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Central blood pressure (BP) is more strongly associated with cardiovascular events than brachial BP. Few studies have described age-related changes in central BP in the general Japanese population. This study aimed to provide reference values for central BP parameters according to the age and sex, using data from 2964 participants aged 40-89 years (mean age: 60 years) who had no history of overt cardiovascular or chronic kidney disease and had not received antihypertensive, lipid-lowering, or hypoglycemic treatments. The central aortic pressure waveform was measured noninvasively by pulse wave analysis using the SphygmoCor XCEL operating system. Central systolic BP (cSBP), pulse pressure (cPP), and augmentation pressure (AP) exhibited a curvilinear relationship with age, with men having significantly higher cSBP but lower cPP and AP than women (cSBP: 132 mmHg vs. 123 mmHg, P < 0.001; cPP, 50 mmHg vs. 55 mmHg, P = 0.001; and AP: 24 mmHg vs. 32 mmHg, P = 0.002). Reference percentiles for cSBP, cPP, and AP were determined for each 5-year age group using the LMS method. Multiple regression analyses showed that age-standardized z-scores for cSBP, cPP, and AP were significantly associated with hypertension, dyslipidemia, diabetes mellitus, abdominal obesity, and heavy smoking, after adjusting for potential confounders. In conclusion, this study provides age- and sex-specific percentiles of central BP parameters in middle-aged and older Japanese adults. These reference values allow a more accurate interpretation of results and may be useful for diagnosis and management of hypertension in clinical practice and for risk stratification in population studies., Competing Interests: Compliance with ethical standards. Conflict of interest: The authors declare no competing interests. Ethical approval: This study was conducted in accordance with the 1964 Declaration of Helsinki and its amendments or equivalent ethical standards. This study was approved by the Ethics Committee of Wakayama Medical University (approval no. G92). Informed consent: All participants signed an informed consent form after the study objectives and experimental procedures were explained., (© 2024. The Author(s), under exclusive licence to The Japanese Society of Hypertension.)

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 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%. 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., (© 2024 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

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 SE TWM , SE AWM , and CSE TWM-AWM during gaming than baseline. Before BE, CSE PW-TWM and CSE PW-AWM during gaming were considerably higher in the GD group than in the HC group. Compared to before BE, both groups had decreased SE TWM and CSE TWM-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. SE TWM , SE AWM , CSE TWM-AWM , CSE PW-TWM , and CSE PW-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.

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., (© 2024. The Author(s).)

The application of deep learning to the classification of pulse waves in Traditional Chinese Medicine (TCM) related to hypertensive target organ damage (TOD) is hindered by challenges such as low classification accuracy and inadequate generalization performance. To address these challenges, we introduce a lightweight transfer learning model named MobileNetV2SCP. This model transforms time-domain pulse waves into 36-dimensional frequency-domain waveform feature maps and establishes a dedicated pre-training network based on these maps to enhance the learning capability for small samples. To improve global feature correlation, we incorporate a novel fusion attention mechanism (SAS) into the inverted residual structure, along with the utilization of 3 × 3 convolutional layers and BatchNorm layers to mitigate model overfitting. The proposed model is evaluated using cross-validation results from 805 cases of pulse waves associated with hypertensive TOD. The assessment metrics, including Accuracy (92.74 %), F1-score (91.47 %), and Area Under Curve (AUC) (97.12 %), demonstrate superior classification accuracy and generalization performance compared to various state-of-the-art models. Furthermore, this study investigates the correlations between time-domain and frequency-domain features in pulse waves and their classification in hypertensive TOD. It analyzes key factors influencing pulse wave classification, providing valuable insights for the clinical diagnosis of TOD., Competing Interests: Declaration of competing interest None declared., (Copyright © 2024 IPEM. Published by Elsevier Ltd. All rights reserved.)

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., (© 2022. The Chinese Journal of Integrated Traditional and Western Medicine Press and Springer-Verlag GmbH Germany, part of Springer Nature.)

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 (SpO 2 ). 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 SpO 2 , LAD, and LVDd performance can be achieved with the maximum error < 15%. While our findings thus demonstrate the potential of pulse wave-based, non-invasive evaluation of the blood-supply capability of patients with HF, they also set the stage for further refinements in health monitoring and deterioration prevention applications., (© 2024. The Author(s).)

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.

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.

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 T ransformer network for R adar-based C ontactless C ontinuous B lood P ressure 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.

