Arterial blood pressure measurement and pulse wave analysis-–their role in enhancing cardiovascular assessment

Enhancing cardiovascular health monitoring: Simultaneous multi-artery cardiac markers recording with flexible and bio-compatible AlN piezoelectric sensors

Continuous monitoring of cardiovascular parameters like pulse wave velocity (PWV), blood pressure wave (BPW), stiffness index (SI), reflection index (RI), mean arterial pressure (MAP), and cardio-ankle vascular index (CAVI) has significant clinical importance for the early diagnosis of cardiovascular diseases (CVDs). Standard approaches, including echocardiography, impedance cardiography, or hemodynamic monitoring, are hindered by expensive and bulky apparatus and accessibility only in specialized facilities. Moreover, noninvasive techniques like sphygmomanometry, electrocardiography, and arterial tonometry often lack accuracy due to external electrical interferences, artifacts produced by unreliable electrode contacts, misreading from placement errors, or failure in detecting transient issues and trends. Here, we report a bio-compatible, flexible, noninvasive, low-cost piezoelectric sensor for continuous and real-time cardiovascular monitoring. The sensor, utilizing a thin aluminum nitride film on a flexible Kapton substrate, is used to extract heart rate, blood pressure waves, pulse wave velocities, and cardio-ankle vascular index from four arterial pulse sites: carotid, brachial, radial, and posterior tibial arteries. This simultaneous recording, for the first time in the same experiment, allows to provide a comprehensive cardiovascular patient's health profile. In a test with a 28-year-old male subject, the sensor yielded the SI = 7.1 ± 0.2 m/s, RI = 54.4 ± 0.5 %, MAP = 86.2 ± 1.5 mmHg, CAVI = 7.8 ± 0.2, and seven PWVs from the combination of the four different arterial positions, in good agreement with the typical values reported in the literature. These findings make the proposed technology a powerful tool to facilitate personalized medical diagnosis in preventing CVDs.

Wearable ultrasound devices: An emerging era for biomedicine and clinical translation

In recent years, personalized diagnosis and treatment have gained significant recognition and rapid development in the biomedicine and healthcare. Due to the flexibility, portability and excellent compatibility, wearable ultrasound (WUS) devices have become emerging personalized medical devices with great potential for development. Currently, with the development of the ongoing advancements in materials and structural design of the ultrasound transducers, WUS devices have improved performance and are increasingly applied in the medical field. In this review, we provide an overview of the design and structure of WUS devices, focusing on their application for diagnosis and treatment of various diseases from a clinical application perspective, and then explore the issues that need to be addressed before clinical translation. Finally, we summarize the progress made in the development of WUS devices, and discuss the current challenges and the future direction of their development. In conclusion, WUS devices usher an emerging era for biomedicine with great clinical promise.

Biocompatible Electronic Skins for Cardiovascular Health Monitoring

Cardiovascular diseases represent a significant threat to the overall well-being of the global population. Continuous monitoring of vital signs related to cardiovascular health is essential for improving daily health management. Currently, there has been remarkable proliferation of technology focused on collecting data related to cardiovascular diseases through daily electronic skin monitoring. However, concerns have arisen regarding potential skin irritation and inflammation due to the necessity for prolonged wear of wearable devices. To ensure comfortable and uninterrupted cardiovascular health monitoring, the concept of biocompatible electronic skin has gained substantial attention. In this review, biocompatible electronic skins for cardiovascular health monitoring are comprehensively summarized and discussed. The recent achievements of biocompatible electronic skin in cardiovascular health monitoring are introduced. Their working principles, fabrication processes, and performances in sensing technologies, materials, and integration systems are highlighted, and comparisons are made with other electronic skins used for cardiovascular monitoring. In addition, the significance of integrating sensing systems and the updating wireless communication for the development of the smart medical field is explored. Finally, the opportunities and challenges for wearable electronic skin are also examined.

Vascular Age Assessed From an Uncalibrated, Noninvasive Pressure Waveform by Using a Deep Learning Approach: The AI-VascularAge Model

BACKGROUND

Aortic stiffness, assessed as carotid-femoral pulse wave velocity, provides a measure of vascular age and risk for adverse cardiovascular disease outcomes, but it is difficult to measure. The shape of arterial pressure waveforms conveys information regarding aortic stiffness; however, the best methods to extract and interpret waveform features remain controversial.

METHODS

We trained a convolutional neural network with fixed-scale (time and amplitude) brachial, radial, and carotid tonometry waveforms as input and negative inverse carotid-femoral pulse wave velocity as label. Models were trained with data from 2 community-based Icelandic samples (N=10 452 participants with 31 126 waveforms) and validated in the community-based Framingham Heart Study (N=7208 participants, 21 624 waveforms). Linear regression rescaled predicted negative inverse carotid-femoral pulse wave velocity to equivalent artificial intelligence vascular age (AI-VA).

RESULTS

The AI-VascularAge model predicted negative inverse carotid-femoral pulse wave velocity with R2=0.64 in a randomly reserved Icelandic test group (n=5061, 16%) and R2=0.60 in the Framingham Heart Study. In the Framingham Heart Study (up to 18 years of follow-up; 479 cardiovascular disease, 200 coronary heart disease, and 213 heart failure events), brachial AI-VA was associated with incident cardiovascular disease adjusted for age and sex (model 1; hazard ratio, 1.79 [95% CI, 1.50-2.40] per SD; P<0.0001) or adjusted for age, sex, systolic blood pressure, total cholesterol, high-density lipoprotein cholesterol, prevalent diabetes, hypertension treatment, and current smoking (model 2; hazard ratio, 1.50 [95% CI, 1.24-1.82] per SD; P<0.0001). Similar hazard ratios were demonstrated for incident coronary heart disease and heart failure events and for AI-VA values estimated from carotid or radial waveforms.

CONCLUSIONS

Our results demonstrate that convolutional neural network-derived AI-VA is a powerful indicator of vascular health and cardiovascular disease risk in a broad community-based sample.

Progress in wearable acoustical sensors for diagnostic applications

With extensive and widespread uses of miniaturized and intelligent wearable devices, continuously monitoring subtle spatial and temporal changes in human physiological states becomes crucial for daily healthcare and professional medical diagnosis. Wearable acoustical sensors and related monitoring systems can be comfortably applied onto human body with a distinctive function of non-invasive detection. This paper reviews recent advances in wearable acoustical sensors for medical applications. Structural designs and characteristics of the structural components of wearable electronics, including piezoelectric and capacitive micromachined ultrasonic transducer (i.e., pMUT and cMUT), surface acoustic wave sensors (SAW) and triboelectric nanogenerators (TENGs) are discussed, along with their fabrication techniques and manufacturing processes. Diagnostic applications of these wearable sensors for detection of biomarkers or bioreceptors and diagnostic imaging have further been discussed. Finally, main challenges and future research directions in these fields are highlighted.

Non-contact high precision pulse-rate monitoring system for moving subjects in different motion states

Remote photoplethysmography (rPPG) enables contact-free monitoring of the pulse rate by using a color camera. The fundamental limitation is that motion artifacts and changes in ambient light conditions greatly affect the accuracy of pulse-rate monitoring. We propose use of a high-speed camera and a motion suppression algorithm with high computational efficiency. This system incorporates a number of major improvements including reproduction of pulse wave details, high-precision pulse-rate monitoring of moving subjects, and excellent scene scalability. A series of quantization methods were used to evaluate the effect of different frame rates and different algorithms in pulse-rate monitoring of moving subjects. The experimental results show that use of 180-fps video and a Plane-Orthogonal-to-Skin (POS) algorithm can produce high-precision pulse-rate monitoring results with mean absolute error can be less than 5 bpm and the relative accuracy reaching 94.5%. Thus, it has significant potential to improve personal health care and intelligent health monitoring.

