Characterization and processing of novel neck photoplethysmography signals for cardiorespiratory monitoring

Epilepsy is a neurological disorder causing serious brain seizures that severely affect the patients' quality of life. Sudden unexpected death in epilepsy (SUDEP), for which no evident decease reason is found after post-mortem examination, is a common cause of mortality. The mechanisms leading to SUDEP are uncertain, but, centrally mediated apneic respiratory dysfunction, inducing dangerous hypoxemia, plays a key role. Continuous physiological monitoring appears as the only reliable solution for SUDEP prevention. However, current seizure-detection systems do not show enough sensitivity and present a high number of intolerable false alarms. A wearable system capable of measuring several physiological signals from the same body location, could efficiently overcome these limitations. In this framework, a neck wearable apnea detection device (WADD), sensing airflow through tracheal sounds, was designed. Despite the promising performance, it is still necessary to integrate an oximeter sensor into the system, to measure oxygen saturation in blood (SpO2) from neck photoplethysmography (PPG) signals, and hence, support the apnea detection decision. The neck is a novel PPG measurement site that has not yet been thoroughly explored, due to numerous challenges. This research work aims to characterize neck PPG signals, in order to fully exploit this alternative pulse oximetry location, for precise cardiorespiratory biomarkers monitoring. In this thesis, neck PPG signals were recorded, for the first time in literature, in a series of experiments under different artifacts and respiratory conditions. Morphological and spectral characteristics were analyzed in order to identify potential singularities of the signals. The most common neck PPG artifacts critically corrupting the signal quality, and other breathing states of interest, were thoroughly characterized in terms of the most discriminative features. An algorithm was further developed to differentiate artifacts from clean PPG signals. Both, the proposed characterization and classification model can be useful tools for researchers to denoise neck PPG signals and exploit them in a variety of clinical contexts. In addition to that, it was demonstrated that the neck also offered the possibility, unlike other body parts, to extract the Jugular Venous Pulse (JVP) non-invasively. Overall, the thesis showed how the neck could be an optimum location for multi-modal monitoring in the context of diseases affecting respiration, since it not only allows the sensing of airflow related signals, but also, the breathing frequency component of the PPG appeared more prominent than in the standard finger location. In this context, this property enabled the extraction of relevant features to develop a promising algorithm for apnea detection in near-real time. These findings could be of great importance for SUDEP prevention, facilitating the investigation of the mechanisms and risk factors associated to it, and ultimately reduce epilepsy mortality.Open Acces

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

Atrial Fibrillation Prediction from Critically Ill Sepsis Patients

Sepsis is defined by life-threatening organ dysfunction during infection and is the leading cause of death in hospitals. During sepsis, there is a high risk that new onset of atrial fibrillation (AF) can occur, which is associated with significant morbidity and mortality. Consequently, early prediction of AF during sepsis would allow testing of interventions in the intensive care unit (ICU) to prevent AF and its severe complications. In this paper, we present a novel automated AF prediction algorithm for critically ill sepsis patients using electrocardiogram (ECG) signals. From the heart rate signal collected from 5-min ECG, feature extraction is performed using the traditional time, frequency, and nonlinear domain methods. Moreover, variable frequency complex demodulation and tunable Q-factor wavelet-transform-based time-frequency methods are applied to extract novel features from the heart rate signal. Using a selected feature subset, several machine learning classifiers, including support vector machine (SVM) and random forest (RF), were trained using only the 2001 Computers in Cardiology data set. For testing the proposed method, 50 critically ill ICU subjects from the Medical Information Mart for Intensive Care (MIMIC) III database were used in this study. Using distinct and independent testing data from MIMIC III, the SVM achieved 80% sensitivity, 100% specificity, 90% accuracy, 100% positive predictive value, and 83.33% negative predictive value for predicting AF immediately prior to the onset of AF, while the RF achieved 88% AF prediction accuracy. When we analyzed how much in advance we can predict AF events in critically ill sepsis patients, the algorithm achieved 80% accuracy for predicting AF events 10 min early. Our algorithm outperformed a state-of-the-art method for predicting AF in ICU patients, further demonstrating the efficacy of our proposed method. The annotations of patients\u27 AF transition information will be made publicly available for other investigators. Our algorithm to predict AF onset is applicable for any ECG modality including patch electrodes and wearables, including Holter, loop recorder, and implantable devices

