Evaluation of the Linear Relationship Between Pulse Arrival Time and Blood Pressure in ICU Patients: Potential and Limitations

A variety of techniques based on the indirect measurement of blood pressure (BP) by Pulse Transit Time (PTT) have been explored over the past few years. Such an approach has the potential in providing continuous and non-invasive beat to beat blood pressure without the use of a cuff. Pulse Arrival Time (PAT) which includes the cardiac pre-ejection period has been proposed as a surrogate of PTT, however, the balance between its questioned accuracy and measurement simplicity has yet to be established. The present work assessed the degree of linear relationship between PAT and blood pressure on 96 h of continuous electrocardiography and invasive radial blood pressure waveforms in a group of 11 young ICU patients. Participants were selected according to strict exclusion criteria including no use of vasoactive medications and presence of clinical conditions associated with cardiovascular diseases. The average range of variation for diastolic BP was 60 to 79 mmHg while systolic BP varied between 123 and 158 mmHg in the study database. The overall Pearson correlation coefficient for systolic and diastolic blood pressure was −0.5 and −0.42, respectively, while the mean absolute error was 3.9 and 7.6 mmHg. It was concluded that the utilization of PAT for the continuous non-invasive blood pressure estimation is rather limited according to the experimental setup, nonetheless the correlation coefficient performed better when the range of variation of blood pressure was high over periods of 30 min suggesting that PAT has the potential to be used as indicator of changes relating to hypertensive or hypotensive episodes

Monitoring of the central blood pressure waveform via a conformal ultrasonic device.

Continuous monitoring of the central-blood-pressure waveform from deeply embedded vessels, such as the carotid artery and jugular vein, has clinical value for the prediction of all-cause cardiovascular mortality. However, existing non-invasive approaches, including photoplethysmography and tonometry, only enable access to the superficial peripheral vasculature. Although current ultrasonic technologies allow non-invasive deep-tissue observation, unstable coupling with the tissue surface resulting from the bulkiness and rigidity of conventional ultrasound probes introduces usability constraints. Here, we describe the design and operation of an ultrasonic device that is conformal to the skin and capable of capturing blood-pressure waveforms at deeply embedded arterial and venous sites. The wearable device is ultrathin (240 μm) and stretchable (with strains up to 60%), and enables the non-invasive, continuous and accurate monitoring of cardiovascular events from multiple body locations, which should facilitate its use in a variety of clinical environments

A review of machine learning techniques in photoplethysmography for the non-invasive cuff-less measurement of blood pressure

Hypertension or high blood pressure is a leading cause of death throughout the world and a critical factor for increasing the risk of serious diseases, including cardiovascular diseases such as stroke and heart failure. Blood pressure is a primary vital sign that must be monitored regularly for the early detection, prevention and treatment of cardiovascular diseases. Traditional blood pressure measurement techniques are either invasive or cuff-based, which are impractical, intermittent, and uncomfortable for patients. Over the past few decades, several indirect approaches using photoplethysmogram (PPG) have been investigated, namely, pulse transit time, pulse wave velocity, pulse arrival time and pulse wave analysis, in an effort to utilise PPG for estimating blood pressure. Recent advancements in signal processing techniques, including machine learning and artificial intelligence, have also opened up exciting new horizons for PPG-based cuff less and continuous monitoring of blood pressure. Such a device will have a significant and transformative impact in monitoring patients’ vital signs, especially those at risk of cardiovascular disease. This paper provides a comprehensive review for non-invasive cuff-less blood pressure estimation using the PPG approach along with their challenges and limitations

CardioCam: Leveraging Camera on Mobile Devices to Verify Users While Their Heart is Pumping

With the increasing prevalence of mobile and IoT devices (e.g., smartphones, tablets, smart-home appliances), massive private and sensitive information are stored on these devices. To prevent unauthorized access on these devices, existing user verification solutions either rely on the complexity of user-defined secrets (e.g., password) or resort to specialized biometric sensors (e.g., fingerprint reader), but the users may still suffer from various attacks, such as password theft, shoulder surfing, smudge, and forged biometrics attacks. In this paper, we propose, CardioCam, a low-cost, general, hard-to-forge user verification system leveraging the unique cardiac biometrics extracted from the readily available built-in cameras in mobile and IoT devices. We demonstrate that the unique cardiac features can be extracted from the cardiac motion patterns in fingertips, by pressing on the built-in camera. To mitigate the impacts of various ambient lighting conditions and human movements under practical scenarios, CardioCam develops a gradient-based technique to optimize the camera configuration, and dynamically selects the most sensitive pixels in a camera frame to extract reliable cardiac motion patterns. Furthermore, the morphological characteristic analysis is deployed to derive user-specific cardiac features, and a feature transformation scheme grounded on Principle Component Analysis (PCA) is developed to enhance the robustness of cardiac biometrics for effective user verification. With the prototyped system, extensive experiments involving 25 subjects are conducted to demonstrate that CardioCam can achieve effective and reliable user verification with over 99% average true positive rate (TPR) while maintaining the false positive rate (FPR) as low as 4%

Flow velocity mapping using contrast enhanced high-frame-rate plane wave ultrasound and image tracking: methods and initial in vitro and in vivo evaluation