Use of a rat model to characterize 35 arterial pulse wave parameters in a comparative study of isoflurane and Zoletil/xylazine anesthesia and the effect of Acanthopanax senticosus extract

BACKGROUND

Information obtained from arterial pulse waveforms (APW) can be useful for characterizing the cardiovascular system. To achieve this, it is necessary to know the detailed characteristics of APWs in different states of an organism, which would allow APW parameters (APW-Ps) to be assigned to particular (patho)physiological conditions. Therefore, our work aimed to characterize 35 APW-Ps in rats under the influence of isoflurane (ISO) and Zoletil/xylazine (ZO/XY) anesthesia and to study the effect of root extract from Acanthopanax senticosus (ASRE) in these anesthetic conditions.

METHODS

The right jugular vein of anesthetized rats was cannulated for the administration of ASRE and the left carotid artery for the detection of APWs from which 35 APW-Ps were evaluated.

RESULTS

We obtained data on 35 APW-Ps, which significantly depended on the anesthesia, and thus, they characterized the cardiovascular system under these two conditions. ASRE transiently modulated all 35 APW-Ps, including a transient decrease in systolic and diastolic blood pressure (BP) and heart rate or increases in pulse BP, dP/dtmax , and systolic and diastolic areas. Whereas the transient effects of ASRE were similar, the extract had prolonged disturbing effects on the cardiovascular system in rats under ZO/XY but not under ISO anesthesia. This negative effect can result from the disturbance caused by ZO/XY anesthesia on the cardiovascular system.

CONCLUSIONS

We characterized 35 APW-Ps of rats under ISO and ZO/XY anesthesia and found that ASRE contains compounds that can modulate the properties of the cardiovascular system, which significantly depended on the status of the cardiovascular system. This should be considered when using ASRE as a nutritional supplement by individuals with cardiovascular problems.

Monitoring blood pressure and cardiac function without positioning via a deep learning-assisted strain sensor array

Continuous and reliable monitoring of blood pressure and cardiac function is of great importance for diagnosing and preventing cardiovascular diseases. However, existing cardiovascular monitoring approaches are bulky and costly, limiting their wide applications for early diagnosis. Here, we developed an intelligent blood pressure and cardiac function monitoring system based on a conformal and flexible strain sensor array and deep learning neural networks. The sensor has a variety of advantages, including high sensitivity, high linearity, fast response and recovery, and high isotropy. Experiments and simulation synergistically verified that the sensor array can acquire high-precise and feature-rich pulse waves from the wrist without precise positioning. By combining high-quality pulse waves with a well-trained deep learning model, we can monitor blood pressure and cardiac function parameters. As a proof of concept, we further constructed an intelligent wearable system for real-time and long-term monitoring of blood pressure and cardiac function, which may contribute to personalized health management, precise and early diagnosis, and remote treatment.

Comparison of effects of peripheral vasculature on tonometric radial pulse and cuff-based brachial pulse waveform as used in estimation of central aortic pressures

OBJECTIVE

Aortic pressure estimation requires reliable peripheral pulse waveform acquisition. The peripheral waveform can change with local vascular effects that can be independent of aortic pressure. This study quantifies the effects of peripheral vasculature changes on radial and brachial waveforms.

DESIGN AND METHOD

In 20 subjects (37± 15 years, 7 female), brachial volumetric displacement (cuff-based) and radial tonometry waveforms were simultaneously measured whilst a cuff around the hand on the same arm was inflated to induce transmural pressures of -60, -30, -15, 0, 15 and 30 mmHg, altering local peripheral resistance and compliance by graded arterial wall unloading. Aortic blood pressure (BP), augmentation index (AIx) and ejection duration were calculated from the measurements using a generalized transfer function. The parameters under unloaded conditions were compared to baseline measurements.

RESULTS

Brachial systolic and diastolic BP did not change throughout the experiment. Altering peripheral resistance and compliance did not significantly change calculated aortic BP values, although changes were nominally greater for radial (maximum +8±1 mmHg) compared to brachial (maximum +2±1 mmHg) waveforms. AIx at 0 mmHg transmural pressure (maximum arterial wall unloading) was higher when derived from radial waveforms (+24±3%, p<0.001) but not when derived from brachial waveforms.

CONCLUSIONS

Localized changes in peripheral resistance and compliance affect tonometer acquired radial waveforms but not volumetric displacement acquired brachial pressure waveforms, as judged by computed central aortic augmentation pressure parameters. This suggests aortic pressure estimation from the brachial cuff waveform is less sensitive to peripheral vasculature disturbances that alter the peripheral arterial pulse morphology.

Continuous Monitoring of Blood Pressure and Vascular Hemodynamic Properties With Miniature Extravascular Hall-Based Magnetic Sensor

Continuous measurement of vascular and hemodynamic parameters could improve monitoring of disease progression and enable timely clinical decision making and therapy surveillance in patients suffering from cardiovascular diseases. However, no reliable extravascular implantable sensor technology is currently available. Here, we report the design, characterization, and validation of an extravascular, magnetic flux sensing device capable of capturing the waveforms of the arterial wall diameter, arterial circumferential strain, and arterial pressure without restricting the arterial wall. The implantable sensing device, comprising a magnet and a magnetic flux sensing assembly, both encapsulated in biocompatible structures, has shown to be robust, with temperature and cyclic-loading stability. Continuous and accurate monitoring of arterial blood pressure and vascular properties was demonstrated with the proposed sensor in vitro with a silicone artery model and validated in vivo in a porcine model mimicking physiologic and pathologic hemodynamic conditions. The captured waveforms were further used to deduce the respiration frequency, the duration of the cardiac systolic phase, and the pulse wave velocity. The findings of this study not only suggest that the proposed sensing technology is a promising platform for accurate monitoring of arterial blood pressure and vascular properties, but also highlight the necessary changes in the technology and the implantation procedure to allow the translation of the sensing device in the clinical setting.

An ultralow power wearable vital sign sensor using an electromagnetically reactive near field

Despite coronavirus disease 2019, cardiovascular disease, the leading cause of global death, requires timely detection and treatment for a high survival rate, underscoring the 24 h monitoring of vital signs. Therefore, telehealth using wearable devices with vital sign sensors is not only a fundamental response against the pandemic but a solution to provide prompt healthcare for the patients in remote sites. Former technologies which measured a couple of vital signs had features that disturbed practical applications to wearable devices, such as heavy power consumption. Here, we suggest an ultralow power (100 μW) sensor that collects all cardiopulmonary vital signs, including blood pressure, heart rate, and the respiration signal. The small and lightweight (2 g) sensor designed to be easily embedded in the flexible wristband generates an electromagnetically reactive near field to monitor the contraction and relaxation of the radial artery. The proposed ultralow power sensor measuring noninvasively continuous and accurate cardiopulmonary vital signs at once will be one of the most promising sensors for wearable devices to bring telehealth to our lives.