Multimodal Signal Processing for Diagnosis of Cardiorespiratory Disorders

This thesis addresses the use of multimodal signal processing to develop algorithms for the automated processing of two cardiorespiratory disorders. The aim of the first application of this thesis was to reduce false alarm rate in an intensive care unit. The goal was to detect five critical arrhythmias using processing of multimodal signals including photoplethysmography, arterial blood pressure, Lead II and augmented right arm electrocardiogram (ECG). A hierarchical approach was used to process the signals as well as a custom signal processing technique for each arrhythmia type. Sleep disorders are a prevalent health issue, currently costly and inconvenient to diagnose, as they normally require an overnight hospital stay by the patient. In the second application of this project, we designed automated signal processing algorithms for the diagnosis of sleep apnoea with a main focus on the ECG signal processing. We estimated the ECG-derived respiratory (EDR) signal using different methods: QRS-complex area, principal component analysis (PCA) and kernel PCA. We proposed two algorithms (segmented PCA and approximated PCA) for EDR estimation to enable applying the PCA method to overnight recordings and rectify the computational issues and memory requirement. We compared the EDR information against the chest respiratory effort signals. The performance was evaluated using three automated machine learning algorithms of linear discriminant analysis (LDA), extreme learning machine (ELM) and support vector machine (SVM) on two databases: the MIT PhysioNet database and the St. Vincent’s database. The results showed that the QRS area method for EDR estimation combined with the LDA classifier was the highest performing method and the EDR signals contain respiratory information useful for discriminating sleep apnoea. As a final step, heart rate variability (HRV) and cardiopulmonary coupling (CPC) features were extracted and combined with the EDR features and temporal optimisation techniques were applied. The cross-validation results of the minute-by-minute apnoea classification achieved an accuracy of 89%, a sensitivity of 90%, a specificity of 88%, and an AUC of 0.95 which is comparable to the best results reported in the literature

Multidimensional embedded MEMS motion detectors for wearable mechanocardiography and 4D medical imaging

