Quantification of vascular function changes under different emotion states: A pilot study

Recent studies have indicated that physiological parameters change with different emotion states. This study aimed to quantify the changes of vascular function at different emotion and sub-emotion states. Twenty young subjects were studied with their finger photoplethysmographic (PPG) pulses recorded at three distinct emotion states: natural (1 minute), happiness and sadness (10 minutes for each). Within the period of happiness and sadness emotion states, two sub-emotion states (calmness and outburst) were identified with the synchronously recorded videos. Reflection index (RI) and stiffness index (SI), two widely used indices of vascular function, were derived from the PPG pulses to quantify their differences between three emotion states, as well as between two sub-emotion states. The results showed that, when compared with the natural emotion, RI and SI decreased in both happiness and sadness emotions. The decreases in RI were significant for both happiness and sadness emotions (both P< 0.01), but the decreases in SI was only significant for sadness emotion (P< 0.01). Moreover, for comparing happiness and sadness emotions, there was significant difference in RI (P< 0.01), but not in SI (P= 0.9). In addition, significant larger RI values were observed with the outburst sub-emotion in comparison with the calmness one for both happiness and sadness emotions (both P< 0.01) whereas significant larger SI values were observed with the outburst sub-emotion only in sadness emotion (P< 0.05). Moreover, gender factor hardly influence the RI and SI results for all three emotion measurements. This pilot study confirmed that vascular function changes with diffenrt emotion states could be quantified by the simple PPG measurement

Sensors for Vital Signs Monitoring

Sensor technology for monitoring vital signs is an important topic for various service applications, such as entertainment and personalization platforms and Internet of Things (IoT) systems, as well as traditional medical purposes, such as disease indication judgments and predictions. Vital signs for monitoring include respiration and heart rates, body temperature, blood pressure, oxygen saturation, electrocardiogram, blood glucose concentration, brain waves, etc. Gait and walking length can also be regarded as vital signs because they can indirectly indicate human activity and status. Sensing technologies include contact sensors such as electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG), non-contact sensors such as ballistocardiography (BCG), and invasive/non-invasive sensors for diagnoses of variations in blood characteristics or body fluids. Radar, vision, and infrared sensors can also be useful technologies for detecting vital signs from the movement of humans or organs. Signal processing, extraction, and analysis techniques are important in industrial applications along with hardware implementation techniques. Battery management and wireless power transmission technologies, the design and optimization of low-power circuits, and systems for continuous monitoring and data collection/transmission should also be considered with sensor technologies. In addition, machine-learning-based diagnostic technology can be used for extracting meaningful information from continuous monitoring data

Hemodynamic parameters assessment: an improvement of methodologies

A associação entre a rigidez arterial e as doenças cardiovasculares é um importante tópico de investigação com vista ao conhecimento da condição hemodinâmica dos pacientes. Vários índices podem ser um indicador da rigidez arterial, a velocidade da onda de pulso (VOP) e o índice de aumentação, são dois exemplos. Outros tópicos relacionados com as ondas reflectidas são um poderoso indicador neste contexto. Nesta tese são usados sensores piezoeléctricos para registar a forma da onda de pressão e algoritmos capazes de fornecer informação acerca de certos parâmetros hemodinâmicos, em alternativa aos dispositivos disponíveis no mercado. A principal motivação para procurar uma alternativa a estes dispositivos relaciona-se com o preço a que estes estão disponíveis. O desenvolvimento de uma bancada de teste capaz de simular as principais características da dinâmica do sistema arterial constitui uma poderosa ferramenta com vista ao desenvolvimento de sondas e validação dos algoritmos usados para a extracção de informação clinicamente relevante. O índice de aumentação foi o principal parâmetro estudado, este foi avaliado por um novo algoritmo baseado na transformada de wavelet, em comparação com outros referenciados na literatura. O seu desempenho foi testado em pulsos a partir de uma simulação realista baseadas em exponenciais, bem como em dados experimentais obtidos em testes “clínicos” com alguns voluntários