Continuous Non-Invasive Blood Pressure Measurement Using 60 GHz-Radar-A Feasibility Study

Blood pressure monitoring is of paramount importance in the assessment of a human's cardiovascular health. The state-of-the-art method remains the usage of an upper-arm cuff sphygmomanometer. However, this device suffers from severe limitations-it only provides a static blood pressure value pair, is incapable of capturing blood pressure variations over time, is inaccurate, and causes discomfort upon use. This work presents a radar-based approach that utilizes the movement of the skin due to artery pulsation to extract pressure waves. From those waves, a set of 21 features was collected and used-together with the calibration parameters of age, gender, height, and weight-as input for a neural network-based regression model. After collecting data from 55 subjects from radar and a blood pressure reference device, we trained 126 networks to analyze the developed approach's predictive power. As a result, a very shallow network with just two hidden layers produced a systolic error of 9.2±8.3 mmHg (mean error ± standard deviation) and a diastolic error of 7.7±5.7 mmHg. While the trained model did not reach the requirements of the AAMI and BHS blood pressure measuring standards, optimizing network performance was not the goal of the proposed work. Still, the approach has displayed great potential in capturing blood pressure variation with the proposed features. The presented approach therefore shows great potential to be incorporated into wearable devices for continuous blood pressure monitoring for home use or screening applications, after improving this approach even further.

Recent progress in flexible pressure sensors based on multiple microstructures: from design to application

Flexible pressure sensors (FPSs) have been widely studied in the fields of wearable medical monitoring and human-machine interaction due to their high flexibility, light weight, sensitivity, and easy integration. To better meet these application requirements, key sensing properties such as sensitivity, linear sensing range, pressure detection limits, response/recovery time, and durability need to be effectively improved. Therefore, researchers have extensively and profoundly researched and innovated on the structure of sensors, and various microstructures have been designed and applied to effectively improve the sensing performance of sensors. Compared with single microstructures, multiple microstructures (MMSs) (including hierarchical, multi-layered and hybrid microstructures) can improve the sensing performance of sensors to a greater extent. This paper reviews the recent research progress in the design and application of FPSs with MMSs and systematically summarizes the types, sensing mechanisms, and preparation methods of MMSs. In addition, we summarize the applications of FPSs with MMSs in the fields of human motion detection, health monitoring, and human-computer interaction. Finally, we provide an outlook on the prospects and challenges for the development of FPSs.

Cellulose Nanocrystal-Based All-3D-Printed Pyro-Piezoelectric Nanogenerator for Hybrid Energy Harvesting and Self-Powered Cardiorespiratory Monitoring toward the Human-Machine Interface

Biomaterials with spontaneous piezoelectric property are highly emerging in recent times for the generation of electricity from mechanical energy sources that are amply available in nature. In this context, pyroelectricity, an integral property of piezoelectric materials, might be an interesting tool in harvesting thermal energy from the fluctuations of temperature. On the other hand, respiration and heart pulse are the significant human vital signs that can be used for early detection and prevention of cardiorespiratory diseases. Here, we report an all-three-dimensional (3D)-printed pyro-piezoelectric nanogenerator (Py-PNG) based on the most abundant and completely biodegradable biopolymer on earth, i.e., cellulose nanocrystal (CNC) for hybrid (mechanical as well as thermal) energy harvesting, and interestingly, the NG could be used as an e-skin sensor for application in self-powered noninvasive cardiorespiratory monitoring for personal healthcare. Notably, the CNC-based device will be biocompatible and economically advantageous due to its biomaterial-based supremacy and huge availability. This is an original approach with 3D geometrical advancement in designing a NG/sensor, where the unique all-3D-printed manner is adopted, and certainly, it has promising potential in reducing the number of processing steps to required equipment during the multilayer fabrication. The all-3D-printed NG/sensor shows outstanding mechano-thermal energy harvesting performance along with sensitivity and is capable of accurate detection of heart pulse as well as respiration, whenever and whichever required without the need of any battery or an external power supply. In addition, we have also extended its application in demonstrating a smart mask-based breath monitoring system. Thus, the real-time cardiorespiratory monitoring provides notable and fascinating information in medical diagnosis, stepping toward biomedical device development and human-machine interface.

Wave reflection quantification analysis and personalized flow wave estimation based on the central aortic pressure waveform

Pulse wave reflections reflect cardiac afterload and perfusion, which yield valid indicators for monitoring cardiovascular status. Accurate quantification of pressure wave reflections requires the measurement of aortic flow wave. However, direct flow measurement involves extra equipment and well-trained operator. In this study, the personalized aortic flow waveform was estimated from the individual central aortic pressure waveform (CAPW) based on pressure-flow relations. The separated forward and backward pressure waves were used to calculate wave reflection indices such as reflection index (RI) and reflection magnitude (RM), as well as the central aortic pulse transit time (PTT). The effectiveness and feasibility of the method were validated by a set of clinical data (13 participants) and the Nektar1D Pulse Wave Database (4,374 subjects). The performance of the proposed personalized flow waveform method was compared with the traditional triangular flow waveform method and the recently proposed lognormal flow waveform method by statistical analyses. Results show that the root mean square error calculated by the personalized flow waveform approach is smaller than that of the typical triangular and lognormal flow methods, and the correlation coefficient with the measured flow waveform is higher. The estimated personalized flow waveform based on the characteristics of the CAPW can estimate wave reflection indices more accurately than the other two methods. The proposed personalized flow waveform method can be potentially used as a convenient alternative for the measurement of aortic flow waveform.

Viewpoint: The Case for Non-Invasive Central Aortic Pressure Monitoring in the Management of Hypertension

Elevated central aortic pressure indices (e.g., systolic pressure and pulse pressure) predict cardiovascular (CV) events and mortality in addition to structural changes (e.g., left ventricular hypertrophy, carotid intima-media thickness and reduced glomerular filtration rate). These elevated risks have been shown in multiple studies to be superior to, and in others, at least as high as that associated with brachial pressures. Threshold values for the diagnosis of elevated central arterial pressures have been defined and can be considered target goals of treatment. Measurements of central arterial pressures can be incorporated into the current approaches to hypertension management utilizing currently available non-invasive devices that measure central pressures during the measurement of brachial BP. The objective of this review is to outline the rationale and evidence supporting incorporation of central aortic pressure monitoring into the care of patients with hypertension.

Short-term effects of personal exposure to temperature variability on cardiorespiratory health based on subclinical non-invasive biomarkers

Growing literatures have explored the cardiorespiratory health effects of the daily temperature, but such effects of temperature variability remain unclear. We investigated the acute associations of personal levels of temperature variability with cardiorespiratory biomarkers. This is a panel study with four repeated measurements among forty eligible college students in Hefei, Anhui Province, China. We collected personal-level temperature data using temperature/humidity data loggers. Temperature variability parameters included diurnal temperature range (DTR), the standard-deviation of temperature (SDT) and temperature variability (TV). Cardiorespiratory health indicators included three BP parameters [systolic BP (SBP), diastolic BP (DBP) and mean article pressure (MAP)], fractional exhaled nitric oxide (FeNO), and four saliva biomarkers [C-reactive protein (CRP), cortisol, alpha-amylase and lysozyme]. Linear mixed-effect models were then used to assess the associations of temperature variability with these cardiorespiratory biomarkers. We found that short-term exposure to the three temperature variability parameters was associated with these cardiorespiratory biomarkers. The magnitude, direction and significance of these associations varied by temperature variability parameters, by biomarkers and by lags of exposure. Specifically, temperature variability parameters were inversely associated with BP and saliva lysozyme; positively associated with airway inflammation biomarkers (FeNO and saliva CRP) and stress response biomarkers (saliva cortisol and alpha-amylase). The results were robust to further control for air pollutants, and these associations were more prominent in females and in subjects with abnormal body mass index. Our findings suggested that acute exposure to temperature variability could significantly alter cardiorespiratory biomarker profiles among healthy young adults in China.