Background: Cardiovascular diseases are the number one cause of death. Of these deaths, almost 80% are due to coronary artery disease (CAD) and cerebrovascular disease. Multidimensional microelectromechanical systems (MEMS) sensors allow measuring the mechanical movement of the heart muscle offering an entirely new and innovative solution to evaluate cardiac rhythm and function. Recent advances in miniaturized motion sensors present an exciting opportunity to study novel device-driven and functional motion detection systems in the areas of both cardiac monitoring and biomedical imaging, for example, in computed tomography (CT) and positron emission tomography (PET). Methods: This Ph.D. work describes a new cardiac motion detection paradigm and measurement technology based on multimodal measuring tools — by tracking the heart’s kinetic activity using micro-sized MEMS sensors — and novel computational approaches — by deploying signal processing and machine learning techniques—for detecting cardiac pathological disorders. In particular, this study focuses on the capability of joint gyrocardiography (GCG) and seismocardiography (SCG) techniques that constitute the mechanocardiography (MCG) concept representing the mechanical characteristics of the cardiac precordial surface vibrations. Results: Experimental analyses showed that integrating multisource sensory data resulted in precise estimation of heart rate with an accuracy of 99% (healthy, n=29), detection of heart arrhythmia (n=435) with an accuracy of 95-97%, ischemic disease indication with approximately 75% accuracy (n=22), as well as significantly improved quality of four-dimensional (4D) cardiac PET images by eliminating motion related inaccuracies using MEMS dual gating approach. Tissue Doppler imaging (TDI) analysis of GCG (healthy, n=9) showed promising results for measuring the cardiac timing intervals and myocardial deformation changes. Conclusion: The findings of this study demonstrate clinical potential of MEMS motion sensors in cardiology that may facilitate in time diagnosis of cardiac abnormalities. Multidimensional MCG can effectively contribute to detecting atrial fibrillation (AFib), myocardial infarction (MI), and CAD. Additionally, MEMS motion sensing improves the reliability and quality of cardiac PET imaging.Moniulotteisten sulautettujen MEMS-liiketunnistimien käyttö sydänkardiografiassa sekä lääketieteellisessä 4D-kuvantamisessa Tausta: Sydän- ja verisuonitaudit ovat yleisin kuolinsyy. Näistä kuolemantapauksista lähes 80% johtuu sepelvaltimotaudista (CAD) ja aivoverenkierron häiriöistä. Moniulotteiset mikroelektromekaaniset järjestelmät (MEMS) mahdollistavat sydänlihaksen mekaanisen liikkeen mittaamisen, mikä puolestaan tarjoaa täysin uudenlaisen ja innovatiivisen ratkaisun sydämen rytmin ja toiminnan arvioimiseksi. Viimeaikaiset teknologiset edistysaskeleet mahdollistavat uusien pienikokoisten liiketunnistusjärjestelmien käyttämisen sydämen toiminnan tutkimuksessa sekä lääketieteellisen kuvantamisen, kuten esimerkiksi tietokonetomografian (CT) ja positroniemissiotomografian (PET), tarkkuuden parantamisessa. Menetelmät: Tämä väitöskirjatyö esittelee uuden sydämen kineettisen toiminnan mittaustekniikan, joka pohjautuu MEMS-anturien käyttöön. Uudet laskennalliset lähestymistavat, jotka perustuvat signaalinkäsittelyyn ja koneoppimiseen, mahdollistavat sydämen patologisten häiriöiden havaitsemisen MEMS-antureista saatavista signaaleista. Tässä tutkimuksessa keskitytään erityisesti mekanokardiografiaan (MCG), joihin kuuluvat gyrokardiografia (GCG) ja seismokardiografia (SCG). Näiden tekniikoiden avulla voidaan mitata kardiorespiratorisen järjestelmän mekaanisia ominaisuuksia. Tulokset: Kokeelliset analyysit osoittivat, että integroimalla usean sensorin dataa voidaan mitata syketiheyttä 99% (terveillä n=29) tarkkuudella, havaita sydämen rytmihäiriöt (n=435) 95-97%, tarkkuudella, sekä havaita iskeeminen sairaus noin 75% tarkkuudella (n=22). Lisäksi MEMS-kaksoistahdistuksen avulla voidaan parantaa sydämen 4D PET-kuvan laatua, kun liikeepätarkkuudet voidaan eliminoida paremmin. Doppler-kuvantamisessa (TDI, Tissue Doppler Imaging) GCG-analyysi (terveillä, n=9) osoitti lupaavia tuloksia sydänsykkeen ajoituksen ja intervallien sekä sydänlihasmuutosten mittaamisessa. Päätelmä: Tämän tutkimuksen tulokset osoittavat, että kardiologisilla MEMS-liikeantureilla on kliinistä potentiaalia sydämen toiminnallisten poikkeavuuksien diagnostisoinnissa. Moniuloitteinen MCG voi edistää eteisvärinän (AFib), sydäninfarktin (MI) ja CAD:n havaitsemista. Lisäksi MEMS-liiketunnistus parantaa sydämen PET-kuvantamisen luotettavuutta ja laatua

