Introduction: Heart Rate Variability (HRV) and Pulse Rate Variability (PRV), are non-invasive techniques for monitoring changes in the cardiac cycle. Both techniques have been used for assessing the autonomic activity. Although highly correlated in healthy subjects, differences in HRV and PRV have been observed under various physiological conditions. The reasons for their disparities in assessing the degree of autonomic activity remains unknown. Methods: To investigate the differences between HRV and PRV, a whole-body cold exposure (CE) study was conducted on 20 healthy volunteers (11 male and 9 female, 30.3 ± 10.4 years old), where PRV indices were measured from red photoplethysmography signals acquired from central (ear canal, ear lobe) and peripheral sites (finger and toe), and HRV indices from the ECG signal. PRV and HRV indices were used to assess the effects of CE upon the autonomic control in peripheral and core vasculature, and on the relationship between HRV and PRV. The hypotheses underlying the experiment were that PRV from central vasculature is less affected by CE than PRV from the peripheries, and that PRV from peripheral and central vasculature differ with HRV to a different extent, especially during CE. Results: Most of the PRV time-domain and Poincaré plot indices increased during cold exposure. Frequency-domain parameters also showed differences except for relative-power frequency-domain parameters, which remained unchanged. HRV-derived parameters showed a similar behavior but were less affected than PRV. When PRV and HRV parameters were compared, time-domain, absolute-power frequency-domain, and non-linear indices showed differences among stages from most of the locations. Bland-Altman analysis showed that the relationship between HRV and PRV was affected by CE, and that it recovered faster in the core vasculature after CE. Conclusion: PRV responds to cold exposure differently to HRV, especially in peripheral sites such as the finger and the toe, and may have different information not available in HRV due to its non-localized nature. Hence, multi-site PRV shows promise for assessing the autonomic activity on different body locations and under different circumstances, which could allow for further understanding of the localized responses of the autonomic nervous system.

Hypertension is one of the most prevalent diseases and is often called the “silent killer” because there are usually no early symptoms. Hypertension is also associated with multiple morbidities, including chronic kidney disease and cardiovascular disease. Early detection and intervention are therefore important. The current routine method for diagnosing hypertension is done using a sphygmomanometer, which can only provide intermittent blood pressure readings and can be confounded by various factors, such as white coat hypertension, time of day, exercise, or stress. Consequently, there is an increasing need for a non-invasive, cuff-less, and continuous blood pressure monitoring device. Multi-site photoplethysmography (PPG) is a promising new technology that can measure a range of features of the pulse, including the pulse transit time of the arterial pulse wave, which can be used to continuously estimate arterial blood pressure. This is achieved by detecting the pulse wave at one body site location and measuring the time it takes for it to reach a second, distal location. The purpose of this review is to analyze the current research in multi-site PPG for blood pressure assessment and provide recommendations to guide future research. In a systematic search of the literature from January 2010 to January 2019, we found 13 papers that proposed novel methods using various two-channel PPG systems and signal processing techniques to acquire blood pressure using multi-site PPG that offered promising results. However, we also found a general lack of validation in terms of sample size and diversity of populations.

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Collecting and analyzing data from sensors embedded in the context of daily life has been widely employed for the monitoring of mental health. Variations in parameters such as movement, sleep duration, heart rate, electrocardiogram, skin temperature, etc., are often associated with psychiatric disorders. Namely, accelerometer data, microphone, and call logs can be utilized to identify voice features and social activities indicative of depressive symptoms, and physiological factors such as heart rate and skin conductance can be used to detect stress and anxiety disorders. Therefore, a wide range of devices comprising a variety of sensors have been developed to capture these physiological and behavioral data and translate them into phenotypes and states related to mental health. Such systems aim to identify behaviors that are the consequence of an underlying physiological alteration, and hence, the raw sensor data are captured and converted into features that are used to define behavioral markers, often through machine learning. However, due to the complexity of passive data, these relationships are not simple and need to be well-established. Furthermore, parameters such as intrapersonal and interpersonal differences need to be considered when interpreting the data. Altogether, combining practical mobile and wearable systems with the right data analysis algorithms can provide a useful tool for the monitoring and management of mental disorders. The current review aims to comprehensively present and critically discuss all available smartphone-based, wearable, and environmental sensors for detecting such parameters in relation to the treatment and/or management of the most common mental health conditions.