Application of machine learning classifiers to arterial disease detection, utilising virtual patient databases

Two of the most common forms of arterial disease are stenosis and aneurysm, estimated to affect between 1% and 20% of the population. Ruptured abdominal aortic aneurysms alone are estimated to be the cause of between 6,000 and 8,000 deaths a year within the United Kingdom. Patients with stenosis have been shown to have a mortality hazard ratio of 1.42 compared to a control population [2], and an unadjusted death rate of 3.35 per 100 person-years compared to 1.23 per 100 person-years in a control population [97]. Current methods for the detection of arterial disease are generally impractical for large scale screening, expensive, or both. If an inexpensive method for the detection of both stenosis and aneurysm is created, that minimises the need for invasive measurements, the cost effectiveness of large scale screening could be improved making both continuous monitoring and screening feasible. One such method is to use easily acquirable haemodynamic measurements at accessible peripheral locations within the circulatory system for diagnosis. Within this thesis an initial exploratory study into the potential of using machine learning classification algorithms to detect arterial disease from such measurements is presented.It is likely that the indicative biomarkers of arterial disease held within pressure and flow-rate profiles consist of micro inter- and intra- measurement details. To facilitate the use of a data driven approach to the discovery of any biomarkers a framework for the creation of virtual patients, through the employment of a mathematical model of blood flow, is presented. This framework is utilised to create a series of virtual patient databases, as the balance between simplicity and realism progresses through the thesis. The most realistic of these databases is made publicly available (https://doi.org/10.5281/zenodo.4549764). The aforementioned framework for the creation of virtual patients is a major contribution of this thesis, and can be applied to a wide range of biological systems given a mathematical description.The synthetic data sets are used to train and subsequently test a series of machine learning classifiers, to predict the presence of both stenosis and aneurysm, using various combinations of pressure and flow-rate measurements. It is shown that the inclusion of a diseased vessel (either stenosis or aneurysm) produces consistent and significant biomarkers in haemodynamic profiles, irrespective of a patients unique underlying arterial network. These biomarkers are found to be differentiable from the natural variability present across a large cohort of patients, showing that arterial disease has a clear and unique effect on pressure and flow-rate profiles. This suggests strong potential in the use of haemodynamic measurements to detect arterial disease

Information Processing for Biological Signals: Application to Laser Doppler Vibrometry

Signals associated with biological activity in the human body can be of great value in clinical and security applications. Since direct measurements of critical biological activity are often difficult to acquire noninvasively, many biological signals are measured from the surface of the skin. This simplifies the signal acquisition, but complicates post processing tasks. Modeling these signals using the underlying physics may not be accurate due to the inherent complexities of the human body. The appropriate use of such models depends on the application of interest. Models developed in this dissertation are motivated by underlying physiology and physics, and are capable of expressing a wide range of signal variability without explicitly invoking physical quantities. An approach for the processing of biological signals is developed using graphical models. Graphical models describe conditional dependence between random variables on a graph. When the graph is a tree, efficient algorithms exist to compute sum-marginals or max-marginals of the joint distribution. Some of the variables correspond to the measured signal, while others may represent the hidden internal dynamics that generate the observed data. Three levels of hidden dynamics are outlined, which enable models to be constructed that track internal dynamics on differing time scales. Expectation maximization algorithms are used to compute parameter estimates. Experimental results of this approach are presented for a novel method of recording bio-mechanical activity using a Laser Doppler Vibrometer. The LDV measures surface velocity on the basis of the Doppler shift. This device is targeted on the neck overlying the carotid artery, and the proximity of the carotid to the skin results in a strong signal. Vibrations and movements from within the carotid are transmitted to the surface of the skin, where they are sensed by the LDV. Changes in the size of the carotid due to variations in blood pressure are sensed at the skin surface. In addition, breathing activity may be inferred from the LDV signal. Individualized models are evaluated systematically on LDV data sets that were acquired under resting conditions on multiple occasions. Model fit is evaluated both within and across recording sessions. Model parameters are interpreted in terms of the underlying physiology. Pressure wave physics in a series of elastic tubes is presented to explore the underlying physics of blood flow in the carotid. Mechanical movements of the carotid walls are related to the underlying pressure, and therefore the cardiovascular activity of the heart and vasculature. This analysis motivates a model that can be estimated from experimental data. Resulting models are interpreted for the LDV signal. The graphical models are applied to the problem of identity verification using the LDV signal. Identity verification is an important problem in which the claimed identity is either accepted or rejected by an automated system. The system design that is used is based on a loglikelihood ratio test using models that are trained during an enrollment phase. A score is computed and compared to a threshold. Performance is given in the form of False Nonmatch and False Match empirical error rates as a function of the threshold. Confidence intervals are computed that take into account correlations between the system decisions

