Development, Validation, and Clinical Application of a Numerical Model for Pulse Wave Velocity Propagation in a Cardiovascular System with Application to Noninvasive Blood Pressure Measurements

High blood pressure blood pressure is an important risk factor for cardiovascular disease and affects almost one-third of the U.S. adult population. Historical cuff-less non-invasive techniques used to monitor blood pressure are not accurate and highlight the need for first principal models. The first model is a one-dimensional model for pulse wave velocity (PWV) propagation in compliant arteries that accounts for nonlinear fluids in a linear elastic thin walled vessel. The results indicate an inverse quadratic relationship (R^2=.99) between ejection time and PWV, with ejection time dominating the PWV shifts (12%). The second model predicts the general relationship between PWV and blood pressure with a rigorous account of nonlinearities in the fluid dynamics, blood vessel elasticity, and finite dynamic deformation of a membrane type thin anisotropic wall. The nonlinear model achieves the best match with the experimental data. To retrieve individual vascular information of a patient, the inverse problem of hemodynamics is presented, calculating local orthotropic hyperelastic properties of the arterial wall. The final model examines the impact of the thick arterial wall with different material properties in the radial direction. For a hypertensive subject the thick wall model provides improved accuracy up to 8.4% in PWV prediction over its thin wall counterpart. This translates to nearly 20% improvement in blood pressure prediction based on a PWV measure. The models highlight flow velocity is additive to the classic pressure wave, suggesting flow velocity correction may be important for cuff-less, non-invasive blood pressure measures. Systolic flow correction of the measured PWV improves the R2 correlation to systolic blood pressure from 0.81 to 0.92 for the mongrel dog study, and 0.34 to 0.88 for the human subjects study. The algorithms and insight resulting from this work can enable the development of an integrated microsystem for cuff-less, non-invasive blood pressure monitoring

Mobile Personal Healthcare System for Non-Invasive, Pervasive and Continuous Blood Pressure Monitoring: A Feasibility Study

Background: Smartphone-based blood pressure (BP) monitor using photoplethysmogram (PPG) technology has emerged as a promising approach to empower users with self-monitoring for effective diagnosis and control ofhypertension (HT). Objective: This study aimed to develop a mobile personal healthcare system for non-invasive, pervasive, and continuous estimation of BP level and variability to be user-friendly to elderly. Methods: The proposed approach was integrated by a self-designed cuffless, calibration-free, wireless and wearable PPG-only sensor, and a native purposely-designed smartphone application using multilayer perceptron machine learning techniques from raw signals. We performed a pilot study with three elder adults (mean age 61.3 ± 1.5 years; 66% women) to test usability and accuracy of the smartphone-based BP monitor. Results: The employed artificial neural network (ANN) model performed with high accuracy in terms of predicting the reference BP values of our validation sample (n=150). On average, our approach predicted BP measures with accuracy \u3e90% and correlations \u3e0.90 (P \u3c .0001). Bland-Altman plots showed that most of the errors for BP prediction were less than 10 mmHg. Conclusions: With further development and validation, the proposed system could provide a cost-effective strategy to improve the quality and coverage of healthcare, particularly in rural zones, areas lacking physicians, and solitary elderly populations

An assesment of the accuracy and role of self-recorded blood pressures in the managment of hypertension

Imperial Users onl

Acoustic sensing as a novel approach for cardiovascular monitoring at the wrist

Cardiovascular diseases are the number one cause of deaths globally. An increased cardiovascular risk can be detected by a regular monitoring of the vital signs including the heart rate, the heart rate variability (HRV) and the blood pressure. For a user to undergo continuous vital sign monitoring, wearable systems prove to be very useful as the device can be integrated into the user's lifestyle without affecting the daily activities. However, the main challenge associated with the monitoring of these cardiovascular parameters is the requirement of different sensing mechanisms at different measurement sites. There is not a single wearable device that can provide sufficient physiological information to track the vital signs from a single site on the body. This thesis proposes a novel concept of using acoustic sensing over the radial artery to extract cardiac parameters for vital sign monitoring. A wearable system consisting of a microphone is designed to allow the detection of the heart sounds together with the pulse wave, an attribute not possible with existing wrist-based sensing methods. Methods: The acoustic signals recorded from the radial artery are a continuous reflection of the instantaneous cardiac activity. These signals are studied and characterised using different algorithms to extract cardiovascular parameters. The validity of the proposed principle is firstly demonstrated using a novel algorithm to extract the heart rate from these signals. The algorithm utilises the power spectral analysis of the acoustic pulse signal to detect the S1 sounds and additionally, the K-means method to remove motion artifacts for an accurate heartbeat detection. The HRV in the short-term acoustic recordings is found by extracting the S1 events using the relative information between the short- and long-term energies of the signal. The S1 events are localised using three different characteristic points and the best representation is found by comparing the instantaneous heart rate profiles. The possibility of measuring the blood pressure using the wearable device is shown by recording the acoustic signal under the influence of external pressure applied on the arterial branch. The temporal and spectral characteristics of the acoustic signal are utilised to extract the feature signals and obtain a relationship with the systolic blood pressure (SBP) and diastolic blood pressure (DBP) respectively. Results: This thesis proposes three different algorithms to find the heart rate, the HRV and the SBP/ DBP readings from the acoustic signals recorded at the wrist. The results obtained by each algorithm are as follows: 1. The heart rate algorithm is validated on a dataset consisting of 12 subjects with a data length of 6 hours. The results demonstrate an accuracy of 98.78%, mean absolute error of 0.28 bpm, limits of agreement between -1.68 and 1.69 bpm, and a correlation coefficient of 0.998 with reference to a state-of-the-art PPG-based commercial device. A high statistical agreement between the heart rate obtained from the acoustic signal and the photoplethysmography (PPG) signal is observed. 2. The HRV algorithm is validated on the short-term acoustic signals of 5-minutes duration recorded from each of the 12 subjects. A comparison is established with the simultaneously recorded electrocardiography (ECG) and PPG signals respectively. The instantaneous heart rate for all the subjects combined together achieves an accuracy of 98.50% and 98.96% with respect to the ECG and PPG signals respectively. The results for the time-domain and frequency-domain HRV parameters also demonstrate high statistical agreement with the ECG and PPG signals respectively. 3. The algorithm proposed for the SBP/ DBP determination is validated on 104 acoustic signals recorded from 40 adult subjects. The experimental outputs when compared with the reference arm- and wrist-based monitors produce a mean error of less than 2 mmHg and a standard deviation of error around 6 mmHg. Based on these results, this thesis shows the potential of this new sensing modality to be used as an alternative, or to complement existing methods, for the continuous monitoring of heart rate and HRV, and spot measurement of the blood pressure at the wrist.Open Acces

