Detecting Vascular Age Using the Analysis of Peripheral Pulse

Development of a recommendation system and data analysis in personalized medicine: an approach towards healthy vascular ageing

Purpose

Understanding early vascular ageing has become crucial for preventing adverse cardiovascular events. To this respect, recent AI-based risk clustering models offer early detection strategies focused on healthy populations, yet their complexity limits clinical use. This work introduces a novel recommendation system embedded in a web app to assess and mitigate early vascular ageing risk, leading patients towards improved cardiovascular health.

Methods

This system employs a methodology that calculates distances within multidimensional spaces and integrates cost functions to obtain personalized optimisation of recommendations. It also incorporates a classification system for determining the intensity levels of the clinical interventions.

Results

The recommendation system showed high efficiency in identifying and visualizing individuals at high risk of early vascular ageing among healthy patients. Additionally, the system corroborated its consistency and reliability in generating personalized recommendations among different levels of granularity, emphasizing its focus on moderate or low-intensity recommendations, which could improve patient adherence to the intervention.

Conclusion

This tool might significantly aid healthcare professionals in their daily analysis, improving the prevention and management of cardiovascular diseases.

Malignant arrhythmias detection using a synthesis-by-analysis modeling method of arterial blood pressure signal

Extreme bradycardia, extreme tachycardia, ventricular flutter fib, and ventricular tachycardia are four malignant arrhythmias (MAs) that lead to sudden cardiac death. It is very important to detect them in daily life. The arterial blood pressure (ABP) signal contains abundant pathological information about four MAs and is easy to be recorded under domestic conditions. Thus, a synthesis-by-analysis (SA) modeling method for ABP signal was proposed to detect the four MAs in this study. The average models of MAs and healthy subjects were obtained by SA modeling, and the change of each ABP wave was quantitively described by twelve parameters of wave models. Then, the probabilistic neural network (PNN) and random forest (RF) are trained to detect the MAs. The experimental data were employed from Fantasia and the 2015 PhysioNet/CinC Challenge databases. The SA modeling results show that some pathological and physiological changes could be extracted from the average models. The two-sample ks-test results between different groups are markedly different (h = 1, p < 0.05). The detection results show that the performances of PPN classifiers are less than that of RF. The kappa coefficients (KC) for the RF classifiers are 97.167 ± 1.46 %, 97.888 ± 0.808 %, 99.895 ± 0.545 %, 98.575 ± 1.683 % and 92.241 ± 1.517 %, respectively. The mean KC is 97.083 ± 0.67 %. Compared to the performance of some existing studies, the proposed method has better performance and is potential to diagnose MAs in m-health.

Photoplethysmography for the Assessment of Arterial Stiffness

This review outlines the latest methods and innovations for assessing arterial stiffness, along with their respective advantages and disadvantages. Furthermore, we present compelling evidence indicating a recent growth in research focused on assessing arterial stiffness using photoplethysmography (PPG) and propose PPG as a potential tool for assessing vascular ageing in the future. Blood vessels deteriorate with age, losing elasticity and forming deposits. This raises the likelihood of developing cardiovascular disease (CVD), widely reported as the global leading cause of death. The ageing process induces structural modifications in the vascular system, such as increased arterial stiffness, which can cause various volumetric, mechanical, and haemodynamic alterations. Numerous techniques have been investigated to assess arterial stiffness, some of which are currently used in commercial medical devices and some, such as PPG, of which still remain in the research space.

