6,252 research outputs found

    A multivariate time-frequency method to characterize the influence of respiration over heart period and arterial pressure

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    Respiratory activity introduces oscillations both in arterial pressure and heart period, through mechanical and autonomic mechanisms. Respiration, arterial pressure, and heart period are, generally, non-stationary processes and the interactions between them are dynamic. In this study we present a methodology to robustly estimate the time course of cross spectral indices to characterize dynamic interactions between respiratory oscillations of heart period and blood pressure, as well as their interactions with respiratory activity. Time-frequency distributions belonging to Cohen's class are used to estimate time-frequency (TF) representations of coherence, partial coherence and phase difference. The characterization is based on the estimation of the time course of cross spectral indices estimated in specific TF regions around the respiratory frequency. We used this methodology to describe the interactions between respiration, heart period variability (HPV) and systolic arterial pressure variability (SAPV) during tilt table test with both spontaneous and controlled respiratory patterns. The effect of selective autonomic blockade was also studied. Results suggest the presence of common underling mechanisms of regulation between cardiovascular signals, whose interactions are time-varying. SAPV changes followed respiratory flow both in supine and standing positions and even after selective autonomic blockade. During head-up tilt, phase differences between respiration and SAPV increased. Phase differences between respiration and HPV were comparable to those between respiration and SAPV during supine position, and significantly increased during standing. As a result, respiratory oscillations in SAPV preceded respiratory oscillations in HPV during standing. Partial coherence was the most sensitive index to orthostatic stress. Phase difference estimates were consistent among spontaneous and controlled breathing patterns, whereas coherence was higher in spontaneous breathing. Parasympathetic blockade did not affect interactions between respiration and SAPV, reduced the coherence between SAPV and HPV and between respiration and HPV. Our results support the hypothesis that non-autonomic, possibly mechanically mediated, mechanisms also contributes to the respiratory oscillations in HPV. A small contribution of sympathetic activity on HPV-SAPV interactions around the respiratory frequency was also observed

    How Does the Body Affect the Mind? Role of Cardiorespiratory Coherence in the Spectrum of Emotions

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    The brain is considered to be the primary generator and regulator of emotions; however, afferent signals originating throughout the body are detected by the autonomic nervous system (ANS) and brainstem, and, in turn, can modulate emotional processes. During stress and negative emotional states, levels of cardiorespiratory coherence (CRC) decrease, and a shift occurs toward sympathetic dominance. In contrast, CRC levels increase during more positive emotional states, and a shift occurs toward parasympathetic dominance. Te dynamic changes in CRC that accompany different emotions can provide insights into how the activity of the limbic system and afferent feedback manifest as emotions. The authors propose that the brainstem and CRC are involved in important feedback mechanisms that modulate emotions and higher cortical areas. That mechanism may be one of many mechanisms that underlie the physiological and neurological changes that are experienced during pranayama and meditation and may support the use of those techniques to treat various mood disorders and reduce stress

    Point process time–frequency analysis of dynamic respiratory patterns during meditation practice

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    Respiratory sinus arrhythmia (RSA) is largely mediated by the autonomic nervous system through its modulating influence on the heart beats. We propose a robust algorithm for quantifying instantaneous RSA as applied to heart beat intervals and respiratory recordings under dynamic breathing patterns. The blood volume pressure-derived heart beat series (pulse intervals, PIs) are modeled as an inverse Gaussian point process, with the instantaneous mean PI modeled as a bivariate regression incorporating both past PIs and respiration values observed at the beats. A point process maximum likelihood algorithm is used to estimate the model parameters, and instantaneous RSA is estimated via a frequency domain transfer function evaluated at instantaneous respiratory frequency where high coherence between respiration and PIs is observed. The model is statistically validated using Kolmogorov–Smirnov goodness-of-fit analysis, as well as independence tests. The algorithm is applied to subjects engaged in meditative practice, with distinctive dynamics in the respiration patterns elicited as a result. The presented analysis confirms the ability of the algorithm to track important changes in cardiorespiratory interactions elicited during meditation, otherwise not evidenced in control resting states, reporting statistically significant increase in RSA gain as measured by our paradigm.National Institutes of Health (U.S.) (Grant R01-HL084502)National Institutes of Health (U.S.) (Grant R01-DA015644)National Institutes of Health (U.S.) (Grant DP1-OD003646)National Institutes of Health (U.S.) (Grant K01-AT00694-01

    Characterization and interpretation of cardiovascular and cardiorespiratory dynamics in cardiomyopathy patients