Short-Term Fasting and Autonomic Control

Obesity is a chronic metabolic disorder associated with increased risk of cardiovascular disease. Evidence suggests that chronic intermittent fasting improves cardiometabolic health and reduces arterial blood pressure. However, the mechanisms underlying the reductions in blood pressure and improved cardiovascular health observed from chronic fasting studies remain unclear. The autonomic nervous system has a central role in the regulation of blood pressure and is essential for cardiovascular homeostasis. We conducted a study to investigate how acute fasting influences autonomic control of blood pressure at rest and during stress. Twenty-five young, healthy, normal weight, normotensive participants were tested twice, once in the fed state (3 hours postprandial) and again in the fasted state (24 hours postprandial). Aim 1 of the study was to determine the influence of an acute fast on hemodynamics, peripheral neural activity, and cardiovascular control at rest. To fulfill this aim we measured 24-hour ambulatory blood pressure for both conditions leading up to an autonomic function test. During the autonomic function test, we controlled breathing at 0.25 Hz and measured blood pressure, heart rate, muscle sympathetic nerve activity, and forearm blood flow for 10 minutes. Fasting reduced overall ambulatory blood pressure and heart rate compared to the fed condition. From the autonomic test we measured enhanced vagal modulation of the heart through 1) increased R-R interval and heart rate variability measured via spectral analysis; 2) Increased spontaneous (rest) and dynamic (Valsalva Maneuver) cardiovagal baroreflex sensitivity indicating enhanced reflexive vagal activation. Fasting did not alter peripheral sympathetic activity or blood pressure during the autonomic test. However, forearm vascular resistance and stroke volume were increased during the fasting condition. Aim 2 investigated if fasting influenced cardiovascular and neural reactivity to a mental stressor (5 min mental arithmetic). Fasting did not augment neural or cardiovascular reactivity to a mental stress challenge. Aim 3 investigated if fasting reduced orthostatic tolerance to intense lower body negative pressure (LBNP). LBNP was applied in a stepwise manner until participants became presyncopal. Fasting reduced the duration of negative pressure participants could tolerate before presyncope occurred. The reduced tolerance to central hypovolemia seems to have been caused by an impaired ability to increase peripheral resistance as measured from the forearm. This dissertation provides novel insight into how systemic energy balance influences autonomic regulation of blood pressure. Specifically, that fasting reduces 24-hour ambulatory blood pressure, increases vagal modulation of the heart, and enhances cardiovagal baroreflex sensitivity

Real-world heart rate norms in the Health eHeart study.

Emerging technology allows patients to measure and record their heart rate (HR) remotely by photoplethysmography (PPG) using smart devices like smartphones. However, the validity and expected distribution of such measurements are unclear, making it difficult for physicians to help patients interpret real-world, remote and on-demand HR measurements. Our goal was to validate HR-PPG, measured using a smartphone app, against HR-electrocardiogram (ECG) measurements and describe out-of-clinic, real-world, HR-PPG values according to age, demographics, body mass index, physical activity level, and disease. To validate the measurements, we obtained simultaneous HR-PPG and HR-ECG in 50 consecutive patients at our cardiology clinic. We then used data from participants enrolled in the Health eHeart cohort between 1 April 2014 and 30 April 2018 to derive real-world norms of HR-PPG according to demographics and medical conditions. HR-PPG and HR-ECG were highly correlated (Intraclass correlation = 0.90). A total of 66,788 Health eHeart Study participants contributed 3,144,332 HR-PPG measurements. The mean real-world HR was 79.1 bpm ± 14.5. The 95th percentile of real-world HR was ≤110 in individuals aged 18-45, ≤100 in those aged 45-60 and ≤95 bpm in individuals older than 60 years old. In multivariable linear regression, the number of medical conditions, female gender, increasing body mass index, and being Hispanic was associated with an increased HR, whereas increasing age was associated with a reduced HR. Our study provides the largest real-world norms for remotely obtained, real-world HR according to various strata and they may help physicians interpret and engage with patients presenting such data