A novel particle filtering method for estimation of pulse pressure variation during spontaneous breathing

Background: We describe the first automatic algorithm designed to estimate the pulse pressure variation (PPVPPV) from arterial blood pressure (ABP) signals under spontaneous breathing conditions. While currently there are a few publicly available algorithms to automatically estimate PPVPPV accurately and reliably in mechanically ventilated subjects, at the moment there is no automatic algorithm for estimating PPVPPV on spontaneously breathing subjects. The algorithm utilizes our recently developed sequential Monte Carlo method (SMCM), which is called a maximum a-posteriori adaptive marginalized particle filter (MAM-PF). We report the performance assessment results of the proposed algorithm on real ABP signals from spontaneously breathing subjects. Results: Our assessment results indicate good agreement between the automatically estimated PPVPPV and the gold standard PPVPPV obtained with manual annotations. All of the automatically estimated PPVPPV index measurements (PPVautoPPVauto) were in agreement with manual gold standard measurements (PPVmanuPPVmanu) within ±4 % accuracy. Conclusion: The proposed automatic algorithm is able to give reliable estimations of PPVPPV given ABP signals alone during spontaneous breathing

Modulations of Heart Rate, ECG, and Cardio-Respiratory Coupling Observed in Polysomnography

The cardiac component of cardio-respiratory polysomnography is covered by ECG and heart rate recordings. However their evaluation is often underrepresented in summarizing reports. As complements to EEG, EOG, and EMG, these signals provide diagnostic information for autonomic nervous activity during sleep. This review presents major methodological developments in sleep research regarding heart rate, ECG and cardio-respiratory couplings in a chronological (historical) sequence. It presents physiological and pathophysiological insights related to sleep medicine obtained by new technical developments. Recorded nocturnal ECG facilitates conventional heart rate variability analysis, studies of cyclical variations of heart rate, and analysis of ECG waveform. In healthy adults, the autonomous nervous system is regulated in totally different ways during wakefulness, slow-wave sleep, and REM sleep. Analysis of beat-to-beat heart-rate variations with statistical methods enables us to estimate sleep stages based on the differences in autonomic nervous system regulation. Furthermore, up to some degree, it is possible to track transitions from wakefulness to sleep by analysis of heart-rate variations. ECG and heart rate analysis allow assessment of selected sleep disorders as well. Sleep disordered breathing can be detected reliably by studying cyclical variation of heart rate combined with respiration-modulated changes in ECG morphology (amplitude of R wave and T wave)

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 145

This bibliography lists 301 reports, articles, and other documents introduced into the NASA scientific and technical information system in August 1975

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

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 /

Tracking Rhythmicity in Biomedical Signals using Sequential Monte Carlo methods

Cyclical patterns are common in signals that originate from natural systems such as the human body and man-made machinery. Often these cyclical patterns are not perfectly periodic. In that case, the signals are called pseudo-periodic or quasi-periodic and can be modeled as a sum of time-varying sinusoids, whose frequencies, phases, and amplitudes change slowly over time. Each time-varying sinusoid represents an individual rhythmical component, called a partial, that can be characterized by three parameters: frequency, phase, and amplitude. Quasi-periodic signals often contain multiple partials that are harmonically related. In that case, the frequencies of other partials become exact integer multiples of that of the slowest partial. These signals are referred to as multi-harmonic signals. Examples of such signals are electrocardiogram (ECG), arterial blood pressure (ABP), and human voice. A Markov process is a mathematical model for a random system whose future and past states are independent conditional on the present state. Multi-harmonic signals can be modeled as a stochastic process with the Markov property. The Markovian representation of multi-harmonic signals enables us to use state-space tracking methods to continuously estimate the frequencies, phases, and amplitudes of the partials. Several research groups have proposed various signal analysis methods such as hidden Markov Models (HMM), short time Fourier transform (STFT), and Wigner-Ville distribution to solve this problem. Recently, a few groups of researchers have proposed Monte Carlo methods which estimate the posterior distribution of the fundamental frequency in multi-harmonic signals sequentially. However, multi-harmonic tracking is more challenging than single-frequency tracking, though the reason for this has not been well understood. The main objectives of this dissertation are to elucidate the fundamental obstacles to multi-harmonic tracking and to develop a reliable multi-harmonic tracker that can track cyclical patterns in multi-harmonic signals

Non-invasive techniques for respiratory information extraction based on pulse photoplethysmogram and electrocardiogram