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 ( E ). 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 Ao ). 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 E Ao (Pearson's r p > 0.95). Carotid distensibility showed a strong negative nonlinear correlation (Spearman's r s < -0.99). CAVI and distensibility exhibited greater resilience to noise compared with PWV indices. Blood pressure-related indices and photoplethysmography (PPG)-based indices showed weaker correlations with E Ao ( r For the first time, 19 noninvasive pulse wave indices for assessing vascular aging were examined together in a single database of nearly 4,000 subjects aged 25-75 yr. The dataset contained precise values of the aortic Young's modulus and other hemodynamic measures for each subject, which enabled us to test each index's ability to measure changes in aortic stiffness while accounting for confounding factors and measurement errors. The study provides freely available tools for analyzing these and additional indices.p and r s < 0.89, | r p | and | r s | < 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 E Ao correlation and noise resilience. PPG-based indices showed potential for daily VA monitoring under minimized noise disturbances. NEW & NOTEWORTHY For the first time, 19 noninvasive pulse wave indices for assessing vascular aging were examined together in a single database of nearly 4,000 subjects aged 25-75 yr. The dataset contained precise values of the aortic Young's modulus and other hemodynamic measures for each subject, which enabled us to test each index's ability to measure changes in aortic stiffness while accounting for confounding factors and measurement errors. The study provides freely available tools for analyzing these and additional indices.

Background: Cardiovascular disease is one of the leading causes of death worldwide. However, according to studies, 90% of cardiovascular diseases can be prevented. Cardiovascular function parameters are an important basis for the diagnosis of cardiovascular diseases. The pulse wave also contains a wealth of physiological and pathological information, which can reflect the trend of cardiac function parameters at an early stage, so the measurement and analysis of the pulse wave signal becomes more and more important. The wearable pulse signal acquisition device has gradually become a new trend. In the mobile health scenario, convenient use is the prerequisite for long-term and rapid health monitoring. The data containing diverse pulse wave signals is the basis for obtaining more comprehensive and accurate human physiopathological information. Accurate data analysis and processing is the key to realizing the important goal of cardiovascular health monitoring., Objective: Based on the concept of mobile health care, wearable devices are developed to obtain physiological signals. The zero-dimensional model and the optimization algorithm are combined to complete the uncertainty quantification of the microcirculation parameters. Then, a feature set containing the cardiovasvular parameters can be constructed. The machine learning algorithm can be used to build a model that can accurately realize cardiovascular disease identification., Methods: This paper adopts laboratory-developed equipment to acquire the wrist pulse wave and fingertip volume pulse wave. A total of 323 samples were collected from healthy populations, hypertensive patients and patients with coronary heart disease (CHD). The pulse blood flow model in fingertip microcirculation is established, and the uncertainty quantification of model parameters is completed based on slime mold algorithm (SMA). After comparing and analyzing the performance of four algorithms on pulse wave classification, the identification model of cardiovascular diseases is established based on the microcirculatory characteristic parameter set and random forest algorithm (RF)., Results: RF showed good classification performance among the four classification algorithms. The identification accuracy of the model built on the microcirculatory characteristic parameter set and RF algorithm all reached more than 88%. The highest recognition accuracy was 95.51% for coronary heart disease samples, 92.11% for healthy samples, and 88.55% for hypertensive samples. It can be seen that the model based on RF algorithm has a good ability to distinguish the characteristic parameters in different cardiovascular health states., Conclusions: The wearable device designed in this paper can facilitate the daily health monitoring of cardiovascular disease. By using the combination of the physical model and machine learning model, the uncertainty quantification of microcirculation parameters and the identification of cardiovascular disease was finally completed. The recognition model based on machine learning provides a new idea and method for the research of cardiovascular health monitoring through pulse waves., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Rui Guo reports financial support was provided by National Natural Science Foundation of China. Rui Guo reports financial support was provided by Science and Technology Commission of Shanghai Municipality., (Copyright © 2023. Published by Elsevier B.V.)

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.

Objectives: 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., Methods: 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., Results: 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)., Conclusions: 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., Competing Interests: Declaration of Competing Interest None declared., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

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.

