581 research outputs found

    Information Transfer in Linear Multivariate Processes Assessed through Penalized Regression Techniques: Validation and Application to Physiological Networks

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    The framework of information dynamics allows the dissection of the information processed in a network of multiple interacting dynamical systems into meaningful elements of computation that quantify the information generated in a target system, stored in it, transferred to it from one or more source systems, and modified in a synergistic or redundant way. The concepts of information transfer and modification have been recently formulated in the context of linear parametric modeling of vector stochastic processes, linking them to the notion of Granger causality and providing efficient tools for their computation based on the state–space (SS) representation of vector autoregressive (VAR) models. Despite their high computational reliability these tools still suffer from estimation problems which emerge, in the case of low ratio between data points available and the number of time series, when VAR identification is performed via the standard ordinary least squares (OLS). In this work we propose to replace the OLS with penalized regression performed through the Least Absolute Shrinkage and Selection Operator (LASSO), prior to computation of the measures of information transfer and information modification. First, simulating networks of several coupled Gaussian systems with complex interactions, we show that the LASSO regression allows, also in conditions of data paucity, to accurately reconstruct both the underlying network topology and the expected patterns of information transfer. Then we apply the proposed VAR-SS-LASSO approach to a challenging application context, i.e., the study of the physiological network of brain and peripheral interactions probed in humans under different conditions of rest and mental stress. Our results, which document the possibility to extract physiologically plausible patterns of interaction between the cardiovascular, respiratory and brain wave amplitudes, open the way to the use of our new analysis tools to explore the emerging field of Network Physiology in several practical applications

    Wearable and Nearable Biosensors and Systems for Healthcare

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    Biosensors and systems in the form of wearables and “nearables” (i.e., everyday sensorized objects with transmitting capabilities such as smartphones) are rapidly evolving for use in healthcare. Unlike conventional approaches, these technologies can enable seamless or on-demand physiological monitoring, anytime and anywhere. Such monitoring can help transform healthcare from the current reactive, one-size-fits-all, hospital-centered approach into a future proactive, personalized, decentralized structure. Wearable and nearable biosensors and systems have been made possible through integrated innovations in sensor design, electronics, data transmission, power management, and signal processing. Although much progress has been made in this field, many open challenges for the scientific community remain, especially for those applications requiring high accuracy. This book contains the 12 papers that constituted a recent Special Issue of Sensors sharing the same title. The aim of the initiative was to provide a collection of state-of-the-art investigations on wearables and nearables, in order to stimulate technological advances and the use of the technology to benefit healthcare. The topics covered by the book offer both depth and breadth pertaining to wearable and nearable technology. They include new biosensors and data transmission techniques, studies on accelerometers, signal processing, and cardiovascular monitoring, clinical applications, and validation of commercial devices

    Assessment Of Blood Pressure Regulatory Controls To Detect Hypovolemia And Orthostatic Intolerance

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    Regulation of blood pressure is vital for maintaining organ perfusion and homeostasis. A significant decline in arterial blood pressure could lead to fainting and hypovolemic shock. In contrast to young and healthy, people with impaired autonomic control due to aging or disease find regulating blood pressure rather demanding during orthostatic challenge. This thesis performed an assessment of blood pressure regulatory controls during orthostatic challenge via traditional as well as novel approaches with two distinct applications 1) to design a robust automated system for early identification of hypovolemia and 2) to assess orthostatic tolerance in humans. In chapter 3, moderate intensity hemorrhage was simulated via lower-body negative pressure (LBNP) with an aim to identify moderate intensity hemorrhage (-30 and -40 mmHg LBNP) from resting baseline. Utilizing features extracted from common vital sign monitors, a classification accuracy of 82% and 91% was achieved for differentiating -30 and -40 mmHg LBNP, respectively from baseline. In chapter 4, cause-and-effect relationship between the representative signals of the cardiovascular and postural systems to ascertain blood pressure homeostasis during standing was performed. The degree of causal interaction between the two systems, studied via convergent cross mapping (CCM), showcased the existence of a significant bi-directional interaction between the representative signals of two systems to regulate blood pressure. Therefore, the two systems should be accounted for jointly when addressing physiology behind fall. Further, in chapter 5, the potential of artificial gravity (2-g) induced via short-arm human centrifuge at feet towards evoking blood pressure regulatory controls analogous to standing was investigated. The observation of no difference in the blood pressure regulatory controls, during 2-g centrifugation compared to standing, strongly supported the hypothesis of artificial hypergravity for mitigating cardiovascular deconditioning, hence minimizing post-flight orthostatic intolerance

