all instances, the standard deviation of the Bland-Altman bias exceeded 8 mm Hg with less than 85% of paired differences falling within 10 mm Hg of the "gold standard."

DISCUSSION

Taken together, the tested device requires additional postprocessing for medical decision making in trauma or emergent care.

Advancing Cuffless Arterial Blood Pressure Waveform Estimation: Time-Series Deep Neural Network Approach

Existing deep learning models for arterial blood pressure (ABP) estimation are becoming increasingly complex. They mainly treat the estimation as a sequence-to-sequence (seq2seq) task, to establish the relationship between input physiological signals and the corresponding BP within the same time frame. However, this approach may overlook the rich temporal information embedded in physiological signals. In this study, we propose a time-series training strategy for ABP waveform prediction. We compared two deep learning models of different sizes - the smaller gMLP and the larger UtransBPNet - in both seq2seq and time-series training ways. The findings indicate that, the models trained with the time-series method achieved significant enhancements in performance compared to their seq2seq counterparts, with mean absolute error (MAE) reductions of 2.0 and 0.9 mmHg for gMLP and UtransBPNet, respectively. This improvement was more pronounced in the smaller, simpler-structured gMLP network. Additionally, the time-series training approach exhibited superior predictive abilities for out-of-distribution data. In conclusion, this straightforward time-series approach offers a novel perspective for developing efficient models for cuffless arterial BP estimation, making it a promising candidate for implementation in edge wearable devices.

Development of a Personalized Multiclass Classification Model to Detect Blood Pressure Variations Associated with Physical or Cognitive Workload

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

Development of beat-by-beat blood pressure monitoring device and nocturnal sec-surge detection algorithm

The nocturnal blood pressure (BP) surge in seconds (sec-surge) is defined as a brief, acute transient BP elevation over several tens of seconds, triggered by obstructive sleep apnea (OSA) and sympathetic hyperactivity. Sec-surge imposes a significant strain on the cardiovascular system, potentially triggering cardiovascular events. Quantitative evaluation of sec-surge level could be valuable in assessing cardiovascular risks. To accurately measure the detailed sec-surge, including its shape as BP rises and falls, we developed a beat-by-beat (BbB) BP monitoring device using tonometry. In addition, we developed an automatic sec-surge detection algorithm to help identify sec-surge cases in the overnight BbB BP data. The device and algorithm successfully detected sec-surges in patients with OSA. Our results demonstrated that sec-surge was associated with left ventricular hypertrophy and arterial stiffness independently of nocturnal BP level or variability. Sec-surge would be worth monitoring for assessing cardiovascular risks, in addition to nocturnal BP level. Nocturnal blood pressure (BP) surge in seconds (sec-surge) places heavy load on the cardiovascular system and can trigger cardiovascular events. To identify sec-surges, we developed a beat-by-beat BP monitoring device and a sec-surge detection algorithm. Furthermore, sec-surge was more related to cardiovascular risks than conventional nocturnal BP parameters.

pyPPG: a Python toolbox for comprehensive photoplethysmography signal analysis

Objective.Photoplethysmography is a non-invasive optical technique that measures changes in blood volume within tissues. It is commonly and being increasingly used for a variety of research and clinical applications to assess vascular dynamics and physiological parameters. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and limited open tools exist for continuous photoplethysmogram (PPG) analysis. Consequently, the primary objective of this research was to identify, standardize, implement and validate key digital PPG biomarkers.Approach.This work describes the creation of a standard Python toolbox, denotedpyPPG, for long-term continuous PPG time-series analysis and demonstrates the detection and computation of a high number of fiducial points and digital biomarkers using a standard fingerbased transmission pulse oximeter.Main results.The improved PPG peak detector had an F1-score of 88.19% for the state-of-the-art benchmark when evaluated on 2054 adult polysomnography recordings totaling over 91 million reference beats. The algorithm outperformed the open-source original Matlab implementation by ∼5% when benchmarked on a subset of 100 randomly selected MESA recordings. More than 3000 fiducial points were manually annotated by two annotators in order to validate the fiducial points detector. The detector consistently demonstrated high performance, with a mean absolute error of less than 10 ms for all fiducial points.Significance.Based on these fiducial points,pyPPGengineered a set of 74 PPG biomarkers. Studying PPG time-series variability usingpyPPGcan enhance our understanding of the manifestations and etiology of diseases. This toolbox can also be used for biomarker engineering in training data-driven models.pyPPGis available onhttps://physiozoo.com/.