Flexible wearable crack strain sensors are currently receiving significant attention because they can be used in a wide range of physiological signal monitoring and human-machine interaction applications. However, sensors with high sensitivity, great repeatability, and wide sensing range remain challenging. Herein, a tunable wrinkle clamp down structure (WCDS) crack strain sensor based on high Poisson's ratio material with high sensitivity, high stability, and wide strain range is proposed. Based on the high Poisson's ratio of the acrylic acid film, the WCDS was prepared by a prestretching process. The wrinkle structures can clamp down the crack to improve the cyclic stability of the crack strain sensor while maintaining its high sensitivity. Moreover, the tensile properties of the crack strain sensor are improved by introducing wrinkles in the bridge-like gold stripes connecting each separated gold flake. Owing to this structure, the sensitivity of the sensor can reach 3627, stable operation over 10 000 cycles is achieved, and the strain range can reach about 9%. In addition, the sensor exhibits low dynamic response and good frequency characteristics. Because of its demonstrated excellent performance, the strain sensor can be used in pulse wave and heart rate monitoring, as well as posture recognition and game control.

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.

As one of the standard electrophysiological signals in the human body, the photoplethysmography contains detailed information about the blood microcirculation and has been commonly used in various medical scenarios, where the accurate detection of the pulse waveform and quantification of its morphological characteristics are essential steps. In this paper, a modular pulse wave preprocessing and analysis system is developed based on the principles of design patterns. The system designs each part of the preprocessing and analysis process as independent functional modules to be compatible and reusable. In addition, the detection process of the pulse waveform is improved, and a new waveform detection algorithm composed of screening-checking-deciding is proposed. It is verified that the algorithm has a practical design for each module, high accuracy of waveform recognition and high anti-interference capability. The modular pulse wave preprocessing and analysis software system developed in this paper can meet the individual preprocessing requirements for various pulse wave application studies under different platforms. The proposed novel algorithm with high accuracy also provides a new idea for the pulse wave analysis process.

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.

Objective: It is unknown whether ultrasound-induced blood-brain barrier (BBB) disruption can promote epileptogenesis and how BBB integrity changes over time after sonication., Methods: 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., Results: 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., Conclusion: Our experiments in mice provide persuasive evidence of the safety of LIPU-induced BBB opening as a therapeutic modality for neurological diseases., Competing Interests: Declaration of competing interest A.C. developed the ultrasound technology used in this study and deposited the patent related to this invention. A.C. is shareholder and consultant for CarThera. The other authors declare no competing interests., (Copyright © 2023 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Song, Liu, Wang, Zhang, Qiao and Wang.)

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.

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.

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.

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.

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.

As the highest percentage of global mortality is caused by several cardiovascular diseases (CVD), maintenance and monitoring of a healthy cardiovascular condition have become the primary concern of each and every individual. Simultaneously, recent progress and advances in wearable pressure sensor technology have provided many pathways to monitor and detect underlying cardiovascular illness in terms of irregularities in heart rate, blood pressure, and blood oxygen saturation. These pressure sensors can be comfortably attached onto human skin or can be implanted on the surface of vascular grafts for uninterrupted monitoring of arterial blood pressure. While the traditional monitoring systems are time-consuming, expensive, and not user-friendly, flexible sensor technology has emerged as a promising and dynamic practice to collect important health information at a comparatively low cost in a reliable and user-friendly way. This Review explores the importance and necessity of cardiovascular health monitoring while emphasizing the role of flexible pressure sensors in monitoring patients' health conditions to avoid adverse effects. A comprehensive discussion on the current research progress along with the real-time impact and accessibility of pressure sensors developed for cardiovascular health monitoring applications has been provided.

Pulse wave behavior is important in cardiovascular pathophysiology and arterial phantoms are valuable for studying arterial function. The ability of phantoms to replicate complex arterial elasticity and anatomy is limited by available materials and techniques. The feasibility of improving phantom performance using functional structure designs producible with practical 3D printing technologies was investigated. A novel corrugated wall approach to separate phantom function from material properties was investigated with a series of designs printed from polyester-polyurethane using a low-cost open-source fused filament fabrication 3D printer. Nonpulsatile pressure-diameter data was collected, and a mock circulatory system was used to observe phantom pulse wave behavior and obtain pulse wave velocities. The measured range of nonpulsatile Peterson elastic strain modulus was 5.6-19 to 12.4-33.0 kPa over pressures of 5-35 mmHg for the most to least compliant designs respectively. Pulse wave velocities of 1.5-5 m s -1 over mean pressures of 7-55 mmHg were observed, comparing favorably to reported in vivo pulmonary artery measurements of 1-4 m s -1 across mammals. Phantoms stiffened with increasing pressure in a manner consistent with arteries, and phantom wall elasticity appeared to vary between designs. Using a functional structure approach, practical low-cost 3D-printed production of simple arterial phantoms with mechanical properties that closely match the pulmonary artery is possible. Further functional structure design development to expand the pressure range and physiologic utility of dir"ectly 3D-printed phantoms appears warranted.