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    Aplicat embargament des de la data de defensa fins el dia 20/5/2022The main objective of this thesis was to study the variability of the cardiac, respiratory and vascular systems through electrocardiographic (ECG), respiratory flow (FLW) and blood pressure (BP) signals, in patients with idiopathic (IDC), dilated (DCM), or ischemic (ICM) disease. The aim of this work was to introduce new indices that could contribute to characterizing these diseases. With these new indices, we propose methods to classify cardiomyopathy patients (CMP) according to their cardiovascular risk or etiology. In addition, a new tool was proposed to reconstruct artifacts in biomedical signals. From the ECG, BP and FLW signals, different data series were extracted: beat to beat intervals (BBI - ECG), systolic and diastolic blood pressure (SBP and DBP - BP), and breathing duration (TT - FLW). -Firstly, we propose a novel artifact reconstruction method applied to biomedical signals. The reconstruction process makes use of information from neighboring events while maintaining the dynamics of the original signal. The method is based on detecting the cycles and artifacts, identifying the number of cycles to reconstruct, and predicting the cycles used to replace the artifact segments. The reconstruction results showed that most of the artifacts were correctly detected, and physiological cycles were incorrectly detected as artifacts in fewer than 1% of the cases. The second part is related to the cardiac death risk stratification of patients based on their left ventricular ejection (LVEF), using the Poincaré plot analysis, and classified as low (LVEF > 35%) or high (LVEF = 35%) risk. The BBI, SBP, and IT series of 46 CMP patients were applied. The linear discriminant analysis and support vector machines (SVM) classification methods were used. When comparing low risk vs high risk, an accuracy of 98 12% was obtained. Our results suggest that a dysfunction in the vagal activity could prevent the body from correctly maintaining circulatory homeostasis Next, we studied cardio-vascular couplings based on heart rate (HRV) and blood pressure (BPV) variability analyses in order to introduce new indices for noninvasive risk stratification in IDC patients. The ECG and BP signals of 91 IDC patients, and 49 healthy subjects were used. The patients were stratified by their sudden cardiac death risk as: high risk (IDCHR), when after two years the subject either died or suffered complications, or low risk (IDCLR) otherwise. Several indices were extracted from the BBI and SBP, and analyzed using the segmented Poincaré plot analysis, the high-resolution joint symbolic dynamics, and the normalized short time partial directed coherence methods. SVM models were built to classify these patients based on their sudden cardiac death risk. The SVM IDCLR vs IDCHR model achieved 98 9% accuracy with an area under the curve (AUC) of 0.96. Our results suggest that IDCHR patients have decreased HRV and increased BPV compared to both the IDCLR patients and the control subjects, suggesting a decrease in their vagal activity and the compensation of sympathetic activity. Lastly, we analyzed the cardiorespiratory interaction associated with the systems related to ICM and DCM disease. We propose an analysis based on vascular activity as the input and output of the baroreflex response. The aim was to analyze the suitability of cardiorespiratory and vascular interactions for the classification of ICM and DCM patients. We studied 41 CMP patients and 39 healthy subjects. Three new sub-spaces were defined: 'up' for increasing values, 'down' for decreasing values, and 'no change' otherwise, and a three-dimensional representation was created for each sub-space that was characterized statistically and morphologically. The resulting indices were used to classify the patients by their etiology through SVM models achieving 92.7% accuracy for ICM vs DCM patients comparison. The results reflected a more pronounced deterioration of the autonomous regulation in DCM patients.El objetivo de esta tesis fue estudiar la variabilidad de los sistemas cardíaco, respiratorio y vascular a través de señales electrocardiográficas (ECG), de flujo respiratorio (FLW) y de presión arterial (BP), en pacientes con cardiopatía idiopática (IDC). dilatada (DCM) o isquémica (ICM). El objetivo de este trabajo fue introducir nuevos indices que contribuyan a caracterizar estas enfermedades. Proponemos métodos para clasificar pacientes con cardiomiopatía (CMP) de acuerdo con su riesgo cardiovascular o etiología. Además, se propuso una nueva herramienta para reconstruir artefactos en señales biomédicas. De las señales de ECG, BP y FLW, se extrajeron diferentes series temporales: intervalos latido-a-latido (BBI - ECG), presión arterial sistólica y diastólica (SBP y DBP - BP) y la duración de la respiración (TT - FLW). En primer lugar, proponemos un método de reconstrucción de artefactos aplicado a señales biomédicas. El proceso de reconstrucción usa la información de eventos vecinos manteniendo la dinámica de la señal. El método se basa en detectar ciclos y artefactos, en identificar el número de ciclos a reconstruir y en predecir los ciclos utilizados para reemplazar los artefactos. La mayoría de los artefactos probados fueron detectados y reconstruidos correctamente y los ciclos fisiológicos fueron detectados incorrectamente como artefactos en menos del 1% de los casos, La segunda parte está relacionada con la estratificación de riesgo de muerte cardiovascular en función de la fracción de eyección ventricular izquierda (FEVI), mediante el análisis de Poincaré, en bajo (FEVI > 35%) y alto riesgo (FEVI 5 35%). Se utilizaron las series BBI, SBP y TT de 46 pacientes con CMP. Se utilizaron para la clasificación el análisis discriminante lineal y las máquinas de soporte vectorial (SVM). Al comparar los pacientes de bajo y alto riesgo, se obtuvo una exactitud del 98%. Los resultados sugieren la disfunción de la actividad vagal en pacientes de alto riesgo. A continuación, estudiamos los acoplamientos cardiovasculares basados en el análisis de la variabilidad de la frecuencia cardiaca (HRV) y la presión arterial (BPV) para introducir nuevos índices de estratificación de riesgo en pacientes con IDC. Se utilizaron las señales de ECG y BP de 91 pacientes con IDC y 49 sujetos sanos. Los pacientes fueron estratificados por su riesgo cardíaco como: alto riesgo (IDCHR), cuando después de dos años el sujeto murió, o bajo riesgo (IDCLR) en otro caso. Se extrajeron indices utilizando el análisis de Poincaré segmentado, la dinámica simbólica articulada de alta resolución y la coherencia parcial dirigida a corto plazo normalizada. Se construyeron modelos SVM para clasificar a estos pacientes en función de su riesgo cardiovascular. El modelo IDCLR vs IDCHR logró una exactitud del 98% con un área bajo la curva de 0.96. Los resultados sugieren que los pacientes IDCHR tienen sus HRV y BPV disminuidos en comparación con los pacientes IDCLR, lo que sugiere una disminución en su actividad vagal y la compensación de la actividad simpática. Finalmente, analizamos la interacción cardiorrespiratoria asociada con los sistemas relacionados con ICM y DCM. Proponemos un análisis basado en la actividad vascular como entrada y salida de la respuesta baroreflectora. El objetivo fue analizar la capacidad de las interacciones cardiorrespiratorias y vasculares para la clasificación de pacientes con ICM y DCM. Estudiamos 41 pacientes con CMP y 39 sujetos sanos. Se definieron tres sub-espacios: 'up' para valores crecientes, 'down' para los decrecientes, y 'no-change' en otro caso, y se creó una representación tridimensional que se caracterizó estadística y morfológicamente. Los indices resultantes se usaron para clasificar a los pacientes por su etiología con modelos SVM que lograron una exactitud de 92% cuando los pacientes ICM y DCM fueron comparados. Los resultados reflejaron un deterioro más pronunciado de la regulación autónoma en pacientes con DCM.Postprint (published version