Pre-eclampsia: early prediction and long-term consequences

Approximately one in ten pregnant women will have their blood pressure recorded above normal at some point during their pregnancy. Pre-eclampsia, the most common hypertensive disorder of pregnancy, affects around 5% of all first time mothers, and is an important cause of foetal and maternal morbidity and mortality worldwide. Efforts to diagnose the condition have been hampered by inability to predict which women are likely to be affected. Multiple pathways are known to be involved in its pathogenesis, and several screening tests have been suggested for its early prediction. None, however, have been sensitive or specific enough to have come into routine medical practice. The work contained in this thesis describes a study which was designed to detect biochemical and clinical markers that could improve ability to predict pre-eclampsia. Over 3900 women were recruited in early pregnancy at four maternity clinics across the West of Scotland; baseline characteristics and information on past medical and obstetric history were obtained. Women were followed up throughout their pregnancy, and information on deliveries obtained from hospital databases. One-hundred and eighty of these women, who had multiple risk factors for pre-eclampsia, attended for further sampling and vascular assessment at gestational weeks 16 and 28. The primary aim of the overall study was to examine whether a proteomic strategy could be used to identify patterns of peptides in urine that detect pre-eclampsia in the first and second trimesters. Using samples from healthy pregnant and non-pregnant women I was able to describe the normal human urinary proteome in pregnancy. By comparing these pregnancy-associated peptides between women who went on to develop pre-eclampsia and matched controls, I was able to identify a pattern of peptides, characterised by collagen fragments, fibrinogen and uromodulin that accurately predicted pre-eclampsia at week 28. No such markers were identified in the first trimester samples. A further aim of the overall study was to identify early pregnancy plasma markers that could help to identify women destined to develop pre-eclampsia. By examining samples from early pregnancy I was able to demonstrate that the angiogenic markers soluble endoglin and placental growth factor are already altered at week 12-16 in women who go on to develop pre-eclampsia. Using a multi-marker approach, I also showed that E-Selectin, an adhesion molecule expressed on endothelial cells which controls interaction between circulating leukocytes and the endothelium, is higher at week 12-16 in women who go on to develop pre-eclampsia. Experiments using samples from later pregnancy, alternative analysis techniques and samples from an independent study population all helped to confirm these novel findings. Endothelial dysfunction is known to play a key role in the development of pre-eclampsia, contributing to the hypertension, proteinuria and oedema seen in affected women. In the risk factor cohort I used vascular function studies to examine whether they supplied additional information to aid in risk stratification. Peripheral arterial tonometry, a novel non-invasive tool for the assessment of microcirculatory endothelial function, was examined in 180 women at gestational weeks 16 and 28. Reactive hyperaemia index (RHI), a measure of endothelial dysfunction calculated from vascular response to arm blood-flow occlusion, did not correlate with maternal factors such as age, BMI and blood pressure. Further, RHI did not help to identify which women would go on to develop pre-eclampsia, when examined at either week 16 or 28. I found that PAT score was negatively correlated with baseline digital pulse amplitude, suggesting that in later pregnancy, when women are more vasodilated, PAT and other techniques which rely on flow-mediated dilatation are less likely to be reliable. I used pulse wave analysis, a well-established method for measuring arterial stiffness and central pressures, to determine whether it supplied additional information about pre-eclampsia risk. This technique has been previously reported to predict pre-eclampsia in early pregnancy. In this cohort of high risk women, no difference was seen at either week 16 or 28 between those who would go on to develop pre-eclampsia and those who would have normotensive pregnancies. Although blood pressure and proteinuria return to normal after pre-eclampsia, evidence has emerged the condition has long-lasting implications; women with a history of pre-eclampsia have an increased risk of cardiovascular disease later in life, suffering stroke or myocardial infarction more frequently than women who had a healthy pregnancy. Conventional risk factors are thought to contribute, but do not fully explain this increased risk. I carried out further vascular function studies in women after pre-eclamptic pregnancy, to examine whether they had ongoing detectable endothelial dysfunction and arterial stiffness. At 6-9 months post-natally, affected women had lower baseline digital pulse amplitude but no other evidence of persistent vascular dysfunction. Taken together, these data provide information about a number of markers that may improve understanding of the pathophysiological mechanisms underlying pre-eclampsia. As well as potentially improving the early prediction of disease, this work represents a highly topical area for further studies. While vascular function analysis does not appear to provide additional information on top of risk factors, these studies also provide useful information on vascular physiology in high-risk pregnancies

