Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Theory and Practice

Advancements in Bio-Integrated Flexible Electronics for Hemodynamic Monitoring in Cardiovascular Healthcare

Cardiovascular diseases (CVDs) remain the leading cause of global mortality, highlighting the urgent need for effective monitoring and prevention strategies. The rapid advancement of flexible sensing technology and the development of conformal sensors have attracted significant attention due to their potential for continuous, real-time assessment of cardiovascular health over extended periods. This review outlines recent advancements in bio-integrated flexible electronics designed for hemodynamic monitoring and broader CVD healthcare applications. It introduces key physiological indicators relevant to hemodynamics, including heart rate, blood pressure, blood flow velocity, and cardiac output. Next, it discusses flexible bio-integrated electronics engineering strategies, such as working principles and configuration designs. Various non-invasive and invasive bio-integrated devices for monitoring these hemodynamic indicators are then presented. Additionally, the review highlights the role of artificial intelligence algorithms and their practical applications in bio-integrated electronics for hemodynamic detection. Finally, it proposes future directions and addresses potential challenges in the field.

Heart-carotid pulse-wave velocity via laser-Doppler vibrometry as a biomarker for arterial stiffening: a feasibility study

Objective. Large artery stiffening leads to an increase in cardiovascular risk and organ damage of the kidneys, brain or the heart. Biomarkers that allow for early detection of this phenomenon are a point of interest in research, with pulse-wave velocity (PWV) having been proven useful in predicting and monitoring arterial stiffness. We previously introduced a laser Doppler vibrometry (LDV) prototype which can measure carotid-femoral PWV (cfPWV). In this work, we assess the feasibility of using the same device to infer heart-carotid pulse-transit time (hcPTT) as a first step towards measuring heart-carotid PWV (hcPWV). The advantage of hcPWV over cfPWV is that the ascending aorta, which is the most distensible segment of the aorta contributing most to total arterial compliance, is included in the arterial pathway.Approach. Signals were simultaneously acquired from a location on the chest (near either the base or the apex of the heart) and the right carotid artery for 100 patients (45% female). Fiducial points on the heart waveforms are associated with opening and closure (second heart sound; S2) of the aortic valve, which can be combined with, respectively, the foot and dicrotic notch (DN) of the carotid waveform to retrieve hcPTT. Considering two distinct heart-signal measurement sites, four hcPTT estimations are evaluated in about 94% of all measurements.Main results. Correlations between these and known predictors of arterial stiffness i.e. age, blood pressure and carotid-femoral PTT via applanation tonometry indicated that combining S2 from a heart-measurement site located at the base of the heart, with the carotid DN yields hcPTT providing convincing correlations with known determinants of arterial stiffness (ρ = 0.377 with age).Significance.We conclude that LDV may provide a corollary biomarker of arterial stiffness, encompassing the ascending aorta.

Flexible, wearable mechano-acoustic sensors for body sound monitoring applications

Body sounds serve as a valuable source of health information, offering insights into systems such as the cardiovascular, pulmonary, and gastrointestinal systems. Additionally, body sound measurements are easily accessible, fast, and non-invasive, which has led to their widespread use in clinical auscultation for diagnosing health conditions. However, conventional devices like stethoscopes are constrained by rigid and bulky designs, limiting their potential for long-term monitoring and often leading to subjective diagnoses. Recently, flexible, wearable mechano-acoustic sensors have emerged as an innovative alternative for body sound auscultation, offering significant advantages over conventional rigid devices. This review explores these advanced sensors, delving into their sensing mechanisms, materials, configurations, and fabrication techniques. Furthermore, it highlights various health monitoring applications of flexible, wearable mechano-acoustic sensors based on body sound auscultation. Finally, the existing challenges and promising opportunities are addressed, providing a snapshot of the current picture and the strategies of future approaches in this rapidly evolving field.

Reliability of an all-in-one wearable sensor for continuous vital signs monitoring in high-risk patients: the NIGHTINGALE clinical validation study

Continuous vital signs monitoring with wearable systems may improve early recognition of patient deterioration on hospital wards. The objective of this study was to determine whether the wearable Checkpoint Cardio's CPC12S, can accurately measure heart rate (HR), respiratory rate (RR), oxygen saturation (SpO2), blood pressure (BP) and temperature continuously. In an observational multicenter method comparison study of 70 high-risk surgical patients admitted to high-dependency wards; HR, RR, SpO2, BP and temperature were simultaneously measured with the CPC12S system and with ICU-grade monitoring systems in four European hospitals. Outcome measures were bias and 95% limits of agreement (LoA). Clinical accuracy was assessed with Clarke Error Grid analyses for HR and RR. A total of 3,212 h of vital signs data (on average 26 h per patient) were analyzed. For HR, bias (95% LoA) of the pooled analysis was 0.0 (-3.5 to 3.4), for RR 1.5 (-3.7 to 7.5) and for SpO2 0.4 (-3.1 to 4.0). The CPC12S system overestimated BP, with a bias of 8.9 and wide LoA (-23.3 to 41.2). Temperature was underestimated with a bias of -0.6 and LoA of -1.7 to 0.6. Clarke Error Grid analyses showed that adequate treatment decisions regarding changes in HR and RR would have been made in 99.2% and 92.0% of cases respectively. The CPC12S system showed high accuracy for measurements of HR. The accuracy of RR, SpO2 were slightly overestimated and core temperature underestimated, with LoA outside the predefined clinical acceptable range. The accuracy of BP was unacceptably low.

Non-Contact Blood Pressure Monitoring Using Radar Signals: A Dual-Stage Deep Learning Network

Emerging radar sensing technology is revolutionizing cardiovascular monitoring by eliminating direct skin contact. This approach captures vital signs through electromagnetic wave reflections, enabling contactless blood pressure (BP) tracking while maintaining user comfort and privacy. We present a hierarchical neural framework that synergizes spatial and temporal feature learning for radar-driven, contactless BP monitoring. By employing advanced preprocessing techniques, the system captures subtle chest wall vibrations and their second-order derivatives, feeding dual-channel inputs into a hierarchical neural network. Specifically, Stage 1 deploys convolutional depth-adjustable lightweight residual blocks to extract spatial features from micro-motion characteristics, while Stage 2 employs a transformer architecture to establish correlations between these spatial features and BP periodic dynamic variations. Drawing on the intrinsic link between systolic (SBP) and diastolic (DBP) blood pressures, early estimates from Stage 2 are used to expand the feature set for the second-stage network, boosting its predictive power. Validation achieved clinically acceptable errors (SBP: -1.09 ± 5.15 mmHg, DBP: -0.26 ± 4.35 mmHg). Notably, this high degree of accuracy, combined with the ability to estimate BP at 2 s intervals, closely approximates real-time, beat-to-beat monitoring, representing a pivotal breakthrough in non-contact BP monitoring.

