Análisis de la interacción cardíaca y respiratoria en pacientes con cardiomiopatía y pacientes en proceso de extubación

Study and evaluation of patients with heart failure related with ischemic and dilated cardiomyopathy is interesting for the clinical practice. Similarly, the analysis of the behavior of the respiratory system of patients undergoing extubation process is still research topic, and is particularly important to determine the optimal moment for the weaning process. In Both cases, the study of the cardiac and respiratory systems, and their interaction can contribute to identify new medical process, and increase the knowledge of the pathologies studied. The main goal of this research is the analysis and characterization of the behavior of the cardiovascular system, the respiratory system, and the cardiorespiratory interaction in patients with chronic heart failure, and patients assisted by mechanical ventilation on the processes of weaning trials. We propose the extraction of information that allows to enhance the knowledge of the physiological behavior, and improve the diagnosis and stratification of these patients. Cardiomyopathy is a disease of the cardiac muscle reducing the heart’s pumping ability. Patients with chronic heart failure (CHF) usually present changes in the respiratory behavior, with periodical changes in the tidal volume, known as periodic breathing (PB). In order to study chronic heart failure patients, we propose to analyse the cardiorespiratory interaction through characteristics extracted, in time and frequency domain, from the respiratory flow (FLW), respiratory volume (VOL), electrocardiogram ECG, and blood pressure (BP) signals. The study was performed in 50 patients with dilated cardiomyopathy (19 patients), and ischemic cardiomyopathy (31 patients). A periodic respiratory pattern is considered as an indicator of the severity of these disease. Using the respiratory flow envelope, we calculated the modulation index (M) associated to the periodic breathing. First part of the study considered CHF patients classified based on the modulation index. The study was performed considering 35 segments of signals non modulated (GN, M75%). Considering these time segments, we studied parameters extracted from the cardiac, respiratory and cardiorespiratory interaction related to the envelope of the respiratory flow signal. The main objective is to analyze differences on the cardiovascular behavior between non modulated and modulated patients. Studying respiratory signals, the main differences were found on the end expiratory lung volume (EELV). Considering the parameters extracted from the ECG, were obtained difference on the spectrum of the upward and downward slope of the QRS complex. Differences were also found on the heart rate variability. From the analysis of the magnitude squared coherence (MSC) between the series and the envelope the main differences were found in the frequency domain on the band of very low frequency. This result suggest that the periodic changes on the tidal volume are presented in the cardiac behavior. CHF patients were also studied through an unsupervised classification based on the K-means method. Time and frequency features extracted from the series were used to perform a 2 clusters classification. Based on these cluster were analyzed the clinical information. Additional, were study the modulation index on these clusters. Results suggest a strong correlation between feature extracted form ECG series and the modulation of the respiratory. Clusters calculated form BP series are related with the blood volume on the ventricle. Mechanical ventilation guarantee a correct alveolar ventilation in patients with respiratory failure. Weaning trial is the process of transfer the respiratory effort from the mechanical ventilator to the patient. This study investigated the contribution of spectral signals of heart rate variability (HRV) and respiratory flow, and their coherence to classifying patients on weaning process from mechanical ventilation. A total of 121 candidates for weaning, undergoing spontaneous breathing tests, were analyzed: 73 were successfully weaned (GS), 33 failed to maintain spontaneous breathing so were reconnected (GF), and 15 were extubated after the test but reintubated within 48 h (GR). The power spectral density and MSC of HRV and respiratory flow signals were estimated. Dimensionality reduction was performed using principal component analysis (PCA) over the spectral signals. Considering this new space formed by the PCA were calculated a fuzzy K-nearest neighbor classification. Best classification index, applying PCA and fKNN methods, were obtained considering the spectral signal of the MSC between HRV and FLW. The classifiers present a good balance between sensibility and specificity, and high accuracy of 92% comparing GS vs. GF, 86% classifying GS vs. GR, and 83% classifying GS vs. GFR. Reintubated patients is the most complex group for the analysis and classification, because at the beginning of the trial the behavior is similar to the success patients, but before 48 h the evolution of the respiratory pattern is more comparable to the failure patients. However, applying our method the index of accuracy, sensibility and specificity comparing GR against the other groups are above 80%. Spectral analysis of weaning trial patients were complemented with a nonlinear study based on the recurrence plot (RP) method. Additional, to the individual RP analysis