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Aims The aim was to investigate the relationship between microvascular function, cardiovascular risk profile, and subclinical atherosclerotic burden. Methods and results The study enrolled 3809 individuals, 50-65 years old, participating in the population-based observational cross-sectional Swedish CArdioPulmonary bioImage Study. Microvascular function was assessed in forearm skin using an arterial occlusion and release protocol determining peak blood oxygen saturation (OxyP). Cardiovascular risk was calculated using the updated Systematic Coronary Risk Evaluation [SCORE2; 10-year risk of fatal and non-fatal cardiovascular disease (CVD) events]. The OxyP was compared with coronary artery calcification score (CACS) and to plaques in the carotid arteries. Individuals with OxyP values in the lowest quartile (Q1; impaired microvascular function) had a mean SCORE2 of 5.8% compared with 3.8% in those with the highest values of OxyP (Q4), a relative risk increase of 53%. The risk of having a SCORE2 > 10% was five times higher for those in Q1 (odds ratio: 4.96, 95% confidence interval: 2.76-8.93) vs. Q4 when adjusting for body mass index and high-sensitivity C-reactive protein. The OxyP was lower in individuals with CACS > 0 and in those with both carotid plaques and CACS > 0, compared with individuals without subclinical atherosclerotic burdens (87.5 +/- 5.6% and 86.9 +/- 6.0%, vs. 88.6 +/- 5.8%, P < 0.01). Conclusion In a population without CVD or diabetes mellitus, impaired microvascular function is associated with cardiovascular risk profiles such as higher SCORE2 risk and CACS. We suggest that OxyP may serve as a microcirculatory functional marker of subclinical atherosclerosis and CVD risk that is not detected by structural assessments., Funding Agencies|Swedish Research Council [2021-06432, 2021-03690]; Swedish Heart-Lung Foundation [20210184]; Sweden's innovation agency (VINNOVA) via the programme MedTech4Health [2016-02211]; Knut and Alice Wallenberg Foundation; VINNOVA; University of Gothenburg; Sahlgrenska University Hospital; Karolinska Institutet; Stockholm County Council; Linkoping University; University Hospital; Lund University; Skane University Hospital; Umea University; Uppsala University

Background: Flowmotion is the rhythmical variations in measured skin blood flow that arise due to global and local regulation of the vessels and can be studied using frequency analysis of time-resolved blood flow signals. It has the potential to reveal clinically useful information about microvascular diseases, but the spatial heteroge-neous nature of the microvasculature makes interpretation difficult. However, recent technological advances in multi-exposure laser speckle contrast imaging (MELSCI) enable new possibilities for simultaneously studying spatial and temporal variations in flowmotion.Aim: To develop a method for flowmotion analysis of MELSCI perfusion images. Furthermore, to investigate the spatial and temporal variations in flowmotion in forearm baseline skin perfusion.Method: In four healthy subjects, forearm skin perfusion was imaged at 15.6 fps for 10 min in baseline. The time -trace signal in each pixel was analyzed using the wavelet transform and summarized in five physiologically relevant frequency intervals, resulting in images of flowmotion. Furthermore, a method for reducing the effect of motion artifacts in the flowmotion analysis was developed.Results: The flowmotion images displayed patterns of high spatial heterogeneity that differed between the fre-quency intervals. The spatial variations in flowmotion, quantified as the coefficient of variation, was between 11 % and 31 % in four subjects. Furthermore, significant temporal variations in flowmotion were also observed, indicating the importance of a spatiotemporal analysis.Conclusion: The new imaging technique reveals significant spatial differences in flowmotion that cannot be ob-tained with single-point measurements. The results indicate that global statistics of flowmotion, such as the mean value in a large region of interest, is more representative of the microcirculation than data measured only in a single point. Therefore, imaging techniques have potential to increase the clinical usef, Funding Agencies|Swedens Innovation Agency VINNOVA [2019-01522]; Swedish Society of Medicine [SLS-961560]; Swedish government and the county councils [RO-960844]