The photoplethysmogram (PPG) signal is widely used in pulse oximeters and smartwatches. A fundamental step in analysing the PPG is the detection of heartbeats. Several PPG beat detection algorithms have been proposed, although it is not clear which performs best. Objective: This study aimed to: (i) develop a framework with which to design and test PPG beat detectors; (ii) assess the performance of PPG beat detectors in different use cases; and (iii) investigate how their performance is affected by patient demographics and physiology. Approach: Fifteen beat detectors were assessed against electrocardiogram-derived heartbeats using data from eight datasets. Performance was assessed using the F 1 score, which combines sensitivity and positive predictive value. Main results: Eight beat detectors performed well in the absence of movement with F 1 scores of ≥90% on hospital data and wearable data collected at rest. Their performance was poorer during exercise with F 1 scores of 55%-91%; poorer in neonates than adults with F 1 scores of 84%-96% in neonates compared to 98%-99% in adults; and poorer in atrial fibrillation (AF) with F 1 scores of 92%-97% in AF compared to 99%-100% in normal sinus rhythm. Significance: Two PPG beat detectors denoted 'MSPTD' and 'qppg' performed best, with complementary performance characteristics. This evidence can be used to inform the choice of PPG beat detector algorithm. The algorithms, datasets, and assessment framework are freely available., (Creative Commons Attribution license.)

Cardiovascular disease is one of the leading causes of death worldwide. Long-term and real-time monitoring of cardiovascular indicators is required to detect abnormalities and conduct early intervention in time. To this end, the development of flexible wearable/implantable sensors for real-time monitoring of various vital signs has aroused extensive interest among researchers. Among the different kinds of sensors, mechanical sensors can reflect the direct information of pressure fluctuations in the cardiovascular system with the advantages of high sensitivity and suitable flexibility. Herein, we first introduce the recent advances of four kinds of mechanical sensors for cardiovascular system monitoring, based on capacitive, piezoresistive, piezoelectric, and triboelectric principles. Then, the physio-mechanical mechanisms in the cardiovascular system and their monitoring are described, including pulse wave, blood pressure, heart rhythm, endocardial pressure, etc. Finally, we emphasize the importance of real-time physiological monitoring in the treatment of cardiovascular disease and discuss its challenges in clinical translation.

In recent decades, cuffless blood pressure monitoring technology has been a point of research in the field of health monitoring and public media. Based on the web of science database, this paper evaluated the publications in the field from 1990 to 2020 using bibliometric analysis, described the developments in recent years, and presented future research prospects in the field. Through the comparative analysis of keywords, citations, H-index, journals, research institutions, national authors and reviews, this paper identified research hotspots and future research trends in the field of cuffless blood pressure monitoring. From the results of the bibliometric analysis, innovative methods such as machine learning technologies related to pulse transmit time and pulse wave analysis have been widely applied in blood pressure monitoring. The 2091 articles related to cuffless blood pressure monitoring technology were published in 1131 journals. In the future, improving the accuracy of monitoring to meet the international medical blood pressure standards, and achieving portability and miniaturization will remain the development goals of cuffless blood pressure measurement technology. The application of flexible electronics and machine learning strategy in the field will be two major development directions to guide the practical applications of cuffless blood pressure monitoring technology.

Objective: Pulse waveform contains abundant physiological and pathological information. The condition of surrounding arteries can be reflected sensitively by the contour and derivative changes of pulse waves. In order to express these changes objectively, the pulse wave needs to be quantified., Methods: This study provides a novel quantification method for pulse waveform in the entire cardiac cycle. It involves two new quantification parameters k1 and k2 to display the waveform change caused by the superimposition of wave reflection in the systolic reflex period, which is the most significant changes period. In this method, multi parameters were fused by Kalman filter to obtain an optimal estimation, involving the new parameters and other parameters: k0 for the early systolic period, C1 and C2 for diastole period, and K for pulse pressure., Results: Use correlation analysis to verify the effectiveness of new parameters that the coefficient is 0.7 between them and the typical augmentation index (AIx). The quantification results of 462 single-cycle pulse waves have consistent change trends with aging in 25-75 different age groups. For respiration analysis, the correlation coefficients are all greater than 0.6, even achieved 0.8 in six multi-cycle data between Kalman optimal estimation and breath wave., Conclusion: This method has quantified the waveform change with physiological status, and these quantification parameters can display the detail of each period., Significance: It will be used to verify waveform recognition accuracy and has a vast potential to detect diseases., Competing Interests: Declaration of Competing Interest The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted, (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

