Evaluation of Respiratory Muscles Activity by means of Cross Mutual Information Function at Different Levels of Ventilatory Effort

Analysis of respiratory muscles activity is an effective technique for the study of pulmonary diseases such as obstructive sleep apnea syndrome (OSAS). Respiratory diseases, especially those associated with changes in the mechanical properties of the respiratory apparatus, are often associated with disruptions of the normally highly coordinated contractions of respiratory muscles. Due to the complexity of the respiratory control, the assessment of OSAS related dysfunctions by linear methods are not sufficient. Therefore, the objective of this study was the detection of diagnostically relevant nonlinear complex respiratory mechanisms. Two aims of this work were: 1) to assess coordination of respiratory muscles contractions through evaluation of interactions between respiratory signals and myographic signals through nonlinear analysis by means of cross mutual information function (CMIF); 2) to differentiate between functioning of respiratory muscles in patients with OSAS and in normal subjects. Electromyographic(EMG) and mechanomyographic (MMG) signals were recorded from three respiratory muscles: genioglossus, sternomastoid and diaphragm. Inspiratory pressure and flow were also acquired. All signals were measured in eight patients with OSAS and eight healthy subjects during an increased respiratory effort while awake. Several variables were defined and calculated from CMIF in order to describe correlation between signals. The results indicate different nonlinear couplings of respiratory muscles in both populations. This effect is progressively more evident at higher levels of respiratory effort

Multivariate assessment of linear and non-linear causal coupling pathways within the central-autonomic-network in patients suffering from schizophrenia

