Advanced bioimpedance signal processing techniques for hemodynamic monitoring during anesthesia

Cardiac output (CO) defines the blood flow arriving from the heart to the different organs in the body and it is thus a primary determinant of global 02 transport. Cardiac output has traditionally been measured using invasive methods, whose risk sometimes exceeds the advantages of a cardiac output monitoring. In this context, the minimization of risk in new noninvasive technologies for CO monitoring could translate into major advantages for clinicians, hospitals and patients: ease of usage and availability, reduced recovery time, and improved patient outcome. Impedance Cardiography (ICG) is a promising noninvasive technology for cardiac output monitoring but available information on the ICG signals is more scare than other physiological signals such as the electrocardiogram (ECG). The present Doctoral Thesis contributes to the development of signal treatment techniques for the ICG in order to create an innovative hemodynamic monitor. First, an extensive literature review is provided regarding the basics of the clinical background in which cardiac output monitoring is used and concerning the state of the art of cardiac output monitors on the market. This Doctoral Thesis has produced a considerable amount of clinical data which is also explained in detail. These clinical data are also useful to complement the theoretical explanation of patient indices such as heart rate variability, blood flow and blood pressure. In addition, a new method to create synthetic biomedical signals with known time-frequency characteristics is introduced. One of the first analysis in this Doctoral Thesis studies the time difference between peak points of the heart beats in the ECG and the ICG: the RC segment. This RC segment is a measure of the time delay between electrical and mechanical activity of the heart. The relationship of the RC segment with blood pressure and heart interval is analyzed. The concordance of beat durations of both the electrocardiogram and the impedance cardiogram is one of the key results to develop new artefact detection algorithms and the RC could also have an impact in describing the hemodynamics of a patient. Time-frequency distributions (TFDs) are also used to characterize how the frequency content in impedance cardiography signals change with time. Since TFDs are calculated using concrete kernels, a new method to select the best kernel by using synthetic signals is presented. Optimized TFDs of ICG signals are then calculated to extract severa! features which are used to discriminate between different anesthesia states in patients undergoing surgery. TFD-derived features are also used to describe the whole surgical operations. Relationships between TFD-derived features are analyzed and prediction models for cardiac output are designed. These prediction models prove that the TFD-derived features are related to the patients' cardiac output. Finally, a validation study for the qCO monitor is presented. The qCO monitor has been designed using sorne of the techniques which are consequence of this Doctoral Thesis. The main outputs of this work have been protected with a patent which has already been filed. As a conclusion, this Doctoral Thesis has produced a considerable amount of clinical data and a variety of analysis and processing techniques of impedance cardiography signals which have been included into commercial medical devices already available on the market.El gasto cardíaco (GC) define el flujo de sangre que llega desde el corazón a los distintos órganos del cuerpo y es, por tanto, un determinante primario del transporte global de oxígeno. Se ha medido tradicionalmente usando métodos invasivos cuyos riesgos excedían en ocasiones las ventajas de su monitorización. En este contexto, la minimización del riesgo de la monitorización del gasto cardíaco en nuevas tecnologías no invasivas podría traducirse en mayores ventajas para médicos, hospitales y pacientes: facilidad de uso, disponibilidad del equipamiento y menor tiempo de recuperación y mejores resultados en el paciente. La impedancio-cardiografía o cardiografía de impedancia (ICG} es una prometedora tecnología no invasiva para la monitorización del gasto cardíaco. Sin embargo, la información disponible sobre las señales de ICG es más escasa que otras señales fisiológicas como el electrocardiograma (ECG). La presente Tesis Doctoral contribuye al desarrollo de técnicas de tratamiento de señal de ICG para así crear un monitor hemodinámico innovador. En primer lugar, se proporciona una extensa revisión bibliográfica sobre los aspectos básicos del contexto clínico en el que se utiliza la monitorización del gasto cardíaco así como sobre el estado del arte de los monitores de gasto cardíaco que existen en el mercado. Esta Tesis Doctoral ha producido una considerable cantidad de datos clínicos que también se explican en detalle. Dichos datos clínicos también son útiles para complementar las explicaciones teóricas de los índices de paciente de variabilidad cardíaca y el flujo y la presión sanguíneos. Además, se presenta un nuevo método de creación de señales sintéticas biomédicas con características de tiempo-frecuencia conocidas. Uno de los primeros análisis de esta Tesis Doctoral estudia la diferencia temporal entre los picos de los latidos cardíacos del ECG y del ICG: el segmento RC. Este segmento RC es una medida del retardo temporal entre la actividad eléctrica y mecánica del corazón. Se analiza la relación del segmento RC con la presión arterial y el intervalo cardíaco. La concordancia entre la duración de los latidos del ECG y del ICG es uno de los resultados claves para desarrollar nuevos algoritmos de detección de artefactos y el segmento RC también podría ser relevante en la descripción de la hemodinámica de los pacientes. Las distribuciones de tiempo-frecuencia (TFD, por sus siglas en inglés) se utilizan para caracterizar cómo el contenido de las señales de impedancia cardiográfica cambia con el tiempo. Dado que las TFDs deben calcularse usando núcleos (kernels, en inglés) concretos, se presenta un nuevo método para seleccionar el mejor núcleo mediante el uso de señales sintéticas. Las TFDs de ICG optimizadas se calculan para extraer distintas características que son usadas para discriminar entre los diferentes estados de anestesia en pacientes sometidos a procesos quirúrgicos. Las características derivadas de las distribuciones de tiempo-frecuencia también son utilizadas para describir las operaciones quirúrgicas durante toda su extensión temporal. La relación entre dichas características son analizadas y se proponen distintos modelos de predicción para el gasto cardíaco. Estos modelos de predicción demuestran que las características derivadas de las distribuciones tiempo-frecuencia de señales de ICG están relacionadas con el gasto cardíaco de los pacientes. Finalmente, se presenta un estudio de validación del monitor qCO, diseñado con alguna de las técnicas que son consecuencia de esta Tesis Doctoral. Las principales conclusiones de este trabajo han sido protegidas con una patente que ya ha sido registrada. Como conclusión, esta Tesis Doctoral ha producido una considerable cantidad de datos clínicos y una variedad de técnicas de procesado y análisis de señales de cardiografía de impedancia que han sido incluidas en dispositivos biomédicos disponibles en el mercad

