Multi-scale Order Recurrence Plot based deterministic analysis on Heart Rate Variability in Congestive Heart Failure Assessment

Congestive heart failure (CHF) is a cardiovascular disease associated with the abnormal autonomic nervous system (ANS). Heart rate variability analysis (HRV) is the main method for the quantitative evaluation of autonomic nervous function. Common analytical methods of HRV include time domain, frequency domain, and nonlinear methods. However, these methods generally ignore the short-term volatility of heart rate and autonomic ganglion law. Therefore, this study proposes a new parameter to analyze heart rate variability-determination of a multi-scale order recurrence plot (MSORP_DET). This method can analyze the HRV in patients with heart failure on multiple time scales. This study analyzed the R-R interval in 24-hour HRV data from 98 samples (54 normal subjects and 44 patients with CHF). The results showed that MSORP_DET could significantly distinguish CHF patients from normal subjects (p<0.001). Moreover, the accuracy rate of screening patients with CHF reached the maximum of 81.6% by using the combination of low frequency/high frequency (LF/HF) and MSORP_DET, compared with 78.6% when using LF/HF alone. Therefore, MSORP_DET can be used as a new index to screen patients with CHF and reveal that the rhythm of ANS in patients with heart failure is more complex than that in normal people

Novel Framework for Nonlinear HRV Analysis and its Physiological Interpretation

La inclusión de métodos no lineales aplicados a señales de variabilidad del ritmo cardiaco (HRV, del inglés Heart Rate Variability) proporciona una nueva visión en la caracterización de anomalías en el contexto de las enfermedades cardiacas o patologías como la insuficiencia cardiaca o la fibrilación auricular, por nombrar algunas. Se ha demostrado que alteraciones en el sistema nervioso autónomo (ANS, del inglés Autonomic Nervous System), el cuál modula el ritmo cardiaco, conllevan a cambios en los patrones no lineales de la HRV. Sin embargo, la incertidumbre, todavía presente, en los mecanismos que subyacen a variaciones fisiológicas o patofisiológicas en los índices no lineales de la HRV, junto con el alto tiempo que requieren los algoritmos para la estimación de estos índices, representan el cuello de botella para su aplicación en la práctica clínica.Después de una breve introducción sobre los temas abordados en esta la tesis en el capítulo 1, el segundo capítulo, el capítulo 2, está dedicado a la primera gran contribución de esta tesis, que consiste en la propuesta y desarrollo de una metodología con el fin de reducir el coste computacional asociado a la caracterización no lineal de la HRV. El esquema propuesto es muy eficaz, reduciendo el tiempo de cálculo a unos pocos segundos para el análisis no lineal de señales de HRV de corta longitud (5 minutos). Con respecto a la interpretación del análisis no lineal de la HRV, es importante señalar que hay una serie de factores que afectan a su cálculo y deben tenerse en cuenta al comparar diferentes estudios de la literatura. Las características de las series de HRV, como la frecuencia de muestreo, así como la selección de valores de parámetros en los métodos no lineales, tienen un impacto en los resultados de los índices no lineales de la HRV y, en algunas circunstancias, pueden dar lugar a interpretaciones erróneas. Uno de los principales objetivos del capítulo 3 es estudiar la influencia de la tasa de muestreo en los índices no lineales de la HRV y proponer alternativas para atenuar esta influencia. Los métodos propuestos incluyen, por una parte, la corrección de la frecuencia cardiaca de las estimaciones de la HRV mediante fórmulas de regresión individuales o basadas en la población y, por otra, el preprocesamiento de las series temporales de HRV mediante modelos de interpolación o de point-process. El capítulo 4 se centra en investigar el efecto de la selección del valor de los parámetros requeridos para el cálculo de ciertos índices no lineales de la HRV (por ejemplo, la entropía aproximada) y proponiendo un nuevo índice independiente de la definición del valor de éstos parámetros a-priori. Este novedoso índice se denomina entropía multidimensional aproximada. El análisis no lineal de la HRV, incluido el nuevo índice propuesto, se aplica al estudio de afecciones asociadas a alteraciones de la modulación cardiaca del ANS, como el envejecimiento y la insuficiencia cardiaca congestiva (CHF, del inglés Congestive Heart Failure). Por un lado, todos los índices no lineales de la HRV evaluados ven disminuidos significativamente sus valores en las personas mayores en comparación con los jóvenes ambos grupos en condiciones de reposo en posición de decubito supino. Por otro lado, los pacientes con insuficiencia cardiaca muestran valores más altos de los índices no lineales significativamente con respecto al grupo de sujetos sanos, en ambos casos analizando el período nocturno. Además, el análisis no lineal de la HRV es evaluada en respuesta a provocaciones simpáticas, inducidas por el cambio de la posición supina a la posición de pie o por la administración de atropina, donde se observa una disminución en todos los índicesno lineales estimados.El capítulo 5 está dedicado a la evaluación del rendimiento del análisis no lineal de la HRV en el triaje de la administración profiláctica con el fin de prevenir los episodios de hipotension causados por la anestesia espinal durante el parto por cesárea. El estudio se realiza en colaboración con el Servicio de Anestesia del Hospital Universitario Miguel Servet (Zaragoza, España). Debido a que la profilaxis puede producir efectos secundarios en el feto, el desafío consiste en predecir los casos normotensos para los cuales se puede prescindir del tratamiento profilactico. La hipótesis de esta tesis se basa en el hecho de que la alteración de la regulación del ANS causada por el último período de embarazo y la proximidad a la cirugía podría reflejarse en los índices no lineales de la HRV, lo que podría ayudar a predecir los casos que deriven en hipotension y normotension con mayor precisión que cuando se utilizan solamente variables demográficas. Es importante destacar que las propuestas metodológicas para el análisis no lineal de la HRV desarrolladas en la tesis se aplican en la caracterización de otras señales cardiovasculares, como la señal fotopletismografica de pulso. Las series temporales derivadas de esta señal, que incluyen información del sistema vascular periférico, se incorporan en un clasificador basado en la regresión logística junto con los índices no lineales de la HRV. El clasificador propuesto alcanza un 76,5% de sensibilidad y un 72,2% de precisión en la detección de los casos normotensos, proporcionando así información pertinente y objetiva respaldando la decisión final del equipo médico.En el capítulo 6 se presentan las principales conclusiones derivadas de la tesis y se consideran futuras ampliaciones en base a las investigaciones llevadas a cabo. Se hace hincapié en la contribución de la tesis al desarrollo de metodologías novedosas para caracterizar de manera más robusta los índices no lineales de la HRV e interpretar con fiabilidad los resultados correspondientes. Basándose en las metodologías desarrolladas, se investigan las condiciones o patologías asociadas con alteraciones en la modulación autonómica de la actividad cardiaca y se destaca la contribución del análisis no lineal de la HRV para su caracterización. En conclusión, entre los objetivos metodológicos desarrollados en esta tesis se encuentran: i) la propuesta de un esquema de trabajo para incrementar la fiabilidad de la estimación de la dimensión de correlación, usando un algoritmo que reduce la carga computacional, facilitando su aplicabilidad en la práctica clínica; ii) el desarrollo de métodos alternativos para atenuar la dependencia de los índices no lineales de la HRV con el ritmo cardiaco medio; iii) la propuesta de un índice no lineal de la HRV multidimensional independiente de la definición a priori de parámetros para su estimación. Además, los objetivos relacionados con la aplicación clínica de lascontribuciones metodológicas son: i) la caracterización del efecto del envejecimiento en los índices no lineales de la HRV; ii) la evaluación de la complejidad e irregularidad del ritmo cardiaco en pacientes que sufren de insuficiencia cardiaca comparada con sujetos sanos; iii) la mejora de la eficacia de la profilaxis para la prevención de eventos de hipotensión después de anestesia espinal durante parto programado por cesárea.<br /