Psoriaasi, atoopilise dermatiidi ja ateroskleroosi metaboloomne profileerimine

Väitekirja elektrooniline versioon ei sisalda publikatsiooneMetaboloomika on teadusharu, mis tegeleb madalmolekulaarsete ühendite mõõtmise ja analüüsimisega. Nendeks on aminohapped, biogeensed amiinid, süsivesikud, rasvhapped, nukleiinhapped või peptiidid, mis võivad olla nii eksogeenset kui ka endogeenset päritolu. Nende ainete samaaegne mõõtmine võimaldab näha ainevahetusradade otsest peegeldust, nö. metaboloomset sõrmejälge. Psoriaas on laialt levinud krooniline põletikuline nahahaigus, mis esineb kuni 1%-l lastest ja 2%-3% üldpopulatsioonist. Haiguse teke on seotud mitme põhjusega, sealhulgas geneetiline eelsoodumus ja vastuvõtlikkus, keskkonna mõjutegurid koos immuunsüsteemi düsfunktsiooni ja nahabarjääri häirega. Atoopiline dermatiit on laialt levinud ja kompleksne nahahaigus, mis mõjutab kuni 15% lapsi ja täiskasvanuid üldpopulatsioonis. Kuigi enamik lapsi kasvab haigusest välja, hõlmab see teatud juhtudel ka täiskasvanuid, mõjutades patsientide heaolu ja põhjustades rida kaasuvaid haigusi, sealhulgas allergiad, astma, tähelepanuhäired ning aneemiat. Ateroskleroos on põletikuline haigus, hõlmates arterite seinu, kuhu kogunevad põletikulised rakud ja lipiidid. See viib arterite ahenemiseni, mis võib päädida trombi tekkega, põhjustades infarkti. Ateroskleroosi kõige levinumad vormid on perifeerne arterite haigus ja koronaar-arteri haigus, millest mõlemast on saanud suured rahvatervise probleemid. Käesoleva doktoritöö peamiseks eesmärgiks oli analüüsida psoriaasi, atoopilise dermatiidi ja ateroskleroosi patsientide metaboloomseid profiile ning hinnata sarnasusi ja erinevusi leitud metaboliitides.Metabolomics concerns with the measurement and analysis of small molecule compounds (< 1 kDa, e.g. amino acids, biogenic amines, carbohydrates, fatty acids, nucleic acids, peptides) of both exogenous and endogenous origins. These are the substrates and products of various chemical reactions within metabolic pathways. Psoriasis (PS) is a widespread chronic inflammatory skin disease affecting 2%-3% of the population in the world. The disease is considered to be multifactorial with a number of key contributing factors including genetic predisposition and susceptibility, environmental influences along with immune dysfunction and the disruption of the skin barrier. Atopic dermatitis (AD) is a widespread and complex condition that affects up to 15% adults and children worldwide. Although children have an increased prevalence of atopic dermatitis, many adults remain affected throughout their life. Atherosclerosis is classified as an inflammatory disease that involves the arterial wall and is characterized by the continuous accumulation of inflammatory cells and lipids within the intima of large arteries. The metabolomic profiles of patients with psoriasis and atopic dermatitis were explored to find possible disease-specific metabolites that could be used to characterise and better understand the underlying mechanisms of the disease pathogenesis. The application of the established methods was expanded to peripheral arterial disease and coronary arterial disease to further search for similarities and differences in the metabolomic profiles of the diseaseshttps://www.ester.ee/record=b522842