Advanced bioimpedance signal processing techniques for hemodynamic monitoring during anesthesia

Aplicat embargament des de la data de defensa fins els maig 2020.Cardiac output (CO) defines the blood flow arriving from the heart to the different organs in the body and it is thus a primary determinant of global 02 transport. Cardiac output has traditionally been measured using invasive methods, whose risk sometimes exceeds the advantages of a cardiac output monitoring. In this context, the minimization of risk in new noninvasive technologies for CO monitoring could translate into major advantages for clinicians, hospitals and patients: ease of usage and availability, reduced recovery time, and improved patient outcome. Impedance Cardiography (ICG) is a promising noninvasive technology for cardiac output monitoring but available information on the ICG signals is more scare than other physiological signals such as the electrocardiogram (ECG). The present Doctoral Thesis contributes to the development of signal treatment techniques for the ICG in order to create an innovative hemodynamic monitor. First, an extensive literature review is provided regarding the basics of the clinical background in which cardiac output monitoring is used and concerning the state of the art of cardiac output monitors on the market. This Doctoral Thesis has produced a considerable amount of clinical data which is also explained in detail. These clinical data are also useful to complement the theoretical explanation of patient indices such as heart rate variability, blood flow and blood pressure. In addition, a new method to create synthetic biomedical signals with known time-frequency characteristics is introduced. One of the first analysis in this Doctoral Thesis studies the time difference between peak points of the heart beats in the ECG and the ICG: the RC segment. This RC segment is a measure of the time delay between electrical and mechanical activity of the heart. The relationship of the RC segment with blood pressure and heart interval is analyzed. The concordance of beat durations of both the electrocardiogram and the impedance cardiogram is one of the key results to develop new artefact detection algorithms and the RC could also have an impact in describing the hemodynamics of a patient. Time-frequency distributions (TFDs) are also used to characterize how the frequency content in impedance cardiography signals change with time. Since TFDs are calculated using concrete kernels, a new method to select the best kernel by using synthetic signals is presented. Optimized TFDs of ICG signals are then calculated to extract severa! features which are used to discriminate between different anesthesia states in patients undergoing surgery. TFD-derived features are also used to describe the whole surgical operations. Relationships between TFD-derived features are analyzed and prediction models for cardiac output are designed. These prediction models prove that the TFD-derived features are related to the patients' cardiac output. Finally, a validation study for the qCO monitor is presented. The qCO monitor has been designed using sorne of the techniques which are consequence of this Doctoral Thesis. The main outputs of this work have been protected with a patent which has already been filed. As a conclusion, this Doctoral Thesis has produced a considerable amount of clinical data and a variety of analysis and processing techniques of impedance cardiography signals which have been included into commercial medical devices already available on the market.El gasto cardíaco (GC) define el flujo de sangre que llega desde el corazón a los distintos órganos del cuerpo y es, por tanto, un determinante primario del transporte global de oxígeno. Se