On the standardization of approximate entropy: multidimensional approximate entropy index evaluated on short-term HRV time series

Background. Nonlinear heart rate variability (HRV) indices have extended the description of autonomic nervous system (ANS) regulation of the heart. One of those indices is approximate entropy, ApEn, which has become a commonly used measure of the irregularity of a time series. To calculate ApEn, a priori definition of parameters like the threshold on similarity and the embedding dimension is required, which has been shown to be critical for interpretation of the results. Thus, searching for a parameter-free ApEn-based index could be advantageous for standardizing the use and interpretation of this widely applied entropy measurement. Methods. A novel entropy index called multidimensional approximate entropy, , is proposed based on summing the contribution of maximum approximate entropies over a wide range of embedding dimensions while selecting the similarity threshold leading to maximum ApEn value in each dimension. Synthetic RR interval time series with varying levels of stochasticity, generated by both MIX(P) processes and white/pink noise, were used to validate the properties of the proposed index. Aging and congestive heart failure (CHF) were characterized from RR interval time series of available databases. Results. In synthetic time series, values were proportional to the level of randomness; i.e., increased for higher values of P in generated MIX(P) processes and was larger for white than for pink noise. This result was a consequence of all maximum approximate entropy values being increased for higher levels of randomness in all considered embedding dimensions. This is in contrast to the results obtained for approximate entropies computed with a fixed similarity threshold, which presented inconsistent results for different embedding dimensions. Evaluation of the proposed index on available databases revealed that aging was associated with a notable reduction in values. On the other hand, evaluated during the night period was considerably larger in CHF patients than in healthy subjects. Conclusion. A novel parameter-free multidimensional approximate entropy index, , is proposed and tested over synthetic data to confirm its capacity to represent a range of randomness levels in HRV time series. values are reduced in elderly patients, which may correspond to the reported loss of ANS adaptability in this population segment. Increased values measured in CHF patients versus healthy subjects during the night period point to greater irregularity of heart rate dynamics caused by the disease