    Autonomic nervous system biomarkers from multi-modal and model-based signal processing in mental health and illness

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    Esta tesis se centra en técnicas de procesado multimodal y basado en modelos de señales para derivar parámetros fisiológicos, es decir, biomarcadores, relacionados con el sistema nervioso autónomo (ANS). El desarrollo de nuevos métodos para derivar biomarcadores de ANS no invasivos en la salud y la enfermedad mental ofrece la posibilidad de mejorar la evaluación del estrés y la monitorización de la depresión. Para este fin, el presente documento se estructura en tres partes principales. En la Parte I, se proporciona unaintroducción a la salud y la enfermedad mental (Cap. 1). Además, se presenta un marco teórico para investigar la etiología de los trastornos mentales y el papel del estrés en la enfermedad mental (Cap. 2). También se destaca la importancia de los biomarcadores no invasivos para la evaluación del ANS, prestando especial atención en la depresión clínica (Cap. 3, 4). En la Parte II, se proporciona el marco metodológico para derivar biomarcadores del ANS. Las técnicas de procesado de señales incluyen el análisis conjunto de la variabilidad del rítmo cardíaco (HRV) y la señal respiratoria (Cap. 6), técnicas novedosas para derivar la señal respiratoria del electrocardiograma (ECG) (Cap. 7) y un análisis robusto que se basa en modelar la forma de ondas del pulso del fotopletismograma (PPG) (Ch. 8). En la Parte III, los biomarcadores del ANS se evalúan en la quantificacióndel estrés (Cap. 9) y en la monitorización de la depresión (Ch. 10).Parte I: La salud mental no solo está relacionada con ese estado positivo de bienestar, en el que un individuo puede enfrentar a las situaciones estresantes de la vida, sino también con la ausencia de enfermedad mental. La enfermedad o trastorno mental se puede definir como un trastorno emocional, cognitivo o conductual que causa un deterioro funcional sustancial en una o más actividades importantes de la vida. Los trastornos mentales más comunes, que muchas veces coexisten, son la ansiedad y el trastorno depresivo mayor (MDD). La enfermedad mental tiene un impacto negativo en la calidad de vida, ya que se asocia con pérdidas considerables en la salud y el funcionamiento, y aumenta ignificativamente el riesgo de una persona de padecer enfermedades ardiovasculares.Un instigador común que subyace a la comorbilidad entre el MDD, la patologíacardiovascular y la ansiedad es el estrés mental. El estrés es común en nuestra vida de rítmo rapido e influye en nuestra salud mental. A corto plazo, ANS controla la respuesta cardiovascular a estímulos estresantes. La regulación de parámetros fisiológicos, como el rítmo cardíaco, la frecuencia respiratoria y la presión arterial, permite que el organismo responda a cambios repentinos en el entorno. Sin embargo, la adaptación fisiológica a un fenómeno ambiental que ocurre regularmente altera los sistemas biológicos involucrados en la respuesta al estrés. Las alteraciones neurobiológicas en el cerebro pueden alterar lafunción del ANS. La disfunción del ANS y los cambios cerebrales estructurales tienen un impacto negativo en los procesos cognitivos, emocionales y conductuales, lo que conduce al desarrollo de una enfermedad mental.Parte II: El desarrollo de métodos novedosos para derivar biomarcadores del ANS no invasivos ofrece la posibilidad de mejorar la evaluacón del estrés en individuos sanos y la disfunción del ANS en pacientes con MDD. El análisis conjunto de varias bioseñales (enfoquemultimodal) permite la cuantificación de interacciones entre sistemas biológicos asociados con ANS, mientras que el modelado de bioseãles y el análisis posterior de los parámetros del modelo (enfoque basado en modelos) permite la cuantificación robusta de cambios en mecanismos fisiológicos relacionados con el ANS. Un método novedoso, quetiene en cuenta los fenómenos de acoplo de fase y frecuencia entre la respiración y las señales de HRV para evaluar el acoplo cardiorrespiratorio no lineal cuadrático se propone en el Cap. 6.3. En el Cap. 7 se proponen nuevas técnicas paramejorar lamonitorización de la respiración. En el Cap. 8, para aumentar la robustez de algunas medidas morfológicas que reflejan cambios en el tonno arterial, se considera el modelado del pulso PPG como una onda principal superpuesta con varias ondas reflejadas.Parte III: Los biomarcadores del ANS se evalúan en la cuantificación de diferentes tipos de estrés, ya sea fisiológico o psicológico, en individuos sanos, y luego, en la monitorización de la depresión. En presencia de estrés mental (Cap. 9.1), inducido por tareas cognitivas, los sujetos sanos muestran un incremento en la frecuencia respiratoria y un mayor número de interacciones no lineales entre la respiración y la seãl de HRV. Esto podría estar asociado con una activación simpática, pero también con una respiración menos regular. En presencia de estrés hemodinámico (Cap. 9.2), inducido por un cambio postural, los sujetos sanos muestran una reducción en el acoplo cardiorrespiratoriono lineal cuadrático, que podría estar relacionado con una retracción vagal. En presencia de estrés térmico (Cap. 9.3), inducido por la exposición a emperaturas ambientales elevadas, los sujetos sanos muestran un aumento del equilibrio simpatovagal. Esto demuestra que los biomarcadores ANS son capaces de evaluar diferentes tipos de estrés y pueden explorarse más en el contexto de la monitorización de la depresión. En el Cap. 10, se evalúan las diferencias en la función del ANS entre elMDD y los sujetos sanos durante un protocolo de estrés mental, no solo con los valores brutos de los biomarcadores del ANS, sino también con los índices de reactividad autónoma, que reflejan la capacidad deun individuo para afrontar con una situación desafiante. Los resultados muestran que la depresión se asocia con un desequilibrio autonómico, que se caracteriza por una mayor actividad simpática y una reducción de la distensibilidad arterial. Los índices de reactividad autónoma cuantificados por cambios, entre etapas de estrés y de recuperación, en los sustitutos de la rigidez arterial, como la pérdida de amplitud de PPG en las ondas reflejadas, muestran el mejor rendimiento en términos de correlación con el grado de la depresión, con un coeficiente de correlación r = −0.5. La correlación negativa implicaque un mayor grado de depresión se asocia con una disminución de la reactividadautónoma. El poder discriminativo de los biomarcadores