Wireless Dynamic Light Scattering Sensors Detect Microvascular Changes Associated With Ageing and Diabetes

This article presents clinical results of wireless portable dynamic light scattering sensors that implement laser Doppler flowmetry signal processing. It has been verified that the technology can detect microvascular changes associated with diabetes and ageing in volunteers. Studies were conducted primarily on wrist skin. Wavelet continuous spectrum calculation was used to analyse the obtained time series of blood perfusion recordings with respect to the main physiological frequency ranges of vasomotions. In patients with type 2 diabetes, the area under the continuous wavelet spectrum in the endothelial, neurogenic, myogenic, and cardio frequency ranges showed significant diagnostic value for the identification of microvascular changes. Aside from spectral analysis, autocorrelation parameters were also calculated for microcirculatory blood flow oscillations. The groups of elderly volunteers and patients with type 2 diabetes, in comparison with the control group of younger healthy volunteers, showed a statistically significant decrease of the normalised autocorrelation function in time scales up to 10 s. A set of identified parameters was used to test machine learning algorithms to classify the studied groups of young controls, elderly controls, and diabetic patients. Our conclusion describes and discusses the classification metrics that were found to be most effective.

Automatic COVID-19 severity assessment from HRV

COVID-19 is known to be a cause of microvascular disease imputable to, for instance, the cytokine storm inflammatory response and the consequent blood coagulation. In this study, we propose a methodological approach for assessing the COVID-19 presence and severity based on Random Forest (RF) and Support Vector Machine (SVM) classifiers. Classifiers were applied to Heart Rate Variability (HRV) parameters extracted from photoplethysmographic (PPG) signals collected from healthy and COVID-19 affected subjects. The supervised classifiers were trained and tested on HRV parameters obtained from the PPG signals in a cohort of 50 healthy subjects and 93 COVID-19 affected subjects, divided into two groups, mild and moderate, based on the support of oxygen therapy and/or ventilation. The most informative feature set for every group's comparison was determined with the Least Absolute Shrinkage and Selection Operator (LASSO) technique. Both RF and SVM classifiers showed a high accuracy percentage during groups' comparisons. In particular, the RF classifier reached 94% of accuracy during the comparison between the healthy and minor severity COVID-19 group. Obtained results showed a strong capability of RF and SVM to discriminate between healthy subjects and COVID-19 patients and to differentiate the two different COVID-19 severity. The proposed method might be helpful for detecting, in a low-cost and fast fashion, the presence and severity of COVID-19 disease; moreover, these reasons make this method interesting as a starting point for future studies that aim to investigate its effectiveness as a possible screening method.

Pulse Wave Analysis Method of Cardiovascular Parameters Extraction for Health Monitoring

OBJECTIVE

A pulse waveform is regarded as an information carrier of the cardiovascular system, which contains multiple interactive cardiovascular parameters reflecting physio-pathological states of bodies. Hence, multiple parameter analysis is increasingly meaningful to date but still cannot be easily achieved one by one due to the complex mapping between waveforms. This paper describes a new analysis method based on waveform recognition aimed for extracting multiple cardiovascular parameters to monitor public health. The objective of this new method is to deduce multiple cardiovascular parameters for a target pulse waveform based on waveform recognition to a most similar reference waveform in a given database or pattern library.

METHODS

The first part of the methodology includes building the sub-pattern libraries and training classifier. This provides a trained classifier and the sub-pattern library with reference pulse waveforms and known parameters. The second part is waveform analysis. The target waveform will be classified and output a state category being used to select the corresponding sub-pattern library with the same state. This will reduce subsequent recognition scope and computation costs. The mainstay of this new analysis method is improved dynamic time warping (DTW). This improved DTW and K-Nearest Neighbors (KNN) were applied to recognize the most similar waveform in the pattern library. Hence, cardiovascular parameters can be assigned accordingly from the most similar waveform in the pattern library.

RESULTS

Four hundred and thirty eight (438) randomly selected pulse waveforms were tested to verify the effectiveness of this method. The results show that the classification accuracy is 96.35%. Using statistical analysis to compare the target sample waveforms and the recognized reference ones from within the pattern library, most correlation coefficients are beyond 0.99. Each set of cardiovascular parameters was assessed using the Bland-Altman plot. The extracted cardiovascular parameters are in strong agreement with the original verifying the effectiveness of this new approach.