Peripheral blood flow analysis is used for noninvasive assessment of the cardiovascular system. The aim was to investigate the possibility of applying Hilbert-Huang spectral analysis (HH) to localize the mechanisms of nonlinear regulation of blood perfusion dynamics in the soft tissues of distal areas of the hands in human. In 9 healthy subjects, synchronous recording of photoplethysmograms (PPG) from the index fingers of the hands was performed. Using a band-pass filter, a cardiac signal was extracted from the PPG, which was separated into the carrier and modulating components. The latter was subjected to decomposition into empirical modes by HH method. Five modes were detected, for which the coherence between the hands was evaluated. The frequencies of the modes fell within the conventional frequency intervals of skin blood flow oscillations, and the coherence was 0.6. A decreased depth of modulation in the left hand compared to the right hand was shown. The results indicate a central origin of the nonlinear regulation of the dynamics of tissue blood perfusion in the hands., (© 2022. Pleiades Publishing, Ltd.)

The task to develop a mechanism for predicting the hemodynamic parameters values based on non-invasive hydrocuff technology of a pulse wave signal fixation is described in this study. The advantages and disadvantages of existing methods of recording the ripple curve are noted in the published materials. This study proposes a new hydrocuff method for hemodynamic parameters and blood pressure values measuring. A block diagram of the device being developed is presented. Algorithms for processing the pulse wave contour are presented. A neural network applying necessity for the multiparametric feature space formation is substantiated. The pulse wave contours obtained using hydrocuff technology of oscillation formation for various age groups are presented. According to preliminary estimates, by the moment of the dicrotic surge formation, it is possible to judge the ratio of the heart and blood vessels work, which makes it possible to form an expanded feature space of significant parameters based on neural network classifiers. This study presents the characteristics accounted for creating a database for training a neural network.

Flexible sensor arrays are widely used for wearable physiological signal recording applications. A high density sensor array requires the signal readout to be compatible with multiple channels. This paper presents a highly-integrated remote health monitoring system integrating a flexible pressure sensor array with a multi-channel wireless readout chip. The custom-designed chip features 64 voltage readout channels, a power management unit, and a wireless transceiver. The whole chip fabricated in a 65 nm complementary metal-oxide-semiconductor (CMOS) process occupies 3.7 × 3.7 mm 2 , and the core blocks consume 2.3 mW from a 1 V supply in the wireless recording mode. The proposed multi-channel system is validated by measuring the ballistocardiogram (BCG) and pulse wave, which paves the way for future portable remote human physiological signals monitoring devices.

Objective: Pulse wave has been considered as a message carrier in the cardiovascular system (CVS), capable of inferring CVS conditions while diagnosing cardiovascular diseases (CVDs). Clarification and prediction of cardiovascular function by means of powerful feature-abstraction capability of machine learning method based on pulse wave is of great clinical significance in health monitoring and CVDs diagnosis, which remains poorly studied., Methods: Here we propose a machine learning (ML)-based strategy aiming to achieve a fast and accurate prediction of three cardiovascular function parameters based on a 412-subject database of pulse waves. We proposed and optimized an ML-based model with multi-layered, fully connected network while building up two high-quality pulse wave datasets comprising a healthy-subject group and a CVD-subject group to predict arterial compliance (AC), total peripheral resistance (TPR), and stroke volume (SV), which are essential messengers in monitoring CVS conditions., Results: Our ML model is validated through consistency analysis of the ML-predicted three cardiovascular function parameters with clinical measurements and is proven through error analysis to have capability of achieving a high-accurate prediction on TPR and SV for both healthy-subject group (accuracy: 85.3%, 86.9%) and CVD-subject group (accuracy: 88.3%, 89.2%)., Discussion: The independent sample t-test proved that our subject groups could represent the typical physiological characteristics of the corresponding population. While we have more subjects in our datasets rather than previous studies after strict data screening, the proposed ML-based strategy needs to be further improved to achieve a disease-specific prediction of heart failure and other CVDs through training with larger datasets and clinical measurements., Conclusion: Our study points to the feasibility and potential of the pulse wave-based prediction of physiological and pathological CVS conditions in clinical application., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Ballistocardiography (BCG) is considered a good alternative to HRV analysis with its non-contact and unobtrusive acquisition characteristics. However, consensus about its validity has not yet been established. In this study, 50 healthy subjects (26.2 ± 5.5 years old, 22 females, 28 males) were invited. Comprehensive statistical analysis, including Coefficients of Variation (CV), Lin’s Concordance Correlation Coefficient (LCCC), and Bland-Altman analysis (BA ratio), were utilized to analyze the consistency of BCG and ECG signals in HRV analysis. If the methods gave different answers, the worst case was taken as the result. Measures of consistency such as Mean, SDNN, LF gave good agreement (the absolute value of CV difference < 2%, LCCC > 0.99, BA ratio < 0.1) between J-J (BCG) and R-R intervals (ECG). pNN50 showed moderate agreement (the absolute value of CV difference < 5%, LCCC > 0.95, BA ratio < 0.2), while RMSSD, HF, LF/HF indicated poor agreement (the absolute value of CV difference ≥ 5% or LCCC ≤ 0.95 or BA ratio ≥ 0.2). Additionally, the R-R intervals were compared with P-P intervals extracted from the pulse wave (PW). Except for pNN50, which exhibited poor agreement in this comparison, the performances of the HRV indices estimated from the PW and the BCG signals were similar.