of the HRV series, inspiratory time series (TI ), and respiratory time series (TTot), were performed the study of the cardiorespiratory interaction through the cross recurrence plot (CRP) and joint recurrence plot (JRP). One of the main issues for the application of the recurrence technique is the correct selection of the embedding dimension (ε). We propose the application of the method based on the analysis of ROC curves comparing observational noise against the signals of the study. This method allow the selection of the optimal ε for each data series. Recurrence matrix where characterized based on the parameters extracted applying the recurrence quantification analysis (RQA). Comparing the different groups that the main differences was observed on the determinism and stationarity of the signals. Patients of the success group show higher values of determinism, suggesting that time series extracted form de GE patients during the weaning trials are more stationary comparing with the orders groups. Analyzing the result obtained from JRP, it concludes that patients from GS show a higher mutual recurrence between HRV and the time series (TI and (TTot. Based on the RQA parameters obtained for each time series extracted, was proposed a supervised classification applying support vector machine technique (SVM). The best classification obtained were abode the 80% in accuracy. The results obtained suggest that recurrence plot and especially joint recurrence plot techniques could improve the discrimination and characterization of patients on weaning trialsEl estudio y la evaluación de pacientes con problemas cardíacos relacionados con cardiomiopatía isquémica o dilatada son de gran interés para la práctica clínica. Igualmente, el análisis del comportamiento del sistema respiratorio de pacientes en proceso de extubación continúa siendo tema de investigación, y un reto en la práctica clínica. En ambos casos, el estudio de los sistemas cardíaco y respiratorio, y de su interacción cardiorespiratoria pueden contribuir a definir nuevos procesos clínicos, y a un mayor conocimiento de las patologías estudiadas. Pacientes con fallo cardíaco crónico (CHF) a menudo presentan aumentos y disminuciones periódicas en el volumen de tidal. Para el estudio de estos pacientes se propone el análisis de la interacción cardiorespiratoria, a través de la caracterización en el dominio temporal y frecuencial de las señales de flujo (FLW) y volumen (VOL) respiratorio, la señal ECG y la señal de presión sanguínea (BP). Se analizaron 50 pacientes diagnosticados con cardiomiopatía dilatada (19 pacientes), y cardiomiopatía isquémica (31 pacientes). A partir de la envolvente de la señal FLW se calculó el índice de modulación (M) asociado con el comportamiento periódico de la respiración. Para el estudio fueron considerados 35 segmentos de señal no modulada (GN, M75%). Las principales diferencias se encontraron al analizar los parámetros relacionados con el volumen pulmonar al final de la espiración, los valores espectrales de las pendientes de subida y de bajada del complejo QRS, y la variabilidad del ritmo cardíaco (HRV). El análisis de la magnitud de la coherencia al cuadrado (MSC) entre las series extraídas y la envolvente de la señal FLW presentó las principales diferencias en la banda de muy baja frecuencia (VLF), con valores más elevados en los pacientes del grupo GH. Teniendo en cuenta los parámetros temporales y espectrales de las series extraídas de las señales cardiovascular y respiratoria se realizó una clasificación de los pacientes CHF aplicando el método no supervisado K-means. Al analizar las señales de presión sanguínea se observa una marcada correlación entre los clusters formados por el clasificador y los parámetros de volumen de sangre en los ventrículos. El destete es el proceso de transferencia del trabajo respiratorio desde el ventilador mecánico al paciente. Se propone evaluar las componentes espectrales de la HRV, de la señal FLW, y de su coherencia espectral para la identificación de pacientes exitosos (GE) en el test de respiración espontánea, pacientes que fracasan (GF) en el proceso de destete, y pacientes que habiendo superado la prueba inicial de respiración espontánea, antes de 48 h tuvieron que ser reintubados y reconectados al ventilador mecánico (GR). Para este estudio se analizaron las señales ECG y FLW de 121 pacientes asistidos mediante ventilación mecánica y sometidos a la prueba de tubo en T para la extubación. La caracterización del comportamiento cardiorespiratorio se realizó aplicando un análisis de componentes principales (PCA) a los espectros de las señales. En particular, se estudió la magnitud de la coherencia al cuadrado (MSC) como medida espectral del acople entre las señales HRV y FLW. Los pacientes fueron clasificados aplicando la técnica fuzzy K vecinos más cercanos (fKNN). Los mejores índices de clasificación se obtuvieron al considerar la señal espectral de la MSC entre la HRV y FLW, obteniéndose clasificaciones superiores al 80%. El estudio espectral de los pacientes en proceso de extubación se complementó con un estudio no lineal basado en la técnica de recurrence plot (RP). Adicionalmente, al estudio individual de las series de HRV, los tiempos de inspiración (Ti), y los tiempos de duración del ciclo respiratorio (TTot), se realizó el análisis de su interacción aplicando el método de joint recurrence plot (JRP). Pacientes del grupo GE presentaron valores más elevados al compararlos con los otros grupos