CONCLUSION

This new method using waveform recognition shows promising results that can directly extract multiple cardiovascular parameters from waveforms with high accuracy. This new approach is efficient and effective and is very promising for future continuous monitoring of cardiovascular health.

Wrist pulse signal based vascular age calculation using mixed Gaussian model and support vector regression

Purpose

Vascular age (VA) is the direct index to reflect vascular aging, so it plays a particular role in public health. How to obtain VA conveniently and cheaply has always been a research hotspot. This study proposes a new method to evaluate VA with wrist pulse signal.

Methods

Firstly, we fit the pulse signal by mixed Gaussian model (MGM) to extract the shape features, and adopt principal component analysis (PCA) to optimize the dimension of the shape features. Secondly, the principal components and chronological age (CA) are respectively taken as the independent variables and dependent variable to establish support vector regression (SVR) model. Thirdly, the principal components are fed into the SVR model to predicted the vascular aging of each subject. The predicted value is regarded as the description of VA. Finally, we compare the correlation coefficients of VA with pulse width (PW), inflection point area ratio (IPA), Ratio b/a (RBA), augmentation index (AIx), diastolic augmentation index (DAI) and pulse transit time (PTT) with those of CA with these six indices.

Results

Compared with the CA, the VA is closer to PW (r = 0.539, P < 0.001 to r = 0.589, P < 0.001 in men; r = 0.325, P < 0.001 to r = 0.400, P < 0.001 in women), IPA (r =  - 0.446, P < 0.001 to r =  - 0.534, P < 0.001 in men; r =  - 0.623, P < 0.001 to r =  - 0.660, P < 0.001 in women), RBA (r = 0.328, P < 0.001 to r = 0.371, P < 0.001 in women), AIx (r = 0.659, P < 0.001 to r = 0.738, P < 0.001 in men; r = 0.547, P < 0.001 to r = 0.573, P < 0.001 in women), DAI (r = 0.517, P < 0.001 to r = 0.532, P < 0.001 in men; r = 0.507, P < 0.001 to r = 0.570, P < 0.001 in women) and PTT (r = 0.526, P < 0.001 to r = 0.659, P < 0.001 in men; r = 0.577, P < 0.001 to r = 0.814, P < 0.001 in women).

Conclusion

The VA is more representative of vascular aging than CA. The method presented in this study provides a new way to directly and objectively assess vascular aging in public health.

COVID-19 detection using a model of photoplethysmography (PPG) signals

Objective:Coronavirus disease 2019 (COVID-19) targets several tissues of the human body; among these, a serious impact has been observed in the microvascular system. The aim of this study was to verify the presence of photoplethysmographic (PPG) signal modifications in patients affected by COVID-19 at different levels of severity.

Approach: The photoplethysmographic signal was evaluated in 93 patients with COVID-19 of different severity (46: grade 1; 47: grade 2) and in 50 healthy control subjects. A pre-processing step removes the long-term trend and segments of each pulsation in the input signal. Each pulse is approximated with a model generated from a multi-exponential curve, and a Least Squares fitting algorithm determines the optimal model parameters. Using the parameters of the mathematical model, three different classifiers (Bayesian, SVM and KNN) were trained and tested to discriminate among healthy controls and patients with COVID, stratified according to the severity of the disease. Results are validated with the leave-one-subject-out validation method.

Main results: Results indicate that the fitting procedure obtains a very high determination coefficient (above 99% in both controls and pathological subjects). The proposed Bayesian classifier obtains promising results, given the size of the dataset, and variable depending on the classification strategy. The optimal classification strategy corresponds to 79% of accuracy, with 90% of specificity and 67% of sensibility.

Significance:The proposed approach opens the possibility of introducing a low cost and non-invasive screening procedure for the fast detection of COVID-19 disease, as well as a promising monitoring tool for hospitalized patients, with the purpose of stratifying the severity of the disease.