Significance: Laser speckle contrast imaging (LSCI) gives a relative measure of microcirculatory perfusion. However, due to the limited information in single-exposure LSCI, models are inaccurate for skin tissue due to complex effects from e.g. static and dynamic scatterers, multiple Doppler shifts, and the speed-distribution of blood. It has been demonstrated how to account for these effects in laser Doppler flowmetry (LDF) using inverseMonte Carlo (MC) algorithms. This allows for a speed-resolved perfusion measure in absolute units %RBC x mm/s, improving the physiological interpretation of the data. Until now, this has been limited to a single-point LDF technique but recent advances inmulti-exposure LSCI (MELSCI) enable the analysis in an imaging modality. Aim: To present a method for speed-resolved perfusion imaging in absolute units %RBC x mm/s, computed from multi-exposure speckle contrast images. Approach: An artificial neural network (ANN) was trained on a large simulated dataset of multi- exposure contrast values and corresponding speed-resolved perfusion. The dataset was generated using MC simulations of photon transport in randomized skin models covering a wide range of physiologically relevant geometrical and optical tissue properties. The ANN was evaluated on in vivo data sets captured during an occlusion provocation. Results: Speed-resolved perfusion was estimated in the three speed intervals 0 to 1 mm/s, 1 to 10 mm/s, and > 10 mm/s, with relative errors 9.8%, 12%, and 19%, respectively. The perfusion had a linear response to changes in both blood tissue fraction and blood flow speed and was less affected by tissue properties compared with single-exposure LSCI. The image quality was subjectively higher compared with LSCI, revealing previously unseen macro- and microvascular structures. Conclusions: The ANN, trained on modeled data, calculates speed-resolved perfusion in absolute units from multi-exposure speckle contrast. This method facilitates t, Funding Agencies|Swedens Innovation Agency VINNOVA via the programs Swelife and MedTech4Health [2017-01435, 2019-01522]

We present in vivo optical properties of skin tissue assessed using spatially resolved diffuse reflectance spectroscopy. Data was collected in a sub-study of the Swedish CArdioPulmonary bioImage Study (SCAPIS). Measurements were performed on 3,809 subjects, 50-65 years, on the volar side of the forearm with the PeriFlux 6000 EPOS system. The analysis consisted of an inverse Monte Carlo method where modeled spectra were non-linearly fitted to measured diffuse reflectance spectra at 0.4 and 1.2-mm source-detector fiber separations, respectively, between 475 and 850 nm. The model consisted of one epidermis layer with adaptable thickness and absorption caused by melanin, and two dermis layers. The upper dermis had a fixed thickness of 0.2 mm and the lower an infinite thickness. The absorption in the dermis layers depended on varying amounts of hemoglobin and its oxygen saturation. The reduced scattering coefficient, with three adaptable parameters, was equal in all layers in the model. Absorption coefficient in epidermis times epidermis thickness, reflecting the total amount of melanin, was 0.19 +/- 0.16 [-] at 570 nm with a significant difference between winter (0.12 +/- 0.10) and summer (0.26 +/- 0.19). Melanin absorption implies an average of 3.9% tissue fraction of melanosomes in epidermis. Absorption coefficient at the 570 nm isosbestic point was 0.19 +/- 0.11 mm(-1) in the upper dermis layer and 0.10 +/- 0.05 mm(-1) in the lower. The reduced scattering coefficient was 2.3 +/- 0.5 mm(-1) at 570 nm. Average sampling depth for all wavelengths and both separations was 0.43 +/- 0.03 mm.