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

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    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

    Tetravariate point-process model for the continuous characterization of cardiovascular-respiratory dynamics during passive postural changes

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    In this study, we present a new methodology for time-varying characterization of cardiovascular (CV) control, which includes RR interval (RRI), systolic arterial pressure (SAP), respiration (RSP) and pulse transit time (PTT). Within a multivariate model, CV dynamics are represented as stochastic point processes whose means has a tetravariate autoregressive structure. Such framework provides the simultaneous time-frequency assessment of: (i) both arms of the SAP-RRI loop, along baroreflex and mechanical feedforward pathways; (ii) Respiratory sinus arrhythmia (RSA), through the direct evaluation of the interactions between RSP and the RRI; (iii) the coupling between cardio-respiratory activity and vascular tone through quantification of the interactions between PTT and the other CV variables. We validated the model by characterizing CV control in 16 healthy subjects during a tilt table test, and we were able to confirm a satisfactory model's goodness-of-ft. We further estimated transfer function gains, instantaneous powers and directed coherences, and observed that RSP strongly drove respiratory-related oscillations in all the other CV variables, and that PTT depended on RRI dynamics rather than blood pressure variations. During head-up tilt, baroreflex sensitivity and RSA decreased, while the gain from RRI to SAP increased, thus confirming previous physiological characterizations. © 2012 CCAL

    A Time-Varying Non-Parametric Methodology for Assessing Changes in QT Variability Unrelated to Heart Rate Variability