Non-Pharmacological Mitigation of Acute Mental Stress-Induced Sympathetic Arousal: Comparison Between Median Nerve Stimulation and Auricular Vagus Nerve Stimulation

Acute mental stress is a common experience in daily life, significantly affecting both physiological and psychological well-being. While traditional pharmacological interventions can be effective, they often accompany undesirable side effects. Non-pharmacological alternatives, such as non-invasive transcutaneous peripheral neuromodulation, have promise in mitigating acute stress-induced arousal, possibly with fewer side effects. Median nerve stimulation (MNS) and auricular vagus nerve stimulation (AVNS), in particular, have demonstrated notable potential. However, efficacy and mechanism of action pertaining to MNS and AVNS remain largely unknown. This paper comparatively investigated MNS and AVNS in terms of efficacy and mechanism of action in the context of mitigating acute stress-induced arousal. Using an experimental dataset collected from 19 healthy participants who experienced acute mental stressors as well as MNS and AVNS, we showed that (i) MNS and AVNS are both effective in mitigating acute stress-induced cardiovascular arousal with MNS modestly superior to AVNS in terms of a synthetic multi-modal variable derived from physio-markers representing heart rate, blood pressure, stroke volume, cardiac output, and peripheral vasoconstriction: 74% vs. 71% in explainability; 86% vs. 69% in stimulation consistency; 0.77 vs. 0.40 in stimulation sensitivity; and 34% vs. 19% in stimulation effectiveness, respectively; and that (ii) MNS and AVNS mitigate acute stress-induced cardiovascular arousal in a distinct mechanism of action: MNS primarily mitigates the arousal pertaining to the physio-markers representing heart rate and peripheral vasoconstriction, while AVNS mitigates the arousal pertaining to the physio-markers representing heart rate, blood pressure, stroke volume, cardiac output, and peripheral vasoconstriction. These findings may help to support future device development for addressing acute mental stress-induced arousal through MNS or AVNS, and they pave the way toward a better understanding of how to quantify the efficacy of such interventions.

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.

Video-based estimation of blood pressure

In this work, we propose a non-contact video-based approach that estimates an individual's blood pressure. The estimation of blood pressure is critical for monitoring hypertension and cardiovascular diseases such as coronary artery disease or stroke. Estimation of blood pressure is typically achieved using contact-based devices which apply pressure on the arm through a cuff. Such contact-based devices are cost-prohibitive as well as limited in their scalability due to the requirement of specialized equipment. The ubiquity of mobile phones and video-based capturing devices motivates the development of a non-contact blood pressure estimation method-Video-based Blood Pressure Estimation (V-BPE). We leverage the time difference of the blood pulse arrival at two different locations in the body (Pulse Transit Time) and the inverse relation between the blood pressure and the velocity of blood pressure pulse propagation in the artery to analytically estimate the blood pressure. Through statistical hypothesis testing, we demonstrate that Pulse Transit Time-based approaches to estimate blood pressure require knowledge of subject specific blood vessel parameters, such as the length of the blood vessel. We utilize a combination of computer vision techniques and demographic information (such as the height and the weight of the subject) to capture and incorporate the aforementioned subject specific blood vessel parameters into our estimation of blood pressure. We demonstrate the robustness of V-BPE by evaluating the efficacy of blood pressure estimation in demographically diverse, outside-the-lab conditions. V-BPE is advantageous in three ways; 1) it is non-contact-based, reducing the possibility of infection due to contact 2) it is scalable, given the ubiquity of video recording devices and 3) it is robust to diverse demographic scenarios due to the incorporation of subject specific information.

Simplified single neuron model for robust local pulse wave velocity sensing using a tetherless bioimpedance device

Pulse arrival time (PAT), Pulse transit time (PTT), and Pulse Wave Velocity (PWV) have all been used as metrics for assessing a number of cardiovascular applications, including arterial stiffness and cuffless blood pressure monitoring. These have been measured using various sensing methods, including electrocardiogram (ECG) with photoplethysmogram (PPG), two PPG sensors, or two Bioimpedance (BioZ) sensors. Our study addresses the mathematical inaccuracies of previous bioimpedance approaches and incorporates PTT weights for the peak-peak (PTTpp), middle-middle (PTTmm), and foot-foot (PTTff) segments of the sensing signal into a single neuron model to determine a more accurate and stable PWV. In addition, we developed a tetherless bioimpedance device and compared our PTT estimation approaches, which yielded PWV across six subjects and two different arteries. Specifically, using our model, we found that the most reliable combination of weights corresponding to PTTpp, PTTmm, and PTTff was (0.260, 0.704, 0.036) for the brachial artery and (0.104, 0.858, 0.038) for radial artery. This model consistently yielded stable values across repetitions, with PWV values of 5.2 m/s, 5.3 m/s, and 5.9 m/s for the brachial artery and values of 5.8 m/s, 6.6 m/s, and 6.5 m/s for the radial artery. This system and model offer the possibility of obtaining higher reliability PTT and PWV values yielding better monitoring of cardiovascular health measures such as blood pressure and arterial stiffness.

Efficient multi-view fusion and flexible adaptation to view missing in cardiovascular system signals

The progression of deep learning and the widespread adoption of sensors have facilitated automatic multi-view fusion (MVF) about the cardiovascular system (CVS) signals. However, prevalent MVF model architecture often amalgamates CVS signals from the same temporal step but different views into a unified representation, disregarding the asynchronous nature of cardiovascular events and the inherent heterogeneity across views, leading to catastrophic view confusion. Efficient training strategies specifically tailored for MVF models to attain comprehensive representations need simultaneous consideration. Crucially, real-world data frequently arrives with incomplete views, an aspect rarely noticed by researchers. Thus, the View-Centric Transformer (VCT) and Multitask Masked Autoencoder (M2AE) are specifically designed to emphasize the centrality of each view and harness unlabeled data to achieve superior fused representations. Additionally, we systematically define the missing-view problem for the first time and introduce prompt techniques to aid pretrained MVF models in flexibly adapting to various missing-view scenarios. Rigorous experiments involving atrial fibrillation detection, blood pressure estimation, and sleep staging-typical health monitoring tasks-demonstrate the remarkable advantage of our method in MVF compared to prevailing methodologies. Notably, the prompt technique requires finetuning <3 % of the entire model's data, substantially fortifying the model's resilience to view missing while circumventing the need for complete retraining. The results demonstrate the effectiveness of our approaches, highlighting their potential for practical applications in cardiovascular health monitoring. Codes and models are released at URL.

A Synthetic Multi-Modal Variable to Capture Cardiovascular Responses to Acute Mental Stress and Transcutaneous Median Nerve Stimulation

OBJECTIVE

To develop a novel synthetic multi-modal variable capable of capturing cardiovascular responses to acute mental stress and the stress-mitigating effect of transcutaneous median nerve stimulation (TMNS), as an initial step toward the overarching goal of enabling closed-loop controlled mitigation of the physiological response to acute mental stress.

METHODS

Using data collected from 40 experiments in 20 participants involving acute mental stress and TMNS, we examined the ability of six plausibly explainable physio-markers to capture cardiovascular responses to acute mental stress and TMNS. Then, we developed a novel synthetic multi-modal variable by fusing the six physio-markers based on numerical optimization and compared its ability to capture cardiovascular responses to acute mental stress and TMNS against the six physio-markers in isolation.

RESULTS

The synthetic multi-modal variable showed explainable responses to acute mental stress and TMNS in more experiments (24 vs ≤19). It also exhibited superior consistency, balanced sensitivity, and robustness compared to individual physio-markers.

CONCLUSION

The results showed the promise of the synthetic multi-modal variable as a means to measure cardiovascular responses to acute mental stress and TMNS. However, the results also suggested the potential necessity to develop a personalized synthetic multi-modal variable.

SIGNIFICANCE

The findings of this work may inform the realization of TMNS-enabled closed-loop control systems for the mitigation of sympathetic arousal to acute mental stress by leveraging physiological measurements that can readily be implemented in wearable form factors.