The Hybrid Excess and Decay (HED) model: an automated approach to characterising changes in the photoplethysmography pulse waveform

Photoplethysmography offers a widely used, convenient and non-invasive approach to monitoring basic indices of cardiovascular function, such as heart rate and blood oxygenation. Systematic analysis of the shape of the waveform generated by photoplethysmography might be useful to extract estimates of several physiological and psychological factors influencing the waveform. Here, we developed a robust and automated method for such a systematic analysis across individuals and across different physiological and psychological contexts. We describe a psychophysiologically-relevant model, the Hybrid Excess and Decay (HED) model, which characterises pulse wave morphology in terms of three underlying pressure waves and a decay function. We present the theoretical and practical basis for the model and demonstrate its performance when applied to a pharmacological dataset of 105 participants receiving intravenous administrations of the sympathomimetic drug isoproterenol (isoprenaline). We show that these parameters capture photoplethysmography data with a high degree of precision and, moreover, are sensitive to experimentally-induced changes in interoceptive arousal within individuals. We conclude by discussing the possible value in using the HED model as a complement to standard measures of photoplethysmography signals.

Topical Reappraisal of Molecular Pharmacological Approaches to Endothelial Dysfunction in Diabetes Mellitus Angiopathy

Diabetes mellitus (DM) is a frequent medical problem, affecting more than 4% of the population in most countries. In the context of diabetes, the vascular endothelium can play a crucial pathophysiological role. If a healthy endothelium—which is a dynamic endocrine organ with autocrine and paracrine activity—regulates vascular tone and permeability and assures a proper balance between coagulation and fibrinolysis, and vasodilation and vasoconstriction, then, in contrast, a dysfunctional endothelium has received increasing attention as a potential contributor to the pathogenesis of vascular disease in diabetes. Hyperglycemia is indicated to be the major causative factor in the development of endothelial dysfunction. Furthermore, many shreds of evidence suggest that the progression of insulin resistance in type 2 diabetes is parallel to the advancement of endothelial dysfunction in atherosclerosis. To present the state-of-the-art data regarding endothelial dysfunction in diabetic micro- and macroangiopathy, we constructed this literature review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). We interrogated five medical databases: Elsevier, PubMed, PMC, PEDro, and ISI Web of Science.

Photoplethysmogram based vascular aging assessment using the deep convolutional neural network

Arterial stiffness due to vascular aging is a major indicator during the assessment of cardiovascular risk. In this study, we propose a method for age estimation by applying deep learning to a photoplethysmogram (PPG) for the non-invasive assessment of the vascular age. The proposed deep learning-based age estimation model consists of three convolutional layers and two fully connected layers, and was developed as an explainable artificial intelligence model with Grad-Cam to explain the contribution of the PPG waveform characteristic to vascular age estimation. The deep learning model was developed using a segmented PPG by pulse from a total of 752 adults aged 20-89 years, and the performance was quantitatively evaluated using the mean absolute error, root-mean-squared-error, Pearson's correlation coefficient, and coefficient of determination between the actual and estimated ages. As a result, a mean absolute error of 8.1 years, root mean squared error of 10.0 years, correlation coefficient of 0.61, and coefficient of determination of 0.37, were obtained. A Grad-Cam, used to determine the weight that the input signal contributes to the result, was employed to verify the contribution to the age estimation of the PPG segment, which was high around the systolic peak. The results of this study suggest that a convolutional-neural-network-based explainable artificial intelligence model outperforms existing models without an additional feature detection process. Moreover, it can provide a rationale for PPG-based vascular aging assessment.

Detecting sepsis from photoplethysmography: strategies for dataset preparation

Sepsis is one of the most frequent causes of death in Intensive Care Units, and its prognosis greatly depend on timeliness of diagnosis. MIMIC-III database is a frequent source of data for developing method for automatic sepsis detection. However, the heterogeneity of data jeopardize the feasibility of the task. In this work we propose a selection strategy for generating high quality data suitable for training a sepsis detection system based on the utilization of only plethysmographic data. Clinical relevance A system for detecting sepsis based only on PPG may be potentially at virtually no cost in any case clinicians suspect the possibility of developing sepsis.