Characteristics of sarcopenia subjects in arterial pulse spectrum analysis

Aims

Sarcopenia is significantly associated with the number of cardiovascular and metabolic diseases, however, the underlying pathophysiological processes are largely unknown. This study performed harmonic index of finger photoplethysmography (PPG) waveforms with the aims of distinguishing different arterial pulse waveform signals between sarcopenia, presarcopenia, dynapenia, and healthy subjects.

Methods

Sixty-eight subjects were enrolled and obtained 1-min PPG signals, then were assigned to four age-matched groups: control, dynapenia, presarcopenia, and sarcopenia which definition according to Asian Working Group for Sarcopenia (AWGS): 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. Harmonics 1-10 of the PPG waveform were obtained and calculated each of the amplitude proportions (C _n ), standard deviations (SD _n ), coefficients of variations (CV _n ), and vascular elasticity index (VEI) for to evaluating the blood-pressure harmonic variability.

Results

The prevalence of sarcopenia in women gender (8 out of 9, 88.9%, p = 0.046) and osteoporosis in dynapenia (7 out of 16, 43.8%, p = 0.005) were significant higher. Among the four groups, compared with control, dynapenia, and presarcopenia, sarcopenia had largest SD _n -values for harmonics 1, 2, 3, and 5 (ratio 1, 2, 3, 5 = 0.354, 0.209, 0.137, 0.074); whereas sarcopenia had largest coefficients of variations (CV _n ) values for harmonics 1, 2, 3 and 10 (ratio 1, 2, 3, 10 = 0.263, 0.310, 0.402, 0.791). Besides, the Δ odds ratio of ratio 3, 4,and 6 tertile values were significantly increased in sarcopenia and possible sarcopenia group compared with control group. Subjects with sarcopenia had significantly higher VEI in mean, SD, and CV of the PPG waveform (mean = 2.332, SD = 1.479, CV = 0.634, p = 0.007) among the groups and the results of binary logistic regression analysis in the tertiles met statistical significance between the sarcopenia and non-sarcopenia groups whether adjusted or unadjusted (adjusted odds ratio 6.956, p = 0.030, unadjusted odds ratio 3.937, p = 0.039).

Conclusions

The elasticity of vessels among sarcopenia groups in lower-frequency components of harmonic ratio in which we defined as VEI showed a significantly highest VEI mean, SD, and CV in sarcopenia indicates the poorer elasticity of the arteries. The present findings showed finger PPG waveform measurements may be useful for early detection of vascular diseases with patients with sarcopenia in a non-invasive and easy-to-perform technique which may expand the clinical applicability in the future.

Kirigami-Inspired Pressure Sensors for Wearable Dynamic Cardiovascular Monitoring

Continuously and accurately monitoring pulse-wave signals is critical to prevent and diagnose cardiovascular diseases. However, existing wearable pulse sensors are vulnerable to motion artifacts due to the lack of proper adhesion and conformal interface with human skin during body movement. Here, a highly sensitive and conformal pressure sensor inspired by the kirigami structure is developed to measure the human pulse wave on different body artery sites under various prestressing pressure conditions and even with body movement. COMSOL multiphysical field coupling simulation and experimental testing are used to verify the unique advantages of the kirigami structure. The device shows a superior sensitivity (35.2 mV Pa^-1 ) and remarkable stability (>84 000 cycles). Toward practical applications, a wireless cardiovascular monitoring system is developed for wirelessly transmitting the pulse signals to a mobile phone in real-time, which successfully distinguished the pulse waveforms from different participants. The pulse waveforms measured by the kirigami inspired pressure sensor are as accurate as those provided by the commercial medical device. Given the compelling features, the sensor provides an ascendant way for wearable electronics to overcome motion artifacts when monitoring pulse signals, thus representing a solid advancement toward personalized healthcare in the era of the Internet of Things.

Ferroelectricity-Coupled 2D-MXene-Based Hierarchically Designed High-Performance Stretchable Triboelectric Nanogenerator

Triboelectric nanogenerators based on the state-of-the-art functional materials and device engineering provide an exciting platform for future multifunctional electronics, but it remains challenging to realize due to the lack of in-depth understanding on the functional properties of nanomaterials that are compatible with microstructural engineering. In this study, a high-performance stretchable (∼60% strain) triboelectric nanogenerator is demonstrated via an interlocked microstructural device configuration sandwiched between silver-nanowire-(Ag-NW) electrodes and hierarchically engineered spongy thermoplastic polyurethane (TPU) polymer composite with ferroelectric barium-titanate-coupled (BTO-coupled) 2D MXene (Ti₃C₂T_x) nanosheets. The use of MXene results in an increase in the dielectric constant whereas the dielectric loss is lowered via coupling with the ferroelectricity of BTO, which increases the overall output performance of the nanogenerator. The spongy nature of the composite film increases the capacitance variation under deformation, which results in improved energy-conversion efficiency (∼79%) and pressure sensitivity (4.6 VkPa^-1 and 2.5 mAkPa^-1) of the device. With the quantum-mechanically calculated electronic structure, the device converts biomechanical energy to electrical energy and generates an open-circuit output voltage of 260 V, short-circuit output current of 160 mA/m², and excellent power output of 6.65 W/m², which is sufficient to operate several consumer electronics. Owing to its superior pressure sensitivity and efficiency, the device enables a broad range of applications including real-time clinical human vital-sign monitoring, acoustic sensing, and multidimensional gesture-sensing functionality of a robotic hand. Considering the ease of fabrication, excellent functionality of the hierarchical polymer nanocomposite, and outstanding energy-harvesting performance of nanogenerators, this work is expected to stimulate the development of next-generation self-powered technology.

Noninvasive estimation of aortic pressure waveform based on simplified Kalman filter and dual peripheral artery pressure waveforms

BACKGROUND AND OBJECTIVE

Aortic pressure (P_a) is important for the diagnosis of cardiovascular disease. However, its direct measurement is invasive, not risk-free, and relatively costly. In this paper, a new simplified Kalman filter (SKF) algorithm is employed for the reconstruction of the P_a waveform using dual peripheral artery pressure waveforms.

METHODS

P_a waveforms obtained in a previous study were collected from 25 patients. Simultaneously, radial and femoral pressure waveforms were generated from two simulation experiments, using transfer functions. In the first, the transfer function is a known finite impulse response; and in the second, it is derived from a tube-load model. To analyze the performance of the proposed SKF algorithm, variable amounts of noise were added to the observed output signal, to give a range of signal-to-noise ratios (SNRs). Additionally, central aortic, brachial and femoral pressure waveforms were simultaneously collected from 2 Sprague-Dawley rats and the measured and reconstructed P_a waveforms were compared.