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 /

Proteomic, circulating and functional biomarkers of cardiovascular disease

Cardiovascular disease is the leading cause of morbidity and mortality in the Western world, mainly through cerebrovascular and coronary artery related events. Cardiovascular disease is a chronic progressive disease with different stages. These stages can be assessed by a variety of biomarkers. Biomarker quantification can be used for different purposes: screening, prediction of disease recurrence, therapeutic monitoring, diagnosis and prognostication. Noninvasive, inexpensive diagnostic tests currently applied in clinical practice have a relative high rate of false positive and false negative results. Therefore further refinement of the diagnostic process could improve clinical care. Regarding prognostication the need for improvement also remains as current risk models only predict a small quantity of occurring cardiovascular events. The concept of the cardiovascular continuum postulates that cardiovascular disease consists of a chain of events, is initiated by numerous cardiovascular risk factors and subsequently progresses through pathophysiological processes, ultimately leading to end-stage heart failure. For that reason cardiovascular diseases are chronic progressive conditions and can be divided into different stages, such as early tissue dysfunction or subclinical atherosclerosis prior to development of clinically overt disease. Biomarkers suitable for prognostication and diagnosis can differ at each stage. The general aim of this thesis was therefore the investigation of a variety of biomarkers in diagnosis and prediction of cardiovascular disease at different stages of the cardiovascular continuum, as covered by three different study cohorts contributing to this thesis. This included several approaches: the comparison of central and peripheral pulse pressure in middle aged hypertensive patients in regards of their prognostic potential; the application of established circulating, functional and structural biomarkers to the diagnostic process of coronary artery disease in stable angina patients; the development/refinement of a urinary proteomic biomarker for coronary artery disease and the examination of its diagnostic potential in stable angina patients. Biomarkers successful in the diagnosis of coronary artery disease were included in multiple biomarker models. Aside from biomarker development for the general population, investigations of specific cohorts, such as patients with certain diseases and belonging to certain age groups or sharing specific biochemical features provided advances in the past. To estimate the potential of a biomarker in risk prediction association studies with surrogate biomarkers are applicable. We collected a cohort of middle-aged hypertensive patients to assess if central pulse pressure, derived from non-invasive assessment of arterial stiffness, could improve risk prediction. Central pulse pressure has been previously shown to have prognostic value in populations with end-stage renal failure, coronary artery disease and high prevalence of diabetes mellitus. Considering the prognostic information of peripheral pulse pressure in the elderly, the hypothesis that central pulse pressure could improve risk prediction is comprehensive and was investigated as part of this thesis. This was accomplished by comparing the strength of correlation between central or peripheral pulse pressure and these surrogate biomarkers. When compared to peripheral pulse pressure, central pulse pressure had stronger associations with aortic pulse wave velocity, carotid intima-media thickness, and left ventricular mass index, but equal association with the albumin:creatinine ratio. In contrast, after adjustment for age, mean arterial pressure, heart rate and hypertension status there was no significant difference between central and peripheral pulse pressure for prediction of listed surrogate biomarkers in multivariate analysis. These results suggested that central pulse pressure is unlikely to provide more prognostic information than peripheral pulse pressure in middle-aged hypertensive patients. The diagnosis of coronary artery disease is clinically relevant in symptomatic patients, either acute or stable. The diagnosis of stable flow limiting coronary artery disease is especially challenging as non-cardiac as well as other cardiac conditions can mimic symptoms. Non-invasive diagnostic tools have either moderate sensitivities or specificities, or are not widely available. Therefore new biomarkers for the diagnosis of flow limiting coronary artery disease have the potential to improve current diagnostic strategies. This could be accomplished adjacent to existing biomarkers or by replacement of such, due to cost effectiveness, better discriminatory etc. As part of this thesis, a biomarker identification and validation study was conducted into urinary proteomics of coronary artery disease. First we tried to replicate a study conducted by our research group in the past. Therein, an established coronary artery disease specific polypeptide pattern was unable to differentiate between patients with severe coronary artery disease and healthy controls despite strong cohort similarities to the original study. We therefore recalibrated the urinary polypeptide pattern using an enlarged biomarker discovery cohort and adjusted the pattern for lipid lowering and angiotensin converting enzyme inhibitor treatment effects. We calculated a score from the resulting polypeptide pattern, which identified coronary artery disease patients with a sensitivity of 79% and a specificity of 88% in a biomarker validation cohort. As the next step of biomarker development we performed a diagnostic validation study. The investigated clinical cohort consisted of stable angina patients with or without coronary artery disease. The new polypeptide pattern score was unable to differentiate between these two groups. The score however correlated strongly with coronary artery disease extent as measured by the Gensini score, implying that urinary proteomics in the diagnosis of coronary artery disease is promising, yet requires further effort before clinical employment. In addition to the urinary proteomic biomarker development second diagnostic approach was selected. As coronary artery disease is a complex chronic disease, the combination of different biomarkers should result in a better discrimination between stable angina patients with or without coronary artery disease. This approach attempts to position the individual as precisely as possible on the cardiovascular continuum including serologic, functional vascular and imaging biomarkers of subclinical atherosclerosis. Serologic markers thereby present a plasma proteomic approach covering pathophysiological processes with known correlation or causative for coronary artery disease. Functional and structural changes of the peripheral vasculature resemble the coronary artery system. We investigated circulating biomarkers and vascular biomarkers separately. A variety of circulating biomarkers differentiated patients with severe coronary artery disease from healthy control subjects. When patients with stable angina and with or without coronary artery disease as diagnosed by coronary angiography were investigated no statistically significant differences could be detected for circulating biomarkers. In the same study a microvascular biomarker, the reactive hyperaemia index, and a macrovascular biomarker, the carotide plaque score, were able to differentiated between cases and controls. Both markers either added separately or together improved the risk classification of exercise treadmill test results. This suggests that a multiple biomarker approach in the diagnosis of coronary artery disease in stable angina patients could be successful. Different aspects of the cardiovascular continuum can be applied to diagnosis and prognostication of cardiovascular disease. In this regard we were able to show, that early processes such as endothelial dysfunction or later processes such as plaque formation can support the diagnostic process. However, randomly collected circulating biomarkers might be unable to do this. Our finding that central pulse pressure is unlikely to have more prognostic value in middle aged hypertensive patients underlines that biomarkers can be useful in specific patient collectives but not necessarily in all cohorts. Instead of applying established biomarkers, also new biomarkers can be developed. Urine proteomics showed great promise in this regard, as specific polypeptide patterns reflect coronary artery disease and are strongly correlated to its extent

