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Objective. Pulse pressure waves contain information about human physiology. There is a need for a simple, accurate way to know cardiovascular health in the clinic, so as to realize the implementation of convenient and effective early health monitoring for patients with arteriosclerosis. Approach. This study proposes an arteriosclerosis assessment method based on fitting a lognormal function, along with improving a conventional electronic sphygmomanometer. During the deflation phase of blood pressure measurement, the cuff pressure was kept constant (40 mmHg) and an additional 10 s of pulse signal was acquired. To derive the pulse pressure waveforms for a single cycle, the acquired pulse data of 101 cases were preprocessed in this study, including filtering for noise removal, onset point identification, removal of baseline drift, and normalization. In this study, an improved pulse resolution algorithm is proposed for the multimodal problem of the pulse wave, combining waveform matching and threshold setting, and finally obtaining the resolution parameters of the lognormal function with an average error less than 1.5%. Main results. According to the correlation analysis, the resolved parameters A 1, W 2, C 2, W 3, and C 3 were significantly correlated with brachial-ankle Pulse Wave Velocity, and the absolute correlation range in 0.17–0.53, which can be used as a reference index for arteriosclerosis. An arteriosclerosis assessment model was constructed based on the support vector mechanism, and the prediction accuracy was 91.1%. Significance. This study provides a new solution idea for the arteriosclerosis assessment method as well as the pulse resolution algorithm, which has a greater reference value. [ABSTRACT FROM AUTHOR]

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Introduction: Numerous studies have shown that natural mechanical waves have the potential to assess the elastic properties of the myocardium. When the Aortic and Mitral valves close, a shear wave is produced, which provides insights into tissue stiffness. In addition, the Atrial Kick (AK) generates a wave similar to Pulse Waves (PWs) in arteries, providing another way to assess tissue stiffness. However, tissue anisotropy can also impact PW propagation, which is currently underexplored. This study aims to address this gap by investigating the impact of anisotropy on PW propagation in a phantom. Methods: Tube phantoms were created using Polyvinyl Alcohol (PVA). Anisotropy was induced between two sets of two freeze-thaw cycles by stretching and twisting the material. The study first tests and validates the procedure of making helical anisotropic vessel phantoms using the shear wave imaging technique (by estimating the shear wave speed at different probe angles). Using plane wave ultrasound tomography synchronized with a peristaltic pump, 3D high frame rate imaging is performed and used to detect the 3D propagation pattern of PW for each manufactured vessel phantom. Finally, the study attempts to extract the anisotropic coefficient of the vessel using pulse wave propagation angle. Results: The Shear wave imaging results obtained for the isotropic vessel show very similar values for each probe angle. On the contrary, the results obtained for the axial anisotropy vessel show a region with a higher shear wave speed at about 0°, corresponding to the long axis of the vessel. Finally, the results obtained for the helical anisotropy depicted increasing shear wave velocity value from -20° to 20°. For the axial phantom, the wavefront of the pulse wave is perpendicular to the long axis of the vessel, while oriented for the helical anisotropic vessels phantom. The pulse wave propagation angle increased with the number of twists made during the vessel manufacturing. Discussion: The results show that anisotropy can be induced in PVA vessel phantoms by stretching and twisting the material in freeze-thaw cycles. The findings also suggest that vessel anisotropy affects pulse wave propagation angles. Estimating the pulse wave propagation angle may be interesting in characterizing tissue anisotropy in organs where such waves are naturally present. [ABSTRACT FROM AUTHOR]

Individuals with gaming disorder (GD) show emotional dysregulation and autonomic dysfunction in daily life. Although studies have shown that the relaxation method of breathing exercise (BE) improves cardiopulmonary synchrony, the physiological regularity and synchrony of GD remain unclear. In this study, we investigated the regularities of pulse wave (PW), thoracic wall movement (TWM), and abdominal wall movement (AWM) using sample entropy (SE) and assessed the vascular-respiratory and TWM-AWM synchrony using cross-sample entropy (CSE). Twenty individuals with GD and 26 healthy control (HC) individuals participated in baseline, gaming, and recovery stages, both before and after BEs. The results showed that both groups had significantly higher SETWM, SEAWM, and CSETWM-AWM during gaming than baseline. Before BE, CSEPW-TWM and CSEPW-AWM during gaming were considerably higher in the GD group than in the HC group. Compared to before BE, both groups had decreased SETWM and CSETWM-AWM during gaming, particularly in the HC group. Online gaming may induce pulse wave and respiratory irregularities, as well as thoracic–abdominal wall movement asynchrony. Individuals with GD who engage in prolonged gaming periods may exhibit lower vascular–respiratory synchrony compared to the HC group. SETWM, SEAWM, CSETWM-AWM, CSEPW-TWM, and CSEPW-AWM may serve as biomarkers for assessing the risk of GD. BE may improve TWM regularity and vascular–respiratory synchrony during gaming, potentially alleviating addictive behavior. [ABSTRACT FROM AUTHOR]

Motivation is a primary driver of goal-directed behavior. Therefore, the development of cost-effective and easily applicable systems to objectively quantify motivational states is needed. To achieve our goal, this study investigated the feasibility of classifying high- and low-motivation states by machine learning based on a diversity of features obtained by non-contact measurement of physiological responses and facial expression analysis. A random forest classifier with feature selection yielded modest success in the classification of high- and low-motivation states. Further analysis linked high-motivation states to the indices of autonomic nervous system activation reflective of reduced sympathetic activation and stronger, more intense expressions of happiness. The performance of motivational state classification systems should be further improved by incorporating different varieties of non-contact measurements. [ABSTRACT FROM AUTHOR]

Abstract Background Left ventricular enlargement (LVE) is a common manifestation of cardiac remodeling that is closely associated with cardiac dysfunction, heart failure (HF), and arrhythmias. This study aimed to propose a machine learning (ML)-based strategy to identify LVE in HF patients by means of pulse wave signals. Method We constructed two high-quality pulse wave datasets comprising a non-LVE group and an LVE group based on the 264 HF patients. Fourier series calculations were employed to determine if significant frequency differences existed between the two datasets, thereby ensuring their validity. Then, the ML-based identification was undertaken by means of classification and regression models: a weighted random forest model was employed for binary classification of the datasets, and a densely connected convolutional network was utilized to directly estimate the left ventricular diastolic diameter index (LVDdI) through regression. Finally, the accuracy of the two models was validated by comparing their results with clinical measurements, using accuracy and the area under the receiver operating characteristic curve (AUC-ROC) to assess their capability for identifying LVE patients. Results The classification model exhibited superior performance with an accuracy of 0.91 and an AUC-ROC of 0.93. The regression model achieved an accuracy of 0.88 and an AUC-ROC of 0.89, indicating that both models can quickly and accurately identify LVE in HF patients. Conclusion The proposed ML methods are verified to achieve effective classification and regression with good performance for identifying LVE in HF patients based on pulse wave signals. This study thus demonstrates the feasibility and potential of the ML-based strategy for clinical practice while offering an effective and robust tool for diagnosing and intervening ventricular remodeling.

Background: Left ventricular enlargement (LVE) is a common manifestation of cardiac remodeling that is closely associated with cardiac dysfunction, heart failure (HF), and arrhythmias. This study aimed to propose a machine learning (ML)-based strategy to identify LVE in HF patients by means of pulse wave signals. Method: We constructed two high-quality pulse wave datasets comprising a non-LVE group and an LVE group based on the 264 HF patients. Fourier series calculations were employed to determine if significant frequency differences existed between the two datasets, thereby ensuring their validity. Then, the ML-based identification was undertaken by means of classification and regression models: a weighted random forest model was employed for binary classification of the datasets, and a densely connected convolutional network was utilized to directly estimate the left ventricular diastolic diameter index (LVDdI) through regression. Finally, the accuracy of the two models was validated by comparing their results with clinical measurements, using accuracy and the area under the receiver operating characteristic curve (AUC-ROC) to assess their capability for identifying LVE patients. Results: The classification model exhibited superior performance with an accuracy of 0.91 and an AUC-ROC of 0.93. The regression model achieved an accuracy of 0.88 and an AUC-ROC of 0.89, indicating that both models can quickly and accurately identify LVE in HF patients. Conclusion: The proposed ML methods are verified to achieve effective classification and regression with good performance for identifying LVE in HF patients based on pulse wave signals. This study thus demonstrates the feasibility and potential of the ML-based strategy for clinical practice while offering an effective and robust tool for diagnosing and intervening ventricular remodeling. [ABSTRACT FROM AUTHOR]

In this paper, the propagation of pulse wave in a deformable elastic vessel filled with inviscid blood is studied. Starting from the stress–strain relationship of blood vessel wall, momentum conservation equation and the Naiver–Stokes equation, the basic equations describing the wall motion and blood flow are established. By utilizing reductive perturbation technique and long wave approximation theory, the basic equations are simplified into a classical third-order mKdV equation with variable coefficients. In order to describe the propagation characteristics of pulse wave more accurately, a fifth-order variable-coefficient mKdV equation is derived. Then, the tanh-function method is applied to find the localized traveling wave solutions of these equations. Based on these localized traveling wave solutions, we further investigate the effects of higher order terms and initial vessel deformation on the characteristics of pulse wave propagation, blood flow velocity and the volume of blood flow. The results show that the higher-order nonlinear and dispersion terms lead to the distortion of the wave, while the initial deformation of the tube wall will influence the wave amplitude and wave width. [ABSTRACT FROM AUTHOR]

IntroductionNumerous studies have shown that natural mechanical waves have the potential to assess the elastic properties of the myocardium. When the Aortic and Mitral valves close, a shear wave is produced, which provides insights into tissue stiffness. In addition, the Atrial Kick (AK) generates a wave similar to Pulse Waves (PWs) in arteries, providing another way to assess tissue stiffness. However, tissue anisotropy can also impact PW propagation, which is currently underexplored. This study aims to address this gap by investigating the impact of anisotropy on PW propagation in a phantom.MethodsTube phantoms were created using Polyvinyl Alcohol (PVA). Anisotropy was induced between two sets of two freeze-thaw cycles by stretching and twisting the material. The study first tests and validates the procedure of making helical anisotropic vessel phantoms using the shear wave imaging technique (by estimating the shear wave speed at different probe angles). Using plane wave ultrasound tomography synchronized with a peristaltic pump, 3D high frame rate imaging is performed and used to detect the 3D propagation pattern of PW for each manufactured vessel phantom. Finally, the study attempts to extract the anisotropic coefficient of the vessel using pulse wave propagation angle.ResultsThe Shear wave imaging results obtained for the isotropic vessel show very similar values for each probe angle. On the contrary, the results obtained for the axial anisotropy vessel show a region with a higher shear wave speed at about 0°, corresponding to the long axis of the vessel. Finally, the results obtained for the helical anisotropy depicted increasing shear wave velocity value from −20° to 20°. For the axial phantom, the wavefront of the pulse wave is perpendicular to the long axis of the vessel, while oriented for the helical anisotropic vessels phantom. The pulse wave propagation angle increased with the number of twists made during the vessel manufacturing.DiscussionThe results show that anisotropy can be induced in PVA vessel phantoms by stretching and twisting the material in freeze-thaw cycles. The findings also suggest that vessel anisotropy affects pulse wave propagation angles. Estimating the pulse wave propagation angle may be interesting in characterizing tissue anisotropy in organs where such waves are naturally present.

In this work, a bidirectional fluid‒structure coupling finite element analysis model of the abdominal aorta was established, with the various vascular elastic modulus as the main parameters for atherosclerosis, taking into consideration blood's dynamic viscosity and compressibility. Pressure and velocity pulse-wave propagation were investigated through the application of a full-coupling analysis algorithm. The effect of atherosclerosis degree on the propagation characteristics of pulse waves in the bifurcated abdominal aorta was quantitatively analyzed. Arterial bifurcation can cause substantial attenuation on the peak of pressure pulse waveform and an increase in wave velocity during the cardiac cycle. The elastic modulus and bifurcation properties of the arterial wall directly affected the peak value and wave propagation velocity of the pressure pulse wave. The preliminary results of this work will be crucial in guiding the evolution of the pressure pulse wave and the initial diagnosis of atherosclerotic disease through the waveform.

Objective: To investigate the correlation between tongue and pulse indicators and the outcome of live birth in patients undergoing frozen-thawed embryo transfer (FET), as well as the association between these indicators and patients’ endocrine parameters. Methods: This study was conducted at Reproductive Medicine Center, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China, from March 8, 2021 to January 5, 2022. Patients undergoing FET were divided into live birth and non-live birth groups according to their live birth outcome. The differences between the endocrine parameters [basic follicle stimulating hormone (b FSH), basic luteinizing hormone (b LH), basic estradiol (b E2), basic progesterone (b P), basal endometrial thickness, follicle stimulating hormone (FSH) on endometrial transition day, luteinizing hormone (LH) on endometrial transition day, estradiol (E2) on endometrial transition day, progesterone (P) on endometrial transition day, and endometrial thickness on endometrial transition day] and the tongue and pulse indicators [tongue body (TB)-L, TB-a, TB-b, tongue coating (TC)-L, TC-a, TC-b, perAll, perPart, h1, h4, h5, t1, h1/t1, and h4/h1] of patients in the two groups were analyzed, with the correlation between these variables analyzed as well using Spearman’s correlation coefficient. Multivariate logistic regression was employed to identify the influential factors in the live birth prediction models across various datasets, including Model 1 consisting of endocrine indicators only, Model 2 solely consisting of tongue and pulse indicators, and Model 3 consisting of both tongue, pulse, and endocrine indicators, as well as to evaluate efficacy of the models derived from different datasets. Results: This study included 78 patients in live birth group and 144 patients in non-live birth group. Compared with non-live birth group, live birth group exhibited higher levels of TB-L (P = 0.01) and TB-a (P = 0.04), while demonstrated lower levels of b FSH (P = 0.01), perAll (P = 0.04), and h4/h1 (P = 0.03). The Spearman’s correlation coefficient analysis revealed statistically significant correlation (P < 0.05) between TB-L, TB-b, TC-L, TC-b, perAll, perPart, h4, h5, t1, h1/t1 and b FSH, b LH, basal endometrial thickness, LH on endometrial transition day, E2 on endometrial transition day, P on endometrial transition day, and endometrial thickness on endometrial transition day in live birth group. The multivariate logistic regression analysis showed that the prediction Model 3 for live birth outcome [area under the curve (AUC): 0.917, 95% confidence interval (CI): 0.863 − 0.971, P < 0.001] surpassed the Model 1 (AUC: 0.698, 95% CI: 0.593 − 0.803, P = 0.001), or the Model 2 (AUC: 0.790, 95% CI: 0.699 − 0.880, P < 0.001). The regression equations for the live birth outcomes, integrating tongue and pulse indicators with endocrine parameters, included the following measures: FSH on endometrial transition day [odds ratio (OR): 0.523, P = 0.025], LH on endometrial transition day (OR: 1.277, P = 0.029), TB-L (OR: 2.401, P = 0.001), perPart (OR: 1.018, P = 0.013), h1 (OR: 0.065, P = 0.021), t1 (OR: 4.354, P = 0.024), and h4/h1 (OR: 0.018, P = 0.016). Conclusion: In infertility patients undergoing FET, there exists a correlation between tongue and pulse indicators and endocrine parameters. The corporation of tongue and pulse indicators significantly improved the predictive capability of the model for live birth outcomes. Specifically, tongue and pulse indicators such as TB-L, perPart, h1, t1, and h4/h1 exhibited a discernible correlation with the ultimate live birth outcomes.

Abstract Pulse wave, as a message carrier in the cardiovascular system (CVS), enables inferring CVS conditions while diagnosing cardiovascular diseases (CVDs). Heart failure (HF) is a major CVD, typically requiring expensive and time-consuming treatments for health monitoring and disease deterioration; it would be an effective and patient-friendly tool to facilitate rapid and precise non-invasive evaluation of the heart’s blood-supply capability by means of powerful feature-abstraction capability of machine learning (ML) based on pulse wave, which remains untouched yet. Here we present an ML-based methodology, which is verified to accurately evaluate the blood-supply capability of patients with HF based on clinical data of 237 patients, enabling fast prediction of five representative cardiovascular function parameters comprising left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter (LVDd), left ventricular end-systolic diameter (LVDs), left atrial dimension (LAD), and peripheral oxygen saturation (SpO2). Two ML networks were employed and optimized based on high-quality pulse wave datasets, and they were validated consistently through statistical analysis based on the summary independent-samples t-test (p > 0.05), the Bland–Altman analysis with clinical measurements, and the error-function analysis. It is proven that evaluation of the SpO2, LAD, and LVDd performance can be achieved with the maximum error

Objective To investigate the correlation between tongue and pulse indicators and the outcome of live birth in patients undergoing frozen-thawed embryo transfer (FET), as well as the association between these indicators and patients' endocrine parameters. Methods This study was conducted at Reproductive Medicine Center, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China, from March 8, 2021 to January 5, 2022. Patients undergoing FET were divided into live birth and non-live birth groups according to their live birth outcome. The differences between the endocrine parameters [basic follicle stimulating hormone (b FSH), basic luteinizing hormone (b LH), basic estradiol (b E2), basic progesterone (b P), basal endometrial thickness, follicle stimulating hormone (FSH) on endometrial transition day, luteinizing hormone (LH) on endometrial transition day, estradiol (E2) on endometrial transition day, progesterone (P) on endometrial transition day, and endometrial thickness on endometrial transition day] and the tongue and pulse indicators [tongue body (TB)-L, TB-a, TB-b, tongue coating (TC)-L, TC-a, TC-b, perAll, perPart, h1, h4, h5, t1, h1/t1, and h4/h1] of patients in the two groups were analyzed, with the correlation between these variables analyzed as well using Spearman's correlation coefficient. Multivariate logistic regression was employed to identify the influential factors in the live birth prediction models across various datasets, including Model 1 consisting of endocrine indicators only, Model 2 solely consisting of tongue and pulse indicators, and Model 3 consisting of both tongue, pulse, and endocrine indicators, as well as to evaluate efficacy of the models derived from different datasets. Results This study included 78 patients in live birth group and 144 patients in non-live birth group. Compared with non-live birth group, live birth group exhibited higher levels of TB-L (P = 0.01) and TB-a (P = 0.04), while demonstrated lower levels of b FSH (P = 0.01), perAll (P = 0.04), and h4/h1 (P = 0.03). The Spearman's correlation coefficient analysis revealed statistically significant correlation (P < 0.05) between TB-L, TB-b, TC-L, TC-b, perAll, perPart, h4, h5, t1, h1/t1 and b FSH, b LH, basal endometrial thickness, LH on endometrial transition day, E2 on endometrial transition day, P on endometrial transition day, and endometrial thickness on endometrial transition day in live birth group. The multivariate logistic regression analysis showed that the prediction Model 3 for live birth outcome [area under the curve (AUC): 0.917, 95% confidence interval (CI): 0.863 - 0.971, P < 0.001] surpassed the Model 1 (AUC: 0.698, 95% CI: 0.593 - 0.803, P = 0.001), or the Model 2 (AUC: 0.790, 95% CI: 0.699 - 0.880, P < 0.001). The regression equations for the live birth outcomes, integrating tongue and pulse indicators with endocrine parameters, included the following measures: FSH on endometrial transition day [odds ratio (OR): 0.523, P = 0.025], LH on endometrial transition day (OR: 1.277, P = 0.029), TB-L (OR: 2.401, P = 0.001), perPart (OR: 1.018, P = 0.013), h1 (OR: 0.065, P = 0.021), t1 (OR: 4.354, P = 0.024), and h4/h1 (OR: 0.018, P = 0.016). Conclusion In infertility patients undergoing FET, there exists a correlation between tongue and pulse indicators and endocrine parameters. The corporation of tongue and pulse indicators significantly improved the predictive capability of the model for live birth outcomes. Specifically, tongue and pulse indicators such as TB-L, perPart, h1, t1, and h4/h1 exhibited a discernible correlation with the ultimate live birth outcomes. [ABSTRACT FROM AUTHOR]

Objective: To develop a multimodal deep-learning model for classifying Chinese medicine constitution, i.e., the balanced and unbalanced constitutions, based on inspection of tongue and face images, pulse waves from palpation, and health information from a total of 540 subjects. Methods: This study data consisted of tongue and face images, pulse waves obtained by palpation, and health information, including personal information, life habits, medical history, and current symptoms, from 540 subjects (202 males and 338 females). Convolutional neural networks, recurrent neural networks, and fully connected neural networks were used to extract deep features from the data. Feature fusion and decision fusion models were constructed for the multimodal data. Results: The optimal models for tongue and face images, pulse waves and health information were ResNet18, Gate Recurrent Unit, and entity embedding, respectively. Feature fusion was superior to decision fusion. The multimodal analysis revealed that multimodal data compensated for the loss of information from a single mode, resulting in improved classification performance. Conclusions: Multimodal data fusion can supplement single model information and improve classification performance. Our research underscores the effectiveness of multimodal deep learning technology to identify body constitution for modernizing and improving the intelligent application of Chinese medicine. [ABSTRACT FROM AUTHOR]

Individuals with gaming disorder (GD) show emotional dysregulation and autonomic dysfunction in daily life. Although studies have shown that the relaxation method of breathing exercise (BE) improves cardiopulmonary synchrony, the physiological regularity and synchrony of GD remain unclear. In this study, we investigated the regularities of pulse wave (PW), thoracic wall movement (TWM), and abdominal wall movement (AWM) using sample entropy (SE) and assessed the vascular-respiratory and TWM-AWM synchrony using cross-sample entropy (CSE). Twenty individuals with GD and 26 healthy control (HC) individuals participated in baseline, gaming, and recovery stages, both before and after BEs. The results showed that both groups had significantly higher SETWM, SEAWM, and CSETWM-AWM during gaming than baseline. Before BE, CSEPW-TWM and CSEPW-AWM during gaming were considerably higher in the GD group than in the HC group. Compared to before BE, both groups had decreased SETWM and CSETWM-AWM during gaming, particularly in the HC group. Online gaming may induce pulse wave and respiratory irregularities, as well as thoracic–abdominal wall movement asynchrony. Individuals with GD who engage in prolonged gaming periods may exhibit lower vascular–respiratory synchrony compared to the HC group. SETWM, SEAWM, CSETWM-AWM, CSEPW-TWM, and CSEPW-AWM may serve as biomarkers for assessing the risk of GD. BE may improve TWM regularity and vascular–respiratory synchrony during gaming, potentially alleviating addictive behavior.

Motivation is a primary driver of goal-directed behavior. Therefore, the development of cost-effective and easily applicable systems to objectively quantify motivational states is needed. To achieve our goal, this study investigated the feasibility of classifying high- and low-motivation states by machine learning based on a diversity of features obtained by non-contact measurement of physiological responses and facial expression analysis. A random forest classifier with feature selection yielded modest success in the classification of high- and low-motivation states. Further analysis linked high-motivation states to the indices of autonomic nervous system activation reflective of reduced sympathetic activation and stronger, more intense expressions of happiness. The performance of motivational state classification systems should be further improved by incorporating different varieties of non-contact measurements.

Sensor data has been used in social security and welfare infrastructures such as insurance and medical care to provide personalized products and services; there is a risk that attackers can alter sensor data to obtain unfair benefits. We consider that one of the attack methods to modify sensor data is to attack the wearer's body to modify biometric information. In this study, we propose a noninvasive attack method to modify the sensor value of a photoplethysmogram. The proposed method can disappear pulse wave peaks by pressurizing the upper arm with air pressure to control blood volume. Seven subjects experiencing a rest environment and five subjects experiencing an after-exercise environment wore five different models of smartwatches, and three pressure patterns were performed. It was confirmed in both situations that the displayed heart rate decreased from the true heart rate. [ABSTRACT FROM AUTHOR]

Contactless continuous blood pressure (BP) monitoring is of great significance for daily healthcare. Radar-based continuous monitoring methods typically extract time-domain features manually such as pulse transit time (PTT) to calculate the BP. However, breathing and slight body movements usually distort the features extracted from pulse-wave signals, especially in long-term continuous monitoring, and manually extracted features may have limited performance for BP estimation. This article proposes a Transformer network for Radar-based Contactless Continuous Blood Pressure monitoring (TRCCBP). A heartbeat signal-guided single-beat pulse wave extraction method is designed to obtain pure pulse-wave signals. A transformer network-based blood pressure estimation network is proposed to estimate BP, which utilizes convolutional layers with different scales, a gated recurrent unit (GRU) to capture time-dependence in continuous radar signal and multi-head attention modules to capture deep temporal domain characteristics. A radar signal dataset captured in an indoor environment containing 31 persons and a real medical situation containing five persons is set up to evaluate the performance of TRCCBP. Compared with the state-of-the-art method, the average accuracy of diastolic blood pressure (DBP) and systolic blood pressure (SBP) is 4.49 mmHg and 4.73 mmHg, improved by 12.36 mmHg and 8.80 mmHg, respectively. The proposed TRCCBP source codes and radar signal dataset have been made open-source online for further research. [ABSTRACT FROM AUTHOR]

Vascular aging (VA) involves structural and functional changes in blood vessels that contribute to cardiovascular disease. Several noninvasive pulse wave (PW) indices have been proposed to assess the arterial stiffness component of VA in the clinic and daily life. This study investigated 19 of these indices, identified in recent review articles on VA, by using a database comprising 3,837 virtual healthy subjects aged 25-75 yr, each with unique PW signals simulated under various levels of artificial noise to mimic real measurement errors. For each subject, VA indices were calculated from filtered PW signals and compared with the precise theoretical value of aortic Young's modulus (EAo). In silico PW indices showed age-related changes that align with in vivo population studies. The cardio-ankle vascular index (CAVI) and all pulse wave velocity (PWV) indices showed strong linear correlations with EAo (Pearson's rp > 0.95). Carotid distensibility showed a strong negative nonlinear correlation (Spearman's rs < -0.99). CAVI and distensibility exhibited greater resilience to noise compared withPWVindices.Blood pressure-related indices and photoplethysmography (PPG)-based indices showed weaker correlations with EAo (rp and rs < 0.89, ΙrpΙ and ΙrsΙ < 0.84, respectively). Overall, blood pressure-related indices were confounded by more cardiovascular properties (heart rate, stroke volume, duration of systole, large artery diameter, and/or peripheral vascular resistance) compared with other studied indices, and PPG-based indices were most affected by noise. In conclusion, carotid- femoral PWV, CAVI and carotid distensibility emerged as the superior clinical VA indicators, with a strong EAo correlation and noise resilience. PPG-based indices showed potential for dailyVAmonitoring underminimized noise disturbances. [ABSTRACT FROM AUTHOR]

A theoretical analysis of the literature data on the pulse-wave propagation velocity using a combination of electrocardiographic and photoplethysmographic signals was carried out. Its diagnostic value in medical practice lies in its use as a noninvasive method for measuring blood pressure, as well as for determining arterial stiffness. Some studies have shown a relationship between the velocity of pulse-wave propagation and risk factors for cardiovascular diseases. Pulse-wave propagation velocity is an early marker and predictor of cardiovascular risk in both people with normal blood pressure and people with hypertension; it is used as the "gold standard" in determining arterial stiffness; however, the issue of accuracy and automation of measuring the pulse-wave propagation velocity remains unresolved. [ABSTRACT FROM AUTHOR]

Pulse wave measurement is a highly prominent technique used in biomedical diagnostics. The development of novel cost-effective pulse wave sensors will pave the way to more advanced healthcare technologies. This work reports on a pulse wave optical fiber sensor interrogated by a smartphone. The sensor performance was tested in terms of signal to noise ratio, repeatability of demodulated signal and suitability of demodulated signals for the extraction of information about direct and reflected waves. The analysis showed that the observed fluctuations of signal parameters are caused by variability of the state of the cardiovascular system and not by the system noise. [ABSTRACT FROM AUTHOR]

Flow-mediated skin fluorescence (FMSF) at 460 nm is a non-invasive method for assessing dynamic changes in the reduced form of nicotinamide adenine dinucleotide (NADH) and microcirculation in forearm skin under varying conditions of tissue perfusion. Typically, fluorescence increases during ischaemia, but atypical cases show a temporary signal decrease instead of a constant increase. This study aimed to explore the clinical implications of atypical FMSF patterns in patients with newly diagnosed untreated hypertension. NADH fluorescence and pulse wave analysis were performed on 65 patients. Differences in peripheral and arterial pulse pressure profiles were examined based on FMSF curve courses. Patients with atypical curve courses had significantly (p < 0.05 or lower for all) higher heart rate, peripheral and central diastolic pressure, tension time index, central rate pressure product, shorter diastole duration, and reservoir pressure–time integral. Hypertensive patients with atypical FMSF signals had less advantageous blood pressure profiles. Although the underlying factors causing these symptoms are unknown, the atypical FMSF pattern may reflect increased sympathetic stimulation and vascular resistance. The visual assessment of the FMSF curve may have important clinical implications that deserve further investigation. [ABSTRACT FROM AUTHOR]

Differential privacy is a powerful technique that protects the privacy of individuals in a dataset by adding controlled randomness. With the increasing developments in smart sensors, the use of various biometric database is expanding. If privacy protections coexist with advanced use of the biometric database, wider utilization is expected. One of the promising approaches is to apply differential privacy to biometric information, which is attracting attention in use cases such as Google. By adding Laplace noise to biometric information, differential privacy can be added. Our aim is to focus on peak to peak interval of electrocardiogram. It is useful bio-information because it is possible to know not only heart disease but also various physical conditions such as exercise amount, activity amount, fatigue, sleep based on it. In this study, we demonstrated that differential privacy can be applied to obtain the sleep apnea index from PPIs with Laplace noise. The observed correlations were 0.96 to 0.99 for the corresponding PPIs with Laplace noise. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular diseases (CVDs), being the culprit for one-third of deaths globally, constitute a challenge for biomedical instrumentation development, especially for early disease detection. Pulsating arterial blood flow, providing access to cardiac-related parameters, involves the whole body. Unobtrusive and continuous acquisition of electrical bioimpedance (EBI) and photoplethysmography (PPG) constitute important techniques for monitoring the peripheral arteries, requiring novel approaches and clever means. Methods: In this work, five peripheral arteries were selected for EBI and PPG signal acquisition. The acquisition sites were evaluated based on the signal morphological parameters. A small-data-based deep learning model, which increases the data by dividing them into cardiac periods, was proposed to evaluate the continuity of the signals. Results: The highest sensitivity of EBI was gained for the carotid artery (0.86%), three times higher than that for the next best, the posterior tibial artery (0.27%). The excitation signal parameters affect the measured EBI, confirming the suitability of classical 100 kHz frequency (average probability of 52.35%). The continuity evaluation of the EBI signals confirmed the advantage of the carotid artery (59.4%), while the posterior tibial artery (49.26%) surpasses the radial artery (48.17%). The PPG signal, conversely, commends the location of the posterior tibial artery (97.87%). Conclusions: The peripheral arteries are highly suitable for non-invasive EBI and PPG signal acquisition. The posterior tibial artery constitutes a candidate for the joint acquisition of EBI and PPG signals in sensor-fusion-based wearable devices—an important finding of this research. [ABSTRACT FROM AUTHOR]

Photoplethysmography (PPG) is an established technology for detecting pulse rate and pulse wave irregularities. However, whether temporal variations in pulse wave amplitudes, reflecting a combination of acute hemodynamic or autonomic responses to changes in overall vascular function, carry prognostic information remains unclear. To quantify nocturnal temporal pulse wave amplitude (PWA) attenuations and evaluate its association with long-term cardiovascular (CV) events in a large, racially diverse sample of men and women. Temporal PWA attenuations were determined based on the slopes between the upper and lower envelopes of PPGs derived from overnight polysomnography of 1957 participants (899 men, 1058 women, mean age 68.2 ± 9.1 years) of the Multi-Ethnic Study of Atherosclerosis. The nocturnal PWA attenuation index was defined as the cumulative duration of all PWA attenuation events relative to total sleep duration. Nocturnal PWA attenuation index was greater in men than in women by almost 13% (16.3 ± 8.9% vs. 14.4 ± 7.9%, p < 0.001). The nocturnal PWA attenuation index was highest in Chinese-American participants (17.9 ± 9.2%) and lowest in African-Americans (13.5 ± 8.1%). During a median follow-up of 4.9 years, 94 CV events occurred. In multivariable Cox proportional hazard analysis adjusted for typical confounders, the nocturnal PWA attenuation index <15.2% was associated with CV events (HR = 1.58 [1.02–2.45], p = 0.042). Nocturnal PWA attenuation index is inversely associated with the risk of CV events, particularly in men and African-Americans. The PPG-derived nocturnal PWA attenuation index could be simply obtained from smart wearable consumer devices and may provide a low-cost, accessible and scalable CV risk marker. • Temporal pulse wave amplitude attenuations in photoplethysmography (PPG) waveforms carry prognostic information. • Nocturnal pulse wave amplitude attenuations are inversely associated with cardiovadvular events. • PPG-derived nocturnal pulse wave amplitude attenuations could be simply obtained from smart wearable consumer devices. • Pulse wave amplitude attenuations may provide a low cost, accessible and scalable cardiovascular risk marker in the future. [ABSTRACT FROM AUTHOR]

Arterial pulse waves (PWs) such as blood pressure and photoplethysmogram (PPG) signals contain a wealth of information on the cardiovascular (CV) system that can be exploited to assess vascular age and identify individuals at elevated CV risk. We review the possibilities, limitations, complementarity, and differences of reduced-order, biophysical models of arterial PW propagation, as well as theoretical and empirical methods for analyzing PW signals and extracting clinically relevant information for vascular age assessment. We provide detailed mathematical derivations of these models and theoretical methods, showing how they are related to each other. Finally, we outline directions for future research to realize the potential of modeling and analysis of PW signals for accurate assessment of vascular age in both the clinic and in daily life. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background and Objectives: Impedance plethysmography and Doppler ultrasound, in most cases, are considered as independent methods for analyzing hemodynamics. This work shows the presence of similarities in the shape of pulse waves recorded by the two indicated methods at rest and during exercise tests. The dynamics of the volume and velocity of blood flow in the radial artery was studied at rest, during an occlusive test and a test with a deep breath. Materials and Methods: The method of impedance rheography was used to determine the dynamics of the blood volume in the artery, and the method of ultrasound dopplerography was used to determine the linear velocity of arterial blood flow. The equation that considers the irregular distribution of erythrocytes velocity in the cross-section of a blood vessel has been obtained for a correct quantitative description of the dynamics of the volumetric blood flow velocity. Results: It has been determined that both the deep breath test and the occlusive test lead to vasodilation of the radial artery. In this case, the test with deep breath causes the appearance of an additional peak in the diastole which agrees in time with the negative (retrograde) diastolic peak of the blood flow velocity. Comparative analysis of the integrated velocity and volume waves demonstrates phase matching and a linear dependence of the shape of these waves at rest which are disrupted during the deep breath test. Conclusions: The proposed equation for calculating volumetric blood flow enables one to study the processes of autoregulation of blood flow in vessels by controlling the balance of changes in blood volume and velocity by the methods of impedance rheography and ultrasound dopplerography and can potentially form the basis for the development of appropriate methods of functional diagnostics.

The extensive research on wearable devices has led to devices with various shapes and mounting locations. Wearable devices are often used to record the user’s biometric information, and methods have been proposed to detect physical abnormalities from the acquired data. Among various kinds of biometric data, pulse data has been used in methods such as heart rate monitoring and emotion recognition. The most common type of pulse sensor uses photoplethysmography (PPG), which irradiates a green LED on the skin and measures pulse data from changes in the light reflected from blood vessels. PPG sensors have been implemented in commercially available wearable devices such as smartwatches. However, a PPG sensor requires blood flow for data acquisition, and when a smartwatch is worn on an artificial body part such as a prosthetic hand or a robotic arm, data cannot be acquired because there is no blood flow. In this study, we propose a method that enables a PPG sensor to measure arbitrary pulse data by using a display. If this method is successful, it will enable pulse data measured at the junction of a living limb and an artificial limb to be input to the display; then, a smartwatch attached to the artificial limb will read the same pulse data. In this paper, we focus on the heart rate and report the results of an experiment in which a target heart rate was input and the display was controlled accordingly to determine whether the target heart rate could be obtained by a smartwatch. We implemented a display drawing program and conducted the evaluation with five kinds of smartwatches and four kinds of displays. The results showed that the error between the target heart rate and the heart rate acquired by the smartwatch was within 3 beats per minute (bpm) in many cases when the target heart rate was set to 60–100 bpm. When the target heart rate was set to 40–55 and 105–200 bpm, the heart rate could also be input into the smartwatch with a small error under certain conditions. Moreover, when generated PPG data was imported into heart rate variability (HRV) analysis software, it was recognized as a pulse wave in the same way as real PPG data obtained from a person. We compared the heart rate, RR interval, and SDNN calculated from the real and generated PPG data, and we confirmed that the proposed method could generate stable PPG data. On the other hand, when the waveforms were compared, the generated PPG waveform differed greatly from the real PPG waveform, which indicated that the software could calculate the heart rate, RR interval, SDNN, and LF/HF ratio regardless of the waveform. This result suggests that the calculation of these parameters without verifying the waveform would be vulnerable to an attack with fake PPG data.

Micro-electro-mechanical system (MEMS) pressure sensors play a significant role in pulse wave acquisition. However, existing MEMS pulse pressure sensors bound with a flexible substrate by gold wire are vulnerable to crush fractures, leading to sensor failure. Additionally, establishing an effective mapping between the array sensor signal and pulse width remains a challenge. To solve the above problems, we propose a 24-channel pulse signal acquisition system based on a novel MEMS pressure sensor with a through-silicon-via (TSV) structure, which connects directly to a flexible substrate without gold wire bonding. Firstly, based on the MEMS sensor, we designed a 24-channel pressure sensor flexible array to collect the pulse waves and static pressure. Secondly, we developed a customized pulse preprocessing chip to process the signals. Finally, we built an algorithm to reconstruct the three-dimensional pulse wave from the array signal and calculate the pulse width. The experiments verify the high sensitivity and effectiveness of the sensor array. In particular, the measurement results of pulse width are highly positively correlated with those obtained via infrared images. The small-size sensor and custom-designed acquisition chip meet the needs of wearability and portability, meaning that it has significant research value and commercial prospects. [ABSTRACT FROM AUTHOR]

It is unknown whether ultrasound-induced blood–brain barrier (BBB) disruption can promote epileptogenesis and how BBB integrity changes over time after sonication. To gain more insight into the safety profile of ultrasound (US)-induced BBB opening, we determined BBB permeability as well as histological modifications in C57BL/6 adult control mice and in the kainate (KA) model for mesial temporal lobe epilepsy in mice after sonication with low-intensity pulsed ultrasound (LIPU). Microglial and astroglial changes in ipsilateral hippocampus were examined at different time points following BBB disruption by respectively analyzing Iba1 and glial fibrillary acidic protein immunoreactivity. Using intracerebral EEG recordings, we further studied the possible electrophysiological repercussions of a repeated disrupted BBB for seizure generation in nine non-epileptic mice. LIPU-induced BBB opening led to transient albumin extravasation and reversible mild astrogliosis, but not to microglial activation in the hippocampus of non-epileptic mice. In KA mice, the transient albumin extravasation into the hippocampus mediated by LIPU-induced BBB opening did not aggravate inflammatory processes and histologic changes that characterize the hippocampal sclerosis. Three LIPU-induced BBB opening did not induce epileptogenicity in non-epileptic mice implanted with depth EEG electrodes. Our experiments in mice provide persuasive evidence of the safety of LIPU-induced BBB opening as a therapeutic modality for neurological diseases. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Soils contaminated with hydrocarbons is a repetitive site condition that may occur at locations adjacent to underground fuel tanks and other facilities. The main purpose of this study is to characterize and assess possibilities of reusing contaminated soil in useful applications such as roadway embankments. Accordingly, controlled amounts of diesel were mixed with clays and sands using percentages ranging from 0 to 13.5%. Unconfined compression, direct shear, California bearing ratio, and other tests were conducted on the diesel-mixed soil, along with the ultrasonic test. The ultrasonic test provided measures for the pulse wave velocity and received frequency, which was correlated with other soil parameters such as elastic modulus, resilient modulus, and California bearing ratio. From the main outcomes, it was found that small amounts of diesel can help improved soil shear strength. For instance, adding 5% diesel increased the soil cohesion by 63% and the unconfined compressive strength from 0.14 to 0.21 N/mm2. In sands, the California bearing ratio increased by almost double by adding 5% diesel, which is essential for roadway embankments especially in remote locations. Damping coefficient of soil was determined at different levels of diesel using the ultrasonic wave decay envelops. The ultrasonic waves were analyzed using fast Fourier transform to determine useful correlations between the wave frequency and the diesel content starting from 0 to 13.5%, and it was realized that an uncomplicated ultrasonic test can be acceptably used to assess static and dynamic properties of clean as well as contaminated soils. [ABSTRACT FROM AUTHOR]

Adequate personalized numerical simulation of hemodynamic indices in coronary arteries requires accurate identification of the key parameters. Elastic properties of coronary vessels produce a significant effect on the accuracy of simulations. Direct measurements of the elasticity of coronary vessels are not available in the general clinic. Pulse wave velocity (AoPWV) in the aorta correlates with aortic and coronary elasticity. In this work, we present a neural network approach for estimating AoPWV. Because of the limited number of clinical cases, we used a synthetic AoPWV database of virtual subjects to train the network. We use an additional set of AoPWV data collected from real patients to test the developed algorithm. The developed neural network predicts brachial–ankle AoPWV with a root-mean-square error (RMSE) of 1.3 m/s and a percentage error of 16 % . We demonstrate the relevance of a new technique by comparing invasively measured fractional flow reserve (FFR) with simulated values using the patient data with constant ( 7.5 m/s) and predicted AoPWV. We conclude that patient-specific identification of AoPWV via the developed neural network improves the estimation of FFR from 4.4 % to 3.8 % on average, with a maximum difference of 2.8 % in a particular case. Furthermore, we also numerically investigate the sensitivity of the most useful hemodynamic indices, including FFR, coronary flow reserve (CFR) and instantaneous wave-free ratio (iFR) to AoPWV using the patient-specific data. We observe a substantial variability of all considered indices for AoPWV below 10 m/s and weak variation of AoPWV above 15 m/s. We conclude that the hemodynamic significance of coronary stenosis is higher for the patients with AoPWV in the range from 10 to 15 m/s. The advantages of our approach are the use of a limited set of easily measured input parameters (age, stroke volume, heart rate, systolic, diastolic and mean arterial pressures) and the usage of a model-generated (synthetic) dataset to train and test machine learning methods for predicting hemodynamic indices. The application of our approach in clinical practice saves time, workforce and funds. [ABSTRACT FROM AUTHOR]

Nowadays, with the increase in the rhythm of life, the relevance of using express diagnostics methods for human health state estimation has significantly increased. We present a new express diagnostics method based on non-invasive measurements (the pulse wave shape, heart rate, blood pressure, and oxygen saturation of blood vessels and tissues). A feature of these measurements is that they can be carried out both in the hospital and at home. The new compact and portable optical hardware–software complex has been developed to measure tissue oxygen saturation. This complex makes it possible to reduce the measurement time from 60 min to 7–8 min, which reduces the likelihood of artifacts in the measurement process and increases its reliability. A new technique has been developed to carry out these measurements. A new optical sensor based on a line of charge-coupled devices has been developed to register a pulse wave in the far peripheral zone. The developed new technique for processing the pulse waveform and data on the oxygen saturation of hemoglobin in the blood and tissues allows a person to obtain additional information about their state of health independently. It will help to make conclusions about taking the necessary measures. This additional information allows the attending physician to provide more effective control over the course of treatment of the patient at any time since the methods of express diagnostics proposed by us have no restrictions on the number of applications. The functional state of more than 300 patients was studied. The results of various measurements are presented. [ABSTRACT FROM AUTHOR]

BACKGROUND: The wrist pulse wave under the optimal pulse pressure plays an important role in detecting human body's physiological and pathological information. Wavelet threshold filtering is a common method for pulse wave de-noising. However, traditional filtering methods cannot smoothen the whole pulse wave well and highlight the details. OBJECTIVE: In view of this, an attempt is made in this paper to propose the pulse wave denoising algorithm for pulse wave under optimal pulse pressure according to the translation invariant wavelet transform (TIWT) and the new threshold function. METHODS: Firstly, by using hyperbolic tangent curve and combining the advantages of soft threshold function and hard threshold function, the new threshold function is derived. Secondly, based on the TIWT, pseudo-Gibbs phenomenon gets suppressed. RESULTS: The experiments show that in comparison to the traditional wavelet filtering algorithm, the novel algorithm can better maintain the pulse wave geometric characteristics and has a higher signal to noise ratio (SNR). CONCLUSION: The TIWT with improved new threshold compensates the shortcomings of the traditional wavelet threshold denoising methods in a better way. It lays a foundation for extracting time-domain characteristics of pulse wave. [ABSTRACT FROM AUTHOR]

The electromyogram (EMG) is a waveform representation of the action potential generated by muscle cells using electrodes. EMG acquired using surface electrodes is called surface EMG (sEMG), and it is the acquisition of muscle action potentials transmitted by volume conduction from the skin. Surface electrodes require disposable conductive gel or adhesive tape to be attached to the skin, which is costly to run, and the tape is hard on the skin when it is removed. Muscle activity can be evaluated by acquiring muscle potentials and analyzing quantitative, temporal, and frequency factors. It is also possible to evaluate muscle fatigue because the frequency of the EMG becomes lower as the muscle becomes fatigued. Research on human activity recognition from EMG signals has been actively conducted and applied to systems that support arm and hand functions. This paper proposes a method for recognizing the muscle activity state of the arm using pulse wave data (PPG: Photoplethysmography) and a method for estimating EMG using pulse wave data. This paper assumes that the PPG sensor is worn on the user's wrist to measure the heart rate. The user also attaches an elastic band to the upper arm, and when the user exerts a force on the arm, the muscles of the upper arm contract. The arteries are then constricted, and the pulse wave measured at the wrist becomes weak. From the change in the pulse wave, the muscle activity of the arm can be recognized and the number of action potentials of the muscle can be estimated. From the evaluation experiment with five subjects, three types of muscle activity were recognized with 80+%, and EMG was estimated with approximately 20% error rate. [ABSTRACT FROM AUTHOR]

Pulse wave carries abundant physiological and pathological data about the human body. Understanding the effect of blood compressibility on the transmission qualities of pulse waves is necessary to detect, cure, and prevent cardiovascular problems. In this research, the fluid-solid coupling finite element analysis model of artery system was developed using COMSOL MultiPhysics software. The influence of blood compressibility on pulse wave propagation properties in the brachial artery was investigated using the blood volume compression modulus. Results show that the influence of blood compressibility on pulse wave propagation is relatively weak and may be ignored when evaluating the compressibility of blood and that the volume compression modulus swings 2GPa over and beyond the typical range of the human body. The influence of blood compressibility on the peak value of pressure pulse wave is minor. The attenuation amplitude of the peak value of a pulse wave is bigger for the case with compressible blood than those of case with incompressible blood, and the difference between compressible and incompressible models is less than 1%. But blood compressibility has a clear impact on pulse wave velocity. It is found that the pulse wave velocity is greater than that of blood flow velocity. These findings will be valuable to investigate deeply for evaluating pressure and velocity pulse waves, as well as in providing a waveform-based preliminary diagnosis of symptoms in human body. [ABSTRACT FROM AUTHOR]

Contactless continuous blood pressure (BP) monitoring is of great significance for daily healthcare. Radar-based continuous monitoring methods typically extract time-domain features manually such as pulse transit time (PTT) to calculate the BP. However, breathing and slight body movements usually distort the features extracted from pulse-wave signals, especially in long-term continuous monitoring, and manually extracted features may have limited performance for BP estimation. This article proposes a Transformer network for Radar-based Contactless Continuous Blood Pressure monitoring (TRCCBP). A heartbeat signal-guided single-beat pulse wave extraction method is designed to obtain pure pulse-wave signals. A transformer network-based blood pressure estimation network is proposed to estimate BP, which utilizes convolutional layers with different scales, a gated recurrent unit (GRU) to capture time-dependence in continuous radar signal and multi-head attention modules to capture deep temporal domain characteristics. A radar signal dataset captured in an indoor environment containing 31 persons and a real medical situation containing five persons is set up to evaluate the performance of TRCCBP. Compared with the state-of-the-art method, the average accuracy of diastolic blood pressure (DBP) and systolic blood pressure (SBP) is 4.49 mmHg and 4.73 mmHg, improved by 12.36 mmHg and 8.80 mmHg, respectively. The proposed TRCCBP source codes and radar signal dataset have been made open-source online for further research.

Sensor data has been used in social security and welfare infrastructures such as insurance and medical care to provide personalized products and services; there is a risk that attackers can alter sensor data to obtain unfair benefits. We consider that one of the attack methods to modify sensor data is to attack the wearer’s body to modify biometric information. In this study, we propose a noninvasive attack method to modify the sensor value of a photoplethysmogram. The proposed method can disappear pulse wave peaks by pressurizing the upper arm with air pressure to control blood volume. Seven subjects experiencing a rest environment and five subjects experiencing an after-exercise environment wore five different models of smartwatches, and three pressure patterns were performed. It was confirmed in both situations that the displayed heart rate decreased from the true heart rate.

Today, cardiovascular diseases threaten human health worldwide. In clinical practice, it has been concluded that analyzing the pulse waveform can provide clinically valuable information for the diagnosis of cardiovascular diseases. Accordingly, continuous and accurate monitoring of the pulse wave is essential for the prevention and detection of cardiovascular diseases. Wearable triboelectric nanogenerators (TENGs) are emerging as a pulse wave monitoring biotechnology due to their compelling characteristics, including being self-powered, light-weight, and wear-resistant, as well as featuring user-friendliness and superior sensitivity. Herein, a comprehensive review is conducted on the progress of wearable TENGs for pulse wave monitoring. Firstly, the four modes of operation of TENG are briefly described. Secondly, TENGs for pulse wave monitoring are classified into two categories, namely wearable flexible film-based TENG sensors and textile-based TENG sensors. Next, the materials, fabrication methods, working mechanisms, and experimental performance of various TENG-based sensors are summarized. It concludes by comparing the characteristics of the two types of TENGs and discussing the potential development and challenges of TENG-based sensors in the diagnosis of cardiovascular diseases and personalized healthcare.

Introduction: Hemodynamic diagnosis indexes (HDIs) can comprehensively evaluate the health status of the cardiovascular system (CVS), particularly for people older than 50 years and prone to cardiovascular disease (CVDs). However, the accuracy of non-invasive detection remains unsatisfactory. We propose a non-invasive HDIs model based on the non-linear pulse wave theory (NonPWT) applied to four limbs.Methods: This algorithm establishes mathematical models, including pulse wave velocity and pressure information of the brachial and ankle arteries, pressure gradient, and blood flow. Blood flow is key to calculating HDIs. Herein, we derive blood flow equation for different times of the cardiac cycle considering the four different distributions of blood pressure and pulse wave of four limbs, then obtain the average blood flow in a cardiac cycle, and finally calculate the HDIs.Results: The results of the blood flow calculations reveal that the average blood flow in the upper extremity arteries is 10.78 ml/s (clinically: 2.5–12.67 ml/s), and the blood flow in the lower extremity arteries is higher than that in the upper extremity. To verify model accuracy, the consistency between the clinical and calculated values is verified with no statistically significant differences (p < 0.05). Model IV or higher-order fitting is the closest. To verify the model generalizability, considering the risk factors of cardiovascular diseases, the HDIs are recalculated using model IV, and thus, consistency is verified (p < 0.05 and Bland-Altman plot).Conclusion: We conclude our proposed algorithmic model based on NonPWT can facilitate the non-invasive hemodynamic diagnosis with simpler operational procedures and reduced medical costs.

Pulse wave carries abundant physiological and pathological data about the human body. Understanding the effect of blood compressibility on the transmission qualities of pulse waves is necessary to detect, cure, and prevent cardiovascular problems. In this research, the fluid–solid coupling finite element analysis model of artery system was developed using COMSOL MultiPhysics software. The influence of blood compressibility on pulse wave propagation properties in the brachial artery was investigated using the blood volume compression modulus. Results show that the influence of blood compressibility on pulse wave propagation is relatively weak and may be ignored when evaluating the compressibility of blood and that the volume compression modulus swings 2GPa over and beyond the typical range of the human body. The influence of blood compressibility on the peak value of pressure pulse wave is minor. The attenuation amplitude of the peak value of a pulse wave is bigger for the case with compressible blood than those of case with incompressible blood, and the difference between compressible and incompressible models is less than 1%. But blood compressibility has a clear impact on pulse wave velocity. It is found that the pulse wave velocity is greater than that of blood flow velocity. These findings will be valuable to investigate deeply for evaluating pressure and velocity pulse waves, as well as in providing a waveform-based preliminary diagnosis of symptoms in human body.

Pulse wave and pulse rate are important indicators of cardiovascular health. Technologies that can check the pulse by contacting the skin with optical sensors built into smart devices have been developed. However, this may cause inconvenience, such as foreign body sensation. Accordingly, studies have been conducted on non-contact pulse rate measurements using facial videos focused on the indoors. Moreover, since the majority of studies are conducted indoors, the error in the pulse rate measurement in outdoor environments, such as an outdoor bench, car and drone, is high. In this paper, to deal with this issue, we focus on developing a robust pulse measurement method based on facial videos taken in diverse environments. The proposed method stably detects faces by removing high-frequency components of face coordinate signals derived from fine body tremors and illumination conditions. It optimizes for extracting skin color changes by reducing illumination-caused noise using the Cg color difference component. The robust pulse wave is extracted from the Cg signal using FFT–iFFT with zero-padding. It can eliminate signal-filtering distortion effectively. We demonstrate that the proposed method relieves pulse rate measurement problems, producing 3.36, 5.81, and 6.09 bpm RMSE for an outdoor bench, driving car, and flying drone, respectively. [ABSTRACT FROM AUTHOR]

BACKGROUND: Arteriosclerosis is one of the diseases that endanger human health. There is a large amount of information in pulse wave signals to reflect the degree of arteriosclerosis. OBJECTIVE: The degree of arteriosclerosis is assessed by analyzing pulse wave signal and calculating multi-scale entropy values. METHODS: A method based on the multiscale cross-approximate entropy of the pulse wave of the human finger is proposed to assess the degree of arteriosclerosis. A total of 86 subjects were divided into three groups. The data of 1000 pulse cycles were selected in the experiment, and the multiscale cross-approximate entropy was calculated for the climb time and pulse wave peak interval. Independent sample t-test analysis gives the small-scale cross-approximate entropy of the two time series of climb time and pulse wave peak interval as p < 0.001 in Groups 1 and 2. The large-scale cross-approximate entropy of the two time series of climb time and pulse wave peak interval is p < 0.017 in Groups 2 and 3. RESULTS: Using the proposed algorithm, the results showed that the small-scale cross-approximate entropy of climb time and pulse wave peak interval could reflect the degree of arteriosclerosis in the human body from the perspective of autonomic nerve function. The large-scale cross-approximate entropy of climb time and pulse wave peak interval confirmed the effect of diabetes on the degree of arteriosclerosis. CONCLUSIONS: The results demonstrate the multiscale cross-approximate entropy is a comprehensive index to evaluate the degree of human arteriosclerosis. [ABSTRACT FROM AUTHOR]

Czech physiologist Penaz tried to overcome limitations of invasive pulse-contour methods (PCM) in clinical applications by a non-invasive method (finger mounted BP cuff) for continuous arterial waveform detection and beat-to-beat analysis. This discovery resulted in significant interest in human physiology and non-invasive examination of hemodynamic parameters, however has limitations because of the distal BP recording using a volume-clamp method. Thus, we propose a validation of beat-to-beat signal analysis acquired by novel a brachial occlusion-cuff (suprasystolic) principle and signal obtained from Finapres during a forced expiratory effort against an obstructed airway (Valsalva maneuver). Twelve healthy adult subjects [2 females, age = (27.2 ± 5.1) years] were in the upright siting position, breathe through the mouthpiece (simultaneously acquisition by brachial blood pressure monitor and Finapres) and at a defined time were asked to generate positive mouth pressure for 20 s (Valsalva). For the purpose of signal analysis, we proposed parameter a "Occlusion Cuff Index" (OCCI). The assumption about similarities between measured signals (suprasystolic brachial pulse waves amplitudes and Finapres's MAP) were proved by averaged Pearson's correlation coefficient ( r - = 0.60, p < 0.001). The averaged Pearson's correlation coefficient for the comparative analysis of OCCI between methods was r - = 0.88, p < 0.001. The average percent change of OCCI during maneuver: 8% increase, 19% decrease and percent change of max/min ratio is 35%. The investigation of brachial pulse waves measured by novel brachial blood pressure monitor shows positive correlation with Finapres and the parameter OCCI shows promise as an index, which could describe changes during beat-to-beat cardiac cycles. [ABSTRACT FROM AUTHOR]

Pulse wave measurement is a highly prominent technique used in biomedical diagnostics. The development of novel cost-effective pulse wave sensors will pave the way to more advanced healthcare technologies. This work reports on a pulse wave optical fiber sensor interrogated by a smartphone. The sensor performance was tested in terms of signal to noise ratio, repeatability of demodulated signal and suitability of demodulated signals for the extraction of information about direct and reflected waves. The analysis showed that the observed fluctuations of signal parameters are caused by variability of the state of the cardiovascular system and not by the system noise.

Methods of testing the elasticity of walls of human veins and arteries are considered. The problems that arise when using them are noted. The necessity of developing nondestructive methods for testing the elasticity of the walls of human veins and arteries is substantiated. A technique for monitoring changes in their elasticity using noninvasive methods of pulse wave recording is proposed. The results of comparing data on changes in the elasticity of the walls of veins and arteries obtained using various methods of pulse wave recording and invasive methods are presented. The advantages and disadvantages of the proposed technique using various pulse waves are noted. [ABSTRACT FROM AUTHOR]