del ANS se aprecia también por su alto rendimiento diagnóstico para clasificar a los sujetos como MDD o sanos, con una precisión de 80.0%. Por lo tanto, se puede concluir que los biomarcadores del ANS pueden usarse para evaluar el estrés y que la distensibilidad arterial deteriorada podría constituir un biomarcador de salud mental útil en el seguimiento de la depresión.This dissertation is focused on multi-modal and model-based signal processing techniques for deriving physiological parameters, i.e. biomarkers, related to the autonomic nervous system (ANS). The development of novel approaches for deriving noninvasive ANS biomarkers in mental health and illness offers the possibility to improve the assessment of stress and the monitoring of depression. For this purpose, the present document is structured in three main parts. In Part I, an introduction to mental health and illness is provided (Ch. 1). Moreover, a theoretical framework for investigating the etiology of mental disorders and the role of stress in mental illness is presented (Ch. 2). The importance of noninvasive biomarkers for ANS assessment, paying particular attention in clinical depression, is also highlighted (Ch. 3, 4). In Part II, themethodological framework for deriving ANS biomarkers is provided. Signal processing techniques include the joint analysis of heart rate variability (HRV) and respiratory signals (Ch. 6), novel techniques for deriving the respiratory signal from electrocardiogram (ECG) (Ch. 7), and a robust photoplethysmogram(PPG)waveform analysis based on amodel-based approach (Ch. 8). In Part III, ANS biomarkers are evaluated in stress assessment (Ch. 9) and in the monitoring of depression (Ch. 10). Part I:Mental health is not only related to that positive state ofwell-being, inwhich an individual can cope with the normal stresses of life, but also to the absence of mental illness. Mental illness or disorder can be defined as an emotional, cognitive, or behavioural disturbance that causes substantial functional impairment in one or more major life activities. The most common mental disorders, which are often co-occurring, are anxiety and major depressive disorder (MDD). Mental illness has a negative impact on the quality of life, since it is associated with considerable losses in health and functioning, and increases significantly a person’s risk for cardiovascular diseases. A common instigator underlying the co-morbidity between MDD, cardiovascular pathology, and anxiety is mental stress. Stress is common in our fast-paced society and strongly influences our mental health. In the short term, ANS controls the cardiovascular response to stressful stimuli. Regulation of physiological parameters, such as heart rate, respiratory rate, and blood pressure, allows the organism to respond to sudden changes in the environment. However, physiological adaptation to a regularly occurring environmental phenomenon alters biological systems involved in stress response. Neurobiological alterations in the brain can disrupt the function of the ANS. ANS dysfunction and structural brain changes have a negative impact on cognitive, emotional, and behavioral processes, thereby leading to development of mental illness. Part II: The development of novel approaches for deriving noninvasive ANS biomarkers offers the possibility to improve the assessment of stress in healthy individuals and ANS dysfunction in MDD patients. Joint analysis of various biosignals (multi-modal approach) allows for the quantification of interactions among biological systems associated with ANS, while the modeling of biosignals and subsequent analysis of the model’s parameters (model-based approach) allows for the robust quantification of changes in physiological mechanisms related to the ANS. A novel method, which takes into account both phase and frequency locking phenomena between respiration and HRV signals, for assessing quadratic nonlinear cardiorespiratory coupling is proposed in Ch. 6.3. Novel techniques for improving the monitoring of respiration are proposed in Ch. 7. In Ch. 8, to increase the robustness for some morphological measurements reflecting arterial tone changes, the modeling of the PPG pulse as amain wave superposed with several reflected waves is considered. Part III: ANS biomarkers are evaluated in the assessment of different types of stress, either physiological or psychological, in healthy individuals, and then, in the monitoring of depression. In the presence of mental stress (Ch. 9.1), induced by cognitive tasks, healthy subjects show an increment in the respiratory rate and higher number of nonlinear interactions between respiration and HRV signal, which might be associated with a sympathetic activation, but also with a less regular breathing. In the presence of hemodynamic stress (Ch. 9.2), induced by a postural change, healthy subjects show a reduction in strength of the quadratic nonlinear cardiorespiratory coupling, whichmight be related to a vagal withdrawal. In the presence of heat stress (Ch. 9.3), induced by exposure to elevated environmental temperatures, healthy subjects show an increased sympathovagal balance. This demonstrates that ANS biomarkers are able to assess different types of stress and they can be further explored in the context of depression monitoring. In Ch. 10, differences in ANS function between MDD and healthy subjects during a mental stress protocol are assessed, not only with the raw values of ANS biomarkers but also with autonomic reactivity indices, which reflect the ability of an individual to copewith a challenging situation. Results show that depression is associated with autonomic imbalance, characterized by increased sympathetic activity and reduced arterial compliance. Autonomic reactivity indices quantified by changes, from stress to recovery, in arterial stiffness surrogates, such as the PPG amplitude loss in wave reflections, show the best performance in terms of correlation with depression severity, yielding to correlation coefficient r = −0.5. The negative correlation implies that a higher degree of depression is associated with a decreased autonomic reactivity. The discriminative power of ANS biomarkers is supported by their high diagnostic performance for classifying subjects as having MDD or not, yielding to accuracy of 80.0%. Therefore, it can be concluded that ANS biomarkers can be used for assessing stress and that impaired arterial compliance might constitute a biomarker of mental health useful in the monitoring of depression.<br /