Assessment and Mechanisms of Autonomic Function in Health and Disease

The autonomic nervous system is a master regulator of homeostasis, and the conviction that autonomic outflow is important on a patient-by-patient, minute-to-minute basis in both health and disease is the motivation for this thesis. The dissertation explores three aims that advance our understanding of the autonomic nervous system by elucidating the molecular mechanisms of autonomic regulation, validating widely used techniques for autonomic assessment, and developing and applying a new method to assess sympathetic vascular control. The first aim of the dissertation was to investigate the role of the Rho kinase pathway as a mediator of the autonomic effects of central angiotensin-II. This study was performed in conscious, chronically instrumented rabbits that received intracerebroventricular infusions of angiotensin-II, angiotensin-II with the specific Rho kinase inhibitor Fasudil, Fasudil alone, or a vehicle control over two weeks. Baseline hemodynamics were assessed daily, and cardiac and global vasomotor sympathetic tone was assessed by the hemodynamic response to autonomic blockers. Angiotensin-II raised blood pressure and cardiac and global vasomotor sympathetic outflow in a Rho-kinase dependent manner. In a separate cohort, renal sympathetic nerve activity was directly recorded and sympathetic baroreflex sensitivity was assessed, providing clear evidence that angiotensin-II increases renal sympathetic nerve activity and impairs baroreflex control thereof via a Rho kinase-dependent mechanism. In summary, the pressor, sympatho-excitatory, and baroreflex dysfunction caused by central angiotensin-II depend on Rho kinase activation. The second aim was to investigate the relationship between measures of pulse rate variability obtained by a chronically implanted arterial pressure telemeter with measures of heart rate variability derived by the standard electrocardiogram and the ability of pulse rate variability to reflect the autonomic contributions of heart rate variability. This study was conducted in conscious rabbits chronically instrumented with epicardial leads and arterial pressure telemeters. The autonomic contribution to pulse rate variability was assessed by pharmacological blockade, and the intrinsic variability of pulse rate was assessed by ventricular pacing. This study showed that pulse rate variability is a generally acceptable surrogate for heart rate variability for time- and frequency-domain measures, but the additional contribution of respiration to and the differing nonlinear properties of pulse rate variability should be considered by investigators. The third aim was to critically test the idea that the renal sympathetic nerves do not participate in the physiological control of renal blood flow. This study was conducted in conscious rabbits that underwent unilateral renal denervation and chronic instrumentation with arterial pressure telemeters and bilateral renal blood flow probes. Using time-varying transfer function analysis, this study showed active, rhythmic vasoconstriction of the renal vasculature with baroreflex properties in normally innervated kidneys, consistent with sympathetic vasomotion, which was absent in denervated kidneys. This refutes the long-held idea that sympathetic control of the renal vasculature is not physiological and has important applications to the burgeoning field of therapeutic renal denervation for cardiovascular disease

Sex Differences in Cardiac Autonomic Modulation and Baroreflex Sensitivity Following Differential Exercise Training

Maintained balance between sympathetic and parasympathetic tone within the cardiac autonomic systems is a vital component of cardiovascular regulation. The alteration of baroreflex function can contribute to chronic parasympathetic withdrawal and subsequent sympathetic dominance that is often seen in the development and progression of cardiovascular diseases. Heart rate variability (HRV) and baroreflex sensitivity (BRS) are non-invasive clinical measures utilized to assess baroreflex control and cardiac autonomic modulation, respectively. Aerobic exercise (AE) training has been shown to increase HRV and BRS; however, little is known concerning the response of HRV or baroreflex function to resistance exercise (RE) training. The purpose of this study was to assess the potential sex differences of short-term aerobic training versus resistance training on HRV and BRS in a hypertensive population. A 2x2x2 design was utilized to analyze mode (resistance vs. aerobic) x time (pre- versus post-training) x sex (male versus female). Forty pre- to stage-1 essential hypertensives between the ages of 33 and 60 years old (20 men, 20 women) underwent either AE training [30 minutes of treadmill exercise, 3 days per week at 65% of peak oxygen consumption (VO2 peak)] or RE training (3 sets of 10 repetitions for 9 major muscle groups, 3 days per week at 10 repetition maximum). Body mass index (BMI), body composition assessment, electrocardiogram (ECG) recordings, beat-to-beat blood pressure (BP), and heads up tilt tests were performed at baseline pre and post 4 week training period. An increase in BRS was seen in both sexes following AE training; however, RE training, showed decreases in BRS in males and no change in females. Following RE training decreases in HRV as indicated by the low frequency to high frequency (LF: HF) ratio were seen in males, and increases in HRV were observed within females. These data show that 4 weeks of moderate intensity aerobic training results in increases in BRS and HRV in both sexes. The decrease in BRS seen in males following 4 weeks of RE training may be related to an increase in arterial stiffness in hypertensive individuals