Cardiovascular Dynamics in COVID-19: A Heart Rate Variability Investigation

COVID-19 is known to be a cause of microvascular disease due, for example, to the cytokine storm inflammatory response and the result of blood coagulation. This study reports an investigation on Heart Rate Variability (HRV) extracted from photoplethysmography (PPG) signals measured from healthy subjects and COVID-19 affected patients. We aimed to determine a statistical difference between HRV parameters among subjects' groups. Specifically, statistical analysis through Mann-Whitney U Test (MWUT) was applied to compare 42 dif-ferent parameters extracted from PPG signals of 143 subjects: 50 healthy subjects (i.e. group 0) and 93 affected from COVID-19 patients stratified through increasing COVID severity index (i.e. groups 1 and 2). Results showed significant statistical differences between groups in several HRV parameters. In particular, Multiscale Entropy (MSE) analysis provided the master key in patient stratification assessment. In fact, MSE11, MSE12, MSE15, MSE16, MSE17, MSE18, MSE19 and MSE20 keep statistical significant difference during all the comparisons between healthy subjects and patients from all the pathological groups. Our preliminary results suggest that it could be possible to distinguish between healthy and COVID-19 affected subjects based on cardiovascular dynamics. This study opens to future evaluations in using machine learning models for automatic decision-makers to distinguish between healthy and COVID-19 subjects, as well as within COVID-19 severity levels. Clinical Relevance - This establishes the possibility to distin-guish healthy subjects from COVID-19 affected patients basing on HRV parameters monitored non invasively by PPG.

The pulse waveform quantification method basing on contour and derivative

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.

Discrimination of the Cognitive Function of Community Subjects Using the Arterial Pulse Spectrum and Machine-Learning Analysis

Early identification of cognitive impairment would allow affected patients to receive care at earlier stage. Changes in the arterial stiffness have been identified as a prominent pathological feature of dementia. This study aimed to verify if applying machine-learning analysis to spectral indices of the arterial pulse waveform can be used to discriminate different cognitive conditions of community subjects. 3-min Radial arterial blood pressure waveform (BPW) signals were measured noninvasively in 123 subjects. Eight machine-learning algorithms were used to evaluate the following 4 pulse indices for 10 harmonics (total 40 BPW spectral indices): amplitude proportion and its coefficient of variation; phase angle and its standard deviation. Significant differences were noted in the spectral pulse indices between Alzheimer’s-disease patients and control subjects. Using them as training data (AUC = 70.32% by threefold cross-validation), a significant correlation (R² = 0.36) was found between the prediction probability of the test data (comprising community subjects at two sites) and the Mini-Mental-State-Examination score. This finding illustrates possible physiological connection between arterial pulse transmission and cognitive function. The present findings from pulse-wave and machine-learning analyses may be useful for discriminating cognitive condition, and hence in the development of a user-friendly, noninvasive, and rapid method for the early screening of dementia.

Vascular ageing and peripheral pulse: an improved model for assessing their relationship