ha medido tradicionalmente usando métodos invasivos cuyos riesgos excedían en ocasiones las ventajas de su monitorización. En este contexto, la minimización del riesgo de la monitorización del gasto cardíaco en nuevas tecnologías no invasivas podría traducirse en mayores ventajas para médicos, hospitales y pacientes: facilidad de uso, disponibilidad del equipamiento y menor tiempo de recuperación y mejores resultados en el paciente. La impedancio-cardiografía o cardiografía de impedancia (ICG} es una prometedora tecnología no invasiva para la monitorización del gasto cardíaco. Sin embargo, la información disponible sobre las señales de ICG es más escasa que otras señales fisiológicas como el electrocardiograma (ECG). La presente Tesis Doctoral contribuye al desarrollo de técnicas de tratamiento de señal de ICG para así crear un monitor hemodinámico innovador. En primer lugar, se proporciona una extensa revisión bibliográfica sobre los aspectos básicos del contexto clínico en el que se utiliza la monitorización del gasto cardíaco así como sobre el estado del arte de los monitores de gasto cardíaco que existen en el mercado. Esta Tesis Doctoral ha producido una considerable cantidad de datos clínicos que también se explican en detalle. Dichos datos clínicos también son útiles para complementar las explicaciones teóricas de los índices de paciente de variabilidad cardíaca y el flujo y la presión sanguíneos. Además, se presenta un nuevo método de creación de señales sintéticas biomédicas con características de tiempo-frecuencia conocidas. Uno de los primeros análisis de esta Tesis Doctoral estudia la diferencia temporal entre los picos de los latidos cardíacos del ECG y del ICG: el segmento RC. Este segmento RC es una medida del retardo temporal entre la actividad eléctrica y mecánica del corazón. Se analiza la relación del segmento RC con la presión arterial y el intervalo cardíaco. La concordancia entre la duración de los latidos del ECG y del ICG es uno de los resultados claves para desarrollar nuevos algoritmos de detección de artefactos y el segmento RC también podría ser relevante en la descripción de la hemodinámica de los pacientes. Las distribuciones de tiempo-frecuencia (TFD, por sus siglas en inglés) se utilizan para caracterizar cómo el contenido de las señales de impedancia cardiográfica cambia con el tiempo. Dado que las TFDs deben calcularse usando núcleos (kernels, en inglés) concretos, se presenta un nuevo método para seleccionar el mejor núcleo mediante el uso de señales sintéticas. Las TFDs de ICG optimizadas se calculan para extraer distintas características que son usadas para discriminar entre los diferentes estados de anestesia en pacientes sometidos a procesos quirúrgicos. Las características derivadas de las distribuciones de tiempo-frecuencia también son utilizadas para describir las operaciones quirúrgicas durante toda su extensión temporal. La relación entre dichas características son analizadas y se proponen distintos modelos de predicción para el gasto cardíaco. Estos modelos de predicción demuestran que las características derivadas de las distribuciones tiempo-frecuencia de señales de ICG están relacionadas con el gasto cardíaco de los pacientes. Finalmente, se presenta un estudio de validación del monitor qCO, diseñado con alguna de las técnicas que son consecuencia de esta Tesis Doctoral. Las principales conclusiones de este trabajo han sido protegidas con una patente que ya ha sido registrada. Como conclusión, esta Tesis Doctoral ha producido una considerable cantidad de datos clínicos y una variedad de técnicas de procesado y análisis de señales de cardiografía de impedancia que han sido incluidas en dispositivos biomédicos disponibles en el mercadoPostprint (published version