History and evolution of blood pressure measurement

Hypertension is the leading cause of morbidity and mortality worldwide. Hypertension mostly accompanies no symptoms, and therefore blood pressure (BP) measurement is the only way for early recognition and timely treatment. Methods for BP measurement have a long history of development and improvement. Invasive method via arterial cannulation was first proven possible in the 1800's. Subsequent scientific progress led to the development of the auscultatory method, also known as Korotkoff' sound, and the oscillometric method, which enabled clinically available BP measurement. However, hypertension management status is still poor. Globally, less than half of adults are aware of their hypertension diagnosis, and only one-third of them being treated are under control. Novel methods are actively investigated thanks to technological advances such as sensors and machine learning in addition to the clinical needs for easier and more convenient BP measurement. Each method adopts different technologies with its own specific advantages and disadvantages. Promises of novel methods include comprehensive information on out-of-office BP capturing dynamic short-term and long-term fluctuations. However, there are still pitfalls such as the need for regular calibration since most novel methods capture relative BP changes rather than an absolute value. In addition, there is growing concern on their accuracy and precision as conventional validation protocols are inappropriate for cuffless continuous methods. In this article, we provide a comprehensive overview of the past and present of BP measurement methods. Novel and emerging technologies are also introduced with respect to their potential applications and limitations.

The Investigation of Bio-impedance Analysis at a Wrist Phantom with Two Pulsatile Arteries

PURPOSE

Bio-impedance analysis (BIA) has been widely investigated for hemodynamic monitoring. However, previous works rarely modelled two synchronously pulsatile arteries (representing the radial and ulnar arteries) in the wrist/forearm model. This work aims to clarify and quantify the influences of two pulsatile arteries on BIA.

METHODS

First, two blood-filled arteries were structured in a 3D wrist segment using the finite element method (FEM). Afterwards, an easy-to-produce two-arteries artificial wrist was fabricated with two components: gelatine-based surrounding tissue phantom and saline blood phantom. A syringe driver was utilised to constrict the arteries, and the impedance signals were measured using a Multi-frequency Impedance Analyser (MFIA).

RESULTS

Both simulation and experimental results demonstrated the non-negligible influences of the ulnar artery on the overall BIA, inducing unwanted resistance changes to the acquired signals from the radial artery. The phantom experiments revealed the summation of the individual resistance changes caused by a single pulsatile artery was approximately equal to the measured resistance change caused by two synchronously pulsatile arteries, confirming the measured impedance signal at the wrist contains the pulsatile information from both arteries.

CONCLUSION

This work is the first simulation and phantom investigation into two synchronously pulsatile arteries under BIA in the distal forearm, providing a better insight and understanding in the morphology of measured impedance signals. Future research can accordingly select either a small spacing 4-spot electrode configuration for a single artery sensing or a band electrode configuration for overall pulsatile arteries sensing. A more accurate estimation of blood volume change and pulse wave analysis (PWA) could help to develop cuffless blood pressure measurement (BPM).

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.

Accuracy and User Acceptability of 24-hour Ambulatory Blood Pressure Monitoring by a Prototype Cuffless Multi-Sensor Device Compared to a Conventional Oscillometric Device

OBJECTIVE

24-hour ambulatory blood pressure monitoring (24ABPM) is state of the art in out-of-office blood pressure (BP) monitoring. Due to discomfort and technical limitations related to cuff-based 24ABPM devices, methods for non-invasive and continuous estimation of BP without the need for a cuff have gained interest. The main aims of the present study were to compare accuracy of a pulse arrival time (PAT) based BP-model and user acceptability of a prototype cuffless multi-sensor device (cuffless device), developed by Aidee Health AS, with a conventional cuff-based oscillometric device (ReferenceBP) during 24ABPM.

METHODS

Ninety-five normotensive and hypertensive adults underwent simultaneous 24ABPM with the cuffless device on the chest and a conventional cuff-based oscillometric device on the non-dominant arm. PAT was calculated using the electrocardiogram (ECG) and photoplethysmography (PPG) sensors incorporated in the chest-worn device. The cuffless device recorded continuously, while ReferenceBP measurements were taken every 20 minutes during daytime and every 30 minutes during nighttime. Two-minute PAT-based BP predictions corresponding to the ReferenceBP measurements were compared with ReferenceBP measurements using paired t-tests, bias, and limits of agreement.