Im Bereich der Zeitreihenanalyse richtet sich das Interesse zunehmend darauf, wie Einblicke in die Interaktions- und Regulationsprozesse von pathophysiologischen- und physiologischen Zuständen erlangt werden können. Neuste Fortschritte in der nichtlinearen Dynamik, der Informationstheorie und der Netzwerktheorie liefern dabei fundiertes Wissen über Kopplungswege innerhalb (patho)physiologischer (Sub)Systeme. Kopplungsanalysen zielen darauf ab, ein besseres Verständnis dafür zu erlangen, wie die verschiedenen integrierten regulatorischen (Sub)Systeme mit ihren komplexen Strukturen und Regulationsmechanismen das globale Verhalten und die unterschiedlichen physiologischen Funktionen auf der Ebene des Organismus beschreiben. Insbesondere die Erfassung und Quantifizierung der Kopplungsstärke und -richtung sind wesentliche Aspekte für ein detaillierteres Verständnis physiologischer Regulationsprozesse. Ziel dieser Arbeit war die Charakterisierung kurzfristiger unmittelbarer zentral-autonomer Kopplungspfade (top-to-bottom und bottom to top) durch die Kopplungsanalysen der Herzfrequenz, des systolischen Blutdrucks, der Atmung und zentraler Aktivität (EEG) bei schizophrenen Patienten und Gesunden. Dafür wurden in dieser Arbeit neue multivariate kausale und nicht-kausale, lineare und nicht-lineare Kopplungsanalyseverfahren (HRJSD, mHRJSD, NSTPDC) entwickelt, die in der Lage sind, die Kopplungsstärke und -richtung, sowie deterministische regulatorische Kopplungsmuster innerhalb des zentralen-autonomen Netzwerks zu quantifizieren und zu klassifizieren. Diese Kopplungsanalyseverfahren haben ihre eigenen Besonderheiten, die sie einzigartig machen, auch im Vergleich zu etablierten Kopplungsverfahren. Sie erweitern das Spektrum neuartiger Kopplungsansätze für die Biosignalanalyse und tragen auf ihre Weise zur Gewinnung detaillierter Informationen und damit zu einer verbesserten Diagnostik/Therapie bei. Die Hauptergebnisse dieser Arbeit zeigen signifikant schwächere nichtlineare zentral-kardiovaskuläre und zentral-kardiorespiratorische Kopplungswege und einen signifikant stärkeren linearen zentralen Informationsfluss in Richtung des Herzkreislaufsystems auf, sowie einen signifikant stärkeren linearen respiratorischen Informationsfluss in Richtung des zentralen Nervensystems in der Schizophrenie im Vergleich zu Gesunden. Die detaillierten Erkenntnisse darüber, wie die verschiedenen zentral-autonomen Netzwerke mit paranoider Schizophrenie assoziiert sind, können zu einem besseren Verständnis darüber führen, wie zentrale Aktivierung und autonome Reaktionen und/oder Aktivierung in physiologischen Netzwerken unter pathophysiologischen Bedingungen zusammenhängen.In the field of time series analysis, increasing interest focuses on insights gained how the coupling pathways of regulatory mechanisms work in healthy and ill states. Recent advances in non-linear dynamics, information theory and network theory lead to a new sophisticated body of knowledge about coupling pathways within (patho)physiological (sub)systems. Coupling analyses aim to provide a better understanding of how the different integrated physiological (sub)systems, with their complex structures and regulatory mechanisms, describe the global behaviour and distinct physiological functions at the organism level. In particular, the detection and quantification of the coupling strength and direction are important aspects for a more detailed understanding of physiological regulatory processes. This thesis aimed to characterize short-term instantaneous central-autonomic-network coupling pathways (top-to-bottom and bottom to top) by analysing the coupling of heart rate, systolic blood pressure, respiration and central activity (EEG) in schizophrenic patients and healthy participants. Therefore, new multivariate causal and non-causal linear and non-linear coupling approaches (HRJSD, mHRJSD, NSTPDC) that are able to determine the coupling strength and direction were developed. Whereby, the HRJSD and mHRJSD approaches allow the quantification and classification of deterministic regulatory coupling patterns within and between the cardiovascular- the cardiorespiratory system and the central-autonomic-network were developed. These coupling approaches have their own unique features, even as compared to well-established coupling approaches. They expand the spectrum of novel coupling approaches for biosignal analysis and thus contribute in their own way to detailed information obtained, and thereby contribute to improved diagnostics/therapy. The main findings of this thesis revealed significantly weaker non-linear central-cardiovascular and central-cardiorespiratory coupling pathways, and significantly stronger linear central information flow in the direction of the cardiac- and vascular system, and a significantly stronger linear respiratory information transfer towards the central nervous system in schizophrenia in comparison to healthy participants. This thesis provides an enhanced understanding of the interrelationship of central and autonomic regulatory mechanisms in schizophrenia. The detailed findings on how variously-pronounced, central-autonomic-network pathways are associated with paranoid schizophrenia may enable a better understanding on how central activation and autonomic responses and/or activation are connected in physiology networks under pathophysiological conditions

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

The cardiorespiratory network in healthy first-degree relatives of schizophrenic patients

Impaired heart rate- and respiratory regulatory processes as a sign of an autonomic dysfunction seems to be obviously present in patients suffering from schizophrenia. Since the linear and non-linear couplings within the cardiorespiratory system with respiration as an important homeostatic control mechanism are only partially investigated so far for those subjects, we aimed to characterize instantaneous cardiorespiratory couplings by quantifying the casual interaction between heart rate (HR) and respiration (RESP). Therefore, we investigated causal linear and non-linear cardiorespiratory couplings of 23 patients suffering from schizophrenia (SZO), 20 healthy first-degree relatives (REL) and 23 healthy subjects, who were age-gender matched (CON). From all participants’ heart rate (HR) and respirations (respiratory frequency, RESP) were investigated for 30 min under resting conditions. The results revealed highly significant increased HR, reduced HR variability, increased respiration rates and impaired cardiorespiratory couplings in SZO in comparison to CON. SZO were revealed bidirectional couplings, with respiration as the driver (RESP → HR), and with weaker linear and non-linear coupling strengths when RESP influencing HR (RESP → HR) and with stronger linear and non-linear coupling strengths when HR influencing RESP (HR → RESP). For REL we found only significant increased HR and only slightly reduced cardiorespiratory couplings compared to CON. These findings clearly pointing to an underlying disease-inherent genetic component of the cardiac system for SZO and REL, and those respiratory alterations are only clearly present in SZO seem to be connected to their mental emotional states