Significance Knowledge of optical properties is important to accurately model light propagation in tissue, but in vivo reference data are sparse.Aim The aim of our study was to present in vivo skin optical properties from a large Swedish cohort including 3809 subjects using a three-layered skin model and spatially resolved diffuse reflectance spectroscopy (Periflux PF6000 EPOS).Approach Diffuse reflectance spectra (475 to 850 nm) at 0.4 and 1.2 mm source-detector separations were analyzed using an inverse Monte Carlo method. The model had one epidermis layer with variable thicknesses and melanin-related absorptions and two dermis layers with varying hemoglobin concentrations and equal oxygen saturations. The reduced scattering coefficient was equal across all layers.Results Median absorption coefficients (mm (- 1)) in the upper dermis ranged from 0.094 at 475 nm to 0.0048 at 850 nm and similarly in the lower dermis from 0.059 to 0.0035. The reduced scattering coefficient (mm( - 1)) ranged from 3.22 to 1.20, and the sampling depth (mm) ranged from 0.23 to 0.38 (0.4 mm separation) and from 0.49 to 0.68 (1.2 mm separation). There were differences in optical properties across sex, age groups, and BMI categories.Conclusions Reference material for skin optical properties is presented., Funding Agencies|Swedish Research Council; Swedish Research Council [2021-06432, 2021-03690]; Swedish Heart-Lung Foundation [20210184]; Sweden's Innovation Agency (VINNOVA) [2016-02211]; Knut and Alice Wallenberg Foundation; Swedish Research Council; VINNOVA; University of Gothenburg; Sahlgrenska University Hospital; Karolinska Institutet; Stockholm County Council; Linkoeping University; University Hospital; Lund University; Skane University Hospital; Umea University; Uppsala University

Background Vasomotion is the spontaneous oscillation in vascular tone in the microcirculation and is believed to be a physiological mechanism facilitating the transport of blood gases and nutrients to and from tissues. So far, Laser Doppler flowmetry has constituted the gold standard for in vivo vasomotion analysis. Materials and methods We applied vasomotion analysis to speed-resolved perfusion, oxygen saturation, red blood cell tissue (RBC) tissue fraction, and average vessel diameter from five healthy individuals at rest measured by the newly developed Periflux 6000 EPOS system over 10 minutes. Magnitude scalogram and the time-averaged wavelet spectra were divided into frequency intervals reflecting endothelial, neurogenic, myogenic, respiratory, and cardiac function. Results Recurrent high-intensity periods of the myogenic, neurogenic, and endothelial frequency intervals were found. The neurogenic activity was considerably more pronounced for the oxygen saturation, RBC tissue fraction, and vessel diameter signals, than for the perfusion signals. In a correlation analysis we found that changes in perfusion in the myogenic, neurogenic, and endothelial frequency intervals precede changes in the other signals. Furthermore, changes in average vessel diameter were in general negatively correlated to the other signals in the same frequency intervals, indicating the importance of capillary recruitment. Conclusion We conclude that vasomotion can be observed in signals reflecting speed resolved perfusion, oxygen saturation, RBC tissue fraction, and vessel diameter. The new parameters enable new aspects of the microcirculation to be observed.

Obesity is a widespread chronic illness which affects over 40% of the US adult population and its world-wide prevalence has increased over the years impacting both low and high-income countries. Obesity has been linked to higher risk of non-communicable diseases such as cardiovascular disease, type-2 diabetes, dyslipidemia, hypertension, among others. Currently the mostly prescribed regimes to combat chronic illness associated with obesity are efforts to change diet, behavior, and physical activity. Wearable devices have the potential of helping users reduce their obesity levels as these devices can easily acquire and communicate biometric data with users and clinicians. However, these technologies depend on optical sensors that are sensitive to molecular skin composition. We hypothesize that individuals with high BMI levels will present changes in skin optical properties when compared to their non-obese counterparts. Our objective is to capture skin optical properties at the wrist among a diverse cohort using a commercial optical system for research use. To meet an appropriate power, the human study, composed of males and females, is conducted with 100 adult participants. Statistical methods, including linear regression and t-tests, are used to determine interactions between measured data and participant demographics. We believe these results can improve design of optical wearables for the obese., Funding Agencies|National Science Foundation Engineering Research Center for Precise Advanced Technologies and Health Systems for Underserved Populations (PATHS-UP) [1648451]