RESULTS

The proposed SKF algorithm outperforms canonical correlation analysis (CCA), which is the current state-of-the-art blind system identification method for the non-invasive estimation of central aortic blood pressure. It is also shown that the proposed SKF algorithm is more noise-tolerant than the CCA algorithm over a wide range of SNRs.

CONCLUSION

The simulations and animal experiments illustrate that the proposed SKF algorithm is accurate and stable in the face of low SNRs. Improved methods for estimating central blood pressure as a measure of cardiac load adds to their value as a prognostic and diagnostic tool.

A Review of Noninvasive Methodologies to Estimate the Blood Pressure Waveform

Accurate estimation of blood pressure (BP) waveforms is critical for ensuring the safety and proper care of patients in intensive care units (ICUs) and for intraoperative hemodynamic monitoring. Normal cuff-based BP measurements can only provide systolic blood pressure (SBP) and diastolic blood pressure (DBP). Alternatively, the BP waveform can be used to estimate a variety of other physiological parameters and provides additional information about the patient's health. As a result, various techniques are being proposed for accurately estimating the BP waveforms. The purpose of this review is to summarize the current state of knowledge regarding the BP waveform, three methodologies (pressure-based, ultrasound-based, and deep-learning-based) used in noninvasive BP waveform estimation research and the feasibility of employing these strategies at home as well as in ICUs. Additionally, this article will discuss the physical concepts underlying both invasive and noninvasive BP waveform measurements. We will review historical BP waveform measurements, standard clinical procedures, and more recent innovations in noninvasive BP waveform monitoring. Although the technique has not been validated, it is expected that precise, noninvasive BP waveform estimation will be available in the near future due to its enormous potential.

Wearable Pressure Sensors for Pulse Wave Monitoring

Cardiovascular diseases remain the leading cause of death worldwide. The rapid development of flexible sensing technologies and wearable pressure sensors have attracted keen research interest and have been widely used for long-term and real-time cardiovascular status monitoring. Owing to compelling characteristics, including light weight, wearing comfort, and high sensitivity to pulse pressures, physiological pulse waveforms can be precisely and continuously monitored by flexible pressure sensors for wearable health monitoring. Herein, an overview of wearable pressure sensors for human pulse wave monitoring is presented, with a focus on the transduction mechanism, microengineering structures, and related applications in pulse wave monitoring and cardiovascular condition assessment. The conceptualizations and methods for the acquisition of physiological and pathological information related to the cardiovascular system are outlined. The biomechanics of arterial pulse waves and the working mechanism of various wearable pressure sensors, including triboelectric, piezoelectric, magnetoelastic, piezoresistive, capacitive, and optical sensors, are also subject to systematic debate. Exemple applications of pulse wave measurement based on microengineering structured devices are then summarized. Finally, a discussion of the opportunities and challenges that wearable pressure sensors face, as well as their potential as a wearable intelligent system for personalized healthcare is given in conclusion.

Piezoelectric Dynamics of Arterial Pulse for Wearable Continuous Blood Pressure Monitoring

Piezoelectric arterial pulse wave dynamics are traditionally considered to be similar to those of typical blood pressure waves. However, achieving accurate continuous blood pressure wave monitoring based on arterial pulse waves remains challenging, because the correlation between piezoelectric pulse waves and their related blood pressure waves is unclear. To address this, the correlation between piezoelectric pulse waves and blood pressure waves is first elucidated via theoretical, simulation, and experimental analysis of these dynamics. Based on this correlation, the authors develop a wireless wearable continuous blood pressure monitoring system, with better portability than conventional systems that are based on the pulse wave velocity between multiple sensors. They explore the feasibility of achieving wearable continuous blood pressure monitoring without motion artifacts, using a single piezoelectric sensor. These findings eliminate the controversy over the arterial pulse wave piezoelectric response, and can potentially be used to develop a portable wearable continuous blood pressure monitoring device for the early prevention and daily control of hypertension.

Assessing hemodynamics from the photoplethysmogram to gain insights into vascular age: a review from VascAgeNet

The photoplethysmogram (PPG) signal is widely measured by clinical and consumer devices, and it is emerging as a potential tool for assessing vascular age. The shape and timing of the PPG pulse wave are both influenced by normal vascular aging, changes in arterial stiffness and blood pressure, and atherosclerosis. This review summarizes research into assessing vascular age from the PPG. Three categories of approaches are described: 1) those which use a single PPG signal (based on pulse wave analysis), 2) those which use multiple PPG signals (such as pulse transit time measurement), and 3) those which use PPG and other signals (such as pulse arrival time measurement). Evidence is then presented on the performance, repeatability and reproducibility, and clinical utility of PPG-derived parameters of vascular age. Finally, the review outlines key directions for future research to realize the full potential of photoplethysmography for assessing vascular age.

Implementation and validation of a novel noninvasive blood pressure measurement algorithm based on oscillometric method with obtaining international hypertension standards

Background:

One of the most prevalent methods in noninvasive blood pressure (BP) measurement with cuff is oscillometric, which has two different types of deflation, including linear and step deflation. With this approach, in addition to designing a novel algorithm by the step deflation method, a sample of its module was constructed and validated during clinical tests in different hospitals.

Method:

In this study, by controlling the valve, the pressure would be deflated through optimized steps. By real-time processing on the obtained signal from the pressure sensor, pulses in each step would be extracted. After that, in offline mode, mean arterial pressure is estimated based on curve fitting.

Result:

A BP simulator, various modules, and an auditory method were used to validate the algorithm and its results. During clinical tests, 80 people (men and women), 11 dialysis patients, and 69 non-dialysis (healthy or with other diseases) in the age range of 17–85 years participated.

Conclusion:

The obtained results compared with the BP simulator are in the standard range according to the international medical standards of the British Hypertension Society (BHS) and the US Association for the Advancement of Medical Instrumentation (AAMI), which are the global standard of comparison in this field.

A Delineator for Arterial Blood Pressure Waveform Analysis Based on a Deep Learning Technique

A deep learning technique based on semantic segmentation was implemented into the blood pressure detection points field. Two models were trained and evaluated in terms of a reference detector. The proposed methodology outperforms the reference detector in two of the three classic benchmarks and on signals from a public database that were modified with realistic test maneuvers and artifacts. Both models differentiate regions with valid information and artifacts. So far, no other delineator had shown this capacity.

Pulse wave velocity measurement along the ulnar artery in the wrist region using a high frequency ultrasonic array

A pulse wave velocity (PWV) measurement method performed above a small blood vessel using an ultrasonic probe is studied and reported in this paper. These experimentations are carried out using a high-frequency probe (14-22 MHz), allowing a high level of resolution compatible with the vessel dimensions, combined with an open research ultrasound scanner. High frame-rate (HFR) imaging (10 000 frames per second) is used for a precise PWV estimation. The measurements are performed in-vivo on a healthy volunteer. The probe is placed above the ulnar artery on the wrist in order to make longitudinal scans. In addition to conventional duplex ultrasound evaluation, the measurement of the PWV using this method at this location could strengthen the detection and diagnosis of cardiovascular diseases (CVDs), in particular for arm artery diseases (AADs). Moreover, these experimentations are also carried out within the scope of a demonstration for a potential miniaturized and wearable device (i.e., a probe with fewer elements, typically less than 32, and its associated electronics). The study has shown results coherent with expected PWV and also promising complementary results such as intima-media thickness (IMT) with spatiotemporal resolution on the order of 6.2 μm and 0.1 ms.

Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research

Vascular ageing biomarkers have been found to be predictive of cardiovascular risk independently of classical risk factors, yet are not widely used in clinical practice. In this review, we present two basic approaches for using machine learning (ML) to assess vascular age: parameter estimation and risk classification. We then summarize their role in developing new techniques to assess vascular ageing quickly and accurately. We discuss the methods used to validate ML-based markers, the evidence for their clinical utility, and key directions for future research. The review is complemented by case studies of the use of ML in vascular age assessment which can be replicated using freely available data and code.

Determination of aortic pulse transit time based on waveform decomposition of radial pressure wave

Carotid-femoral pulse transit time (cfPTT) is a widely accepted measure of central arterial stiffness. The cfPTT is commonly calculated from two synchronized pressure waves. However, measurement of synchronized pressure waves is technically challenging. In this paper, a method of decomposing the radial pressure wave is proposed for estimating cfPTT. From the radial pressure wave alone, the pressure wave can be decomposed into forward and backward waves by fitting a double triangular flow wave. The first zero point of the second derivative of the radial pressure wave and the peak of the dicrotic segment of radial pressure wave are used as the peaks of the fitted double triangular flow wave. The correlation coefficient between the measured wave and the estimated forward and backward waves based on the decomposition of the radial pressure wave was 0.98 and 0.75, respectively. Then from the backward wave, cfPTT can be estimated. Because it has been verified that the time lag estimation based on of backward wave has strong correlation with the measured cfPTT. The corresponding regression function between the time lag estimation of backward wave and measured cfPTT is y = 0.96x + 5.50 (r = 0.77; p < 0.001). The estimated cfPTT using radial pressure wave decomposition based on the proposed double triangular flow wave is more accurate and convenient than the decomposition of the aortic pressure wave based on the triangular flow wave. The significance of this study is that arterial stiffness can be directly estimated from a noninvasively measured radial pressure wave.

Ambulatory Cardiovascular Monitoring Via a Machine-Learning-Assisted Textile Triboelectric Sensor

Wearable bioelectronics for continuous and reliable pulse wave monitoring against body motion and perspiration remains a great challenge and highly desired. Here, a low-cost, lightweight, and mechanically durable textile triboelectric sensor that can convert subtle skin deformation caused by arterial pulsatility into electricity for high-fidelity and continuous pulse waveform monitoring in an ambulatory and sweaty setting is developed. The sensor holds a signal-to-noise ratio of 23.3 dB, a response time of 40 ms, and a sensitivity of 0.21 µA kPa-1 . With the assistance of machine learning algorithms, the textile triboelectric sensor can continuously and precisely measure systolic and diastolic pressure, and the accuracy is validated via a commercial blood pressure cuff at the hospital. Additionally, a customized cellphone application (APP) based on built-in algorithm is developed for one-click health data sharing and data-driven cardiovascular diagnosis. The textile triboelectric sensor enabled wireless biomonitoring system is expected to offer a practical paradigm for continuous and personalized cardiovascular system characterization in the era of the Internet of Things.

Validation of Central Pressure Estimation in Patients with an Aortic Aneurysm Before and After Endovascular Repair

PURPOSE

The aim of this study was to investigate if non-invasive central pressure estimations are accurate in patients with an abdominal aortic aneurysm, before and after endovascular repair. Secondary evaluation was if measurement-accuracy was dependent on anatomical characteristics.

METHODS

Procedural invasive and non-invasive pressure-measurements were performed simultaneously both before and after endovascular repair in 20 patients with an infrarenal abdominal aortic aneurysm. Invasive catheter measurements were performed in the abdominal aorta. A tonometric device was used to perform non-invasive pressure-wave-analysis at the radial artery. A generalized transfer-function was used to generate an ascending aortic waveform for both measurements, allowing for direct comparison.

RESULTS

Pre-treatment the mean differences between methods were - 5.5 mmHg (p = .904), - 11.8 (p < .001), and - 7.2 mmHg (p = .124) for central systolic, diastolic, and mean pressure, respectively. The accuracy was dependent of aneurysm sac volume and intraluminal thrombus volume. Post-treatment limits of agreement were smaller for all pressure parameters compared to pre-treatment. The mean differences were 6.5 mmHg (p = .007), - 6.4 (p < .020), and 1.6 mmHg (p = .370) for central systolic, diastolic, and mean pressure, respectively.

CONCLUSION

In untreated AAA's the accuracy of non-invasive central pressure estimation was acceptable (mean difference between 5 and 10 mmHg) when compared to invasive pressures, but dependent of AAA characteristics. After EVAR the accuracy of central pressure estimation improved (reduction of 75% of the mean difference between pre and post measurements) TRIAL REGISTRATION NUMBER: NCT03469388; 3-5-2018.

Non-Invasive Hemodynamics Monitoring System Based on Electrocardiography via Deep Convolutional Autoencoder

This study evaluates cardiovascular and cerebral hemodynamics systems by only using non-invasive electrocardiography (ECG) signals. The Massachusetts General Hospital/Marquette Foundation (MGH/MF) and Cerebral Hemodynamic Autoregulatory Information System Database (CHARIS DB) from the PhysioNet database are used for cardiovascular and cerebral hemodynamics, respectively. For cardiovascular hemodynamics, the ECG is used for generating the arterial blood pressure (ABP), central venous pressure (CVP), and pulmonary arterial pressure (PAP). Meanwhile, for cerebral hemodynamics, the ECG is utilized for the intracranial pressure (ICP) generator. A deep convolutional autoencoder system is applied for this study. The cross-validation method with Pearson’s linear correlation (R), root mean squared error (RMSE), and mean absolute error (MAE) are measured for the evaluations. Initially, the ECG is used to generate the cardiovascular waveform. For the ABP system—the systolic blood pressure (SBP) and diastolic blood pressures (DBP)—the R evaluations are 0.894 ± 0.004 and 0.881 ± 0.005, respectively. The MAE evaluations for SBP and DBP are, respectively, 6.645 ± 0.353 mmHg and 3.210 ± 0.104 mmHg. Furthermore, for the PAP system—the systolic and diastolic pressures—the R evaluations are 0.864 ± 0.003 mmHg and 0.817 ± 0.006 mmHg, respectively. The MAE evaluations for systolic and diastolic pressures are, respectively, 3.847 ± 0.136 mmHg and 2.964 ± 0.181 mmHg. Meanwhile, the mean CVP evaluations are 0.916 ± 0.001, 2.220 ± 0.039 mmHg, and 1.329 ± 0.036 mmHg, respectively, for R, RMSE, and MAE. For the mean ICP evaluation in cerebral hemodynamics, the R and MAE evaluations are 0.914 ± 0.003 and 2.404 ± 0.043 mmHg, respectively. This study, as a proof of concept, concludes that the non-invasive cardiovascular and cerebral hemodynamics systems can be potentially investigated by only using the ECG signal.