Objective.Vascular ageing is associated with several alterations, including arterial stiffness and endothelial dysfunction. Such alterations represent an independent factor in the development of cardiovascular disease (CVD). In our previous works we demonstrated the alterations occurring in the vascular system are themselves reflected in the shape of the peripheral waveform; thus, a model that describes the waveform as a sum of Gaussian curves provides a set of parameters that successfully discriminate betweenunder(≤35 years old) andoversubjects (>35 years old). In the present work, we explored the feasibility of a new decomposition model, based on a sum of exponential pulses, applied to the same problem.Approach.The first processing step extracts each pulsation from the input signal and removes the long-term trend using a cubic spline with nodes between consecutive pulsations. After that, a Least Squares fitting algorithm determines the set of optimal model parameters that best approximates each single pulse. The vector of model parameters gives a compact representation of the pulse waveform that constitutes the basis for the classification step. Each subject is associated to his/her 'representative' pulse waveform, obtained by averaging the vector parameters corresponding to all pulses. Finally, a Bayesan classifier has been designed to discriminate the waveforms of under and over subjects, using the leave-one-subject-out validation method.Main results.Results indicate that the fitting procedure reaches a rate of 96% in under subjects and 95% in over subjects and that the Bayesan classifier is able to correctly classify 91% of the subjects with a specificity of 94% and a sensibility of 84%.Significance.This study shows a sensible vascular age estimation accuracy with a multi-exponential model, which may help to predict CVD.

Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research

Vascular ageing biomarkers have been found to be predictive of cardiovascular risk independently of classical risk factors, yet are not widely used in clinical practice. In this review, we present two basic approaches for using machine learning (ML) to assess vascular age: parameter estimation and risk classification. We then summarize their role in developing new techniques to assess vascular ageing quickly and accurately. We discuss the methods used to validate ML-based markers, the evidence for their clinical utility, and key directions for future research. The review is complemented by case studies of the use of ML in vascular age assessment which can be replicated using freely available data and code.

Discrimination of vascular aging using the arterial pulse spectrum and machine-learning analysis

Aging contributes to the progression of vascular dysfunction and is a major nonreversible risk factor for cardiovascular disease. The aim of this study was to determine the effectiveness of using arterial pulse-wave measurements, frequency-domain pulse analysis, and machine-learning analysis in distinguishing vascular aging. Radial pulse signals were measured noninvasively for 3 min in 280 subjects aged 40-80 years. The cardio-ankle vascular index (CAVI) was used to evaluate the arterial stiffness of the subjects. Forty frequency-domain pulse indices were used as features, comprising amplitude proportion (C_n), coefficient of variation of C_n, phase angle (P_n), and standard deviation of P_n (n = 1-10). Multilayer perceptron and random forest with supervised learning were used to classify the data. The detected differences were more prominent in the subjects aged 40-50 years. Several indices differed significantly between the non-vascular-aging group (aged 40-50 years; CAVI <9) and the vascular-aging group (aged 70-80 years). Fivefold cross-validation revealed an excellent ability to discriminate the two groups (the accuracy was >80%, and the AUC was >0.8). For subjects aged 50-60 and 60-70 years, the detection accuracies of the two trained algorithms were around 80%, with AUCs of >0.73 for both, which indicated acceptable discrimination. The present method of frequency-domain analysis may improve the index reliability for further machine-learning analyses of the pulse waveform. The present noninvasive and objective methodology may be meaningful for developing a wearable-device system to reduce the threat of vascular dysfunction induced by vascular aging.

Inter-Patient Classification With Encoded Peripheral Pulse Series and Multi-Task Fusion CNN: Application in Type 2 Diabetes

Diabetes mellitus, a chronic disease associated with elevated accumulation of glucose in the blood, is generally diagnosed through an invasive blood test such as oral glucose tolerance test (OGTT). An effective method is proposed to test type 2 diabetes using peripheral pulse waves, which can be measured fast, simply and inexpensively by a force sensor on the wrist over the radial artery. A self-designed pulse waves collection platform includes a wristband, force sensor, cuff, air tubes, and processing module. A dataset was acquired clinically for more than one year by practitioners. A group of 127 healthy candidates and 85 patients with type 2 diabetes, all between the ages of 45 and 70, underwent assessments in both OGTT and pulse data collection at wrist arteries. After preprocessing, pulse series were encoded as images using the Gramian angular field (GAF), Markov transition field (MTF), and recurrence plots (RPs). A four-layer multi-task fusion convolutional neural network (CNN) was developed for feature recognition, the network was well-trained within 30 minutes based on our server. Compared to single-task CNN, multi-task fusion CNN was proved better in classification accuracy for nine of twelve settings with empirically selected parameters. The results show that the best accuracy reached 90.6% using an RP with threshold ϵ of 6000, which is competitive to that using state-of-the-art algorithms in diabetes classification.