Ultrasound imaging is the most widely used method for visualising and quantifying blood flow in medical practice, but existing techniques have various limitations in terms of imaging sensitivity, field of view, flow angle dependence, and imaging depth. In this study, we developed an ultrasound imaging velocimetry approach capable of visualising and quantifying dynamic flow, by combining high-frame-rate plane wave ultrasound imaging, microbubble contrast agents, pulse inversion contrast imaging and speckle image tracking algorithms. The system was initially evaluated in vitro on both straight and carotid-mimicking vessels with steady and pulsatile flows and in vivo in the rabbit aorta. Colour and spectral Doppler measurements were also made. Initial flow mapping results were compared with theoretical prediction and reference Doppler measurements and indicate the potential of the new system as a highly sensitive, accurate, angle-independent and full field-of-view velocity mapping tool capable of tracking and quantifying fast and dynamic flows

Development of a PC interfaced blood pressure meter (E-BPMS)

Blood pressure is one of the fundamental vital signs, and its measurement is of great importance to medical professionals and the general public alike. Nowadays, there are several types of blood pressure meter available manufactured from various companies. In order to meet the demand on telemedicine and technology advancement, a new form of blood pressure meter is desirable. This prototype of blood pressure meter is interfaced with a personal computer (PC) which able to simulate the measurement process in real time. The proposed system was named e-BPMS (Electronic Blood Pressure Measurement System) suggests the usage of both hardware and software in determining blood pressure reading. Hardware elements operate on oscillometric principle which gives the results in terms of systolic, diastolic and MAP (Mean Arterial Pressure). Furthermore, these results will be presented and simulated on the software. The e-BPMS interface was developed by using Visual Basic 6.0 language which highlights the user friendly attributes. Moreover, the simulated waveform will evaluate the blood pressure and gives the blood pressure value. This application shows significant improvement on the overall performance and gives reliable results. The framework used to design e-BPMS is easy to understand and it can be extended further to endorse new application area

pyPPG: A Python toolbox for comprehensive photoplethysmography signal analysis

Photoplethysmography is a non-invasive optical technique that measures changes in blood volume within tissues. It is commonly and increasingly used for in a variety of research and clinical application to assess vascular dynamics and physiological parameters. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and open tools exist for continuous photoplethysmogram (PPG) analysis. Consequently, the primary objective of this research was to identify, standardize, implement and validate key digital PPG biomarkers. This work describes the creation of a standard Python toolbox, denoted pyPPG, for long-term continuous PPG time series analysis recorded using a standard finger-based transmission pulse oximeter. The improved PPG peak detector had an F1-score of 88.19% for the state-of-the-art benchmark when evaluated on 2,054 adult polysomnography recordings totaling over 91 million reference beats. This algorithm outperformed the open-source original Matlab implementation by ~5% when benchmarked on a subset of 100 randomly selected MESA recordings. More than 3,000 fiducial points were manually annotated by two annotators in order to validate the fiducial points detector. The detector consistently demonstrated high performance, with a mean absolute error of less than 10 ms for all fiducial points. Based on these fiducial points, pyPPG engineers a set of 74 PPG biomarkers. Studying the PPG time series variability using pyPPG can enhance our understanding of the manifestations and etiology of diseases. This toolbox can also be used for biomarker engineering in training data-driven models. pyPPG is available on physiozoo.orgComment: The manuscript was submitted to "Physiological Measurement" on September 5, 202

Echocardiography combined with cardiopulmonary exercise testing for the prediction of outcome in idiopathic pulmonary arterial hypertension

BACKGROUND: Right ventricular (RV) function is a major determinant of exercise intolerance and outcome in idiopathic pulmonary arterial hypertension (IPAH). The aim of the study was to evaluate the incremental prognostic value of echocardiography of the RV and cardiopulmonary exercise testing (CPET) on long-term prognosis in these patients. METHODS: One hundred-thirty treatment-naïve IPAH patients were enrolled and prospectively followed. Clinical worsening (CW) was defined by a reduction in 6-minute walk distance plus an increase in functional class, or non elective hospitalization for PAH, or death. Baseline evaluation included clinical, hemodynamic, echocardiographic and CPET variables. Cox regression modeling with c-statistic and bootstrapping validation methods were done. RESULTS: During a mean period of 528 ± 304 days, 54 patients experienced CW (53%). Among demographic, clinical and hemodynamic variables at catheterization, functional class and cardiac index were independent predictors of CW (Model-1). With addition of echocardiographic and CPET variables (Model-2), peak O2 pulse (peak VO2/heart rate) and RV fractional area change (RVFAC) independently improved the power of the prognostic model (AUC: 0.81 vs 0.66, respectively; p=0.005). Patients with low RVFAC and low O2 pulse (low RVFAC + low O2 pulse) and high RVFAC+low O2 pulse showed 99.8 and 29.4 increase in the hazard ratio, respectively (relative risk -RR- of 41.1 and 25.3, respectively), compared with high RVFAC+high O2 pulse (p=0.0001). CONCLUSIONS: Echocardiography combined with CPET provides relevant clinical and prognostic information. A combination of low RVFAC and low O2 pulse identifies patients at a particularly high risk of clinical deterioration