The Different Facets of Heart Rate Variability in Obstructive Sleep Apnea

Obstructive sleep apnea (OSA), a heterogeneous and multifactorial sleep related breathing disorder with high prevalence, is a recognized risk factor for cardiovascular morbidity and mortality. Autonomic dysfunction leads to adverse cardiovascular outcomes in diverse pathways. Heart rate is a complex physiological process involving neurovisceral networks and relative regulatory mechanisms such as thermoregulation, renin-angiotensin-aldosterone mechanisms, and metabolic mechanisms. Heart rate variability (HRV) is considered as a reliable and non-invasive measure of autonomic modulation response and adaptation to endogenous and exogenous stimuli. HRV measures may add a new dimension to help understand the interplay between cardiac and nervous system involvement in OSA. The aim of this review is to introduce the various applications of HRV in different aspects of OSA to examine the impaired neuro-cardiac modulation. More specifically, the topics covered include: HRV time windows, sleep staging, arousal, sleepiness, hypoxia, mental illness, and mortality and morbidity. All of these aspects show pathways in the clinical implementation of HRV to screen, diagnose, classify, and predict patients as a reasonable and more convenient alternative to current measures.Peer Reviewe

Novel Technologies for the Diagnosis and Treatment of Posttraumatic Stress Disorder

The brain and the heart share an active and reciprocal dialogue, continuously modulating each other's function. For individuals who have experienced traumatic events, the reminders of these events affect both the brain and heart due to this intimate relationship, and can later develop into posttraumatic stress disorder (PTSD) due to the repeated activation of trauma-related neuropathways and autonomic imbalance. Electrical stimulation of the vagus nerve —the longest cranial nerve, which regulates the autonomic state—using an implantable device is a potential treatment method to address such imbalance. Noninvasive vagal nerve stimulation (nVNS) devices offer inexpensive and low-risk alternatives to surgical implants, but their effects on the physiology are not well understood. Real-time, noninvasively obtained biomarkers are required to tailor therapy and to close the loop for automated delivery. This dissertation focuses on identifying and developing noninvasive technologies for nVNS in the context of PTSD. Identification of noninvasive measures that can diagnose and treat PTSD is imperative for at-home usage and for developing closed-loop systems. This research first focuses on how noninvasive sensing modalities could be instrumented and used in conjunction with signal processing and machine learning methods to quantify an individual’s autonomic state. Second, a mechanistic, sham-controlled, randomized, double blind study on the use of nVNS for dampening stress response is investigated in multiple dimensions: downstream physiological effects and biochemical biomarkers, with a particular focus on real-time physiological biomarkers and their potential for closing the loop for machine learning guided personalized neuromodulation. The broader impacts of this research cover accessible, low-cost diagnosis and treatment options for patients with stress-related neuropsychiatric disorders, which are important public health problems and projected to increase due to COVID-19 pandemic. The sensing modalities, algorithms, biomarkers, and methodologies detailed in this dissertation lay the groundwork for future efforts to objectively diagnose and treat neuropsychiatric disorders remotely, outside of clinical settings.Ph.D

Cardiac sympathovagal activity initiates a functional brain-body response to emotional processing