Can Blood Flow be Used to Monitor Changes in Peripheral Vascular Function That Occur in Response to Segmental Vibration Exposure?

OBJECTIVES

Laser Doppler blood flow measurements have been used for diagnosis or detection of peripheral vascular dysfunction. This study used a rat tail model of vibration-induced vascular injury to determine how laser Doppler measurements were affected by acute and repeated exposures to vibration, and to identify changes in the Doppler signal that were associated with the exposure.

METHODS

Blood flow was measured immediately after a single exposure to vibration, or before vibration exposure on days 1, 5, 10, 15, and 20 of a 20 days exposure.

RESULTS

After a single exposure to vibration, average tail blood flow was reduced. With 20 days of exposure, there was a reduction in the amplitude of the arterial pulse on days 10 to 20 in vibrated rats and days 15 to 20 in control rats.

CONCLUSIONS

More detailed statistical analyses of laser Doppler data may be needed to identify early changes in peripheral circulation after exposure to vibration.

Pulse decomposition analysis in photoplethysmography imaging

OBJECTIVE

Photoplethysmography imaging (PPGI) has gained immense attention over the last few years but only a few works have addressed morphological analysis so far. Pulse wave decomposition (PWD), i.e. the decomposition of a pulse wave by a varying number of kernels, allows for such analyses. This work investigates the applicability of PWD algorithms in the context of PPGI.

APPROACH

We used simulated and experimental data to compare various PWD algorithms from the literature regarding their robustness against noise and motion artifacts while preserving morphological information as well as regarding their ability to reveal physiological changes by PPGI.

MAIN RESULTS

Our experiments prove that algorithms that combine Gamma and Gaussian distributions outperform other choices. Further, algorithms with two kernels exhibit the highest robustness against noise and motion artifacts (improvement in [Formula: see text] of 14.09 %) while preserving the morphology similarly to algorithms using more kernels. Lastly, we showed that PWD can reveal physiological changes upon distal stimuli by PPGI.

SIGNIFICANCE

This work proves the feasibility of pulse decomposition analysis in PPGI, particularly for algorithms with a low number of kernels, and opens up novel applications for PPGI. Not only for PPGI but for future research on PWD in general, our findings have importance as they elucidate differences between PWD algorithms and emphasize the importance of using initial values. To support such future research, we have released the algorithms and simulated data to the public.

Pulse decomposition analysis in camera-based photoplethysmography

Imaging photoplethysmography (iPPG) is an interesting alternative to laser speckle contrast imaging for the analysis of spatio-temporal patterns in the cutaneous microcirculation. Recent years have witnessed the development of sophisticated techniques for the non-invasive extraction of vascular-related features. These techniques, referred to as pulse decomposition algorithms (PDA), most often involve the analysis of photoplethysmographic waves. This study validated the use of a multi-Gaussian (PDA) for the automatic mapping of iPPG pulse waveforms acquired with a standard camera. We show that iPPG-based PDA can reveal differences in skin perfusion in response to cold stimuli. The study thus proves the potential for morphological analyses of the iPPG pulse waveform.

Assessment of cutaneous microcirculation by laser Doppler flowmetry in type 1 diabetes

BACKGROUND

The burden of type 1 diabetes (T1D) is growing worldwide, stressing the requirement to limit the threat of its long-term complications. In this regard, the development of methods for the early diagnosis and non-invasive monitoring of vascular abnormalities is widely recognized as one of the greatest priorities of the clinical research in this field.

OBJECTIVE

To assess the deterioration of physiological properties extracted from laser Doppler flowmetry (LDF) signals of microvascular perfusion and, secondly, to investigate their association with the quality of long-term metabolic control.