RESULTS

Mean (SD) of ReferenceBP compared to PAT-based daytime and nighttime systolic BP (SBP) were 129.7 (13.8) mmHg versus 133.6 (20.9) mmHg and 113.1 (16.5) mmHg versus 131.9 (23.4) mmHg. Ninety-five % limits of agreements were [-26.7, 34.6 mmHg] and [-20.9, 58.4 mmHg] for daytime and nighttime SBP respectively. The cuffless device was reported to be significantly more comfortable and less disturbing than the ReferenceBP device during 24ABPM.

CONCLUSIONS

In the present study, we demonstrated that a general PAT-based BP model had unsatisfactory agreement with ambulatory BP during 24ABPM, especially during nighttime. If sufficient accuracy can be achieved, cuffless BP devices have promising potential for clinical assessment of BP due to the opportunities provided by continuous BP measurements during real-life conditions and high user acceptability.

Selecting patients for interventional procedures to treat hypertension

Purpose: Interventional approaches to treat hypertension are an emerging option that may be suitable for patients whose BP control cannot be achieved with lifestyle and/or pharmacotherapy and possibly for those who do not wish to take drug therapy.Materials and Methods: Interventional strategies include renal denervation with radiofrequency, ultrasound and alcohol-mediated platforms as well as baroreflex activation therapy and cardiac neuromodulation therapy. Presently renal denervation is the most advanced of the therapeutic options and is currently being commercialised in the EU.Results: It is apparent that RDN is effective in both unmedicated patients and patients with more severe hypertension including those with resistant hypertension.Conclusion: However, at present there is no evidence for the use of RDN in patients with secondary forms of hypertension and thus evaluation to rule these out is necessary before proceeding with a procedure. Furthermore, there are numerous pitfalls in the diagnosis and management of secondary hypertension which need to be taken into consideration. Finally, prior to performing an intervention it is appropriate to document presence/absence of hypertension-mediated organ damage.

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.

Smartphone-Based Blood Pressure Monitoring via the Oscillometric Finger Pressing Method: Analysis of Oscillation Width Variations Can Improve Diastolic Pressure Computation

OBJECTIVE

Oscillometric finger pressing is a potential method for absolute blood pressure (BP) monitoring via a smartphone. The user presses their fingertip against a photoplethysmography-force sensor unit on a smartphone to steadily increase the external pressure on the underlying artery. Meanwhile, the phone guides the finger pressing and computes systolic BP (SP) and diastolic BP (DP) from the measured blood volume oscillations and finger pressure. The objective was to develop and evaluate reliable finger oscillometric BP computation algorithms.

METHODS

The collapsibility of thin finger arteries was exploited in an oscillometric model to develop simple algorithms for computing BP from the finger pressing measurements. These algorithms extract features from "width" oscillograms (oscillation width versus finger pressure functions) and the conventional "height" oscillogram for markers of DP and SP. Finger pressing measurements were obtained using a custom system along with reference arm cuff BP measurements from 22 subjects. Measurements were also obtained during BP interventions in some subjects for 34 total measurements.

RESULTS

An algorithm employing the average of width and height oscillogram features predicted DP with correlation of 0.86 and precision error of 8.6 mmHg with respect to the reference measurements. Analysis of arm oscillometric cuff pressure waveforms from an existing patient database provided evidence that the width oscillogram features are better suited to finger oscillometry.

CONCLUSION

Analysis of oscillation width variations during finger pressing can improve DP computation.

SIGNIFICANCE

The study findings may help in converting widely available devices into truly cuffless BP monitors for improving hypertension awareness and control.

Current evidence suggests that estimating blood pressure from convenient ECG waveforms alone is not viable

Cuffless blood pressure (BP) measurement could improve hypertension awareness and control and is being widely pursued. Some have proposed to estimate BP from the electrocardiogram (ECG) alone despite little physiological basis. In this minireview, we extracted the most relevant articles related to ECG-based BP estimation. Our findings suggest that, as expected, estimating BP from ECG does not appear to be viable. Most notably, we have not found any evidence that ECG features can track BP changes. At best, certain ECG features may indicate heart disease and thus correlate with high BP, but this may not be clinically useful.

Current and Developing Technologies for BP Monitoring

PURPOSE OF REVIEW

To discuss new and emerging technologies for blood pressure measurement and monitoring, including the limitations of current blood pressure measurement techniques, hopes for new device technologies, and the current barriers impeding change in this space.