Smart wearables provide an opportunity to monitor health in daily life and are emerging as potential tools for detecting cardiovascular disease (CVD). Wearables such as fitness bands and smartwatches routinely monitor the photoplethysmogram signal, an optical measure of the arterial pulse wave that is strongly influenced by the heart and blood vessels. In this survey, we summarize the fundamentals of wearable photoplethysmography and its analysis, identify its potential clinical applications, and outline pressing directions for future research in order to realize its full potential for tackling CVD.

With the increasing use of wearable devices equipped with various sensors, information on human activities, biometrics, and surrounding environments can be obtained via sensor data at any time and place. When such devices are attached to arbitrary body parts and multiple devices are used to capture body-wide movements, it is important to estimate where the devices are attached. In this study, we propose a method that estimates the load positions of wearable devices without requiring the user to perform specific actions. The proposed method estimates the time difference between a heartbeat obtained by an ECG sensor and a pulse wave obtained by a pulse sensor, and it classifies the pulse sensor position from the estimated time difference. Data were collected at 12 body parts from four male subjects and one female subject, and the proposed method was evaluated in both user-dependent and user-independent environments. The average F-value was 1.0 when the number of target body parts was from two to five.

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.

Exercise is a critical factor that impacts arterial stiffness. In this narrative review, we noted multiple findings that could not be reconciled with one another. Some studies indicated that arterial stiffness increases after a regimen of resistance training. However, such studies were limited by a lack of specification of the resistance training protocols, as well as varying results reported from different areas of the body, undermining the internal validity of the studies. Another factor explored in this review was how the order of performing exercises can affect arterial stiffness. Low-intensity resistance training before high-intensity resistance training resulted in increased arterial stiffness, whereas vice versa showed no change in arterial stiffness. Other studies indicated that resistance exercise results in reduced arterial stiffness. Intensity is a variable in studies that produces inconsistent results of arterial stiffness, with some studies suggesting high-intensity resistance training increases arterial stiffness and low-intensity resistance training decreases arterial stiffness, while other studies pointing to a significant decrease in arterial stiffness, regardless of the intensity of resistance training. Demographic factors such as gender, age, and diet play an important role in explaining these differences. In terms of future implications, there is potential clinical significance as increased arterial stiffness serves as a prognostic marker in diagnosing coronary heart disease., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2021, Tabaie et al.)

In the assessment of autonomic function by heart rate variability (HRV), the framework that the power of high-frequency component or its surrogate indices reflects parasympathetic activity, while the power of low-frequency component or LF/HF reflects sympathetic activity has been used as the theoretical basis for the interpretation of HRV. Although this classical framework has contributed greatly to the widespread use of HRV for the assessment of autonomic function, it was obtained from studies of short-term HRV (typically 5‑10 min) under tightly controlled conditions. If it is applied to long-term HRV (typically 24 h) under free-running conditions in daily life, erroneous conclusions could be drawn. Also, long-term HRV could contain untapped useful information that is not revealed in the classical framework. In this review, we discuss the limitations of the classical framework and present studies that extracted autonomic function indicators and other useful biomedical information from long-term HRV using novel approaches beyond the classical framework. Those methods include non-Gaussianity index, HRV sleep index, heart rate turbulence, and the frequency and amplitude of cyclic variation of heart rate., (© 2021. The Author(s).)

