A Method for the Analysis of Respiratory Sinus Arrhythmia Using Continuous Wavelet Transforms

Abstract-A continuous wavelet transform-based method is presented to study the nonstationary strength and phase delay of the respiratory sinus arrhythmia (RSA). The RSA is the cyclic variation of instantaneous heart rate at the breathing frequency. In studies of cardio-respiratory interaction during sleep, paced breathing or postural changes, low respiratory frequencies, and fast changes can occur. Comparison on synthetic data presented here shows that the proposed method outperforms traditional short-time Fourier-transform analysis in these conditions. On the one hand, wavelet analysis presents a sufficient frequency-resolution to handle low respiratory frequencies, for which time frames should be long in Fourier-based analysis. On the other hand, it is able to track fast variations of the signals in both amplitude and phase for which time frames should be short in Fourier-based analysis. Index Terms-Cardio-respiratory interaction, continuous wavelet transform (CWT), heart rate variability (HRV), respiratory sinus arrhythmia (RSA)

Breath-by-Breath Analysis of Cardiorespiratory Interaction for Quantifying Developmental Maturity in Premature Infants

Breath-by-breath analysis of cardiorespiratory interaction for quantifying developmental maturity in premature infants. J Appl Physiol 112: 859-867, 2012. First published December 15, 2011; doi:10.1152/japplphysiol.01152.2011.-In healthy neonates, connections between the heart and lungs through brain stem chemosensory pathways and the autonomic nervous system result in cardiorespiratory synchronization. This interdependence between cardiac and respiratory dynamics can be difficult to measure because of intermittent signal quality in intensive care settings and variability of heart and breathing rates. We employed a phase-based measure suggested by Sch fer and coworkers (Sch fer C, Rosenblum MG, Kurths J, Abel HH. Nature 392: 239-240, 1998) to obtain a breath-by-breath analysis of cardiorespiratory interaction. This measure of cardiorespiratory interaction does not distinguish between cardiac control of respiration associated with cardioventilatory coupling and respiratory influences on the heart rate associated with respiratory sinus arrhythmia. We calculated, in sliding 4-min windows, the probability density of heartbeats as a function of the concurrent phase of the respiratory cycle. Probability density functions whose Shannon entropy had a \u3c 0.1% chance of occurring from random numbers were classified as exhibiting interaction. In this way, we analyzed 18 infant-years of data from 1,202 patients in the Neonatal Intensive Care Unit at University of Virginia. We found evidence of interaction in 3.3 patient-years of data (18%). Cardiorespiratory interaction increased several-fold with postnatal development, but, surprisingly, the rate of increase was not affected by gestational age at birth. We find evidence for moderate correspondence between this measure of cardiorespiratory interaction and cardioventilatory coupling and no evidence for respiratory sinus arrhythmia, leading to the need for further investigation of the underlying mechanism. Such continuous measures of physiological interaction may serve to gauge developmental maturity in neonatal intensive care patients and prove useful in decisions about incipient illness and about hospital discharge

Wavelet entropy as a measure of ventricular beat suppression from the electrocardiogram in atrial fibrillation

A novel method of quantifying the effectiveness of the suppression of ventricular activity from electrocardiograms (ECGs) in atrial fibrillation is proposed. The temporal distribution of the energy of wavelet coefficients is quantified by wavelet entropy at each ventricular beat. More effective ventricular activity suppression yields increased entropies at scales dominated by the ventricular and atrial components of the ECG. Two studies are undertaken to demonstrate the efficacy of the method: first, using synthesised ECGs with controlled levels of residual ventricular activity, and second, using patient recordings with ventricular activity suppressed by an average beat template subtraction algorithm. In both cases wavelet entropy is shown to be a good measure of the effectiveness of ventricular beat suppression

The effect of spontaneous versus paced breathing on EEG, HRV, skin conductance and skin temperature