Driver sleepiness is a contributing factor in many road fatalities. A long-standing goal in driver state research has therefore been to develop a robust sleepiness detection system. It has been suggested that various heart rate variability (HRV) metrics can be used for driver sleepiness classification. However, since heart rate is modulated not only by sleepiness but also by several other time-varying intra-individual factors such as posture, distress, boredom and relaxation, it is relevant to highlight not only the possibilities but also the difficulties involved in HRV-based driver sleepiness classification. This paper investigates the reliability of HRV as a standalone feature for driver sleepiness detection in a realistic setting. Data from three real-road driving studies were used, including 86 drivers in both alert and sleep-deprived conditions. Subjective ratings based on the Karolinska sleepiness scale (KSS) were used as ground truth when training four binary classifiers (k-nearest neighbours, support vector machine, AdaBoost, and random forest). The best performance was achieved with the random forest classifier with an accuracy of 85%. However, the accuracy dropped to 64% for three-class classification and to 44% for subject-independent, leave-one-participant-out classification. The worst results were obtained in the severely sleepy class. The results show that in realistic driving conditions, subject-independent sleepiness classification based on HRV is poor. The conclusion is that more work is needed to control for the many confounding factors that also influence HRV before it can be used as input to a driver sleepiness detection system., Funding Agencies|ADAS&ME Project - European Unions Horizon 2020 Research and Innovation Programme [688900]

Significance: Multi-exposure laser speckle contrast imaging (MELSCI) estimates microcirculatory blood perfusion more accurately than single-exposure LSCI. However, the technique has been hampered by technical limitations due to massive data throughput requirements and nonlinear inverse search algorithms, limiting it to an offline technique where data must be postprocessed. Aim: To present an MELSCI system capable of continuous acquisition and processing of MELSCI data, enabling real-time video-rate perfusion imaging with high accuracy. Approach: The MELSCI algorithm was implemented in programmable hardware (field programmable gate array) closely interfaced to a high-speed CMOS sensor for real-time calculation. Perfusion images were estimated in real-time from the MELSCI data using an artificial neural network trained on simulated data. The MELSCI perfusion was compared to two existing single-exposure metrics both quantitatively in a controlled phantom experiment and qualitatively in vivo. Results: The MELSCI perfusion shows higher signal dynamics compared to both single-exposure metrics, both spatially and temporally where heartbeat-related variations are resolved in much greater detail. The MELSCI perfusion is less susceptible to measurement noise and is more linear with respect to laser Doppler perfusion in the phantom experiment (R-2 = 0.992). Conclusions: The presented MELSCI system allows for real-time acquisition and calculation of high-quality perfusion at 15.6 frames per second. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License., Funding Agencies|Swedish Research CouncilSwedish Research Council [2014-6141]; Swedens Innovation Agency VINNOVAvia the programs Swelife and MedTech4Health [2017-01435, 2019-01522]

Significance: Diffuse reflectance spectroscopy (DRS) is frequently used to assess oxygen saturation and hemoglobin concentration in living tissue. Methods solving the inverse problem may include time-consuming nonlinear optimization or artificial neural networks (ANN) determining the absorption coefficient one wavelength at a time. Aim: To present an ANN-based method that directly outputs the oxygen saturation and the hemoglobin concentration using the shape of the measured spectra as input. Approach: A probe-based DRS setup with dual source-detector separations in the visible wavelength range was used. ANNs were trained on spectra generated from a three-layer tissue model with oxygen saturation and hemoglobin concentration as target. Results: Modeled evaluation data with realistic measurement noise showed an absolute root-mean-square (RMS) deviation of 5.1% units for oxygen saturation estimation. The relative RMS deviation for hemoglobin concentration was 13%. This accuracy is at least twice as good as our previous nonlinear optimization method. On blood-intralipid phantoms, the RMS deviation from the oxygen saturation derived from partial oxygen pressure measurements was 5.3% and 1.6% in two separate measurement series. Results during brachial occlusion showed expected patterns. Conclusions: The presented method, directly assessing oxygen saturation and hemoglobin concentration, is fast, accurate, and robust to noise. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License., Funding Agencies|Swedish Research CouncilSwedish Research Council [2014-6141]; Swedens innovation agency VINNOVA via the program MedTech4Health [2016-02211]; Swedens innovation agency VINNOVA via the program Swelife and MedTech4Health [2017-01435]