Objective. The waveform of a photoplethysmography (PPG) signal depends on the measurement site and individual physiological conditions. Filtering can distort the morphology of the original PPG signal waveform and change the timing of pulse feature points on PPG signals. We aim to quantitatively investigate the effect of PPG signal morphology (related to measurement site) and type of pulse feature on the filtering-induced time shift (TS). Approach. 60 s PPG signals were measured from six body sites (finger, wrist under (volar), wrist upper (dorsal), earlobe, and forehead) of 36 healthy adults. Using infinite impulse response digital filters which are common in PPG signal processing, PPG signals were prefiltered (band-pass, pass and stop bands: >0.5 Hz and <0.2 Hz for high-pass filter, <20 Hz and >30 Hz for low-pass filter) and then filtered (low-pass, pass and stop bands: <3 Hz and >5 Hz). Four pulse feature points were defined and extracted (peak, valley, maximal first derivative, and maximal second derivative). For each subject, overall TS and intra-subject TS variability in feature points were calculated as the mean and standard deviation of TS between prefiltered and filtered PPG signals in 50 cardiac cycles. Statistical testing was performed to investigate the effect of measurement site and type of pulse feature on overall TS and intra-subject TS variability. Main results. Measurement site, type of pulse feature, and their interaction had significant impacts on the overall TS and intra-subject TS variability ( p  < 0.001 for all). Valley and maximal second derivative showed higher overall TS than peak and maximal first derivative. Finger had higher overall TS and lower intra-subject TS variability than other measurement sites. Significance . Measurement site and type of pulse feature can significantly influence the timing of feature points on filtered PPG signals. Filtering parameters should be quoted to support the reproducibility of PPG-related studies., (© 2021 Institute of Physics and Engineering in Medicine.)

This study presents a new blood pressure (BP) estimation algorithm utilizing machine learning (ML). A cuffless device that can measure BP without calibration would be precious for portability, continuous measurement, and comfortability, but unfortunately, it does not currently exist. Conventional BP measurement with a cuff is standard, but this method has various problems like inaccurate BP measurement, poor portability, and painful cuff pressure. To overcome these disadvantages, many researchers have developed cuffless BP estimation devices. However, these devices are not clinically applicable because they require advanced preparation before use, such as calibration, do not follow international standards (81060-1:2007), or have been designed using insufficient data sets. The present study was conducted to combat these issues. We recruited 127 participants and obtained 878 raw datasets. According to international standards, our diverse data set included participants from different age groups with a wide variety of blood pressures. We utilized ML to formulate a BP estimation method that did not require calibration. The present study also conformed to the method required by international standards while calculating the level of error in BP estimation. Two essential methods were applied in this study: (a) grouping the participants into five subsets based on the relationship between the pulse transit time and systolic BP by a support vector machine ensemble with bagging (b) applying the information from the wavelet transformation of the pulse wave and the electrocardiogram to the linear regression BP estimation model for each group. For systolic BP, the standard deviation of error for the proposed BP estimation results with cross-validation was 7.74 mmHg, which was an improvement from 17.05 mmHg, as estimated by the conventional pulse-transit-time-based methods. For diastolic BP, the standard deviation of error was 6.42 mmHg for the proposed BP estimation, which was an improvement from 14.05mmHg. The purpose of the present study was to demonstrate and evaluate the performance of the newly developed BP estimation ML method that meets the international standard for non-invasive sphygmomanometers in a population with a diverse range of age and BP., Competing Interests: KM is employed Connect Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Yamanaka, Morikawa, Morita, Huh and Yamamura.)

This paper reports on a mask-type sensor for simultaneous pulse wave and respiration measurements and eye blink detection that uses only one sensing element. In the proposed sensor, a flexible air bag-shaped chamber whose inner pressure change can be measured by a microelectromechanical system-based piezoresistive cantilever was used as the sensing element. The air bag-shaped chamber is fabricated by wrapping a sponge pad with plastic film and polyimide tape. The polyimide tape has a hole to which the substrate with the piezoresistive cantilever adheres. By attaching the sensor device to a mask where it contacts the nose of the subject, the sensor can detect the pulses and eye blinks of the subject by detecting the vibration and displacement of the nose skin caused by these physiological parameters. Moreover, the respiration of the subject causes pressure changes in the space between the mask and the face of the subject as well as slight vibrations of the mask. Therefore, information about the respiration of the subject can be extracted from the sensor signal using either the low-frequency component (<1 Hz) or the high-frequency component (>100 Hz). This paper describes the sensor fabrication and provides demonstrations of the pulse wave and respiration measurements as well as eye blink detection using the fabricated sensor.