AI-driven system for non-contact continuous nocturnal blood pressure monitoring using fiber optic ballistocardiography

Continuous monitoring of nocturnal blood pressure is crucial for hypertension management and cardiovascular risk assessment. However, current clinical methods are invasive and discomforting, posing challenges. These traditional techniques often disrupt sleep, impacting patient compliance and measurement accuracy. Here we introduce a non-contact system for continuous monitoring of nocturnal blood pressure, utilizing ballistocardiogram signals. The key component of this system is the utilization of advanced, flexible fiber optic sensors designed to capture medical-grade ballistocardiogram signals accurately. Our artificial intelligence model extracts deep learning and fiducial features with physical meanings and implements an efficient, lightweight personalization scheme on the edge device. Furthermore, the system incorporates a crucial algorithm to automatically detect the user's sleeping posture, ensuring accurate measurement of nocturnal blood pressure. The model underwent rigorous evaluation using open-source and self-collected datasets comprising 158 subjects, demonstrating its effectiveness across various blood pressure ranges, demographic groups, and sleep states. This innovative system, suitable for real-world unconstrained sleeping scenarios, allows for enhanced hypertension screening and management and provides new insights for clinical research into cardiovascular complications.

Continuous Estimation of Blood Pressure by Utilizing Seismocardiogram Signal Features in Relation to Electrocardiogram

There is an ongoing search for a reliable and continuous method of noninvasive blood pressure (BP) tracking. In this study, we investigate the feasibility of utilizing seismocardiogram (SCG) signals, i.e., chest motion caused by cardiac activity, for this purpose. This research is novel in examining the temporal relationship between the SCG-measured isovolumic moment and the electrocardiogram (PEPIM). Additionally, we compare these results with the traditionally measured pre-ejection period with the aortic opening marked as an endpoint (PEPAO). The accuracy of the BP estimation was evaluated beat to beat against invasively measured arterial BP. Data were collected on separate days as eighteen sets from nine subjects undergoing a medical procedure with anesthesia. Results for PEPIM showed a correlation of 0.67 ± 0.18 (p < 0.001), 0.66 ± 0.17 (p < 0.001), and 0.67 ± 0.17 (p < 0.001) when compared to systolic BP, diastolic BP, and mean arterial pressure (MAP), respectively. Corresponding results for PEPAO were equal to 0.61 ± 0.22 (p < 0.001), 0.61 ± 0.21 (p < 0.001), and 0.62 ± 0.22 (p < 0.001). Values of PEPIM were used to estimate MAP using two first-degree models, the linear regression model (achieved RMSE of 11.7 ± 4.0 mmHg) and extended model with HR (RMSE of 10.8 ± 4.2 mmHg), and two corresponding second-degree models (RMSE of 10.8 ± 3.7 mmHg and RMSE of 8.5 ± 3.4 mmHg for second-degree polynomial and second-degree extended, respectively). In the intrasubject testing of the second-degree model extended with HR based on PEPIM values, the mean error of MAP estimation in three follow-up measurements was in the range of 7.5 to 10.5 mmHg, without recalibration. This study demonstrates the method's potential for further research, particularly given that both proximal and distal pulses are measured in close proximity to the heart and cardiac output. This positioning may enhance the method's capacity to more accurately reflect central blood pressure compared to peripheral measurements.

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.

Prediction of Left Ventricle Pressure Indices Via a Machine Learning Approach Combining ECG, Pulse Oximetry, and Cardiac Sounds: a Preclinical Feasibility Study

Heart failure (HF) is defined as the inability of the heart to meet body oxygen demand requiring an elevation in left ventricular filling pressures (LVP) to compensate. LVP increase can be assessed in the cardiac catheterization laboratory, but this procedure is invasive and time-consuming to the extent that physicians rather rely on non-invasive diagnostic tools. In this work, we assess the feasibility to develop a novel machine-learning (ML) approach to predict clinically relevant LVP indices. Synchronized invasive (pressure-volume tracings) and non-invasive signals (ECG, pulse oximetry, and cardiac sounds) were collected from anesthetized, closed-chest Göttingen minipigs. Animals were either healthy or had HF with reduced ejection fraction and circa 500 heartbeats were included in the analysis for each animal. The ML algorithm showed excellent prediction of LVP indices estimating, for instance, the end-diastolic pressure with a R2 of 0.955. This novel ML algorithm could assist clinicians in the care of HF patients.

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.

BP-Net: Monitoring "Changes" in Blood Pressure Using PPG With Self-Contrastive Masking

Estimating blood pressure (BP) values from physiological signals (e.g., photoplethysmogram (PPG)) using deep learning models has recently received increased attention, yet challenges remain in terms of models' generalizability. Here, we propose taking a new approach by framing the problem as tracking the "changes" in BP over an interval, rather than directly estimating its value. Indeed, continuous monitoring of acute changes in BP holds promising implications for clinical applications (e.g., hypertensive emergencies). As a solution, we first present a self-contrastive masking (SCM) model, designed to perform pair-wise temporal comparisons within the input signal. We then leverage the proposed SCM model to introduce BP-Net, a model trained to detect elevations/drops greater than a given threshold in the systolic blood pressure (SBP) over an interval, from PPG. Using data from PulseDB, 1) we evaluate the performance of BP-Net on previously unseen subjects, 2) we test BP-Net's ability to generalize across domains by training and testing on different datasets, and 3) we compare the performance of BP-Net with existing PPG-based BP-estimation models in detecting over-threshold SBP changes. Formulating the problem as a binary classification task (i.e., over-threshold SBP elevation/drop or not), BP-Net achieves 75.97%/73.19% accuracy on data from subjects unseen during training. Additionally, the proposed BP-Net outperforms SBP estimations derived from existing PPG-based BP-estimation methods. Overall, by shifting the focus from estimating the value of SBP to detecting over-threshold "changes" in SBP, this work introduces a new potential for using PPG in clinical BP monitoring, and takes a step forward in addressing the challenges related to the generalizability of PPG-based BP-estimation models to unseen subjects.

Oscillometric blood pressure measurements on smartphones using vibrometric force estimation

This paper proposes a smartphone-based method for measuring Blood Pressure (BP) using the oscillometric method. For oscillometry, it is necessary to measure (1) the pressure applied to the artery and (2) the local blood volume change. This is accomplished by performing an oscillometric measurement at the finger's digital artery, whereby a user presses down on the phone's camera with steadily increasing force. The camera is used to capture the blood volume change using photoplethysmography. We devised a novel method for measuring the force applied of the finger without the use of specialized smartphone hardware with a technique called Vibrometric Force Estimation (VFE). The fundamental concept of VFE relies on a phenomenon where a vibrating object is dampened when an external force is applied on to it. This phenomenon can be recreated using the phone's own vibration motor and measured using the phone's Inertial Measurement Unit (IMU). A cross device reliability study with three smartphones of different manufacturers, shape, and prices results in similar force estimation performance across all smartphone models. In an N = 24 proof of concept study of the BP measurement, the smartphone technique achieves a mean absolute error of 9.21 mmHg and 7.77 mmHg of systolic and diastolic BP, respectively, compared to an FDA approved BP cuff. The vision for this technology is not necessarily to replace existing BP monitoring solutions, but rather to introduce a downloadable smartphone software application that could serve as a low-barrier hypertension screening measurement fit for widespread adoption.