A century-long debate on bodily states and emotions persists. While the involvement of bodily activity in emotion physiology is widely recognized, the specificity and causal role of such activity related to brain dynamics has not yet been demonstrated. We hypothesize that the peripheral neural monitoring and control of cardiovascular activity prompts and sustains brain dynamics during an emotional experience, so these afferent inputs are processed by the brain by triggering a concurrent efferent information transfer to the body. To this end, we investigated the functional brain-heart interplay under emotion elicitation in publicly available data from 62 healthy participants using a computational model based on synthetic data generation of EEG and ECG signals. Our findings show that sympathovagal activity plays a leading and causal role in initiating the emotional response, in which ascending modulations from vagal activity precede neural dynamics and correlate to the reported level of arousal. The subsequent dynamic interplay observed between the central and autonomic nervous systems sustains emotional processing. These findings should be particularly revealing for the psychophysiology and neuroscience of emotions. Significance We investigate the temporal dynamics of brain and cardiac activities in healthy subjects who underwent an emotional elicitation through videos. We demonstrate that, within the first few seconds, emotional stimuli modulate the heart activity, which in turn stimulate an emotion-specific cortical response in the brain. Then, the conscious emotional experience is sustained by a bidirectional brain-heart interplay and information exchange. Moreover, the perceived intensity of an emotional stimulus is predicted by the intensity of neural control regulating the heart activity. These findings may constitute the fundamental knowledge linking neurophysiology and psychiatric disorders, including the link between depressive symptoms and cardiovascular disorders

Affective-autonomic states of domestic pigs in the context of coping and animal welfare

Gaining better insight in affective states of farm animals is of importance for understanding their welfare state. One important step in this context is to establish valid proxy measures to objectively assess and interpret an individual’s subjective perception of its environment. This thesis presents a reliable tool for the objective evaluation of affective-autonomic states in free-moving pigs and gains insight into the neurophysiological mechanisms underlying the individual processing of affective states in relation to their valence and arousal dimensions.Die Untersuchung affektiver Zustände von Nutztieren ist für das Verständnis ihres Wohlbefindens von essentieller Bedeutung. Ein wichtiger Schritt in diesem Kontext ist die Etablierung zuverlässiger Messmethoden zur objektiven Beurteilung und Interpretation individueller subjektiver Wahrnehmung. Diese Arbeit stellt eine valide Methode zur objektiven Beurteilung affektiv-autonomer Zustände bei Schweinen dar und vermittelt einen Einblick in die neurophysiologischen Mechanismen, die der individuellen Verarbeitung affektiver Zustände zugrunde liegen

Fourier Transforms

The 21st century ushered in a new era of technology that has been reshaping everyday life, simplifying outdated processes, and even giving rise to entirely new business sectors. Today, contemporary users of products and services expect more and more personalized products and services that can meet their unique needs. In that sense, it is necessary to further develop existing methods, adapt them to new applications, or even discover new methods. This book provides a thorough review of some methods that have an increasing impact on humanity today and that can solve different types of problems even in specific industries. Upgrading with Fourier Transformation gives a different meaning to these methods that support the development of new technologies and have a good projected acceleration in the future

Drive at the rhythm of your own heart: a study on Heart Rate Variability, cognitive functioning and driving performance in Ferrari Driving Academy drivers