Restoration of Remote PPG Signal through Correspondence with Contact Sensor Signal

Photoplethysmography (PPG) is an optical measurement technique that detects changes in blood volume in the microvascular layer caused by the pressure generated by the heartbeat. To solve the inconvenience of contact PPG measurement, a remote PPG technology that can measure PPG in a non-contact way using a camera was developed. However, the remote PPG signal has a smaller pulsation component than the contact PPG signal, and its shape is blurred, so only heart rate information can be obtained. In this study, we intend to restore the remote PPG to the level of the contact PPG, to not only measure heart rate, but to also obtain morphological information. Three models were used for training: support vector regression (SVR), a simple three-layer deep learning model, and SVR + deep learning model. Cosine similarity and Pearson correlation coefficients were used to evaluate the similarity of signals before and after restoration. The cosine similarity before restoration was 0.921, and after restoration, the SVR, deep learning model, and SVR + deep learning model were 0.975, 0.975, and 0.977, respectively. The Pearson correlation coefficient was 0.778 before restoration and 0.936, 0.933, and 0.939, respectively, after restoration.

Hemodynamic modeling of the circle of Willis reveals unanticipated functions during cardiovascular stress

The circle of Willis (CW) allows blood to be redistributed throughout the brain during local ischemia; however, it is unlikely that the anatomic persistence of the CW across mammalian species is driven by natural selection of individuals with resistance to cerebrovascular disease typically occurring in elderly humans. To determine the effects of communicating arteries (CoAs) in the CW on cerebral pulse wave propagation and blood flow velocity, we simulated young, active adult humans undergoing different states of cardiovascular stress (i.e., fear and aerobic exercise) using discrete transmission line segments with stress-adjusted cardiac output, peripheral resistance, and arterial compliance. Phase delays between vertebrobasilar and carotid pulses allowed bidirectional shunting through CoAs: both posteroanterior shunting before the peak of the pulse waveform and anteroposterior shunting after internal carotid pressure exceeded posterior cerebral pressure. Relative to an absent CW without intact CoAs, the complete CW blunted anterior pulse waveforms, although limited to 3% and 6% reductions in peak pressure and pulse pressure, respectively. Systolic rate of change in pressure (i.e., ∂P/∂t) was reduced 15%-24% in the anterior vasculature and increased 23%-41% in the posterior vasculature. Bidirectional shunting through posterior CoAs was amplified during cardiovascular stress and increased peak velocity by 25%, diastolic-to-systolic velocity range by 44%, and blood velocity acceleration by 134% in the vertebrobasilar arteries. This effect may facilitate stress-related increases in blood flow to the cerebellum (improving motor coordination) and reticular-activating system (enhancing attention and focus) via a nitric oxide-dependent mechanism, thereby improving survival in fight-or-flight situations.NEW & NOTEWORTHY Hemodynamic modeling reveals potential evolutionary benefits of the intact circle of Willis (CW) during fear and aerobic exercise. The CW equalizes pulse waveforms due to bidirectional shunting of blood flow through communicating arteries, which boosts vertebrobasilar blood flow velocity and acceleration. These phenomena may enhance perfusion of the brainstem and cerebellum via nitric oxide-mediated vasodilation, improving performance of the reticular-activating system and motor coordination in survival situations.

Flexible Wearable Sensors for Cardiovascular Health Monitoring

Cardiovascular diseases account for the highest mortality globally, but recent advances in wearable technologies may potentially change how these illnesses are diagnosed and managed. In particular, continuous monitoring of cardiovascular vital signs for early intervention is highly desired. To this end, flexible wearable sensors that can be comfortably worn over long durations are gaining significant attention. In this review, advanced flexible wearable sensors for monitoring cardiovascular vital signals are outlined and discussed. Specifically, the functional materials, configurations, mechanisms, and recent advances of these flexible sensors for heart rate, blood pressure, blood oxygen saturation, and blood glucose monitoring are highlighted. Different mechanisms in bioelectric, mechano-electric, optoelectric, and ultrasonic wearable sensors are presented to monitor cardiovascular vital signs from different body locations. Present challenges, possible strategies, and future directions of these wearable sensors are also discussed. With rapid development, these flexible wearable sensors will potentially be applicable for both medical diagnosis and daily healthcare use in tackling cardiovascular diseases.

Soft Wearable Healthcare Materials and Devices

In spite of advances in electronics and internet technologies, current healthcare remains hospital-centred. Disruptive technologies are required to translate state-of-art wearable devices into next-generation patient-centered diagnosis and therapy. In this review, recent advances in the emerging field of soft wearable materials and devices are summarized. A prerequisite for such future healthcare devices is the need of novel materials to be mechanically compliant, electrically conductive, and biologically compatible. It is begun with an overview of the two viable design strategies reported in the literatures, which is followed by description of state-of-the-art wearable healthcare devices for monitoring physical, electrophysiological, chemical, and biological signals. Self-powered wearable bioenergy devices are also covered and sensing systems, as well as feedback-controlled wearable closed-loop biodiagnostic and therapy systems. Finally, it is concluded with an overall summary and future perspective.

CNN-LSTM Prediction Method for Blood Pressure Based on Pulse Wave

Cardiovascular disease (CVD), which seriously threatens human health, can be prevented by blood pressure (BP) measurement. However, convenient and accurate BP measurement is a vital problem. Although the easily-collected pulse wave (PW)-based methods make it possible to monitor BP at all times and places, the current methods still require professional knowledge to process the medical data. In this paper, we combine the advantages of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, to propose a CNN-LSTM BP prediction method based on PW data. In detailed, CNN first extract features from PW data, and then the features are input into LSTM for further training. The numerical results based on real-life data sets show that the proposed method can achieve high predicted accuracy of BP while saving training time. As a result, CNN-LSTM can achieve convenient BP monitoring in daily health.

A Fully Biodegradable Ferroelectric Skin Sensor from Edible Porcine Skin Gelatine

High-performance biodegradable electronic devices are being investigated to address the global electronic waste problem. In this work, a fully biodegradable ferroelectric nanogenerator-driven skin sensor with ultrasensitive bimodal sensing capability based on edible porcine skin gelatine is demonstrated. The microstructure and molecular engineering of gelatine induces polarization confinement that gives rise the ferroelectric properties, resulting in a piezoelectric coefficient (d33) of ≈24 pC N-1 and pyroelectric coefficient of ≈13 µC m-2K-1, which are 6 and 11.8 times higher, respectively, than those of the conventional planar gelatine. The ferroelectric gelatine skin sensor has exceptionally high pressure sensitivity (≈41 mV Pa-1) and the lowest detection limit of pressure (≈0.005 Pa) and temperature (≈0.04 K) ever reported for ferroelectric sensors. In proof-of-concept tests, this device is able to sense the spatially resolved pressure, temperature, and surface texture of an unknown object, demonstrating potential for robotic skins and wearable electronics with zero waste footprint.

Beat-to-beat and spectral analyses of the noninvasive radial pulse and laser-Doppler signals in patients with hypertension

This study performed beat-to-beat and spectral analyses of 20-minute skin-surface laser-Doppler-flowmetry (LDF) and radial blood-pressure-waveform (BPW) signals in order to compare the blood-flow perfusion condition and regulatory mechanisms between essential-hypertension (EHT) patients and aged-matched control subjects. Beat-to-beat LDF analyses yielded the pulse width (PW), AC-to-DC ratio (AD), and their corresponding variability indices (coefficients of variation [CVs]). The relative energy contributions (RECs) of five characteristic frequency peaks (defined as FR1-FR5) were also calculated. Spectral BPW analysis obtained the amplitude proportion (Cn) and phase angle (Pn) of each harmonic component n. PW, AD, AD_CV, and REC of FR2 were significantly smaller in the EHT group than in the control group. Regarding BPW indices, C1, C2, C4, and C5 were significantly larger and P2-P8 were significantly smaller in EHT patients than in controls. The present results indicate that BPW and LDF indices can be used to evaluate the blood-flow perfusion efficiency and microcirculatory regulatory activities in EHT. Sex differences were found, with the effects being more prominent in female patients. These findings may be partly attributable to impairment of endothelial and neural regulatory functions. The present findings might aid the development of new noninvasive methods for reducing the risk of EHT-induced damage.