RECENT FINDINGS

A number of new cuffless devices are being developed and poised to emerge on the marketplace in coming years. There are several different types of technologies and sensors currently under study. New guidelines for validation of cuffless blood pressure devices have recently been developed in anticipation of this change. The current standards for blood pressure device validation are specific to cuff-based technology and are insufficient for validating devices with cuffless-based technologies. In anticipation of a number of new cuffless technologies coming to market in the coming years, three sets of standards have been developed and published in recent years to address this gap.

The Promise and Illusion of Continuous, Cuffless Blood Pressure Monitoring

PURPOSE OF REVIEW

Blood pressure (BP) fluctuations outside of clinic are increasingly recognized for their role in the development of cardiovascular disease, syncope, and premature death and as a promising target for tailored hypertension treatment. However, current cuff-based BP devices, including home and ambulatory devices, are unable to capture the breadth of BP variability across human activities, experiences, and contexts.

RECENT FINDINGS

Cuffless, wearable BP devices offer the promise of beat-to-beat, continuous, noninvasive measurement of BP during both awake and sleep periods with minimal patient inconvenience. Importantly, cuffless BP devices can characterize BP variability, allowing for the identification of patient-specific triggers of BP surges in the home environment. Unfortunately, the pace of evidence, regulation, and validation testing has lagged behind the pace of innovation and direct consumer marketing. We provide an overview of the available technologies and devices for cuffless BP monitoring, considerations for the calibration and validation of these devices, and the promise and pitfalls of the cuffless BP paradigm.

A new method of continuous blood pressure monitoring using multichannel sensing signals on the wrist

Hypertension is a worldwide health problem and a primary risk factor for cardiovascular disease. Continuous monitoring of blood pressure has important clinical value for the early diagnosis and prevention of cardiovascular disease. However, existing technologies for wearable continuous blood pressure monitoring are usually inaccurate, rely on subject-specific calibration and have poor generalization across individuals, which limit their practical applications. Here, we report a new blood pressure measurement method and develop an associated wearable device to implement continuous blood pressure monitoring for new subjects. The wearable device detects cardiac output and pulse waveform features through dual photoplethysmography (PPG) sensors worn on the palmar and dorsal sides of the wrist, incorporating custom-made interface sensors to detect the wearing contact pressure and skin temperature. The detected multichannel signals are fused using a machine-learning algorithm to estimate continuous blood pressure in real time. This dual PPG sensing method effectively eliminates the personal differences in PPG signals caused by different people and different wearing conditions. The proposed wearable device enables continuous blood pressure monitoring with good generalizability across individuals and demonstrates promising potential in personal health care applications.

Estimating Blood Pressure during Exercise with a Cuffless Sphygmomanometer

Accurately measuring blood pressure (BP) is essential for maintaining physiological health, which is commonly achieved using cuff-based sphygmomanometers. Several attempts have been made to develop cuffless sphygmomanometers. To increase their accuracy and long-term variability, machine learning methods can be applied for analyzing photoplethysmogram (PPG) signals. Here, we propose a method to estimate the BP during exercise using a cuffless device. The BP estimation process involved preprocessing signals, feature extraction, and machine learning techniques. To ensure the reliability of the signals extracted from the PPG, we employed the skewness signal quality index and the RReliefF algorithm for signal selection. Thereafter, the BP was estimated using the long short-term memory (LSTM)-based neural network. Seventeen young adult males participated in the experiments, undergoing a structured protocol composed of rest, exercise, and recovery for 20 min. Compared to the BP measured using a non-invasive voltage clamp-type continuous sphygmomanometer, that estimated by the proposed method exhibited a mean error of 0.32 ± 7.76 mmHg, which is equivalent to the accuracy of a cuff-based sphygmomanometer per regulatory standards. By enhancing patient comfort and improving healthcare outcomes, the proposed approach can revolutionize BP monitoring in various settings, including clinical, home, and sports environments.

Physics-informed neural networks for modeling physiological time series for cuffless blood pressure estimation