Arterial pulse wave modeling and analysis for vascular-age studies: a review from VascAgeNet

Aging; Arteriosclerosis; HemodynamicsEnvelliment; Arteriosclerosi; HemodinàmicaEnvejecimiento; Arteriosclerosis; HemodinámicaArterial 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.This article is based upon work from COST Action “Network for Research in Vascular Ageing” (VascAgeNet, CA18216), supported by COST (European Cooperation in Science and Technology, www.cost.eu). This work was supported by British Heart Foundation Grants PG/15/104/31913 (to J.A. and P.H.C.), FS/20/20/34626 (to P.H.C.), and AA/18/6/34223, PG/17/90/33415, SPG 2822621, and SP/F/21/150020 (to A.D.H.); Kaunas University of Technology Grant INP2022/16 (to B.P.); European Research Executive Agency, Marie-Sklodowska Curie Actions Individual Fellowship Grant 101038096 (to S.P.); Istinye University, BAP Project Grant 2019B1 (to S.P.); “la Caixa” Foundation Grant LCF/BQ/PR22/11920008 (to A.G.); and National Institute for Health and Care Research Grant AI AWARD02499 and EU Horizon 2020 Grant H2020 848109 (to A.D.H.)

Gaussian process emulators for 1D vascular models

One-dimensional numerical models of the arterial vasculature are capable of simulating the physics of pulse wave transmission and reflection. These models are computationally efficient and represents and ideal choice with great translational opportunities in healthcare. However, the use of these models in a patient-specific scenario is hampered by the difficulty in measuring the model inputs (parameters, boundary conditions, and initial conditions) in the clinical setting. As a result, most of the model inputs are noisy or missing, and the inputs uncertainty is transmitted to the model outputs. A fundamental step in the model development consists in performing a sensitivity and uncertainty analysis aimed at understanding how variations on the inputs affect the output variability, with the final aim of instruct the measurement process. A typical sensitivity analysis conducted by means of \break Monte Carlo sampling is computationally expensive due to the large number of runs required. A novel approach aimed at reducing the computational time consists in using a statistical emulator capable of mimicking mean and variance behaviours of the 1D deterministic model. In this study, emulators built through Gaussian process method are used to predict outcomes of a 1D finite-volume solver for networks of elastic vessels. The 1D model is discussed and validated showing good agreement with published results. The emulator approach for sensitivity analysis is validated against Monte Carlo sampling and a 99.9% reduction in computational time is obtained. This methodology is further applied in the context of cerebral vasospasm where the sensitivity analysis results are used to identify new biomechanical metrics for this pathology. The novel biomarkers are effective at detecting the cerebral vasospasm better than the currently used one. In particular, the progression of the disease is characterised from an early onset even when the vasospasm is occurring at some distance away from the measurement location

Methods and Instrumentation for Non-Invasive Assessment of the Cardiovascular Condition