This paper describes the experimental results of testing a prototype of a high precision human skin rapid temperature fluctuations measuring instrument. Based on the author's work, an original circuit solution on a miniature semiconductor diode sensor has been designed. The proposed circuitry provides operation in the full voltage range with automatic setting and holding the operating point, as well as the necessary slope of the conversion coefficient (up to 2300 mV/°C), which makes it possible to register fast temperature oscillations from the surface of the human body and other biological objects. Simulation results in the Microcap 12 software and laboratory tests have confirmed all declared design specifications: temperature resolution of 0.01 °C, transducer thermal time constant of 0.05 s. An original thermostat and an experimental setup for the simultaneous registration of the electrocardiogram, pulse wave signals from the Biopac polygraph MP36 and a signal of temperature oscillations from the prototype thermometer have been designed for further investigations. The preliminary test results indicates that using the designed measuring instrument gives a possibility to provide an in-depth study of the relationship between micro- and macro-blood circulations manifested in skin temperature fluctuations.

Another type of natural wave, traced from longitudinal wall motion and propagation along the artery, is observed in our in vivo human carotid artery experiments. We coin it as extension wave (EW) and hypothesize that EW velocity (EWV) is associated with arterial longitudinal stiffness. The EW is thus assumed to complement the pulse wave (PW), whose velocity (PWV) is tracked from the radial wall displacement and linked to arterial circumferential stiffness through the Moens-Korteweg equation, as indicators for arterial mechanical anisotropy quantification by noninvasive high-frame-rate ultrasound. The relationship between directional arterial stiffnesses and the two natural wave speeds was investigated in wave theory, finite-element simulations based on isotropic and anisotropic arterial models, and in vivo human common carotid artery ( n = 10) experiments. Excellent agreement between the theory and simulations showed that EWV was 2.57 and 1.03 times higher than PWV in an isotropic and an anisotropic carotid artery model, respectively, whereas in vivo EWV was consistently lower than PWV in all 10 healthy human subjects. A strong linear correlation was substantiated in vivo between EWV and arterial longitudinal stiffness quantified by a well-validated vascular-guided wave imaging technique (VGWI). We thereby proposed a novel index calculated as EWV 2 /PWV 2 as an alternative to assess arterial mechanical anisotropy. Simulations and in vivo results corroborated the effect of mechanical anisotropy on the propagation of spontaneous waves along the arterial wall. The proposed anisotropy index demonstrated the feasibility of the concurrent EW and PW imaged by high frame-rate ultrasound in grading of arterial wall anisotropy. NEW & NOTEWORTHY An extension wave formed by longitudinal wall displacements was observed by high-frame-rate ultrasound in the human common carotid artery in vivo. A strong correlation between extension wave velocity and arterial longitudinal stiffness complements the well-established pulse wave, which is linked to circumferential stiffness, to noninvasively assess direction-dependent wall elasticity of the major artery. The proposed anisotropy index, which directly reflects arterial wall microstructure and function, might be a potential risk factor for screening (sub-) clinical cardiovascular diseases.

People with autism spectrum disorder (ASD) exhibit atypicality in various domains of behavior. Previous psychophysiological studies have revealed an atypical pattern of autonomic nervous system (ANS) activation induced by psychosocial stimulation. Thus, it might be feasible to develop a novel assessment tool to evaluate the risk of ASD by measuring ANS activation in response to emotional stimulation. The present study investigated whether people with ASD could be automatically classified from neurotypical adults based solely on physiological data obtained by the recently introduced non-contact measurement of pulse wave. We video-recorded faces of adult males with and without ASD while watching emotion-inducing video clips. Features reflective of ANS activation were extracted from the temporal fluctuation of facial skin coloration and entered into a machine-learning algorithm. Though the performance was modest, the gradient boosting classifier succeeded in classifying people with and without ASD, which indicates that facial skin color fluctuation contains information useful for detecting people with ASD. Taking into consideration the fact that the current study recruited only high-functioning adults who have relatively mild symptoms and probably developed some compensatory strategies, ASD screening by non-contact measurement of pulse wave could be a promising assessment tool to evaluate ASD risk., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Doi, Tsumura, Kanai, Masui, Mitsuhashi and Nagasawa.)