Transmission line model as a digital twin for abdominal aortic aneurysm patients

We investigated the potential of the transmission line model as a digital twin of aneurysmal aorta by comparatively analyzing how a uniform lossless tube-load model were fitted to the carotid and femoral artery tonometry waveforms pertaining to (i) 79 abdominal aortic aneurysm (AAA) patients vs their matched controls (CON) and (ii) 35 AAA patients before vs after endovascular aneurysm repair (EVAR). The uniform lossless tube-load model fitted the tonometry waveforms pertaining to AAA as well as CON and EVAR. In addition, the parameters in the tube-load model exhibited physiologically explainable changes: when normalized, both pulse transit time and reflection coefficient increased with AAA and decreased after EVAR, which can be explained by the increase in arterial compliance and the decrease in arterial inertance due to the aortic expansion associated with AAA. In sum, the tube-load model may have the potential as a digital twin to enable personalized AAA monitoring.

Effects of low-pressure Valsalva maneuver on changes in cerebral arterial stiffness and pulse wave velocity

The Valsalva maneuver (VM), commonly used to assess cardiovascular and autonomic nervous system functions, can induce changes in hemodynamic function that may affect cerebral vascular functionality, such as arterial elasticity. This study aimed to investigate the effects of low-pressure VM on cerebral arterial stiffness and cerebral vascular dynamics. Thirty-one healthy young participants (average age 21.58±1.72 years) were recruited for this study. These participants were instructed to maintain an expiratory pressure of 30-35 mmHg for 15 seconds. We measured the vasoconstriction and vasodilation diameters (VCD and VDD) of the common carotid artery (CCA), as well as systolic and diastolic blood pressures (SBP and DBP), before and after VM (PRE_VM and POST_VM). Additionally, we assessed mean arterial pressure (MAP), pulse pressure (PP), pulse wave velocity (PWV), and arterial stiffness. Our findings revealed significant increases in both the VCD and VDD of the CCA (2.15%, p = 0.039 and 4.55%, p<0.001, respectively), MAP (1.67%, p = 0.049), and DBP (1.10%, p = 0.029) following low-pressure VM. SBP showed an increasing trend, but this was not statistically significant (p = 0.108). Interestingly, we observed significant decreases in arterial stiffness and PWV in POST_VM when comparing with PRE_VM (p<0.001 and p<0.001, respectively). In conclusion, our study demonstrated the effectiveness of low-pressure VM in reducing the PWV and stiffness of the CCA. This suggests that low-pressure VM can be a simple and cost-effective method to reduce cerebrovascular stiffness in a brief interval, without the need for specific environmental conditions.

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.

Advancing the Local Pulse Wave Velocity Measurement-Wave Confluence Decomposition Using a Double Gaussian Propagation Model

Background Pulse wave velocity (PWV) is a marker of arterial stiffness and local measurements could facilitate its widescale clinical use. However, confluence of incident and early reflected waves leads to biased spatiotemporal PWV estimates. Objective We introduce the Double Gaussian Propagation Model (DGPM) to measure local PWV in consideration of wave confluence (PWV[Formula: see text]) and compare it against conventional spatiotemporal PWV (PWV[Formula: see text]), with Bramwell-Hill PWV (PWV[Formula: see text]) and blood pressure (BP) as reference measures. Methods Ten subjects ranging from normotension to hypertension were repeatedly measured at rest and with induced PWV changes. Carotid distension waveforms over a 19 mm wide segment were acquired from ultrasonography, simultaneously with noninvasive continuous BP. Per cardiac cycle, the 8-parameter DGPM (amplitude, centroid, width, and velocity, respectively of forward and backward propagating wave) was fitted to the distension waveforms' systolic foot and dicrotic notch complexes. Corresponding PWV[Formula: see text] was computed from linear fittings of respective feature timings and distances. Regression analyses were conducted with PWV[Formula: see text] and PWV[Formula: see text] as predictors, and various PWV and BP measures as response variables. Results Whereas PWV[Formula: see text] correlations were insignificant, PWV[Formula: see text] estimated the reference PWV[Formula: see text] with a significant reduction in errors (P < 0.001), explained up to 65% PWV[Formula: see text] variability at rest, demonstrated higher intra-method consistency and correlated significantly with all BP measures (P < 0.001). Conclusion The proposed DGPM measures local carotid PWV in consideration of wave confluence, showing significant correlations with Bramwell-Hill PWV and BP at two distinct waveform complexes. Thereby PWV[Formula: see text] outperforms the conventional PWV[Formula: see text] in all investigated respects, potentially enabling PWV assessment in routine clinical practice.

A Review of Patient Bed Sensors for Monitoring of Vital Signs

The analysis of biomedical signals is a very challenging task. This review paper is focused on the presentation of various methods where biomedical data, in particular vital signs, could be monitored using sensors mounted to beds. The presented methods to monitor vital signs include those combined with optical fibers, camera systems, pressure sensors, or other sensors, which may provide more efficient patient bed monitoring results. This work also covers the aspects of interference occurrence in the above-mentioned signals and sleep quality monitoring, which play a very important role in the analysis of biomedical signals and the choice of appropriate signal-processing methods. The provided information will help various researchers to understand the importance of vital sign monitoring and will be a thorough and up-to-date summary of these methods. It will also be a foundation for further enhancement of these methods.

Deep learning and remote photoplethysmography powered advancements in contactless physiological measurement

In recent decades, there has been ongoing development in the application of computer vision (CV) in the medical field. As conventional contact-based physiological measurement techniques often restrict a patient's mobility in the clinical environment, the ability to achieve continuous, comfortable and convenient monitoring is thus a topic of interest to researchers. One type of CV application is remote imaging photoplethysmography (rPPG), which can predict vital signs using a video or image. While contactless physiological measurement techniques have an excellent application prospect, the lack of uniformity or standardization of contactless vital monitoring methods limits their application in remote healthcare/telehealth settings. Several methods have been developed to improve this limitation and solve the heterogeneity of video signals caused by movement, lighting, and equipment. The fundamental algorithms include traditional algorithms with optimization and developing deep learning (DL) algorithms. This article aims to provide an in-depth review of current Artificial Intelligence (AI) methods using CV and DL in contactless physiological measurement and a comprehensive summary of the latest development of contactless measurement techniques for skin perfusion, respiratory rate, blood oxygen saturation, heart rate, heart rate variability, and blood pressure.

Continuous blood pressure prediction system using Conv-LSTM network on hybrid latent features of photoplethysmogram (PPG) and electrocardiogram (ECG) signals

Continuous blood pressure (BP) monitoring is essential for managing cardiovascular disease. However, existing devices often require expert handling, highlighting the need for alternative methods to simplify the process. Researchers have developed various methods using physiological signals to address this issue. Yet, many of these methods either fall short in accuracy according to the BHS, AAMI, and IEEE standards for BP measurement devices or suffer from low computational efficiency due to the complexity of their models. To solve this problem, we developed a BP prediction system that merges extracted features of PPG and ECG from two pulses of both signals using convolutional and LSTM layers, followed by incorporating the R-to-R interval durations as additional features for predicting systolic (SBP) and diastolic (DBP) blood pressure. Our findings indicate that the prediction accuracies for SBP and DBP were 5.306 ± 7.248 mmHg with a 0.877 correlation coefficient and 3.296 ± 4.764 mmHg with a 0.918 correlation coefficient, respectively. We found that our proposed model achieved a robust performance on the MIMIC III dataset with a minimum architectural design and high-level accuracy compared to existing methods. Thus, our method not only meets the passing category for BHS, AAMI, and IEEE guidelines but also stands out as the most rapidly accurate deep-learning-based BP measurement device currently available.