The bold vision of AI-driven pervasive physiological monitoring, through the proliferation of off-the-shelf wearables that began a decade ago, has created immense opportunities to extract actionable information for precision medicine. These AI algorithms model input-output relationships of a system that, in many cases, exhibits complex nature and personalization requirements. A particular example is cuffless blood pressure estimation using wearable bioimpedance. However, these algorithms need training over significant amount of ground truth data. In the context of biomedical applications, collecting ground truth data, particularly at the personalized level is challenging, burdensome, and in some cases infeasible. Our objective is to establish physics-informed neural network (PINN) models for physiological time series data that would use minimal ground truth information to extract complex cardiovascular information. We achieve this by building Taylor's approximation for gradually changing known cardiovascular relationships between input and output (e.g., sensor measurements to blood pressure) and incorporating this approximation into our proposed neural network training. The effectiveness of the framework is demonstrated through a case study: continuous cuffless BP estimation from time series bioimpedance data. We show that by using PINNs over the state-of-the-art time series models tested on the same datasets, we retain high correlations (systolic: 0.90, diastolic: 0.89) and low error (systolic: 1.3 ± 7.6 mmHg, diastolic: 0.6 ± 6.4 mmHg) while reducing the amount of ground truth training data on average by a factor of 15. This could be helpful in developing future AI algorithms to help interpret pervasive physiologic data using minimal amount of training data.

Decision Support System Proposal for Medical Evacuations in Military Operations

The area of military operations is a big challenge for medical support. A particularly important factor that allows medical services to react quickly in the case of mass casualties is the ability to rapidly evacuation of wounded soldiers from a battlefield. To meet this requirement, an effective medical evacuation system is essential. The paper presented the architecture of the electronically supported decision support system for medical evacuation during military operations. The system can also be used by other services such as police or fire service. The system meets the requirements for tactical combat casualty care procedures and is composed of following elements: measurement subsystem, data transmission subsystem and analysis and inference subsystem. The system, based on the continuous monitoring of selected soldiers' vital signs and biomedical signals, automatically proposes a medical segregation of wounded soldiers (medical triage). The information on the triage was visualized using the Headquarters Management System for medical personnel (first responders, medical officers, medical evacuation groups) and for commanders, if required. All elements of the architecture were described in the paper.

Emerging sensing and modeling technologies for wearable and cuffless blood pressure monitoring

Cardiovascular diseases (CVDs) are a leading cause of death worldwide. For early diagnosis, intervention and management of CVDs, it is highly desirable to frequently monitor blood pressure (BP), a vital sign closely related to CVDs, during people's daily life, including sleep time. Towards this end, wearable and cuffless BP extraction methods have been extensively researched in recent years as part of the mobile healthcare initiative. This review focuses on the enabling technologies for wearable and cuffless BP monitoring platforms, covering both the emerging flexible sensor designs and BP extraction algorithms. Based on the signal type, the sensing devices are classified into electrical, optical, and mechanical sensors, and the state-of-the-art material choices, fabrication methods, and performances of each type of sensor are briefly reviewed. In the model part of the review, contemporary algorithmic BP estimation methods for beat-to-beat BP measurements and continuous BP waveform extraction are introduced. Mainstream approaches, such as pulse transit time-based analytical models and machine learning methods, are compared in terms of their input modalities, features, implementation algorithms, and performances. The review sheds light on the interdisciplinary research opportunities to combine the latest innovations in the sensor and signal processing research fields to achieve a new generation of cuffless BP measurement devices with improved wearability, reliability, and accuracy.

Wearable and Portable Devices for Acquisition of Cardiac Signals while Practicing Sport: A Scoping Review

Wearable and portable devices capable of acquiring cardiac signals are at the frontier of the sport industry. They are becoming increasingly popular for monitoring physiological parameters while practicing sport, given the advances in miniaturized technologies, powerful data, and signal processing applications. Data and signals acquired by these devices are increasingly used to monitor athletes' performances and thus to define risk indices for sport-related cardiac diseases, such as sudden cardiac death. This scoping review investigated commercial wearable and portable devices employed for cardiac signal monitoring during sport activity. A systematic search of the literature was conducted on PubMed, Scopus, and Web of Science. After study selection, a total of 35 studies were included in the review. The studies were categorized based on the application of wearable or portable devices in (1) validation studies, (2) clinical studies, and (3) development studies. The analysis revealed that standardized protocols for validating these technologies are necessary. Indeed, results obtained from the validation studies turned out to be heterogeneous and scarcely comparable, since the metrological characteristics reported were different. Moreover, the validation of several devices was carried out during different sport activities. Finally, results from clinical studies highlighted that wearable devices are crucial to improve athletes' performance and to prevent adverse cardiovascular events.