Approximately 20-30% of all road fatalities are related to driver sleepiness. A long-lasting goal in driver state research has therefore been to develop a robust sleepiness detection system. Since the alertness level is reflected in autonomous nervous system activity, it has been suggested that various heart rate variability (HRV) metrics can be used as features for driver sleepiness classification. Since the heart rate is modulated by many different factors, and not just by sleepiness, it is relevant to question the high driver sleepiness classification accuracies that have occasionally been presented in the literature. The main objective of this paper is thus to test how well a sleepiness classification system based on HRV features really is. A unique data set with 86 drivers, obtained while driving on real roads in real traffic, both in alert and sleep deprived conditions, was used to train and test a support vector machine (SVM) classifier. Subjective ratings based on the Karolinska sleepiness scale (KSS) was used as ground truth to divide the data into three classes (alert, somewhat sleepy and severely sleepy). Even though nearly all the 24 investigated HRV metrics showed significant differences between sleepiness levels, the SVM results only reached a mean accuracy of 61 %, with the worst results originating from the severely sleepy cases. In summary, the high classification performance that may arise in studies with high experimental control could not be replicated under realistic driving conditions. Future works should focus on how various confounding factors should be accounted for when using HRV based metrics as input to a driver sleepiness detection system.

Spatial frequency domain imaging (SFDI) utilizes a digital light processing (DLP) projector for illuminating turbid media with sinusoidal patterns. The tissue absorption (μa) and reduced scattering coefficient (μ,s) are calculated by analyzing the modulation transfer function for at least two spatial frequencies. We evaluated different illumination strategies with a red, green and blue light emitting diodes (LED) in the DLP, while imaging with a filter mosaic camera, XiSpec, with 16 different multi-wavelength sensitive pixels in the 470-630 nm wavelength range. Data were compared to SFDI by a multispectral camera setup (MSI) consisting of four cameras with bandpass filters centered at 475, 560, 580 and 650 nm. A pointwise system for comprehensive microcirculation analysis was used (EPOS) for comparison. A 5-min arterial occlusion and release protocol on the forearm of a Caucasian male with fair skin was analyzed by fitting the absorption spectra of the chromophores HbO2, Hb and melanin to the estimatedμa. The tissue fractions of red blood cells (fRBC), melanin (/mel) and the Hb oxygenation (S02 ) were calculated at baseline, end of occlusion, early after release and late after release. EPOS results showed a decrease in S02 during the occlusion and hyperemia during release (S02 = 40%, 5%, 80% and 51%). The fRBC showed an increase during occlusion and release phases. The best MSI resemblance to the EPOS was for green LED illumination (S02 = 53%, 9%, 82%, 65%). Several illumination and analysis strategies using the XiSpec gave un-physiological results (e.g. negative S02 ). XiSpec with green LED illumination gave the expected change in /RBC , while the dynamics in S02 were less than those for EPOS. These results may be explained by the calculation of modulation using an illumination and detector setup with a broad spectral transmission bandwidth, with considerable variation in μa of included chromophores. Approaches for either reducing the effective bandwidth of the XiS

We present a first evaluation of a new multi-exposure laser speckle contrast imaging (MELSCI) system for assessing spatial variations in the microcirculatory perfusion. The MELSCI system is based on a 1000 frames per second 1-megapixel camera connected to a field programmable gate arrays (FPGA) capable of producing MELSCI data in realtime. The imaging system is evaluated against a single point laser Doppler flowmetry (LDF) system during occlusionrelease provocations of the arm in five subjects. Perfusion is calculated from MELSCI data using current state-of-the-art inverse models. The analysis displayed a good agreement between measured and modeled data, with an average error below 6%. This strongly indicates that the applied model is capable of accurately describing the MELSCI data and that the acquired data is of high quality. Comparing readings from the occlusion-release provocation showed that the MELSCI perfusion was significantly correlated (R=0.83) to the single point LDF perfusion, clearly outperforming perfusion estimations based on a single exposure time. We conclude that the MELSCI system provides blood flow images of enhanced quality, taking us one step closer to a system that accurately can monitor dynamic changes in skin perfusion over a large area in real-time