Mutual information based data association

Relating information originating from disparate sensors without any attempt to model the environment or the behaviour of any particular object within it is a challenging task. Inspired by human perception, the focus of this paper will be on observing objects moving in space using sensors that operate based on different physical principles and the fact that motion has in principle, greater power to specify properties of an object than purely spatial information captured as a single observation in time. The contribution of this paper include the development of a novel strategy for detecting a set of signals that are statistically dependent and correspond to each other related by a common cause. Mutual Information is proposed as a measure of statistical dependence. The algorithm is evaluated through simulations and three application domains, which includes, (1.) Grouping problem in images, (2.) Data association problem in moving observers with dynamic targets, and (3.) Multi-modal sensor fusion. © 2009 IEEE

Mutual information measures applied to EEG signals for sleepiness characterization

Excessive daytime sleepiness (EDS) is one of the main symptoms of several sleep related disorders with a great impact on the patient lives. While many studies have been carried out in order to assess daytime sleepiness, the automatic EDS detection still remains an open problem. In this work, a novel approach to this issue based on non-linear dynamical analysis of EEG signal was proposed. Multichannel EEG signals were recorded during five maintenance of wakefulness (MWT) and multiple sleep latency (MSLT) tests alternated throughout the day from patients suffering from sleep disordered breathing. A group of 20 patients with excessive daytime sleepiness (EDS) was compared with a group of 20 patients without daytime sleepiness (WDS), by analyzing 60-s EEG windows in waking state. Measures obtained from cross-mutual information function (CMIF) and auto-mutual-information function (AMIF) were calculated in the EEG. These functions permitted a quantification of the complexity properties of the EEG signal and the non-linear couplings between different zones of the scalp. Statistical differences between EDS and WDS groups were found in ß band during MSLT events (. p-value<0.0001). WDS group presented more complexity than EDS in the occipital zone, while a stronger nonlinear coupling between occipital and frontal zones was detected in EDS patients than in WDS. The AMIF and CMIF measures yielded sensitivity and specificity above 80% and AUC of ROC above 0.85 in classifying EDS and WDS patients.Peer ReviewedPostprint (author's final draft

Interacción en sistemas biológicos mediante nuevos índices basados en la dinámica no lineal