Recent Advances in Materials, Devices and Algorithms Toward Wearable Continuous Blood Pressure Monitoring

Continuous blood pressure (BP) tracking provides valuable insights into the health condition and functionality of the heart, arteries, and overall circulatory system of humans. The rapid development in flexible and wearable electronics has significantly accelerated the advancement of wearable BP monitoring technologies. However, several persistent challenges, including limited sensing capabilities and stability of flexible sensors, poor interfacial stability between sensors and skin, and low accuracy in BP estimation, have hindered the progress in wearable BP monitoring. To address these challenges, comprehensive innovations in materials design, device development, system optimization, and modeling have been pursued to improve the overall performance of wearable BP monitoring systems. In this review, we highlight the latest advancements in flexible and wearable systems toward continuous noninvasive BP tracking with a primary focus on materials development, device design, system integration, and theoretical algorithms. Existing challenges, potential solutions, and further research directions are also discussed to provide theoretical and technical guidance for the development of future wearable systems in continuous ambulatory BP measurement with enhanced sensing capability, robustness, and long-term accuracy.

Resonance sonomanometry for noninvasive, continuous monitoring of blood pressure

Cardiovascular disease is the leading cause of death worldwide. Existing methods for continuous, noninvasive blood pressure (BP) monitoring suffer from poor accuracy, uncomfortable form factors, or a need for frequent calibration, limiting their adoption. We introduce a new framework for continuous BP measurement that is noninvasive and calibration-free called resonance sonomanometry. The method uses ultrasound imaging to measure both the arterial dimensions and artery wall resonances that are induced by acoustic stimulation, which offers a direct measure of BP by a fully determined physical model. The approach and model are validated in vitro using arterial mock-ups and then in multiple arteries in human subjects. This approach offers the promise of robust continuous BP measurements, providing significant benefits for early diagnosis and treatment of cardiovascular disease.

A Novel Photo-electro-mechano Sensing Array for the Visualization and Estimation of Tonoarteriogram

Existing cuffless blood pressure (BP) monitoring technologies commonly rely on single-site measurements with unimodal sensor configurations, thereby constraining the precision and visualization of the two-dimensional data. Building upon our previous observations regarding the significant influence of measurement sites on BP evaluation, and leveraging the widely acknowledged utility of electrocardiography (ECG) in BP assessment, in this study, we develop a multimodal photo-electro-mechano tonoarteriographic (TAG) imaging system, enabling continuous visualization of local BP variation and estimation of central BP. The system integrates a 3×3 photoplethysmography (PPG) sensor array, one-lead ECG, and a 2×2 pressure sensor array, allowing to collect simultaneously 15 channel physiological signals. The proposed system was tested with 20 subjects and the experimental results reveal noticeable variations in local pulse transit time (PTT)/BP across different anatomical structures at the measurement site. To further improve the system performance, we designed and tested a flexible 2×4 ultrasound sensor array and demonstrated its feasibility to augment the system's capability for central BP estimation. In summary, the proposed system can not only estimate continuously central BP but also visualize the two-dimensional local BP/PTT variation at the measurement site, holding potential to support clinical decision-making and offering geographic-dependent information for micro-circulation investigations.

Mean Perfusion Pressure Estimation Using Hemodynamics Ohm's Law Towards Unobtrusive Mean Arterial Pressure Estimation

This work proposes a novel approach to estimating mean perfusion pressure, a difference between mean arterial and central venous pressure, using a pulsatile arterial blood pressure and a flow waveform. Using these waveforms, which can be measured, for example, using ultrasound, a vascular resistance downstream at the branch of an arterial tree is estimated. This estimation exploits Ohm's law in hemodynamics during multiple steady states when beat-to-beat variations in arterial pressure occur physiologically. Then, the mean perfusion pressure is estimated by multiplying the vascular resistance with time-averaged flow. A numerical simulation of hemodynamics in a human arterial tree demonstrates the approach. MGHDB records in PhysioNet were used to emulate natural variations of the arterial pressure. The mean and standard deviation of errors in validation are -0.1 and 0.3 mmHg, respectively. This work establishes a theoretical background towards mean arterial pressure estimation and supplements prior works to produce an absolute arterial blood pressure waveform unobtrusively and without calibration.

Investigating the relationship between early cardiovascular disease markers and loneliness in young adults

Loneliness is recognised as a risk factor for cardiovascular disease development. However, it is unclear whether loneliness itself or other closely related mental health symptoms, such as depression and social anxiety, are associated with the development of cardiovascular disease. In the present study, we examined the relationship between loneliness and several early cardiovascular disease markers in young adults, after controlling for depression and social anxiety. Sixty-six young adults (18-35 years old, Mage = 22.70; 75.8% females) completed psychological questionnaires and took part in several physiological tests assessing cardiovascular health (e.g., vascular function). Results revealed higher loneliness was significantly associated with shorter pulse transit time (β = - 0.70, p = 0.002; shorter pulse transit time is a subclinical marker for arterial stiffness). Additionally, results show that while loneliness and depression were both related to vascular dysfunction in young adults, the underlining physiological mechanisms through which they affect vascular function may be different. Specifically, higher loneliness was associated with increased arterial stiffness, whereas depression was associated with increased endothelial dysfunction (β = - 0.43, p = 0.04). Our findings indicate that presence of loneliness and depression in young adults may be accompanied by early indicators of poor cardiovascular health, such as arterial stiffness and endothelial dysfunction. Results from the study further support the link between loneliness and cardiovascular disease development.

Arterial Distension Monitoring Scheme Using FPGA-Based Inference Machine in Ultrasound Scanner Circuit System

This paper presents an arterial distension monitoring scheme using a field-programmable gate array (FPGA)-based inference machine in an ultrasound scanner circuit system. An arterial distension monitoring requires a precise positioning of an ultrasound probe on an artery as a prerequisite. The proposed arterial distension monitoring scheme is based on a finite state machine that incorporates sequential support vector machines (SVMs) to assist in both coarse and fine adjustments of probe position. The SVMs sequentially perform recognitions of ultrasonic A-mode echo pattern for a human carotid artery. By employing sequential SVMs in combination with convolution and average pooling, the number of features for the inference machine is significantly reduced, resulting in less utilization of hardware resources in FPGA. The proposed arterial distension monitoring scheme was implemented in an FPGA (Artix7) with a resource utilization percentage less than 9.3%. To demonstrate the proposed scheme, we implemented a customized ultrasound scanner consisting of a single-element transducer, an FPGA, and analog interface circuits with discrete chips. In measurements, we set virtual coordinates on a human neck for 9 human subjects. The achieved accuracy of probe positioning inference is 88%, and the Pearson coefficient (r) of arterial distension estimation is 0.838.

Hemodynamic Bedside Monitoring Instrument with Pressure and Optical Sensors: Validation and Modality Comparison

Results from two independent clinical validation studies for measuring hemodynamics at the patient's bedside using a compact finger probe are reported. Technology comprises a barometric pressure sensor, and in one implementation, additionally, an optical sensor for photoplethysmography (PPG) is developed, which can be used to measure blood pressure and analyze rhythm, including the continuous detection of atrial fibrillation. The capabilities of the technology are shown in several form factors, including a miniaturized version resembling a common pulse oximeter to which the technology could be integrated in. Several main results are presented: i) the miniature finger probe meets the accuracy requirements of non-invasive blood pressure instrument validation standard, ii) atrial fibrillation can be detected during the blood pressure measurement and in a continuous recording, iii) a unique comparison between optical and pressure sensing mechanisms is provided, which shows that the origin of both modalities can be explained using a pressure-volume model and that recordings are close to identical between the sensors. The benefits and limitations of both modalities in hemodynamic monitoring are further discussed.