El objetivo principal de esta tesis es el desarrollo de métodos no invasivos para la extracción de información respiratoria a partir de dos señales biomédicas ampliamente utilizadas en la rutina clínica: el electrocardiograma (ECG) y la señal fotopletismográfica de pulso (PPG). La motivación de este estudio es la conveniencia de monitorizar información respiratoria a partir de dispositivos no invasivos que permita sustituir las técnicas actuales que podrían interferir con la respiración natural y que presentan inconvenientes en algunas aplicaciones como la prueba de esfuerzo y los estudios del sueño. Además, si estos dispositivos no invasivos son los ya utilizados en la rutina clínica, la información respiratoria extraída de ellos representa un valor añadido que permite tener una visión más completa del paciente. DESARROLLO TEÓRICO Esta tesis se divide en 6 capítulos. El Capítulo 1 introduce la problemática, motivaciones y objetivos del estudio. También introduce el origen fisiológico de las señales estudiadas ECG y PPG, y cómo y por qué tienen información autonómica y respiratoria que se puede extraer de ellas. El Capítulo 2 aborda la obtención de información respiratoria a partir del ECG. Se han propuesto varios métodos para la obtención de la respiración a partir del ECG (EDR, del inglés ¿ECG derived respiration?). Su rendimiento se suele ver muy afectado en entornos altamente no estacionarios y ruidosos como la prueba de esfuerzo. No obstante, se han propuesto algunas alternativas, como una basada en el ángulo de rotación del eje eléctrico (obtenido del ECG), que es el que mejor funciona en prueba de esfuerzo según nuestros conocimientos. Este método requiere de tres derivaciones ortogonales y es muy dependiente de cada una de ellas, i.e., el método no es aplicable o su rendimiento se reduce significativamente si hay algún problema en alguna de las derivaciones requeridas. En el Capítulo 2 se propone un método EDR nuevo basado en las pendientes del QRS y el ángulo de la onda R. El Capítulo 3 aborda a obtención de información respiratoria a partir de la señal PPG. Se propone un método nuevo para obtener la tasa respiratoria a partir de la señal PPG. Explota una modulación respiratoria en la variabilidad de anchura de pulso (PWV) relacionada con la velocidad y dispersión de la onda de pulso. El Capítulo 4 aborda la extracción de información respiratoria a partir de señales PPG registradas con smarthpones (SCPPG), mediante la adaptación de los métodos basados en la señal PPG presentados en el Capítulo 3. En el Capítulo 5 se propone un método para el diagnóstico del síndrome de apnea obstructiva del sueño (OSAS) en niños basado únicamente en la señal PPG. El OSAS es una disfunción relacionada con la respiración y el sueño que se diagnostica mediante polisomnografía (PSG). La PSG es el registro nocturno de muchas señales durante el sueño, siendo muy difícil de aplicar en entornos ambulatorios. El método que presenta esta tesis está enfocado a diagnosticar el OSAS en niños utilizando únicamente la señal PPG que permitiría considerar un diagnóstico ambulatorio con sus ventajas económicas y sociales. Finalmente, el Capítulo 6 resume las contribuciones originales y las conclusiones principales de esta tesis, y propone posibles extensiones del trabajo. CONCLUSIÓN El método presentado en el Capítulo 2 para estimar la tasa respiratoria a partir de las pendientes del complejo QRS y el ángulo de la onda R en el ECG demostró ser robusto en entornos altamente no estacionarios y ruidosos y por tanto ser aplicable durante ejercicio incluyendo entrenamiento deportivo. Además, es independiente de un conjunto específico de derivaciones y, por tanto, un problema en alguna de ellas no implica una reducción considerable del rendimiento. El método presentado en el Capítulo 3 para estimar la tasa respiratoria a partir de la PWV extraída de la señal PPG está mucho menos afectada por el tono simpático que otros métodos presentados en la literatura que suelen basarse en la amplitud y/o la tasa de pulso. Esto permite una mayor precisión que otros métodos basados en PPG. Además, se propone un método para combinar información de diferentes señales respiratorias, y se utiliza para estimar la tasa respiratoria a partir de la PWV en combinación con otros métodos basados en la señal PPG, mejorando la precisión de la estimación incluso en comparación con otros métodos en la literatura que requieren el ECG o la presión sanguínea. Los métodos propuestos en el Capítulo 4 para estimar la tasa respiratoria mediante señales SCPPG estimaron de forma precisa la tasa respiratoria en sus rangos espontáneos habituales (0.2-0.4 Hz) e incluso a tasas más altas (hasta 0.5 Hz o 0.6 Hz, dependiendo del dispositivo utilizado). El único requerimiento es que el smartphone tenga un luz tipo flash y una cámara para grabar una yema del dedo sobre ella. La popularidad de los smartphones los convierte en dispositivos de acceso y aceptación r¿apidos. Así, para la población general es potencialmente aceptable un método que funciona