Premi extraordinari doctorat curs 2010-2011, àmbit d’Enginyeria Enginyeria IndustrialMost biological systems are complex and consist of several interconnected parts whose links can contain additional information which can be hidden from the observer. As a result of the interactions between elements, emergent properties that cannot be explained by the characteristics of isolated elements can arise. Current clinical applications record a high number of different signals that contain information about these physiological systems, providing multichannel data whose interactions can be studied by classical reference methods, generally linear, as the correlation analysis and spectral coherence, and other nonlinear methods that are being defined and developed during recent years, such as nonlinear prediction, entropies, mutual information and phase synchronization. The development, improvement and application of new analytical techniques is a field with obvious social and technological interest, especially when performed by noninvasive techniques, which can improve the processes of rehabilitation and clinical therapy, and also help the development of new diagnostic tools. In this thesis new indexes have been defined in order to evaluate: * The coordination of respiratory muscles in healthy subjects and patients with obstructive sleep apnea syndrome (OSAS) during an effort ventilatory protocol. * The effect on functional connectivity of the brain after administration of a psychoactive drug. * The changes caused by Alzheimer's disease (AD) in the connectivity of the brain. Respiratory muscles provide the mechanical energy that supports respiration. The evaluation of interactions between electromyographic (EMG) and mechanomiographic (MMG) signals of different respiratory muscles, genioglossus, sternomastoid and diaphragm, has allowed the discrimination of coordination patterns of OSAS patients with respect to healthy subjects at low, medium and high respiratory effort during while awake. Analysis and characterization of the electroencephalographic (EEG) and magnetoencephalographic (MEG) signals allows the understanding of brain function to assist in the process of clinical diagnosis of disorders in neurology, psychiatry and pharmacology. In this thesis the interactions within and between different brain regions have been assessed, using new nonlinear indexes which have managed to reflect changes over time in the brain after administration of alprazolam, and to characterize andto differentiate brain connectivity of AD patients with respect to healthy subjects.La mayoría de sistemas biológicos son sistemas complejos que constan de diversas partes interconectadas cuyos vínculos pueden contener información adicional y oculta al observador. Como resultado de estas interacciones entre elementos surgen propiedades emergentes, que no pueden explicarse a partir de las características de los elementos aislados. Las aplicaciones clínicas actuales registran un elevado número de señales diferentes que contienen información sobre estos sistemas fisiológicos, cosa que permite disponer de datos multicanal, cuyas interacciones pueden ser estudiadas mediante métodos clásicos de referencia generalmente lineales, como el análisis de correlación y la coherencia espectral, u otros métodos no lineales que están siendo definidos y desarrollados durante los últimos años, como la predicción no lineal, las entropías, la información mutua o la sincronización de fase. El desarrollo, mejora y aplicación de nuevas técnicas de análisis constituye un campo con evidente interés social y tecnológico, en especial cuando se realiza mediante técnicas no invasivas, que puede proporcionar mejoras en los procesos de rehabilitación y terapia clínica, así como contribuir a desarrollar herramientas de ayuda al diagnóstico. En esta tesis se han definido nuevos índices no lineales que han permitido evaluar: * La coordinación de los músculos respiratorios en sujetos sanos y pacientes con síndrome de apnea obstructiva del sueño (SAOS) durante un protocolo ventilatorio de esfuerzo. * El efecto en la conectividad funcional del cerebro tras la administración de un fármaco psicoactivo. * Los cambios provocados por la enfermedad de Alzheimer (EA) en la conectividad del cerebro. La musculatura respiratoria proporciona la energía mecánica que soporta la respiración. La evaluación de las interacciones entre señales electromiográficas (EMG) y mecanomiográficas (MMG) de diferentes músculos respiratorios -geniogloso, esternocleidomastoideo y diafragma- ha permitido diferenciar el patrón de coordinación de los pacientes con SAOS de los sujetos sanos a niveles bajos, medios y altos de esfuerzo respiratorio durante vigilia. El análisis y caracterización de las señales electroencefalográficas (EEG) y magnetoencefalográficas (MEG) permite la comprensión de la función cerebral para ayudar en el proceso de diagnóstico clínico de disfunciones en neurología, psiquiatría y farmacología. En esta tesis se han evaluado las interacciones en y entre diferentes regiones cerebrales mediante nuevos índices no lineales que han conseguido reflejar los cambios producidos a lo largo del tiempo en el cerebro tras la administración del fármaco alprazolam, así como caracterizar y diferenciar la conectividad cerebral de los pacientes con EA con respecto a sujetos sanos. Las herramientas utilizadas en las aplicaciones mencionadas se basan en las siguientes técnicas de análisis no lineal: * La función de información mutua cruzada, el equivalente no lineal de la función de correlación cruzada, que cuantifica la información compartida entre dos variables aleatorias. * La entropía condicional corregida cruzada, una medida que cuantifica la información restante contenida en una variable aleatoria cuando se conoce totalmente otra variable relacionada, y por lo tanto es una medida complementaria de la información mutua. * La predicción no lineal basada en modelos localmente lineales, una herramienta matemática que permite deducir la evolución de una serie temporal en función de muestras anteriores. Los nuevos índices desarrollados han demostrado la necesidad de evaluar las interacciones en los sistemas biológicos y fisiológicos tanto con métodos lineales como no lineales, para obtener una evaluación más completa de la dinámica subyacente y ayudar en los procesos de diagnóstico de patologías y en el procedimiento de evaluación psicofarmacológica.Award-winningPostprint (published version