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.

Non-invasive pulse arrival time as a surrogate for oscillometric systolic blood pressure changes during non-pharmacological intervention

Background.Non-invasive continuous blood pressure (BP) monitoring is of longstanding interest in various cardiovascular scenarios. In this context, pulse arrival time (PAT), i.e., a surrogate parameter for systolic BP (change), became very popular recently, especially in the context of cuffless BP measurement and dedicated lifestyle interventions. Nevertheless, there is also understandable doubt on its reliability in uncontrolled and mobile settings.Objective.The aim of this work is therefore the investigation whether PAT follows oscillometric systolic BP readings during moderate interventions by physical or mental activity using a medical grade handheld device for non-invasive PAT assessment.Approach.A study was conducted featuring an experimental group performing a physical and a mental task, and a control group. Oscillometric BP and PAT were assessed at baseline and after each intervention. Interventions were selected randomly but then performed sequentially in a counterbalanced order. Multivariate analyses of variance were used to test within-subject and between-subject effects for the dependent variables, followed by univariate analyses for post-hoc testing. Furthermore, correlation analysis was performed to assess the association of intervention effects between BP and PAT.Mainresults.The study included 51 subjects (31 females). Multivariate analysis of variances showed that effects in BP, heart rate, PAT and pulse wave parameters were consistent and significantly different between experimental and control groups. After physical activity, heart rate and systolic BP increased significantly whereas PAT decreased significantly. Mental activity leads to a decrease in systolic BP at stable heart rate. Pulse wave parameters follow accordingly by an increase of PAT and mainly unchanged pulse wave analysis features due to constant heart rate. Finally, also the control group behaviour was accurately registered by the PAT method compared to oscillometric cuff. Correlation analyses revealed significant negative associations between changes of systolic BP and changes of PAT from baseline to the physical task (-0.33 [-0.63, 0.01],p< 0.048), and from physical to mental task (-0.51 [-0.77, -0.14],p= 0.001), but not for baseline to mental task (-0.12 [-0,43,0,20],p= 0.50) in the experimental group.Significance.PAT and the used digital, handheld device proved to register changes in BP and heart rate reliably compared to oscillometric measurements during intervention. Therefore, it might add benefit to future mobile health solutions to support BP management by tracking relative, not absolute, BP changes during non-pharmacological interventions.

Flexible Strain Sensors Based on an Interlayer Synergistic Effect of Nanomaterials for Continuous and Noninvasive Blood Pressure Monitoring

The continuous, noninvasive monitoring of human blood pressure (BP) through the accurate detection of pulse waves has extremely stringent requirements on the sensitivity and stability of flexible strain sensors. In this study, a new ultrasensitive flexible strain sensor based on the interlayer synergistic effect was fabricated through drop-casting and drying silver nanowires and graphene films on polydimethylsiloxane substrates and was further successfully applied for continuous monitoring of BP. This strain sensor exhibited ultrahigh sensitivity with a maximum gauge factor of 34357.2 (∼700% sensitivity enhancement over other major sensors), satisfactory response time (∼85 ms), wide strange range (12%), and excellent stability. An interlayer fracture mechanism was proposed to elucidate the working principle of the strain sensor. The real-time BP values can be obtained by analyzing the relationship between the BP and the pulse transit time. To verify our strain sensor for real-time BP monitoring, our strain sensor was compared with a conventional electrocardiogram-photoplethysmograph method and a commercial cuff-based device and showed similar measurement results to BP values from both methods, with only minor differences of 0.693, 0.073, and 0.566 mmHg in the systolic BP, diastolic BP, and mean arterial pressure, respectively. Furthermore, the reliability of the strain sensors was validated by testing 20 human subjects for more than 50 min. This ultrasensitive strain sensor provides a new pathway for continuous and noninvasive BP monitoring.

A Review on Recent Advancements of Biomedical Radar for Clinical Applications

The field of biomedical radar has witnessed significant advancements in recent years, paving the way for innovative and transformative applications in clinical settings. Most medical instruments invented to measure human activities rely on contact electrodes, causing discomfort. Thanks to its non-invasive nature, biomedical radar is particularly valuable for clinical applications. A significant portion of the review discusses improvements in radar hardware, with a focus on miniaturization, increased resolution, and enhanced sensitivity. Then, this paper also delves into the signal processing and machine learning techniques tailored for radar data. This review will explore the recent breakthroughs and applications of biomedical radar technology, shedding light on its transformative potential in shaping the future of clinical diagnostics, patient and elderly care, and healthcare innovation.

Assessing pressure wave components for aortic stiffness monitoring through spectral regression learning

Aims

The ageing process notably induces structural changes in the arterial system, primarily manifesting as increased aortic stiffness, a precursor to cardiovascular events. While wave separation analysis is a robust tool for decomposing the components of blood pressure waveform, its relationship with cardiovascular events, such as aortic stiffening, is incompletely understood. Furthermore, its applicability has been limited due to the need for concurrent measurements of pressure and flow. Our aim in this study addresses this gap by introducing a spectral regression learning method for pressure-only wave separation analysis.

Methods and results

Leveraging data from the Framingham Heart Study (2640 individuals, 55% women), we evaluate the accuracy of pressure-only estimates, their interchangeability with a reference method based on ultrasound-derived flow waves, and their association with carotid-femoral pulse wave velocity (PWV). Method-derived estimates are strongly correlated with the reference ones for forward wave amplitude ( R 2 = 0.91 ), backward wave amplitude ( R 2 = 0.88 ), and reflection index ( R 2 = 0.87 ) and moderately correlated with a time delay between forward and backward waves ( R 2 = 0.38 ). The proposed pressure-only method shows interchangeability with the reference method through covariate analysis. Adjusting for age, sex, body size, mean blood pressure, and heart rate, the results suggest that both pressure-only and pressure-flow evaluations of wave separation parameters yield similar model performances for predicting carotid-femoral PWV, with forward wave amplitude being the only significant factor (P < 0.001; 95% confidence interval, 0.056-0.097).

Conclusion

We propose an interchangeable pressure-only wave separation analysis method and demonstrate its clinical applicability in capturing aortic stiffening. The proposed method provides a valuable non-invasive tool for assessing cardiovascular health.

MEMS Fingertip Strain Plethysmography for Cuffless Estimation of Blood Pressure

OBJECTIVE

To develop a cuffless method for estimating blood pressure (BP) from fingertip strain plethysmography (SPG) recordings.

METHODS

A custom-built micro-electromechanical systems (MEMS) strain sensor is employed to record heartbeat-induced vibrations at the fingertip. An XGboost regressor is then trained to relate SPG recordings to beat-to-beat systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP) values. For this purpose, each SPG segment in this setup is represented by a feature vector consisting of cardiac time interval, amplitude features, statistical properties, and demographic information of the subjects. In addition, a novel concept, coined geometric features, are introduced and incorporated into the feature space to further encode the dynamics in SPG recordings. The performance of the regressor is assessed on 32 healthy subjects through 5-fold cross-validation (5-CV) and leave-subject-out cross validation (LSOCV).