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    OBJECTIVE: To propose and test a novel methodology to measure changes in QT interval variability (QTV) unrelated to RR interval variability (RRV) in non-stationary conditions. METHODS: Time-frequency coherent and residual spectra representing QTV related (QTVrRRV) and unrelated (QTVuRRV) to RRV, respectively, are estimated using time-frequency Cohen's class distributions. The proposed approach decomposes the non-stationary output spectrum of any two-input one-output model with uncorrelated inputs into two spectra representing the information related and unrelated to one of the two inputs, respectively. An algorithm to correct for the bias of the time-frequency coherence function between QTV and RRV is proposed to provide accurate estimates of both QTVuRRV and QTVrRRV. Two simulation studies were conducted to assess the methodology in challenging non-stationary conditions and data recorded during head-up tilt in 16 healthy volunteers were analyzed. RESULTS: In the simulation studies, QTVuRRV changes were tracked with only a minor delay due to the filtering necessary to estimate the non-stationary spectra. The correlation coefficient between theoretical and estimated patterns was >0.92 even for extremely noisy recordings (SNR in QTV =-10dB). During head-up tilt, QTVrRRV explained the largest proportion of QTV, whereas QTVuRRV showed higher relative increase than QTV or QTVrRRV in all spectral bands (P<0.05 for most pairwise comparisons). CONCLUSION: The proposed approach accurately tracks changes in QTVuRRV. Head-up tilt induced a slightly greater increase in QTVuRRV than in QTVrRRV. SIGNIFICANCE: The proposed index QTVuRRV may represent an indirect measure of intrinsic ventricular repolarization variability, a marker of cardiac instability associated with sympathetic ventricular modulation and sudden cardiac death

    Human emotion characterization by heart rate variability analysis guided by respiration

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    © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksDeveloping a tool which identifies emotions based on their effect on cardiac activity may have a potential impact on clinical practice, since it may help in the diagnosing of psycho-neural illnesses. In this study, a method based on the analysis of heart rate variability (HRV) guided by respiration is proposed. The method was based on redefining the high frequency (HF) band, not only to be centered at the respiratory frequency, but also to have a bandwidth dependent on the respiratory spectrum. The method was first tested using simulated HRV signals, yielding the minimum estimation errors as compared to classical and respiratory frequency centered at HF band based definitions, independently of the values of the sympathovagal ratio. Then, the proposed method was applied to discriminate emotions in a database of video-induced elicitation. Five emotional states, relax, joy, fear, sadness and anger, were considered. The maximum correlation between HRV and respiration spectra discriminated joy vs. relax, joy vs. each negative valence emotion, and fear vs. sadness with p-value = 0.05 and AUC = 0.70. Based on these results, human emotion characterization may be improved by adding respiratory information to HRV analysis.Peer ReviewedPostprint (author's final draft

    Анализ взаимосвязи между центральной нервной и сердечно-сосудистой системами

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    В роботі розглянуто взаємозв’язок між центральною нервовою та серцево-судинними системами. Описані існуючі методи оцінки зв’язку між сигналами варіабельності серцевого ритму і електроенцефалограми людини: кореляція, когерентність, взаємна інформація, ентропія передачі, ймовірність синхронізації. Найбільш перспективними напрямами визнано дослідження нелінійного взаємозв’язку між розглянутими системами, розгляд методів оцінки нелінійного зв’язку між сигналами ЕЕГ та сигналами варіабельності серцевого ритму та їх порівняння. Визначені шляхи покращення існуючих підходів до даної задачі та напрямки подальших досліджень.In the paper the aspects of collaboration and interconnection between central nervous and cardiovascular systems are described. Existing methods to estimate the connectivity between HRV and EEG signals and corresponding up-to-date studies are reviewed. It can be affirmed that there is an apparent interconnection between central nervous and cardiovascular systems on the basis of examined papers. But the definite method of assessment of this interconnection capable to take into account the underlying manner of this connection is yet to be defined. It was determined that further research should be directed into examination of non-linear connectivity between HRV and EEG signals, methods for non-linear connectivity assessment and comparison of their performance. On this basis the new ways to improve the current approaches are expounded.В работе рассмотрена взаимосвязь между центральной нервной и сердечно-сосудистой системой. Описаны существующие методы оценки связи между сигналами вариабельности сердечного ритма и электроэнцефалограммы человека: корреляция, когерентность, взаимная информация, энтропия передачи, вероятность синхронизации. Установлены наиболее перспективные направления исследований: определение нелинейной взаимосвязи между рассмотренными системами, рассмотрение методов оценки нелинейной связи межу сигналами ЭЭГ и сигналами вариабельности сердечного ритма и их сравнение. Обозначены пути улучшения существующих подходов к данной задаче и направления последующих исследований
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