A dissertation submitted in fulfilment of the requirements for the degree Master of Science in Engineering, in the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg. January 2017 JohannesburgIt is well known that emotional stress has a negative impact on people’s health and physical, emotional and mental performance. Previous research has investigated the effects of stress on various aspects of physiology such as respiration, heart rate, heart rate variability (HRV), skin conductance, skin temperature and electrical activity in the brain. Essentially, HRV, Electroencephalography (EEG), skin conductance and skin temperature appear to reflect a stress response or state of arousal. Whilst the relationship between respiration rate, respiration rhythm and HRV is well documented, less is known about the relationship between respiration rate, EEG, skin conductance and skin temperature, whilst HRV is maximum (when there is resonance between HRV and respiration i.e. in phase with one another). This research project aims to investigate the impact that one session of slow paced breathing has on EEG, heart rate variability (HRV), skin conductance and skin temperature. Twenty male participants were randomly assigned to either a control or intervention group. Physiological data were recorded for the intervention and control group during one breathing session, over a short initial baseline (B1), a main session of 12 minutes, and a final baseline (B2). The only difference between the control and intervention groups was that during the main session, the intervention group practiced slow paced breathing (at 6 breaths per minute), while the control group breathed spontaneously. Wavelet transformation was used to analyse EEG data while Fourier transformation was used to analyse HRV. The study shows that slow-paced breathing significantly increases the low frequency and total power of the HRV but does not change the high frequency power of HRV. Furthermore, skin temperature significantly increased for the control group from B1 to Main, and was significantly higher for the control group when compared to the intervention group during the main session. There were no significant skin temperature changes between sessions for the intervention group. Skin conductance increased significantly from Main to B2 for the control group. No significant changes were found between sessions for the intervention group and between groups. EEG theta power at Cz decreased significantly from Main to B2 for the control group only, while theta power decreased at F4 from Main to B2 for both groups. Lastly, beta power at Cz decreased from B1 to B2 for the control group only. This significant effect that slow-paced breathing has on HRV suggests the hypothesis that with frequent practice, basal HRV would increase, and with it, potential benefits such as a reduction in anxiety and improved performance in specific tasks. Slow-paced breathing biofeedback thus shows promise as a simple, cheap, measurable and effective method to reduce the impact of stress on some physiological signals, suggesting a direction for future research.MT201

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

Design, Evaluation, and Application of Heart Rate Variability Analysis Software (HRVAS)

The analysis of heart rate variability (HRV) has become an increasingly popular and important tool for studying many disease pathologies in the past twenty years. HRV analyses are methods used to non-invasively quantify variability within heart rate. Purposes of this study were to design, evaluate, and apply an easy to use and open-source HRV analysis software package (HRVAS). HRVAS implements four major categories of HRV techniques: statistical and time-domain analysis, frequency-domain analysis, nonlinear analysis, and time-frequency analysis. Software evaluations were accomplished by performing HRV analysis on simulated and public congestive heart failure (CHF) data. Application of HRVAS included studying the effects of hyperaldosteronism on HRV in rats. Simulation and CHF results demonstrated that HRVAS was a dependable HRV analysis tool. Results from the rat hyperaldosteronism model showed that 5 of 26 HRV measures were statistically significant (p\u3c0.05). HRVAS provides a useful tool for HRV analysis to researchers

Statistical Coding and Decoding of Heartbeat Intervals

The heart integrates neuroregulatory messages into specific bands of frequency, such that the overall amplitude spectrum of the cardiac output reflects the variations of the autonomic nervous system. This modulatory mechanism seems to be well adjusted to the unpredictability of the cardiac demand, maintaining a proper cardiac regulation. A longstanding theory holds that biological organisms facing an ever-changing environment are likely to evolve adaptive mechanisms to extract essential features in order to adjust their behavior. The key question, however, has been to understand how the neural circuitry self-organizes these feature detectors to select behaviorally relevant information. Previous studies in computational perception suggest that a neural population enhances information that is important for survival by minimizing the statistical redundancy of the stimuli. Herein we investigate whether the cardiac system makes use of a redundancy reduction strategy to regulate the cardiac rhythm. Based on a network of neural filters optimized to code heartbeat intervals, we learn a population code that maximizes the information across the neural ensemble. The emerging population code displays filter tuning proprieties whose characteristics explain diverse aspects of the autonomic cardiac regulation, such as the compromise between fast and slow cardiac responses. We show that the filters yield responses that are quantitatively similar to observed heart rate responses during direct sympathetic or parasympathetic nerve stimulation. Our findings suggest that the heart decodes autonomic stimuli according to information theory principles analogous to how perceptual cues are encoded by sensory systems