en smartphones, pudiendo facilitar la medida de algunas constantes vitales utilizando solo la yema del dedo. El método presentado en el Capítulo 5 para el diagnóstico del OSAS en niños a partir de la PPG obtuvo una precisión suficiente para la clínica, aunque antes de ser aplicado en dicho entorno, el método debería ser validado en una base de datos más grande.The main objective of this thesis is to develop non-invasive methods for respiration information extraction from two biomedical signals which are widely adopted in clinical routine: the electrocardiogram (ECG) and the pulse photoplethysmographic (PPG) signal. This study is motivated by the desirability of monitoring respiratory information from non-invasive devices allowing to substitute the current respiration-monitoring techniques which may interfere with natural breathing and which are unmanageable in some applications such as stress test or sleep studies. Furthermore, if these noninvasive devices are those already used in the clinical routine, the respiratory information obtained from them represents an added value which allows a more complete overview of the patient status. This thesis is divided into 6 chapters. Chapter 1 of this thesis introduces the problematic, motivations and objectives of this study. It also introduces the physiological origin of studied ECG and PPG signals, and why and how they carry autonomic- and respiration-related information which can be extracted from them. Chapter 2 of this thesis addresses the derivation of respiratory information from ECG signal. Several ECG derived respiration (EDR) methods have been presented in literature. Their performance usually decrease considerably in highly non-stationary and noisy environments such as stress test. However, some alternatives aimed to this kind of environments have been presented, such as one based on electrical axis rotation angles (obtained from the ECG), which to the best of our knowledge was the best suited for stress test. This method requires three orthogonal leads, and it is very dependent on each one of those leads, i.e., the performance of the method is significantly decreased if there is any problem at any one of the required leads. A novel EDR method based on QRS slopes and R-wave angle is presented in this thesis. The proposed method demonstrated to be robust in highly non-stationary and noisy environments and so to be applicable to exercise conditions including sports training. Furthermore, it is independent on a specific lead set, and so, a problem at any lead do not imply a significantly reduction of the performance. Chapter 3 addresses the derivation of respiratory information from PPG signals. A novel method for deriving respiratory rate from PPG signal is presented. It exploits respiration-related modulations in pulse width variability (PWV) which is related to pulse wave velocity and dispersion. The proposed method is much less affected by the sympathetic tone than other methods in literature which are usually based on pulses amplitude and/or rate. This leads to highest accuracy than other PPG-based method. Furthermore, a method for combining information from several respiratory signals was developed and used to obtain a respiratory rate estimation from the proposed PWV-based in combination with other known PPG-based methods, improving the accuracy of the estimation and outperforming other methods in literature which involve ECG or BP recording. Chapter 4 addresses the derivation of respiratory information from smartphone- camera-acquired-PPG (SCPPG) signals by adapting the methods for deriving respiratory rate from PPG signal presented in Chapter 3. The proposed method accurately estimates respiratory rate from SCPPG signals at its normal spontaneous ranges (0.2-0.4 Hz) and even at higher rates (up to 0.5 Hz or 0.6 Hz, depending on the used device). The only requirement is that these smartphones and tablets contain a flashlight and a video camera to image a fingertip pressed to it. As smartphones and tablets have become common, they meet the criteria of ready access and acceptance. Hence, a mobile phone/tablet approach has the potential to be widely-accepted by the general population and can facilitate the capability to measure some of the vital signs using only fingertip of the subject. Chapter 5 of this thesis proposes a methodology for obstructive sleep apnea syndrome (OSAS) screening in children just based on PPG signal. OSAS is a sleep-respiration-related dysfunction for which polysomnography (PSG) is the gold standard for diagnosis. PSG consists of overnight recording of many signals during sleep, therefore, it is quite involved and difficult to use in ambulatory scenario. The method presented in this thesis is aimed to diagnose the OSAS in children based just on PPG signal which would allow us to consider an ambulatory diagnosis with both its social and economic advantages. Finally, Chapter 6 summarizes the original contributions and main conclusions of the thesis, and proposes possible extensions of the work