    Sleep Arousal and Cardiovascular Dynamics

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    Sleep arousal conventionally refers to any temporary intrusions of wakefulness into sleep. Arousals are usually considered as a part of normal sleep and rarely result in complete awakening. However, once their frequency increases, they may affect the sleep architecture and lead to sleep fragmentation, resulting in fatigue, poor executive functioning and excessive daytime sleepiness. In the electroencephalogram, arousals mostly appear as a shift of power in frequency to values greater than 16 Hz lasting 3-15 seconds. The general objective of this thesis was to investigate on the nature of sleep arousal and study arousal interaction and association with cardiovascular dynamics. At the first step of this research, an algorithm was developed and evaluated for automatic detection of sleep arousal. The polysomnographic (PSG) data of 9 subjects were analysed and 32 features were derived from a range of biosignals. The extracted features were used to develop kNN classifier model in to differentiate arousal from non-arousal events. The developed algorithm can detect arousal events with the average sensitivity and accuracy of 79% and 95.5%, respectively. The second aim was to investigate cardiovascular dynamics once an arousal occurs. Overnight continuous systolic and diastolic blood pressure (SBP and DSP), spectral components of heart rate variability (HRV) and the pulse transit time of 10 subjects (average arousal number of 51.5 +/- 21.1 per person) were analysed before and after arousal occurrence. Whether each cardiovascular variable increases or decreases was evaluated in different types of arousals through slpoe index (SI). The analysis indicated a post-arousal SBP and DBP elevation and PTT dropping. High frequency component of HRV (HF) dropped at arousal onset whilst low frequency (LF) component shifted. HRV spectral components extracted from ECG, lead I alongside with PTT were utilised for sleep staging in 22 healthy and insomnia subjects using linear and non-linear classifier models. Obtained result shows that developed model by DW-kNN classifier could detect sleep stages with mean accuracy of 73.4% +/- 6.4. An empirical curve fitting model for overnight continuous blood pressure estimation was developed and evaluated using the first and second derivatives of fingertip PPG (VPG, APG) along with ECG. The VPG-based model could estimate systolic and diastolic blood pressure with mean error of 3:96 mmHg with standard deviation of 1.41 mmHg and DBP with 6:88 mmHg with standard deviation of 3.03 mmHg. The QT and RR time intervals are two cardiac variables which represent beat to beat variability and ventricular repolarisation, respectively. PSG dataset of 2659 men aged older than 65 (MrOS Sleep Study) was analysed to compare on RR and QT interval variability pre- and post-arousal onset. The cardiac interval gradients were developed to monitor instantaneous changes pre-and post-onset. Analysis of gradients demonstrated that both RR and QT are likely to start shortening several second prior to onset by average probability of 73% and 64%. The QT/RR linear correlation was significantly rising after arousal inducing regardless of arousal type and associated pathological events (Rpost = 0.218 vs Rpre = 0.047). ANOVA test and Tukey’s honest post-hoc analysis indicated a significant difference between cardiac intervals variability between respiratory, movements and spontaneous arousals. In addition, respiratory disturbance index (RDI) as a measure of sleep apnoea severity was reversely correlated with both QT (RVarQT = -0.251, p 1:1 ms) and greater frequency of sleep arousal, less physical activity and medical history of several cardiovascular disease. Similarly participants in quartile DRR> - 8:8 were likelier to be obese with less physical activity, medical history of COPD and stroke and suffered from severer degree of sleep apnoea (RDI = 28:7 20:4 vs RDI = 25:5 +/- 17:6, p < 0:001). Kaplan-Meier analysis showed that the distribution DRR at arousal onset was significantly associated with cardiovascular (CV) mortality (p < 0:001). Cox proportional hazard regression models also indicated the effect of arousal duration in prediction of CV mortality, where longer arousals had more prognostic value for CV mortality than shorter arousals.Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 202