RESULTS

Mean absolute errors (MAEs) of 3.88 mmHg and 5.45 mmHg were achieved for DBP and SBP estimations, respectively, in the 5-CV setting. LSOCV yielded MAEs of 8.16 mmHg for DBP and 16.81 mmHg for SBP. Through feature importance analysis, 3 geometric and 26 integral-related features introduced in this work were identified as primary contributors to BP estimation. The method exhibited robustness against variations in blood pressure level (normal to critical) and body mass index (underweight to obese), with MAE ranges of [1.28, 4.28] mmHg and [2.64, 7.52] mmHg, respectively.

CONCLUSION

The findings suggest high potential for SPG-based BP estimation at the fingertip.

SIGNIFICANCE

This study presents a fundamental step towards the augmentation of optical sensors that are susceptible to dark skin tones.

Calibration-free blood pressure estimation based on a convolutional neural network

In this study, we conducted research on a deep learning-based blood pressure (BP) estimation model suitable for wearable environments. To measure BP while wearing a wearable watch, it needs to be considered that computing power for signal processing is limited and the input signals are subject to noise interference. Therefore, we employed a convolutional neural network (CNN) as the BP estimation model and utilized time-series electrocardiogram (ECG) and photoplethysmogram (PPG) signals, which are quantifiable in a wearable context. We generated periodic input signals and used differential and thresholding methods to decrease noise in the preprocessing step. We then applied a max-pooling technique with filter sizes of 2 × 1 and 5 × 1 within a 3-layer convolutional neural network to estimate BP. Our method was trained, validated, and tested using 2.4 million data samples from 49 patients in the intensive care unit. These samples, totaling 3.1 GB were obtained from the publicly accessible MIMIC database. As a result of a test with 480,000 data samples, the average root mean square error in BP estimation was 3.41, 5.80, and 2.78 mm Hg in the prediction of pulse pressure, systolic BP (SBP), and diastolic BP (DBP), respectively. The cumulative error percentage less than 5 mm Hg was 68% and 93% for SBP and DBP, respectively. In addition, the cumulative error percentage less than 15 mm Hg was 98% and 99% for SBP and DBP. Subsequently, we evaluated the impact of changes in input signal length (1 cycle vs. 30 s) and the introduction of noise on BP estimation results. The experimental results revealed that the length of the input signal did not significantly affect the performance of CNN-based analysis. When estimating BP using noise-added ECG signals, the mean absolute error (MAE) for SBP and DBP was 9.72 and 6.67 mm Hg, respectively. Meanwhile, when using noise-added PPG signals, the MAE for SBP and DBP was 26.85 and 14.00 mm Hg, respectively. Therefore, this study confirmed that using ECG signals rather than PPG signals is advantageous for noise reduction in a wearable environment. Besides, short sampling frames without calibration can be effective as input signals. Furthermore, it demonstrated that using a model suitable for information extraction rather than a specialized deep learning model for sequential data can yield satisfactory results in BP estimation.

Preserving shape details of pulse signals for video-based blood pressure estimation

In recent years, imaging photoplethysmograph (iPPG) pulse signals have been widely used in the research of non-contact blood pressure (BP) estimation, in which BP estimation based on pulse features is the main research direction. Pulse features are directly related to the shape of pulse signals while iPPG pulse signals are easily disturbed during the extraction process. To mitigate the impact of pulse feature distortion on BP estimation, it is necessary to eliminate interference while retaining valuable shape details in the iPPG pulse signal. Contact photoplethysmograph (cPPG) pulse signals measured at rest can be considered as the undisturbed reference signal. Transforming the iPPG pulse signal to the corresponding cPPG pulse signal is a method to ensure the effectiveness of shape details. However, achieving the required shape accuracy through direct transformation from iPPG to the corresponding cPPG pulse signals is challenging. We propose a method to mitigate this challenge by replacing the reference signal with an average cardiac cycle (ACC) signal, which can approximately represent the shape information of all cardiac cycles in a short time. A neural network using multi-scale convolution and self-attention mechanisms is developed for this transformation. Our method demonstrates a significant improvement in the maximal information coefficient (MIC) between pulse features and BP values, indicating a stronger correlation. Moreover, pulse signals transformed by our method exhibit enhanced performance in BP estimation using different model types. Experiments are conducted on a real-world database with 491 subjects in the hospital, averaging 60 years of age.

Respiratory gating improves correlation between pulse wave transit time and pulmonary artery pressure in experimental pulmonary hypertension

Objective. Since pulse wave transit time (PWTT) shortens as pulmonary artery pressure (PAP) increases it was suggested as a potential non-invasive surrogate for PAP. The state of tidal lung filling is also known to affect PWTT independently of PAP. The aim of this retrospective analysis was to test whether respiratory gating improved the correlation coefficient between PWTT and PAP.Approach. In each one of five anesthetized and mechanically ventilated pigs two high-fidelity pressure catheters were placed, one directly behind the pulmonary valve, and the second one in a distal branch of the pulmonary artery. PAP was raised using the thromboxane A2 analogue U46619 and animals were ventilated in a pressure controlled mode (I:E ratio 1:2, respiratory rate 12/min, tidal volume of 6 ml kg-1). All signals were recorded using the multi-channel platform PowerLab®. The arrival of the pulse wave at each catheter tip was determined using a MATLAB-based modified hyperbolic tangent algorithm and PWTT calculated as the time interval between these arrivals.Main results. Correlation coefficient for PWTT and mean PAP wasr= 0.932 for thromboxane. This correlation coefficient increased considerably when heart beats either at end-inspiration (r= 0.978) or at end-expiration (r= 0.985) were selected (=respiratory gating).Significance. The estimation of mean PAP from PWTT improved significantly when taking the respiratory cycle into account. Respiratory gating is suggested to improve for the estimation of PAP by PWTT.

A Survey on Blood Pressure Measurement Technologies: Addressing Potential Sources of Bias

Regular blood pressure (BP) monitoring in clinical and ambulatory settings plays a crucial role in the prevention, diagnosis, treatment, and management of cardiovascular diseases. Recently, the widespread adoption of ambulatory BP measurement devices has been predominantly driven by the increased prevalence of hypertension and its associated risks and clinical conditions. Recent guidelines advocate for regular BP monitoring as part of regular clinical visits or even at home. This increased utilization of BP measurement technologies has raised significant concerns regarding the accuracy of reported BP values across settings. In this survey, which focuses mainly on cuff-based BP monitoring technologies, we highlight how BP measurements can demonstrate substantial biases and variances due to factors such as measurement and device errors, demographics, and body habitus. With these inherent biases, the development of a new generation of cuff-based BP devices that use artificial intelligence (AI) has significant potential. We present future avenues where AI-assisted technologies can leverage the extensive clinical literature on BP-related studies together with the large collections of BP records available in electronic health records. These resources can be combined with machine learning approaches, including deep learning and Bayesian inference, to remove BP measurement biases and provide individualized BP-related cardiovascular risk indexes.

Non-invasive methods for heart rate measurement in fish based on photoplethysmography

Heart rate is a crucial physiological indicator for fish, but current measurement methods are often invasive or require delicate manipulation. In this study, we introduced two non-invasive and easy-to-operate methods based on photoplethysmography, namely reflectance-type photoplethysmography (PPG) and remote photoplethysmography (rPPG), which we applied to the large yellow croaker (Larimichthys crocea). PPG showed perfect synchronization with electrocardiogram (ECG), with a Pearson's correlation coefficient of 0.99999. For rPPG, the results showed good agreement with ECG. Under active provision of green light, the Pearson's correlation coefficient was 0.966, surpassing the value of 0.947 under natural light. Additionally, the root mean square error was 0.810, which was lower than the value of 1.30 under natural light, indicating not only that the rPPG method had relatively high accuracy but also that green light may have the potential to further improve its accuracy.