Non-Contact Sleep Monitoring

"The road ahead for preventive medicine seems clear. It is the delivery of high quality, personalised (as opposed to depersonalised) comprehensive medical care to all." Burney, Steiger, and Georges (1964) This world's population is ageing, and this is set to intensify over the next forty years. This demographic shift will result in signicant economic and societal burdens (partic- ularly on healthcare systems). The instantiation of a proactive, preventative approach to delivering healthcare is long recognised, yet is still proving challenging. Recent work has focussed on enabling older adults to age in place in their own homes. This may be realised through the recent technological advancements of aordable healthcare sen- sors and systems which continuously support independent living, particularly through longitudinally monitoring deviations in behavioural and health metrics. Overall health status is contingent on multiple factors including, but not limited to, physical health, mental health, and social and emotional wellbeing; sleep is implicitly linked to each of these factors. This thesis focusses on the investigation and development of an unobtrusive sleep mon- itoring system, particularly suited towards long-term placement in the homes of older adults. The Under Mattress Bed Sensor (UMBS) is an unobstrusive, pressure sensing grid designed to infer bed times and bed exits, and also for the detection of development of bedsores. This work extends the capacity of this sensor. Specically, the novel contri- butions contained within this thesis focus on an in-depth review of the state-of-the-art advances in sleep monitoring, and the development and validation of algorithms which extract and quantify UMBS-derived sleep metrics. Preliminary experimental and community deployments investigated the suitability of the sensor for long-term monitoring. Rigorous experimental development rened algorithms which extract respiration rate as well as motion metrics which outperform traditional forms of ambulatory sleep monitoring. Spatial, temporal, statistical and spatiotemporal features were derived from UMBS data as a means of describing movement during sleep. These features were compared across experimental, domestic and clinical data sets, and across multiple sleeping episodes. Lastly, the optimal classier (built using a combina- tion of the UMBS-derived features) was shown to infer sleep/wake state accurately and reliably across both younger and older cohorts. Through long-term deployment, it is envisaged that the UMBS-derived features (in- cluding spatial, temporal, statistical and spatiotemporal features, respiration rate, and sleep/wake state) may be used to provide unobtrusive, continuous insights into over- all health status, the progression of the symptoms of chronic conditions, and allow the objective measurement of daily (sleep/wake) patterns and routines