    Detection of Spatial and Temporal Interactions in Renal Autoregulation Dynamics

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    Renal autoregulation stabilizes renal blood flow to protect the glomerular capillaries and maintain glomerular filtration rates through two mechanisms: tubuloglomerular feedback (TGF) and the myogenic response (MR). It is considered that the feedback mechanisms operate independently in each nephron (the functional unit of the kidney) within a kidney, but renal autoregulation dynamics can be coupled between vascular connected nephrons. It has also been shown that the mechanisms are time-varying and interact with each other. Understanding of the significance of such complex behavior has been limited by absence of techniques capable of monitoring renal flow signals among more than 2 or 3 nephrons simultaneously. The purpose of this thesis was to develop approaches to allow the identification and characterization of spatial and temporal properties of renal autoregulation dynamics. We present evidence that laser speckle perfusion imaging (LSPI) effectively captures renal autoregulation dynamics in perfusion signals across the renal cortex of anaesthetized rats and that spatial heterogeneity of the dynamics is present and can be investigated using LSPI. Next, we present a novel approach to segment LSPI of the renal surface into phase synchronized clusters representing areas with coupled renal autoregulation dynamics. Results are shown for the MR and demonstrate that when a signal is present phase synchronized regions can be identified. We then describe an approach to identify quadratic phase coupling between the TGF and MR mechanisms in time and space. Using this approach we can identify locations across the renal surface where both mechanisms are operating cooperatively. Finally, we show how synchronization between nephrons can be investigated in relation to renal autoregulation effectiveness by comparing phase synchronization estimates from LSPI with renal autoregulation system properties estimated from renal blood flow and blood pressure measurements. Overall, we have developed approaches to 1) capture renal autoregulation dynamics across the renal surface, 2) identify regions with phase synchronized renal autoregulation dynamics, 3) quantify the presence of the TGF-MR interaction across the renal surface, and 4) determine how the above vary over time. The described tools allow for investigations of the significance and mechanisms behind the complex spatial interactions and time-varying properties of renal autoregulation dynamics

    Deep Learning Algorithms for Time Series Analysis of Cardiovascular Monitoring Systems

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    This thesis investigates and develops methods to enable ubiquitous monitoring of the most examined cardiovascular signs, blood pressure, and heart rate. Their continuous measurement can help improve health outcomes, such as the detection of hypertension, heart attack, or stroke, which are the leading causes of death and disability. Recent research into wearable blood pressure monitors sought predominately to utilise a hypothesised relationship with pulse transit time, relying on quasiperiodic pulse event extractions from photoplethysmography local signal characteristics and often used only a fraction of typically bivariate time series. This limitation has been addressed in this thesis by developing methods to acquire and utilise fused multivariate time series without the need for manual feature engineering by leveraging recent advances in data science and deep learning methods that showed great data analysis potential in other domains
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