Microstates of the cortical brain-heart axis

Electroencephalographic (EEG) microstates are brain states with quasi-stable scalp topography. Whether such states extend to the body level, that is, the peripheral autonomic nerves, remains unknown. We hypothesized that microstates extend at the brain-heart axis level as a functional state of the central autonomic network. Thus, we combined the EEG and heartbeat dynamics series to estimate the directional information transfer originating in the cortex targeting the sympathovagal and parasympathetic activity oscillations and vice versa for the afferent functional direction. Data were from two groups of participants: 36 healthy volunteers who were subjected to cognitive workload induced by mental arithmetic, and 26 participants who underwent physical stress induced by a cold pressure test. All participants were healthy at the time of the study. Based on statistical testing and goodness-of-fit evaluations, we demonstrated the existence of microstates of the functional brain-heart axis, with emphasis on the cerebral cortex, since the microstates are derived from EEG. Such nervous-system microstates are spatio-temporal quasi-stable states that exclusively refer to the efferent brain-to-heart direction. We demonstrated brain-heart microstates that could be associated with specific experimental conditions as well as brain-heart microstates that are non-specific to tasks

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

Assessing Variability of EEG and ECG/HRV Time Series Signals Using a Variety of Non-Linear Methods

Time series signals, such as Electroencephalogram (EEG) and Electrocardiogram (ECG) represent the complex dynamic behaviours of biological systems. The analysis of these signals using variety of nonlinear methods is essential for understanding variability within EEG and ECG, which potentially could help unveiling hidden patterns related to underlying physiological mechanisms. EEG is a time varying signal, and electrodes for recording EEG at different positions on the scalp give different time varying signals. There might be correlation between these signals. It is important to know the correlation between EEG signals because it might tell whether or not brain activities from different areas are related. EEG and ECG might be related to each other because both of them are generated from one co-ordinately working body. Investigating this relationship is of interest because it may reveal information about the correlation between EEG and ECG signals. This thesis is about assessing variability of time series data, EEG and ECG, using variety of nonlinear measures. Although other research has looked into the correlation between EEGs using a limited number of electrodes and a limited number of combinations of electrode pairs, no research has investigated the correlation between EEG signals and distance between electrodes. Furthermore, no one has compared the correlation performance for participants with and without medical conditions. In my research, I have filled up these gaps by using a full range of electrodes and all possible combinations of electrode pairs analysed in Time Domain (TD). Cross-Correlation method is calculated on the processed EEG signals for different number unique electrode pairs from each datasets. In order to obtain the distance in centimetres (cm) between electrodes, a measuring tape was used. For most of our participants the head circumference range was 54-58cm, for which a medium-sized I have discovered that the correlation between EEG signals measured through electrodes is linearly dependent on the physical distance (straight-line) distance between them for datasets without medical condition, but not for datasets with medical conditions. Some research has investigated correlation between EEG and Heart Rate Variability (HRV) within limited brain areas and demonstrated the existence of correlation between EEG and HRV. But no research has indicated whether or not the correlation changes with brain area. Although Wavelet Transformations (WT) have been performed on time series data including EEG and HRV signals to extract certain features respectively by other research, so far correlation between WT signals of EEG and HRV has not been analysed. My research covers these gaps by conducting a thorough investigation of all electrodes on the human scalp in Frequency Domain (FD) as well as TD. For the reason of different sample rates of EEG and HRV, two different approaches (named as Method 1 and Method 2) are utilised to segment EEG signals and to calculate Pearson’s Correlation Coefficient for each of the EEG frequencies with each of the HRV frequencies in FD. I have demonstrated that EEG at the front area of the brain has a stronger correlation with HRV than that at the other area in a frequency domain. These findings are independent of both participants and brain hemispheres. Sample Entropy (SE) is used to predict complexity of time series data. Recent research has proposed new calculation methods for SE, aiming to improve the accuracy. To my knowledge, no one has attempted to reduce the computational time of SE calculation. I have developed a new calculation method for time series complexity which could improve computational time significantly in the context of calculating a correlation between EEG and HRV. The results have a parsimonious outcome of SE calculation by exploiting a new method of SE implementation. In addition, it is found that the electrical activity in the frontal lobe of the brain appears to be correlated with the HRV in a time domain. Time series analysis method has been utilised to study complex systems that appear ubiquitous in nature, but limited to certain dynamic systems (e.g. analysing variables affecting stock values). In this thesis, I have also investigated the nature of the dynamic system of HRV. I have disclosed that Embedding Dimension could unveil two variables that determined HRV

Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk

Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis