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Pulse transit time (PTT) represents a potential approach for cuff-less blood pressure (BP) monitoring. Conventionally, PTT is determined by (1) measuring (a) ECG and ear, finger, or toe PPG waveforms or (b) two of these PPG waveforms and (2) detecting the time delay between the waveforms. The conventional PTTs (cPTTs) were compared in terms of correlation with BP in humans. Thirty-two volunteers [50% female; 52 (17) (mean (SD)) years; 25% hypertensive] were studied. The four waveforms and manual cuff BP were recorded before and after slow breathing, mental arithmetic, cold pressor, and sublingual nitroglycerin. Six cPTTs were detected as the time delays between the ECG R-wave and ear PPG foot, R-wave and finger PPG foot [finger pulse arrival time (PAT)], R-wave and toe PPG foot (toe PAT), ear and finger PPG feet, ear and toe PPG feet, and finger and toe PPG feet. These time delays were also detected via PPG peaks. The best correlation by a substantial extent was between toe PAT via the PPG foot and systolic BP [− 0.63 ± 0.05 (mean ± SE); p < 0.001 via one-way ANOVA]. Toe PAT is superior to other cPTTs including the popular finger PAT as a marker of changes in BP and systolic BP in particular.

Subjects with amputation of the lower limbs are at increased risk of cardiovascular mortality and morbidity. We hypothesize that amputation-induced alterations in the arterial tree negatively impact arterial biomechanics, blood pressure and flow behavior. These changes may interact with other biological factors, potentially increasing cardiovascular risk. To evaluate this hypothesis regarding the purely mechanical impact of amputation on the arterial tree, we used a simulation computer model including a detailed one-dimensional (1D) arterial network model (143 arterial segments) coupled with a zero-dimensional (0D) model of the left ventricle. Our simulations included five settings of the arterial network: (1) 4-limbs control, (2) unilateral amputee (right lower limb), (3) bilateral amputee (both lower limbs), (4) trilateral amputee (lower-limbs and right upper-limb), and (5) quadrilateral amputee (lower and upper limbs). Analysis of regional stiffness, as calculated by pulse wave velocity (PWV) for large-, medium- and small-sized arteries, showed that, while aortic stiffness did not change with increasing degree of amputation, stiffness of medium and smaller-sized arteries increased with greater amputation severity. Despite a staged decrease in cardiac output, the systolic and diastolic blood pressure values increased, resulting in an increase in both central and peripheral pulse pressures but with an attenuation of pulse pressure amplification. The most significant increase in peak systolic pressure and decrease in peak systolic blood flow was observed at the site of the abdominal aorta. Wave separation analysis indicated no changes in the shape of the forward and backward wave components. However, the results from wave intensity analysis showed that with extended amputation, there was an increase in peak forward wave intensity and a rise in the inverse peak of the backward wave intensity, suggesting potential alterations in cardiac hemodynamic load. In conclusion, this simulation study showed that biomechanical and hemodynamic changes in the arterial network geometry could interact with additional risk factors to increase the cardiovascular risk in patients with amputations.

We should pay more attention to the long-term monitoring and early warning of type 2 diabetes and its complications. The traditional blood glucose tests are traumatic and cannot effectively monitor the development of diabetic complications. The development of mobile health is changing rapidly. Therefore, we are interested in developing a new noninvasive, economical, and instant-result method to accurately diagnose and monitor type 2 diabetes and its complications. We aimed to determine whether type 2 diabetes and its complications, including hypertension and hyperlipidemia, could be diagnosed and monitored by using pulse wave. We collected the pulse wave parameters from 50 healthy people, 139 diabetic patients without hypertension and hyperlipidemia, 133 diabetic patients with hypertension, 70 diabetic patients with hyperlipidemia, and 75 diabetic patients with hypertension and hyperlipidemia. The pulse wave parameters showing significant differences among these groups were identified. Various machine learning models such as linear discriminant analysis, support vector machines (SVMs), and random forests were applied to classify the control group, diabetic patients, and diabetic patients with complications. There were significant differences in several pulse wave parameters among the 5 groups. The parameters height of tidal wave (h ), time distance between the start point of pulse wave and dominant wave (t ), and width of percussion wave in its one-third height position (W) increase and the height of dicrotic wave (h ) decreases when people develop diabetes. The parameters height of dominant wave (h ), h , and height of dicrotic notch (h ) are found to be higher in diabetic patients with hypertension, whereas h is lower in diabetic patients with hyperlipidemia. For detecting diabetes, the method with the highest out-of-sample prediction accuracy is SVM with polynomial kernel. The algorithm can detect diabetes with 96.35% accuracy. However, all the algorithms have a low accuracy when predicting diabetic patients with hypertension and hyperlipidemia (below 70%). The results demonstrated that the noninvasive and convenient pulse-taking diagnosis described in this paper has the potential to become a low-cost and accurate method to monitor the development of diabetes. We are collecting more data to improve the accuracy for detecting hypertension and hyperlipidemia among diabetic patients. Mobile devices such as sport bands, smart watches, and other diagnostic tools are being developed based on the pulse wave method to improve the diagnosis and monitoring of diabetes, hypertension, and hyperlipidemia.

Flow of cerebrospinal fluid (CSF) through perivascular spaces (PVSs) in the brain is important for clearance of metabolic waste. Arterial pulsations are thought to drive flow, but this has never been quantitatively shown. We used particle tracking to quantify CSF flow velocities in PVSs of live mice. CSF flow is pulsatile and driven primarily by the cardiac cycle. The speed of the arterial wall matches that of the CSF, suggesting arterial wall motion is the principal driving mechanism, via a process known as perivascular pumping. Increasing blood pressure leaves the artery diameter unchanged but changes the pulsations of the arterial wall, increasing backflow and thereby reducing net flow in the PVS. Perfusion-fixation alters the normal flow direction and causes a 10-fold reduction in PVS size. We conclude that particle tracking velocimetry enables the study of CSF flow in unprecedented detail and that studying the PVS in vivo avoids fixation artifacts. Arterial pulsations are thought to drive CSF flow through perivascular spaces (PVSs), but this has never been quantitatively shown. Using particle tracking to quantify CSF flow velocities in PVSs of live mice, the authors show that flow speeds match the instantaneous speeds of the pulsing artery walls that form the inner boundaries of the PVSs.

One of the European gold standard measurement of vascular ageing, a risk factor for cardiovascular disease, is the carotid-femoral pulse wave velocity (cfPWV), which requires an experienced operator to measure pulse waves at two sites. In this work, two machine learning pipelines were proposed to estimate cfPWV from the peripheral pulse wave measured at a single site, the radial pressure wave measured by applanation tonometry. The study populations were the Twins UK cohort containing 3,082 subjects aged from 18 to 110 years, and a database containing 4,374 virtual subjects aged from 25 to 75 years. The first pipeline uses Gaussian process regression to estimate cfPWV from features extracted from the radial pressure wave using pulse wave analysis. The mean difference and upper and lower limits of agreement (LOA) of the estimation on the 924 hold-out test subjects from the Twins UK cohort were 0.2 m/s, and 3.75 m/s & -3.34 m/s, respectively. The second pipeline uses a recurrent neural network (RNN) to estimate cfPWV from the entire radial pressure wave. The mean difference and upper and lower LOA of the estimation on the 924 hold-out test subjects from the Twins UK cohort were 0.05 m/s, and 3.21 m/s & -3.11m/s, respectively. The percentage error of the RNN estimates on the virtual subjects increased by less than 2% when adding 20% of random noise to the pressure waveform. These results show the possibility of assessing the vascular ageing using a single peripheral pulse wave ( e.g . the radial pressure wave), instead of cfPWV. The proposed code for the machine learning pipelines is available from the following online depository ( https://github.com/WeiweiJin/Estimate-Cardiovascular-Risk-from-Pulse-Wave-Signal ).

Studies regarding site characterization are critical to determine the feasibility and model of infrastructure that can be built in an area. One method that is very reliable in site characterization is Multichannel Analysis of Surface Waves (MASW). This method utilizes surface wave (S-wave) propagation to identify the subsurface. Therefore, in this research, an infrastructure assessment was carried out in the North Morowali Regency area using the MASW method. Based on the results of the shear wave velocity profile (Vs), the research area is dominated by clay to dense sand. Therefore, it is highly recommended that the infrastructure model built in the North Morowali area be adapted to the conditions of the sites that dominate the area so that the buildings to be built will last a long time.

Passive surface wave methods are non-invasive, low-cost, and robust approaches to image near-surface shear-wave velocity (Vs) structure using passive seismic sources. A clean and high-resolution dispersion image is critical for surface wave analysis. In practice, however, artifacts or aliasing are almost inevitable in passive surface wave dispersion measurements and seriously pollute the measured dispersion spectra. It is significant to clarify how they are generated, how they affect the dispersion measurement, and how they can be attenuated. We provide the first comprehensive review on artifacts that are frequently observed in high-frequency (>1 Hz) passive surface wave dispersion measurements and summarize them into two general groups: geometry-related artifacts and source-related artifacts. Mathematical derivations and numerical as well as field examples are presented to explain the underlying physics of various artifacts and explore potential solutions and guidelines to attenuate them before and after field observations. This work will help the reader understand the complexity of the measured dispersion spectra and lead to improvements on rapidly advancing passive surface wave methods.

As global warming continues, the Earth's cryosphere is experiencing severe degradation. This study leverages a novel combination of distributed acoustic sensing (DAS) and artificial intelligence to monitor and decipher cryospheric dynamics. We have developed an advanced time‐lapse surface wave analysis workflow to capture shear wave velocity changes (Δv)$({\\Delta }v)$during a 2‐month controlled permafrost thaw experiment in Fairbanks, Alaska. To understand the underlying physical mechanisms of Δv${\\Delta }v$ , multimodal rock‐physics simulations were conducted to associate the observed Δv${\\Delta }v$to hydrological and thermal processes like heating and rainfall events. Furthermore, we employ a physics‐guided deep learning algorithm alongside interpretable techniques to evaluate the impact of various physical factors and shed light on the cryospheric hydro‐thermo coupling mechanisms. This study highlights the potential of using DAS and data‐driven rock‐physics simulation for complex cryosphere monitoring and offers a comprehensive view of the permafrost's thawing dynamics. Plain Language Summary Our study delves into the changes in the cryosphere due to global warming, utilizing an instrumented field site in Fairbanks, Alaska. We used Distributed Acoustic Sensing (DAS), which involves sending light pulses through fiber‐optic cables to detect ground vibrations, to provide insights into the condition of the permafrost. Using data previously collected over a 2 months period, we analyzed the permafrost's response to artificial warming, akin to the effects of climate change. This process involved tracking shear wave velocity changes in the ground, which helped identify the shifts in ice, water, and soil composition within the permafrost. Our findings indicate that permafrost thawing significantly alters shear wave velocity, signaling changes in the structure and water content of the cryosphere. By integrating field observations with computer simulations and deep learning, we unraveled the complex hydro‐thermo interactions within the thawing cryosphere. This research is helpful for understanding how the transformation of the cryosphere affects global climate and local ecosystems, enhancing our capability to predict and manage the ramifications of climate change in Earth's frozen regions. Key Points DAS observes seismic responses related to hydrological and thermal processes within the cryosphere Time‐lapse surface wave analysis delivers high‐resolution shear wave velocity changes within the permafrost Data‐driven rock‐physics simulations predict seismic velocity perturbations and reveal complex hydro‐thermo coupling mechanisms

Continuous, comfortable, convenient (C3), and accurate blood pressure (BP) measurement and monitoring are needed for early diagnosis of various cardiovascular diseases. To supplement the limited C3 BP measurement of existing cuff-based BP technologies, though they may achieve reliable accuracy, cuffless BP measurement technologies, such as pulse transit/arrival time, pulse wave analysis, and image processing, have been studied to obtain C3 BP measurement. One of the recent cuffless BP measurement technologies, innovative machine-learning and artificial intelligence-based technologies that can estimate BP by extracting BP-related features from photoplethysmography (PPG)-based waveforms have attracted interdisciplinary attention of the medical and computer scientists owing to their handiness and effectiveness for both C3 and accurate, i.e., C3A, BP measurement. However, C3A BP measurement remains still unattainable because the accuracy of the existing PPG-based BP methods was not sufficiently justified for subject-independent and highly varying BP, which is a typical case in practice. To circumvent this issue, a novel convolutional neural network(CNN)- and calibration-based model (PPG2BP-Net) was designed by using a comparative paired one-dimensional CNN structure to estimate highly varying intrasubject BP. To this end, approximately 70 % , 20 % , and 10 % of 4185 cleaned, independent subjects from 25,779 surgical cases were used for training, validating, and testing the proposed PPG2BP-Net, respectively and exclusively (i.e., subject-independent modelling). For quantifying the intrasubject BP variation from an initial calibration BP, a novel ‘standard deviation of subject-calibration centring (SDS)’ metric is proposed wherein high SDS represents high intrasubject BP variation from the calibration BP and vice versa. PPG2BP-Net achieved accurately estimated systolic and diastolic BP values despite high intrasubject variability. In 629-subject data acquired after 20 minutes following the A-line (arterial line) insertion, low error mean and standard deviation of 0.209 ± 7.509 and 0.150 ± 4.549 mmHg for highly varying A-line systolic and diastolic BP values, respectively, where their SDSs are 15.375 and 8.745. This study moves one step forward in developing the C3A cuffless BP estimation devices that enable the push and agile pull services.