Tese de doutoramento em Física (Pré-Bolonha), Especialidade de Física Tecnológica, apresentada à Faculdade de Ciências e Tecnologia da Universidade de CoimbraAs doenças cardiovasculares (DCVs) são a principal causa de morte a nível mundial e largamente responsáveis pelos custos crescentes nos sistemas de saúde. Nos últimos anos, a comunidade médica tem vindo a demonstrar um grande interesse na avaliação da rigidez arterial local, pressão arterial central e na análise da onda de pressão, devido aos seus valores preditivos no desenvolvimento deste tipo de patologias. Apesar da sua relevância, estes parâmetros hemodinâmicos permanecem particularmente difíceis de medir na prática clínica, já que a maioria dos dispositivos disponíveis exigem elevados conhecimentos técnicos (introduzindo a dependência de um operador), tecnologias dispendiosas ou apresentam abordagens de análise ineficientes. Este trabalho de investigação encontra assim a sua motivação no potencial impacto que instrumentação não-invasiva, exata e de fácil utilização pode ter na monitorização da condição hemodinâmica e no diagnóstico precoce e acompanhamento de DCVs. Neste contexto, uma nova geração de protótipos baseados na combinação de diferentes tipos de sensores eletromecânicos, bem como um conjunto de algoritmos de processamento de sinal adequados à extração de múltiplos parâmetros hemodinâmicos foram desenvolvidos. Dependendo do marcador de risco cardiovascular a ser avaliado, dois grandes grupos de dispositivos foram projetados. O primeiro grupo, focado na avaliação da rigidez arterial local, explorou uma configuração dupla inovadora com dois sensores acústicos ou piezoelétricos (PZs) para a medição da velocidade da onda de pulso (VOP) e outros índices temporais relevantes, num curto segmento da artéria carótida. O outro grupo, centrado na avaliação contínua da pressão arterial sanguínea (PAS) e onda de pressão arterial (OPA), também na artéria carótida, usou uma unidade vibrador-acelerómetro montada num mesmo suporte que permitiu ao acelerómetro detetar as vibrações produzidas, atenuadas e moduladas em amplitude quando em contacto mecânico com a parede do vaso. Os protótipos desenvolvidos foram extensivamente caracterizados em sistemas de bancada de teste, desenvolvidos para este efeito e capazes de reproduzir a variabilidade de uma ampla gama de situações clinicamente relevantes, bem como em condições in vivo. Relativamente à avaliação da rigidez arterial local, a primeira e segunda gerações de protótipos desenvolvidos apresentaram boa exatidão nos ensaios de resolução temporal realizados em tubos elásticos de bancadas de teste. O algoritmo de correlação cruzada exibiu a capacidade de medir VOPs altas (≈ 19 ms-1 e 14 ms-1) com erros relativos e coeficientes de variação inferiores a 10 % para os diferentes protótipos. Os sinais adquiridos provaram ser robustos e repetíveis, não sofrendo efeitos de crosstalk. Os resultados obtidos no estudo de validação pré-clínica em vinte indivíduos saudáveis com a segunda geração de protótipos foram ainda bastante satisfatórios. As VOPs carotídeas médias obtidas apresentaram uma correlação linear e forte entre si, estando os resultados próximos dos valores obtidos noutros estudos de referência. Além disso, a capacidade de reproduzir perfis de onda pressão distintos usando as sondas PZs foi também mostrada, quer utilizando o processo de desconvolução quer um circuito eletrónico integrador dedicado. No que diz respeito à avaliação da PAS e OPA, o processo de desmodulação produziu excelentes resultados na recuperação da morfologia da onda de pressão em condições de bancada de teste e in vivo. Para os dois protótipos desenvolvidos, várias formas de onda foram extraídas, com exatidão, das portadoras moduladas de aceleração, corrente ou potência elétricas, usando os algoritmos de deteção do envelope e do produto. Na bancada de teste foi possível reproduzir a forma de onda de pressão para posições de aplanação do tubo elástico sucessivamente mais elevadas com um erro quadrático médio de 2.4 ± 0.51 %, quando considerado o melhor método de extração. A eficácia de um novo método de calibração focado na utilização de curvas empíricas que convertem aceleração em pressão foi também demonstrado. Através da conservação da amplitude da portadora de aceleração, foi possível determinar os valores de pressão máximo, mínimo, médio e de pulso com erros relativos inferiores a 10 % em condições de bancada. Além disso, as diferenças de pressão entre o último protótipo desenvolvido e o sistema de referência foram, em média, ≤ 5 ± 8 mmHg, satisfazendo os critérios de exatidão de sistemas de medição de PAS clinicamente validados. Embora estudos de validação clínica sejam ainda necessários, os resultados globais obtidos