Photoplethysmography (PPG) is a photometric technique used for the measurement of volumetric changes in the blood. The recent interest in new applications of PPG has invigorated more fundamental research regarding the origin of the PPG waveform, which since its discovery in 1937, remains inconclusive. A hand full of studies in the recent past have explored various hypotheses for the origin of PPG. These studies relate the PPG to mechanical movement, red blood cell orientation or blood volume variations. Recognising the significance and need to corroborate a theory behind the PPG formation, the present work rigorously investigates the origin of PPG based on a realistic model of light-tissue interactions. A three-dimensional comprehensive Monte Carlo model of finger-PPG was developed and explored to quantify the optical entities pertinent to PPG (e.g., absorbance, reflectance, and penetration depth) as the functions of multiple wavelengths and source-detector separations. Complementary to the simulations, a pilot in vivo investigation was conducted on eight healthy volunteers. PPG signals were recorded using a custom-made multi-wavelength sensor with an adjustable sourcedetector separation. Simulated results illustrate the distribution of photon-tissue interactions in the reflectance PPG geometry. The depth-selective analysis quantifies the contributions of the dermal and subdermal tissue layers in the PPG wave formation. A strong negative correlation (r = -0.96) is found between the ratios of the simulated absorbances and measured PPG amplitudes. This work quantified for the first time the contributions of different tissue layers and sublayers in the formation of the PPG signal.

Atherosclerosis is the most common Cardiovascular Disease (CVD) causing increased morbidity. Although atherosclerosis is a general disease involving several factors, it preferentially affects the wall of the vessel bifurcations. The employment of advanced Computational Fluid Dynamics (CFD) has the potential in shedding more light in the further understanding of the causes of the diseases and perhaps aid in the early diagnosis. In this study, a three-dimensional Fluid Structure Interaction (FSI) study was carried out for a patient specific carotid. By considering physiological conditions, first normal and then with hypertension disease, hemodynamic parameters are evaluated to better understand the genesis and progress of atherosclerotic plaques in the carotid artery bifurcation. Two-way FSI was performed by applying a fully implicit second-order backward Euler differencing scheme using commercial code ANSYS and ANSYS CFX (version 19.0). Arbitrary Lagrangian-Eulerian (ALE) formulation is employed to calculate the arterial response by using the temporal blood response. Due to arterial bifurcation, obvious velocity reduction and backflow formation is observed which decrease shear stress and made it oscillatory at the starting point of the internal carotid artery near the carotid sinus, which resulted in low shear stress. Oscillatory shear index (OSI) signifies oscillatory behavior wall shear stress. By using the anatomically realistic 3D geometry and representative physiological conditions, the results obtained for shear stress are more acceptable. Comparing the results of this study with available literature shows that the regions with low wall shear stress and with OSI value more than 0.3 face potential risk to the plaque development. It is observed that hemodynamics of carotid artery is very much affected by the geometry and flow conditions and relatively low wall shear stress regions were observed post stenosis, which is a known reason for plaque development and progression.

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Heart rate variability (HRV) utilizes the electrocardiogram (ECG) and has been widely studied as a non-invasive indicator of cardiac autonomic activity. Pulse rate variability (PRV) utilizes photoplethysmography (PPG) and recently has been used as a surrogate for HRV. Several studies have found that PRV is not entirely valid as an estimation of HRV and that several physiological factors, including the pulse transit time (PTT) and blood pressure (BP) changes, may affect PRV differently than HRV. This study aimed to assess the relationship between PRV and HRV under different BP states: hypotension, normotension, and hypertension. Using the MIMIC III database, 5 min segments of PPG and ECG signals were used to extract PRV and HRV, respectively. Several time-domain, frequency-domain, and nonlinear indices were obtained from these signals. Bland–Altman analysis, correlation analysis, and Friedman rank sum tests were used to compare HRV and PRV in each state, and PRV and HRV indices were compared among BP states using Kruskal–Wallis tests. The findings indicated that there were differences between PRV and HRV, especially in short-term and nonlinear indices, and although PRV and HRV were altered in a similar manner when there was a change in BP, PRV seemed to be more sensitive to these changes.