Separating the effect of respiration on the heart rate variability using Granger's causality and linear filtering

Lenis, Gustavo; Kircher, Michael; Lázaro, Jesús; Bailón, Raquel; Gil, Eduardo; Dössel, Olaf

doi:10.1016/j.bspc.2016.07.014

Cited by 20 publications

(17 citation statements)

References 46 publications

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“…At the same time, HRV was analysed in conjunction with the oscillatory activity of other physiological signals to improve the understanding and interpretability of the cardiac rhythms, e.g., when they are assessed in the context of cardiovascular and cardiorespiratory control. Indeed, HRV (usually evaluated in terms of beat-to-beat heart period changes represented by the RR interval) was mainly studied in bivariate analysis together with the variations in systolic blood pressure (SBP) [15][16][17][18][19] or in the respiration pattern (RESP) [20,21]. Later, the analysis has been extended to a multivariate setting, where cardiovascular and cardiorespiratory oscillations were evaluated simultaneously, which helped to understand the network of interconnections among variables, shedding light on the combined activity of physiological mechanisms like the baroreflex and the RSA [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Multiscale Information Decomposition Dissects Control Mechanisms of Heart Rate Variability at Rest and During Physiological Stress

Krohová

Faes

Czippelová

et al. 2019

Entropy

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Heart rate variability (HRV; variability of the RR interval of the electrocardiogram) results from the activity of several coexisting control mechanisms, which involve the influence of respiration (RESP) and systolic blood pressure (SBP) oscillations operating across multiple temporal scales and changing in different physiological states. In this study, multiscale information decomposition is used to dissect the physiological mechanisms related to the genesis of HRV in 78 young volunteers monitored at rest and during postural and mental stress evoked by head-up tilt (HUT) and mental arithmetics (MA). After representing RR, RESP and SBP at different time scales through a recently proposed method based on multivariate state space models, the joint information transfer T RESP , SBP → RR is decomposed into unique, redundant and synergistic components, describing the strength of baroreflex modulation independent of respiration ( U SBP → RR ), nonbaroreflex ( U RESP → RR ) and baroreflex-mediated ( R RESP , SBP → RR ) respiratory influences, and simultaneous presence of baroreflex and nonbaroreflex respiratory influences ( S RESP , SBP → RR ), respectively. We find that fast (short time scale) HRV oscillations—respiratory sinus arrhythmia—originate from the coexistence of baroreflex and nonbaroreflex (central) mechanisms at rest, with a stronger baroreflex involvement during HUT. Focusing on slower HRV oscillations, the baroreflex origin is dominant and MA leads to its higher involvement. Respiration influences independent on baroreflex are present at long time scales, and are enhanced during HUT.

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Section: Introductionmentioning

confidence: 99%

Multiscale Information Decomposition Dissects Control Mechanisms of Heart Rate Variability at Rest and During Physiological Stress

Krohová

Faes

Czippelová

et al. 2019

Entropy

View full text Add to dashboard Cite

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“…Granger causality (GC) is one of the model-based methods for connectivity estimation which is based on the supposition that each cause precedes its effects. The GC method is used especially in the absence of a priori data of brain abnormal patterns [1,[17][18][19][20]. The other method which needs a model for connectivity estimation is the dynamic causal model (DCM) which quantifies neural connectivity by assuming a bilinear state space model.…”

Section: Introductionmentioning

confidence: 99%

Effective brain connectivity estimation between active brain regions in autism using the dual Kalman-based method

Rajabioun

Nasrabadi

Shamsollahi

et al. 2019

Biomedical Engineering / Biomedizinische Technik

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Brain connectivity estimation is a useful method to study brain functions and diagnose neuroscience disorders. Effective connectivity is a subdivision of brain connectivity which discusses the causal relationship between different parts of the brain. In this study, a dual Kalman-based method is used for effective connectivity estimation. Because of connectivity changes in autism, the method is applied to autistic signals for effective connectivity estimation. For method validation, the dual Kalman based method is compared with other connectivity estimation methods by estimation error and the dual Kalman-based method gives acceptable results with less estimation errors. Then, connectivities between active brain regions of autistic and normal children in the resting state are estimated and compared. In this simulation, the brain is divided into eight regions and the connectivity between regions and within them is calculated. It can be concluded from the results that in the resting state condition the effective connectivity of active regions is decreased between regions and is increased within each region in autistic children. In another result, by averaging the connectivity between the extracted active sources of each region, the connectivity between the left and right of the central part is more than that in other regions and the connectivity in the occipital part is less than that in others.

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“…The spectrogram is performed using a Hanning window of length 32 s. For respiratory data of non-stationary characteristics, usually the spectrogram of window length around 1 min is applied, [11,19]. However, we have shown in [9] that a window length of 32 s is a better choice in time-varying conditions.…”

Section: Performance Comparison Using the Accuracy Of The If Estimatesmentioning

confidence: 99%

“…Previous studies have shown that multi-component HR signals also have non-stationary characteristics [11,19,21]. Such signals are usually analyzed in the joint time-frequency domain (TFD) because the spectral contents of these signals change with time [22].…”

Section: Introductionmentioning

confidence: 99%

“…Different time-based approaches to estimate the IRF exist, especially for analysis of respiration data for rest and sleep stages, e.g., [17,18]. For non-stationary IRF, estimation related to exercise and stress tests and spectrogram based methods are applied [11,19]. Usually, the extraction of IRF from the respiratory data has been shown to be reliable in these cases.…”

Section: Introductionmentioning

confidence: 99%

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Performance Comparison of Time-Frequency Distributions for Estimation of Instantaneous Frequency of Heart Rate Variability Signals

2017

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Abstract:The instantaneous frequency (IF) of a non-stationary signal is usually estimated from a time-frequency distribution (TFD). The IF of heart rate variability (HRV) is an important parameter because the power in a frequency band around the IF can be used for the interpretation and analysis of the respiratory rate but also for a more accurate analysis of heart rate (HR) signals. In this study, we compare the performance of five states of the art kernel-based time-frequency distributions (TFDs) in terms of their ability to accurately estimate the IF of HR signals. The selected TFDs include three widely used fixed kernel methods: the modified B distribution, the S-method and the spectrogram; and two adaptive kernel methods: the adaptive optimal kernel TFD and the recently developed adaptive directional TFD. The IF of the respiratory signal, which is usually easier to estimate as the respiratory signal is a mono-component with small amplitude variations with time, is used as a reference to examine the accuracy of the HRV IF estimates. Experimental results indicate that the most reliable estimates are obtained using the adaptive directional TFD in comparison to other commonly used methods such as the adaptive optimal kernel TFD and the modified B distribution.

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Separating the effect of respiration on the heart rate variability using Granger's causality and linear filtering

Cited by 20 publications

References 46 publications

Multiscale Information Decomposition Dissects Control Mechanisms of Heart Rate Variability at Rest and During Physiological Stress

Multiscale Information Decomposition Dissects Control Mechanisms of Heart Rate Variability at Rest and During Physiological Stress

Effective brain connectivity estimation between active brain regions in autism using the dual Kalman-based method

Performance Comparison of Time-Frequency Distributions for Estimation of Instantaneous Frequency of Heart Rate Variability Signals

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