Changes in Noninvasive Arterial Stiffness and Central Blood Pressure After Endovascular Abdominal Aneurysm Repair

Purpose:

To evaluate the impact of elective endovascular aneurysm repair (EVAR) on the carotid-femoral pulse wave velocity (cfPWV) and central pressure waveform, through 1-year follow-up.

Materials and Methods:

A tonometric device was used to measure cfPWV and estimate the central pressure waveform in 20 patients with an infrarenal abdominal aortic aneurysm scheduled for elective EVAR. The evaluated central hemodynamic parameters included the central pressures, the augmentation index (AIx), and the subendocardial viability ratio (SEVR). AIx quantifies the contribution of reflected wave to the central systolic pressure, whereas SEVR describes the myocardial perfusion relative to the cardiac workload. Measurements were performed before EVAR, at discharge, and 6 weeks and 1 year after EVAR.

Results:

CfPWV was increased at discharge (12.4±0.4 vs 11.3±0.5 m/s at baseline; p=0.005) and remained elevated over the course of 1-year follow-up (6 weeks: cfPWV = 12.2±0.5 m/s; 1 year: cfPWV = 12.2±0.7 m/s, p<0.05). After an initial drop in systolic central pressure at discharge, all the central pressures increased thereafter up to 1 year, without significant differences compared with baseline. The same was observed for the AIx and SEVR.

Conclusion:

Endovascular aortic aneurysm repair caused an increase in pulse wave velocity compared with baseline, which remained elevated through 1 year follow-up, which may be related to an increased cardiovascular risk. However, no differences in central pressure, augmentation index, and subendocardial viability ration were observed during follow-up.

Changes in Noninvasive Arterial Stiffness and Central Blood Pressure After Endovascular Abdominal Aneurysm Repair

PURPOSE

To evaluate the impact of elective endovascular aneurysm repair (EVAR) on the carotid-femoral pulse wave velocity (cfPWV) and central pressure waveform, through 1-year follow-up.

MATERIALS AND METHODS

A tonometric device was used to measure cfPWV and estimate the central pressure waveform in 20 patients with an infrarenal abdominal aortic aneurysm scheduled for elective EVAR. The evaluated central hemodynamic parameters included the central pressures, the augmentation index (AIx), and the subendocardial viability ratio (SEVR). AIx quantifies the contribution of reflected wave to the central systolic pressure, whereas SEVR describes the myocardial perfusion relative to the cardiac workload. Measurements were performed before EVAR, at discharge, and 6 weeks and 1 year after EVAR.

RESULTS

CfPWV was increased at discharge (12.4±0.4 vs 11.3±0.5 m/s at baseline; p=0.005) and remained elevated over the course of 1-year follow-up (6 weeks: cfPWV = 12.2±0.5 m/s; 1 year: cfPWV = 12.2±0.7 m/s, p<0.05). After an initial drop in systolic central pressure at discharge, all the central pressures increased thereafter up to 1 year, without significant differences compared with baseline. The same was observed for the AIx and SEVR.

CONCLUSION

Endovascular aortic aneurysm repair caused an increase in pulse wave velocity compared with baseline, which remained elevated through 1 year follow-up, which may be related to an increased cardiovascular risk. However, no differences in central pressure, augmentation index, and subendocardial viability ration were observed during follow-up.

Triboelectric Effect Enabled Self-Powered, Point-of-Care Diagnostics: Opportunities for Developing ASSURED and REASSURED Devices

The use of rapid point-of-care (PoC) diagnostics in conjunction with physiological signal monitoring has seen tremendous progress in their availability and uptake, particularly in low- and middle-income countries (LMICs). However, to truly overcome infrastructural and resource constraints, there is an urgent need for self-powered devices which can enable on-demand and/or continuous monitoring of patients. The past decade has seen the rapid rise of triboelectric nanogenerators (TENGs) as the choice for high-efficiency energy harvesting for developing self-powered systems as well as for use as sensors. This review provides an overview of the current state of the art of such wearable sensors and end-to-end solutions for physiological and biomarker monitoring. We further discuss the current constraints and bottlenecks of these devices and systems and provide an outlook on the development of TENG-enabled PoC/monitoring devices that could eventually meet criteria formulated specifically for use in LMICs.

Blood Pressure Morphology Assessment from Photoplethysmogram and Demographic Information Using Deep Learning with Attention Mechanism

Arterial blood pressure (ABP) is an important vital sign from which it can be extracted valuable information about the subject's health. After studying its morphology it is possible to diagnose cardiovascular diseases such as hypertension, so ABP routine control is recommended. The most common method of controlling ABP is the cuff-based method, from which it is obtained only the systolic and diastolic blood pressure (SBP and DBP, respectively). This paper proposes a cuff-free method to estimate the morphology of the average ABP pulse (ABPM¯) through a deep learning model based on a seq2seq architecture with attention mechanism. It only needs raw photoplethysmogram signals (PPG) from the finger and includes the capacity to integrate both categorical and continuous demographic information (DI). The experiments were performed on more than 1100 subjects from the MIMIC database for which their corresponding age and gender were consulted. Without allowing the use of data from the same subjects to train and test, the mean absolute errors (MAE) were 6.57 ± 0.20 and 14.39 ± 0.42 mmHg for DBP and SBP, respectively. For ABPM¯, R correlation coefficient and the MAE were 0.98 ± 0.001 and 8.89 ± 0.10 mmHg. In summary, this methodology is capable of transforming PPG into an ABP pulse, which obtains better results when DI of the subjects is used, potentially useful in times when wireless devices are becoming more popular.

Exploring the Influential Factors on Readers' Continuance Intentions of E-Book APPs: Personalization, Usefulness, Playfulness, and Satisfaction

With the rapid development of mobile devices, users can now read on the screen. Electronic reading (e-reading) has become a common reading style with the growth in online learning or electronic learning (e-learning). E-book applications (APPs) are widely developed and applied for reading on a screen. However, it is difficult for readers to change their reading habits or preference from paper-printed books to digital devices. The study of readers' continuance intention to use e-book APPs is the first step to improving e-reading. This study focuses on the influential factors on undergraduates' continuance intention of e-book APPs, which analyzed and summarized the literature related to the electronic book (e-book) applications (APPs) and undergraduates' continuous intention, combined with the characteristics of the e-book APPs, introduced relevant theories and variables, and established the factors that influence undergraduates' continuous intention of using e-book APPs. On this basis, the paper analyzed the relationship between various influencing factors and their influence on continuous intention. A model composed of five hypotheses was constructed to test the factors influencing undergraduates' continuous intention in e-book APPs. The results indicated that of all research variables, satisfaction is the most important factor that affects continuous intention; Perceived usefulness and perceived playfulness have an indirect effect on continuous intention through satisfaction; personalization has direct effects on perceived usefulness and perceived playfulness, so it also has an indirect effect on continuous intention. The findings of the study will be helpful for designers and developers of e-book APPs and provide e-book APP suggestions for readers as well.