Wearable Continuous Blood Pressure Monitoring Devices Based on Pulse Wave Transit Time and Pulse Arrival Time: A Review

Continuous blood pressure (BP) monitoring is of great significance for the real-time monitoring and early prevention of cardiovascular diseases. Recently, wearable BP monitoring devices have made great progress in the development of daily BP monitoring because they adapt to long-term and high-comfort wear requirements. However, the research and development of wearable continuous BP monitoring devices still face great challenges such as obvious motion noise and slow dynamic response speeds. The pulse wave transit time method which is combined with photoplethysmography (PPG) waves and electrocardiogram (ECG) waves for continuous BP monitoring has received wide attention due to its advantages in terms of excellent dynamic response characteristics and high accuracy. Here, we review the recent state-of-art wearable continuous BP monitoring devices and related technology based on the pulse wave transit time; their measuring principles, design methods, preparation processes, and properties are analyzed in detail. In addition, the potential development directions and challenges of wearable continuous BP monitoring devices based on the pulse wave transit time method are discussed.

The Microsoft Research Aurora Project: Important Findings on Cuffless Blood Pressure Measurement

Conventional blood pressure (BP) measurement devices based on an inflatable cuff only provide a narrow view of the continuous BP profile. Cuffless BP measuring technologies could permit numerous BP readings throughout daily life and thereby considerably improve the assessment and management of hypertension. Several wearable cuffless BP devices based on pulse wave analysis (applied to a photoplethysmography or tonometry waveform) with or without use of pulse arrival time are now available on the market. The key question is: Can these devices provide accurate measurement of BP? Microsoft Research recently published a complex article describing perhaps the most important and highest resource project to date (Aurora Project) on assessing the accuracy of several pulse wave analysis and pulse wave analysis-pulse arrival time devices. The overall results from 1125 participants were clear-cut negative. The present article motivates and describes emerging cuffless BP devices and then summarizes the Aurora Project. The study methodology and findings are next discussed in the context of regulatory-cleared devices, physiology, and related studies, and the study strengths and limitations are pinpointed thereafter. Finally, the implications of the Aurora Project are briefly stated and recommendations for future work are offered to finally realize the considerable potential of cuffless BP measurement in health care.

Mathematical Modeling of Oscillometric Blood Pressure Measurement: A Complete, Reduced Oscillogram Model

OBJECTIVE

Oscillogram modeling is a powerful tool for understanding and advancing popular oscillometric blood pressure (BP) measurement. A reduced oscillogram model relating cuff pressure oscillation amplitude ( ∆O) to external cuff pressure of the artery ( Pe) is: [Formula: see text], where g(P) is the arterial compliance versus transmural pressure ( P) curve, Ps and Pd are systolic and diastolic BP, and k is the reciprocal of the cuff compliance. The objective was to determine an optimal functional form for the arterial compliance curve.

METHODS

Eight prospective, three-parameter functions of the brachial artery compliance curve were compared. The study data included oscillometric arm cuff pressure waveforms and invasive brachial BP from 122 patients covering a 20-120 mmHg pulse pressure range. The oscillogram measurements were constructed from the cuff pressure waveforms. Reduced oscillogram models, inputted with measured systolic and diastolic BP and each parametric brachial artery compliance curve function, were optimally fitted to the oscillogram measurements in the least squares sense.

RESULTS

An exponential-linear function yielded as good or better model fits compared to the other functions, with errors of 7.9±0.3 and 5.1±0.2% for tail-trimmed and lower half-trimmed oscillogram measurements. Importantly, this function was also the most tractable mathematically.

CONCLUSION

A three-parameter exponential-linear function is an optimal form for the arterial compliance curve in the reduced oscillogram model and may thus serve as the standard function for this model henceforth.

SIGNIFICANCE

The complete, reduced oscillogram model determined herein can potentially improve oscillometric BP measurement accuracy while advancing foundational knowledge.

Validating cuffless continuous blood pressure monitoring devices

Cuff-based home blood pressure (BP) devices, which have been the standard for BP monitoring for decades, are limited by physical discomfort, convenience, and their ability to capture BP variability and patterns between intermittent readings. In recent years, cuffless BP devices, which do not require cuff inflation around a limb, have entered the market, offering the promise of continuous beat-to-beat measurement of BP. These devices take advantage of a variety of principles to determine BP, including (1) pulse arrival time, (2) pulse transit time, (3) pulse wave analysis, (4) volume clamping, and (5) applanation tonometry. Because BP is calculated indirectly, these devices require calibration with cuff-based devices at regular intervals. Unfortunately, the pace of regulation of these devices has failed to match the speed of innovation and direct availability to patient consumers. There is an urgent need to develop a consensus on standards by which cuffless BP devices can be tested for accuracy. In this narrative review, we describe the landscape of cuffless BP devices, summarize the current status of validation protocols, and provide recommendations for an ideal validation process for these devices.