neste trabalho para os dois principais tipos de protótipos dão bons indicadores quanto à sua utilização como alternativas válidas aos sistemas atualmente disponíveis, tanto em ambientes clínico quanto de investigação.Cardiovascular diseases (CVDs) are the leading cause of death worldwide and largely responsible for the ever increasing costs in healthcare systems. In the last few years, the medical community has demonstrated a great interest in local arterial stiffness, central blood pressure assessment and pressure waveform analysis, due to their predictive values in the development of this type of pathologies. Despite their significance, these hemodynamic parameters remain particularly challenging to measure in standard clinical practice since most available devices require high technical expertise (introducing operator dependence), burdensome technologies and/or present ineffective analysis approaches. This research work finds its motivation in the potential impact that non-invasive, accurate and easy-to-use instrumentation could have on the monitoring of hemodynamic condition and on the diagnosis and control of early stages of CVDs. In this context, a new generation of prototypes based on the combination of different types of electromechanical sensors, along with a set of signal processing algorithms suited to the extraction of multiple hemodynamic parameters were developed. Two major groups of devices were designed depending on the cardiovascular risk marker to be assessed. The first group, focused on local arterial stiffness evaluation, explored an innovative double headed probe configuration of acoustic or piezoelectric (PZ) sensors for the measurement of pulse wave velocity (PWV) and other relevant time-based indices, in a short segment of the carotid artery. The other main group, centered on the continuous assessment of arterial blood pressure (ABP) and arterial pressure waveform (APW), also at the carotid artery, used a vibrator-accelerometer unit mounted in a common support that enabled the accelerometer to sense the produced vibrations, attenuated and modulated in amplitude when in mechanical contact with the vessel wall. The developed prototypes were extensively characterized in test bench systems, purposely built and capable of reproducing the variability of a wide range of clinically relevant situations, as well as in in vivo conditions. Regarding local arterial stiffness evaluation, the first and second generations of developed prototypes presented good accuracy in time resolution experiments on elastic tubes at the test bench. Cross-correlation algorithm exhibited the capability of measuring high PWVs (≈ 19 ms-1 and 14 ms-1) with relative errors and coefficients of variation lower than 10 % for the different prototypes. The acquired signals proved to be robust and repeatable, not suffering from crosstalk effect. The results obtained in a pre-clinical validation trial of twenty healthy subjects with the second generation of prototypes were very satisfactory, demonstrating that the mean carotid PWVs obtained were linearly and strongly correlated and were in agreement with other reference studies. Additionally, the ability to reproduce distinct wave pressure profiles using the PZs probes was also shown, either using the demodulation algorithm-based process or a special circuit for electronic integration. Concerning APW and ABP assessment, the demodulation process yielded excellent results in recovering the morphology of pressure wave in test bench and in in vivo conditions. For the two developed prototypes, several waveforms were accurately extracted from the acceleration, current or power modulated carriers using the envelope and product detector algorithms. It was possible to reproduce the pressure waveform for successive higher applanation positions of the elastic tube at the test bench with a root mean square error of 2.4 ± 0.51 %, when considering the best extracting method. The effectiveness of a novel calibration method focused on the use of empirical curves which convert acceleration into pressure was also demonstrated. Through the conservation of the acceleration carrier amplitude, it was possible to determine the maximum, minimum, mean and pulse pressure values with relative errors lower than 10 % in bench conditions. Also, the mean pressure differences between the latest prototype and the reference system were, on average, ≤ 5 ± 8 mmHg, satisfying the accuracy criteria of clinically validated ABP devices. Although clinical validation studies are still required, the global results obtained in this work for the two major types of prototypes provide great prospects regarding their use as valid alternatives to currently available systems, both in clinical and research settings