Classifier design for patients on weaning process

The mechanical ventilation (MV) is a therapeutic strategy to mechanically assist or replace spontaneous breathing. With the objective of developing a software support for doctors was performed a study of respiratory signals using the discrete wavelet transform to determine the descriptors to indicate whether the patient can be disconnected from the mechanical ventilator. To reduce the dimensionality of the system was performed a principal component analysis (PCA), establishing three variables optimal, which are the inputs to the classifiers that were analyzed in the article: K-Nearest Neighbor and fuzzy logic.La ventilación mecánica (VM) es una estrategia terapéutica que consiste en asistir o reemplazar mecánicamente la ventilación pulmonar espontánea. Con el objetivo de desarrollar un software de apoyo para los médicos, se realizó un estudio de las señales respiratorias, utilizando la transformada de wavelet discreta, para determinar los descriptores que indiquen si el paciente puede ser desconectado del ventilador mecánico. Para reducir la dimensionalidad del sistema se realizó un análisis de componentes principales (PCA), determinando tres variables óptimas, las cuales son las entradas a los clasificadores que se analizaron en el artículo: K-Nearest Neighbor y lógica difusa

Review and classification of variability analysis techniques with clinical applications

Analysis of patterns of variation of time-series, termed variability analysis, represents a rapidly evolving discipline with increasing applications in different fields of science. In medicine and in particular critical care, efforts have focussed on evaluating the clinical utility of variability. However, the growth and complexity of techniques applicable to this field have made interpretation and understanding of variability more challenging. Our objective is to provide an updated review of variability analysis techniques suitable for clinical applications. We review more than 70 variability techniques, providing for each technique a brief description of the underlying theory and assumptions, together with a summary of clinical applications. We propose a revised classification for the domains of variability techniques, which include statistical, geometric, energetic, informational, and invariant. We discuss the process of calculation, often necessitating a mathematical transform of the time-series. Our aims are to summarize a broad literature, promote a shared vocabulary that would improve the exchange of ideas, and the analyses of the results between different studies. We conclude with challenges for the evolving science of variability analysis

Current Issues and Recent Advances in Pacemaker Therapy

Patients with implanted pacemakers or defibrillators are frequently encountered in various healthcare settings. As these devices may be responsible for, or contribute to a variety of clinically significant issues, familiarity with their function and potential complications facilitates patient management. This book reviews several clinically relevant issues and recent advances of pacemaker therapy: implantation, device follow-up and management of complications. Innovations and research on the frontiers of this technology are also discussed as they may have wider utilization in the future. The book should provide useful information for clinicians involved in the management of patients with implanted antiarrhythmia devices and researchers working in the field of cardiac implants

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