METHODS

Microvascular perfusion was recorded at the hallux of 63 control subjects and 47 T1D patients, whose glycaemic control was characterized in terms of the annual average levels of glycosylated haemoglobin (HbA1c). Pulse Decomposition Analysis was applied to the LDF data, in order to derive non-invasive markers of vascular stiffness based on a multi-Gaussian representation of the peripheral pulse waveforms; furthermore, wavelet transform analysis was used to evaluate the microvascular myogenic vasomotion and, finally, a physiological model of the reactive hyperaemia to a local thermal stimulus at 43 was used to test the integrity of the neurovascular pathways.

RESULTS

Compared to the control group, T1D patients showed a lower microvascular perfusion at baseline, and a larger vasodilatory reserve upon local heating, but no significant difference in myogenic activity. Moreover, the results of the PDA carried out on the LDF pulse waves, indicate the presence of a significant strong relation between large artery stiffness and the overall loss of glycaemic control over the past year.

Acute Vibration Induces Peripheral Nerve Sensitization in a Rat Tail Model: Possible Role of Oxidative Stress and Inflammation

Prolonged occupational exposure to hand-held vibrating tools leads to pain and reductions in tactile sensitivity, grip strength and manual dexterity. The goal of the current study was to use a rat-tail vibration model to determine how vibration frequency influences factors related to nerve injury and dysfunction. Rats were exposed to restraint, or restraint plus tail vibration at 62.5 Hz or 250 Hz. Nerve function was assessed using the current perception threshold (CPT) test. Exposure to vibration at 62.5 and 250 Hz, resulted in a reduction in the CPT at 2000 and 250-Hz electrical stimulation (i.e. increased Aβ and Aδ, nerve fiber sensitivity). Vibration exposure at 250 Hz also resulted in an increased sensitivity of C-fibers to electrical stimulation and thermal nociception. These changes in nerve fiber sensitivity were associated with increased expression of interleukin (IL)-1β and tumor necrosis factor (TNF)-α in ventral tail nerves, and increases in circulating concentrations of IL-1 β in rats exposed to 250-Hz vibration. There was an increase in glutathione, but no changes in other measures of oxidative activity in the peripheral nerve. However, measures of oxidative stress were increased in the dorsal root ganglia (DRG). These changes in pro-inflammatory factors and markers of oxidative stress in the peripheral nerve and DRG were associated with inflammation, and reductions in myelin basic protein and post-synaptic density protein (PSD)-95 gene expression, suggesting that vibration-induced changes in sensory function may be the result of changes at the exposed nerve, the DRG and/or the spinal cord.

Health effects associated with occupational exposure to hand-arm or whole body vibration

Workers in a number of different occupational sectors are exposed to workplace vibration on a daily basis. This exposure may arise through the use of powered-hand tools or hand-transmitted vibration (HTV). Workers might also be exposed to whole body vibration (WBV) by driving delivery vehicles, earth moving equipment, or through use of tools that generate vibration at low dominant frequencies and high amplitudes, such as jackhammers. Occupational exposure to vibration has been associated with an increased risk of musculoskeletal pain in the back, neck, hands, shoulders, and hips. Occupational exposure may also contribute to the development of peripheral and cardiovascular disorders and gastrointestinal problems. In addition, there are more recent data suggesting that occupational exposure to vibration may enhance the risk of developing certain cancers. The aim of this review is to provide an assessment of the occupations where exposure to vibration is most prevalent, and a description of the adverse health effects associated with occupational exposure to vibration. This review will examine (1) various experimental methods used to measure and describe the characteristics of vibration generated by various tools and vehicles, (2) the etiology of vibration-induced disorders, and (3) how these data were employed to assess and improve intervention strategies and equipment that reduces the transmission of vibration to the body. Finally, there is a discussion of the research gaps that need to be investigated to further reduction in the incidence of vibration-induced illnesses and injuries.