Contact pressure-guided wearable dual-channel bioimpedance device for continuous hemodynamic monitoring

Wearable devices for continuous monitoring of arterial pulse waves have the potential to improve the diagnosis, prognosis, and management of cardiovascular diseases. These pulse wave signals are often affected by the contact pressure between the wearable device and the skin, limiting the accuracy and reliability of hemodynamic parameter quantification. Here, we report a continuous hemodynamic monitoring device that enables the simultaneous recording of dual-channel bioimpedance and quantification of pulse wave velocity (PWV) used to calculate blood pressure (BP). Our investigations demonstrate the effect of contact pressure on bioimpedance and PWV. The pulsatile bioimpedance magnitude reached its maximum when the contact pressure approximated the mean arterial pressure of the subject. We employed PWV to continuously quantify BP while maintaining comfortable contact pressure for prolonged wear. The mean absolute error and standard deviation of the error compared to the reference value were determined to be 0.1 ± 3.3 mmHg for systolic BP, 1.3 ± 3.7 mmHg for diastolic BP, and -0.4 ± 3.0 mmHg for mean arterial pressure when measurements were conducted in the lying down position. This research demonstrates the potential of wearable dual-bioimpedance sensors with contact pressure guidance for reliable and continuous hemodynamic monitoring.

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.

Ambient health sensing on passive surfaces using metamaterials

Ambient sensors can continuously and unobtrusively monitor a person's health and well-being in everyday settings. Among various sensing modalities, wireless radio-frequency sensors offer exceptional sensitivity, immunity to lighting conditions, and privacy advantages. However, existing wireless sensors are susceptible to environmental interference and unable to capture detailed information from multiple body sites. Here, we present a technique to transform passive surfaces in the environment into highly sensitive and localized health sensors using metamaterials. Leveraging textiles' ubiquity, we engineer metamaterial textiles that mediate near-field interactions between wireless signals and the body for contactless and interference-free sensing. We demonstrate that passive surfaces functionalized by these metamaterials can provide hours-long cardiopulmonary monitoring with accuracy comparable to gold standards. We also show the potential of distributed sensors and machine learning for continuous blood pressure monitoring. Our approach enables passive environmental surfaces to be harnessed for ambient sensing and digital health applications.

BiGRU-attention for Continuous blood pressure trends estimation through single channel PPG

BACKGROUND

Physiological parameter monitoring based on photoplethysmography (PPG) detection has the advantage of fast, portable, and non-invasive. Changes in the morphology of the PPG waveform can reflect the effect of arterial elasticity changes on blood pressure (BP). However, machine learning models and non-recurrent neural network models typically ignore the time-dependency of continuous PPG data, leading to the decrease of accuracy or the increased calibration frequency.

OBJECTIVE

This paper proposes a BiGRU model with attention to estimate BP trends, which uses a single-channel PPG signal combined with demographic information to estimate continuous BP trends point-by-point and to discuss the impact of calibration cycle.

METHODS

This paper selects 15 typical subjects from two groups with/without cardiovascular disease (CVD) and evaluates the model performance. Regarding the calibration frequency problem, we set two modes of non-calibration and calibration to validate the results of blood pressure trends estimation.

RESULTS

In the calibration mode, the estimation errors (ME ± STD) of SBP for CVD/non-CVD groups are 0.91 ± 10.58 mmHg/0.17 ± 10.06 mmHg respectively, and DBP are 0.34 ± 5.28 mmHg/-0.19 ± 5.36 mmHg; in the non-calibration mode, the estimation errors of SBP for CVD/non-CVD groups are 0.27 ± 9.87 mmHg/-0.82 ± 9.92 mmHg respectively, and DBP are -0.63 ± 3.28 mmHg/0.80 ± 4.93 mmHg.

CONCLUSIONS

The results show that the proposed model has high accuracy in estimating BP levels, which is expected to achieve real-time, long-term continuous BP trends monitoring in wearable devices.

Peripheral artery disease diagnosis based on deep learning-enabled analysis of non-invasive arterial pulse waveforms

This paper intends to investigate the feasibility of peripheral artery disease (PAD) diagnosis based on the analysis of non-invasive arterial pulse waveforms. We generated realistic synthetic arterial blood pressure (BP) and pulse volume recording (PVR) waveform signals pertaining to PAD present at the abdominal aorta with a wide range of severity levels using a mathematical model that simulates arterial blood circulation and arterial BP-PVR relationships. We developed a deep learning (DL)-enabled algorithm that can diagnose PAD by analyzing brachial and tibial PVR waveforms, and evaluated its efficacy in comparison with the same DL-enabled algorithm based on brachial and tibial arterial BP waveforms as well as the ankle-brachial index (ABI). The results suggested that it is possible to detect PAD based on DL-enabled PVR waveform analysis with adequate accuracy, and its detection efficacy is close to when arterial BP is used (positive and negative predictive values at 40 % abdominal aorta occlusion: 0.78 vs 0.89 and 0.85 vs 0.94; area under the ROC curve (AUC): 0.90 vs 0.97). On the other hand, its efficacy in estimating PAD severity level is not as good as when arterial BP is used (r value: 0.77 vs 0.93; Bland-Altman limits of agreement: -32%-+32 % vs -20%-+19 %). In addition, DL-enabled PVR waveform analysis significantly outperformed ABI in both detection and severity estimation. In sum, the findings from this paper suggest the potential of DL-enabled non-invasive arterial pulse waveform analysis as an affordable and non-invasive means for PAD diagnosis.

Performance of wearable watch-type home blood pressure measurement devices in a real-world clinical sample

BACKGROUND

Independent testing of home blood pressure (BP) measurement (HBPM) devices is often lacking, particularly among older and multi-morbid patients.

METHODS

We studied the Bpro G2 (using tonometry), Omron HeartGuide (using occlusive oscillometric technology), and Heartisans (using photoplethysmography) wrist watch HBPM devices against a gold standard brachial sphygmomanometer. To test device performance, we used the ISO81060-2 protocol (though this protocol cannot formally validate cuffless devices). We also used linear mixed models to compare adjusted longitudinal BP measurements between devices. Finally, as a surrogate for usability, we recorded instances of device failure where no BP measurement was returned.

RESULTS

We enrolled 128 participants (median [Q1-Q3] age 53 [40-65] years, 51% male, 46% on antihypertensive drugs), of whom 100 were suitable for the primary analysis. All three devices had mean BP values within 5 mmHg of sphygmomanometry. However, due to insufficient reliability (e.g., wider than accepted standard deviations of mean BP), none of the three devices passed all criteria required by the ISO81060-2 protocol. In adjusted longitudinal analyses, the Omron device also systematically underestimated systolic and diastolic BP (- 8.46 mmHg; 95% CI 6.07, 10.86; p < 0.001; and - 2.53 mmHg; 95% CI - 4.03, - 1.03; p = 0.001; respectively). Nevertheless, compared to the Omron device, BPro and Heartisans devices had increased odds of failure (BPro: odds ratio [OR] 5.24; p < 0.0001; Heartisans: OR 5.61; p < 0.001).

CONCLUSIONS

While we could not formally validate the cuffless devices, our results show that wearable technologies will require improvements to offer reliable BP assessment. This study also highlights the need for validation protocols specifically designed for cuffless BP measurement technologies.

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.