Remote Assessment of the Cardiovascular Function Using Camera-Based Photoplethysmography

Camera-based photoplethysmography (cbPPG) is a novel measurement technique that allows the continuous monitoring of vital signs by using common video cameras. In the last decade, the technology has attracted a lot of attention as it is easy to set up, operates remotely, and offers new diagnostic opportunities. Despite the growing interest, cbPPG is not completely established yet and is still primarily the object of research. There are a variety of reasons for this lack of development including that reliable and autonomous hardware setups are missing, that robust processing algorithms are needed, that application fields are still limited, and that it is not completely understood which physiological factors impact the captured signal. In this thesis, these issues will be addressed. A new and innovative measuring system for cbPPG was developed. In the course of three large studies conducted in clinical and non-clinical environments, the system’s great flexibility, autonomy, user-friendliness, and integrability could be successfully proven. Furthermore, it was investigated what value optical polarization filtration adds to cbPPG. The results show that a perpendicular filter setting can significantly enhance the signal quality. In addition, the performed analyses were used to draw conclusions about the origin of cbPPG signals: Blood volume changes are most likely the defining element for the signal's modulation. Besides the hardware-related topics, the software topic was addressed. A new method for the selection of regions of interest (ROIs) in cbPPG videos was developed. Choosing valid ROIs is one of the most important steps in the processing chain of cbPPG software. The new method has the advantage of being fully automated, more independent, and universally applicable. Moreover, it suppresses ballistocardiographic artifacts by utilizing a level-set-based approach. The suitability of the ROI selection method was demonstrated on a large and challenging data set. In the last part of the work, a potentially new application field for cbPPG was explored. It was investigated how cbPPG can be used to assess autonomic reactions of the nervous system at the cutaneous vasculature. The results show that changes in the vasomotor tone, i.e. vasodilation and vasoconstriction, reflect in the pulsation strength of cbPPG signals. These characteristics also shed more light on the origin problem. Similar to the polarization analyses, they support the classic blood volume theory. In conclusion, this thesis tackles relevant issues regarding the application of cbPPG. The proposed solutions pave the way for cbPPG to become an established and widely accepted technology

Central and peripheral autonomic influences : analysis of cardio-pulmonary dynamics using novel wavelet statistical methods

The development and implementation of novel signal processing techniques, particularly with regard to applications in the clinical environment, is critical to bringing computer-aided diagnoses of disease to reality. One of the most confounding factors in the field of cardiac autonomic response (CAR) research is the influence of the coupling of respiratory oscillations with cardiac oscillations. This research had three objectives. The first was the assessment of central autonomic influence over heart rate oscillations when the pulmonary system is damaged. The second was to assess the link between peripheral and central autonomic control schema by evaluating the heart rate variability (HRV) of people who were able or unable to adapt to the use of integrated lenses for vision, specifically acconrrmodation, correction (adaptive and non-adaptive presbyopes). The third objective was the development of a wavelet-based toolset by which the first two objectives could be achieved. The first tool is a wavelet based entropy measure that quantifies the level of information by assessing not only the entropy levels, but also the distribution of the entropy across frequency bands. The second tool is a wavelet source separation (WayS) method used to separate the respiratory component from the cardiac component, thereby allowing for analysis of the dynamics of the cardiac signal without the confounding influence of the respiratory signal that occurs when the body is perturbed. With regard to hypothesis one, the entropy method was used to separate the COPD study populations with 93% classification accuracy at rest, and with 100% accuracy during exercise. Changes in COPD and control autonomic markers were evident after respiration is removed. Specifically, the LF/HF ratio slightly decreased on average from pre to post reconstruction for controls, increased on average for COPD. In healthy controls, respiration frequency is distributed across multiple bandwidths, causing large decreases in both LF and HF when removed. With respiration effect removed from COPD population, LE dominates autonomic response, indicating that the frequency is concentrated in the HF autonomic region. Decrease in variance of data set increases probability tat smaller changes can be detected in values. The theory set forth in hypothesis two was validated by the quantification of a correlation between peripheral and central autonomic influences, as evidenced by differences in oculomotor adaptability correlating with differences in HRV. Standard Deviation varies with grouping, not with age. Increasing controlled respiration frequencies resulted in adaptive presbyopes and controls displaying similar sympathetic responses, diverging from non-adaptive group. WayS reduced frequency content in ranges concurrent with breathing rate, indicating a robust analysis. The outcome of hypothesis three was the confirmation that wavelet statistical methods possess significant potential for applications in HRV. Entropy can be used in conjunction with cluster analysis to classify patient populations with high accuracy. Using the WayS analysis, the respiration effect can be removed from HRV data sets, providing new insights into autonomic alterations, both central and peripheral, in disease