openRacing driving requires the development of extraordinary sensorimotor skills to deliver high-level peak performances in complex environments characterised by multiple stressors, draining drivers’ physiological and cognitive resources. Although previous research provided evidence in favour of the role played by the Autonomic Nervous System (ANS) and different cognitive and executive functions in supporting the delivery of a high-level driving performance, further research is needed to deepen our understanding of the exact mechanisms linking physiological and psychological resources to the behavioural outcomes of driving. We adopted an evidence-based theoretical model (i.e., the Neurovisceral Integration Perspective; Thayer and Lane, 2000; Thayer et al., 2009; Thayer et al., 2012) and validated techniques and tools, to investigate, in a sample of elite racing drivers mainly scouted for the Ferrari Driver Academy, the relationship between HRV parameters, indexing the individual availability of physiological resources, and a set of measures of cognitive functions thought to be relevant for driving, including non-executive (simple reaction times) and executive (inhibitory control and WM) ones. We also tried to elucidate whether and how these physiological and cognitive variables can be used to predict driving performance, measured using a very ecological task in a realistic driving simulator. Based on previous research, we hypothesised that: (a) time-domain HRV indices of parasympathetic cardiac control would be positively associated with measures of inhibitory control (i.e., the performance at a Go/NoGo task) and WM (i.e., the performance at an N-Back task), but not with those of general readiness (i.e., the performance at an SRT task); (b) that driving performance (as indexed by the best and average lap times recorded) would be predicted by HRV indices, as well as by measures of inhibitory control. The results showed a significant negative correlation between cardiorespiratory coherence and the percentage of commissions at the Go/NoGo task, a negative correlation between coherence and the lap times recorded by the drivers, and a positive correlation between the latter and the mean reaction times (RTs) at the Go trials of the Go/NoGo task. Finally, linear models including coherence, the percentage of commissions at Go/NoGo and the mean RTs at Go trials as independent variables, proved to be able to explain a significant amount of variance in driving performance. Our results replicated some findings previously reported in psychophysiology, cognitive psychology, neuropsychology and sport psychology, extending them to the field of motorsport, and provided further support to the Neurovisceral Integration Perspective. Finally, the linear models developed proved to be able to explain a significant amount of variability in peak driving performance in elite racing drivers, providing a useful tool for their assessment and scouting, as well as for future studies in the field.Racing driving requires the development of extraordinary sensorimotor skills to deliver high-level peak performances in complex environments characterised by multiple stressors, draining drivers’ physiological and cognitive resources. Although previous research provided evidence in favour of the role played by the Autonomic Nervous System (ANS) and different cognitive and executive functions in supporting the delivery of a high-level driving performance, further research is needed to deepen our understanding of the exact mechanisms linking physiological and psychological resources to the behavioural outcomes of driving. We adopted an evidence-based theoretical model (i.e., the Neurovisceral Integration Perspective; Thayer and Lane, 2000; Thayer et al., 2009; Thayer et al., 2012) and validated techniques and tools, to investigate, in a sample of elite racing drivers mainly scouted for the Ferrari Driver Academy, the relationship between HRV parameters, indexing the individual availability of physiological resources, and a set of measures of cognitive functions thought to be relevant for driving, including non-executive (simple reaction times) and executive (inhibitory control and WM) ones. We also tried to elucidate whether and how these physiological and cognitive variables can be used to predict driving performance, measured using a very ecological task in a realistic driving simulator. Based on previous research, we hypothesised that: (a) time-domain HRV indices of parasympathetic cardiac control would be positively associated with measures of inhibitory control (i.e., the performance at a Go/NoGo task) and WM (i.e., the performance at an N-Back task), but not with those of general readiness (i.e., the performance at an SRT task); (b) that driving performance (as indexed by the best and average lap times recorded) would be predicted by HRV indices, as well as by measures of inhibitory control. The results showed a significant negative correlation between cardiorespiratory coherence and the percentage of commissions at the Go/NoGo task, a negative correlation between coherence and the lap times recorded by the drivers, and a positive correlation between the latter and the mean reaction times (RTs) at the Go trials of the Go/NoGo task. Finally, linear models including coherence, the percentage of commissions at Go/NoGo and the mean RTs at Go trials as independent variables, proved to be able to explain a significant amount of variance in driving performance. Our results replicated some findings previously reported in psychophysiology, cognitive psychology, neuropsychology and sport psychology, extending them to the field of motorsport, and provided further support to the Neurovisceral Integration Perspective. Finally, the linear models developed proved to be able to explain a significant amount of variability in peak driving performance in elite racing drivers, providing a useful tool for their assessment and scouting, as well as for future studies in the field

Electrophysiology

The outstanding evolution of recording techniques paved the way for better understanding of electrophysiological phenomena within the human organs, including the cardiovascular, ophthalmologic and neural systems. In the field of cardiac electrophysiology, the development of more and more sophisticated recording and mapping techniques made it possible to elucidate the mechanism of various cardiac arrhythmias. This has even led to the evolution of techniques to ablate and cure most complex cardiac arrhythmias. Nevertheless, there is still a long way ahead and this book can be considered a valuable addition to the current knowledge in subjects related to bioelectricity from plants to the human heart