Use of cardiorespiratory coherence to separate spectral bands of the heart rate variability

Daoud, Mohammed; Ravier, Philippe; Buttelli, Olivier

doi:10.1016/j.bspc.2018.08.003

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…The main limitation of this work is the independent acquisitions of respective RR and respiratory variables, and the subsequent separate methods of analysis. A synchronized acquisition using a common time scale would have allowed a coherence or cross-spectral analysis for a more accurate assessment of common frequencies between HRV and E peaks (Daoud et al 2018). Therefore, the causality between periodic breathing and HRV oscillations in the LF band might be considered as speculative.…”

Section: Discussionmentioning

confidence: 99%

Low-frequency ventilatory oscillations in hypoxia are a major contributor to the low-frequency component of heart rate variability

et al. 2019

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Heart rate variability (HRV) may be influenced by several factors, such as environment (hypoxia, hyperoxia, hypercapnia) or physiological demand (exercise). In this retrospective study, we tested the hypothesis that inter-beats (RR) intervals in healthy subjects exercising under various environmental stresses exhibit oscillations at the same frequency than ventilatory oscillations. MethodsSpectra from RR intervals and ventilation ( E) were collected from 37 healthy young male subjects who participated in five previous studies focused on ventilatory oscillations (or periodic breathing) during exercise in hypoxia, hyperoxia and hypercapnia. Bland & Altman test and multivariate regressions were then performed to compare respective frequencies and changes in peak powers of the two signals. ResultsFast Fourier analysis of RR and E signals showed that RR was oscillating at the same frequency than periodic breathing, i.e. ~0.09 Hz (11 seconds). During exercise, in these various conditions, the difference between minimum and maximum HRV peak power was positively correlated to the same change in ventilation peak power (P < 0.05).Low Frequency (LF) peak power was correlated to tidal volume (P < 0.01) and breathing frequency (P < 0.001). ConclusionsThis study suggests that low frequency ventilatory oscillations in hypoxia are a major contributor of the LF band power of heart rate variability.

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Section: Discussionmentioning

confidence: 99%

Low-frequency ventilatory oscillations in hypoxia are a major contributor to the low-frequency component of heart rate variability

et al. 2019

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“…The indices for our examination were: (a) linear measures of heart rate variability: mean value and standard deviation (Task Force Guidelines, 1996) (b) short term exponent α 1 as a fractal measure which in heart rate strongly correlates with changes in low and high frequency oscillations (sympathetic and parasympathetic activity) (Weippert et al, 2015;Shiau, 2018); (c) long term exponent α 2 as a fractal measure which in heart rate spectrum corresponds to a very low frequency band (Francis et al, 2002); (d) multiscaling entropy at short time scales (1-4 samples, MSE 1−4 ), related to fast oscillations, respiratory and predominately vagal control (Silva et al, 2016); (e) multiscaling entropy at long time scales (5-10 samples, MSE 5−10 ), related to slow oscillations, predominately of sympathetic control (Silva et al, 2016); (f) spectral coherence (Coh RRI−Resp ), reflecting the presence (Daoud et al, 2018) and degree (Faes and Nollo, 2011) of linear cardiac and respiratory oscillatory synchronization; (g) short scale and long scale cross DFA (ρ 1 and ρ 2 , respectively Podobnik and Stanley, 2008;Horvatic et al, 2011;Podobnik et al, 2011;Zebende, 2011;Kristoufek, 2015;Kwapien et al, 2015 as the parameters of cross correlations of fractal RRI and respiratory variations; and (h) short and long scale cross MSE (X MSE1−4 and X MSE5−10 , respectively) as the measure of cross correlation in MSE domain (Costa et al, 2005). Programs for Cross DFA and cross MSE are available within Supplementary Data Sheet 1.…”

Section: Data Processingmentioning

confidence: 99%

Slow 0.1 Hz Breathing and Body Posture Induced Perturbations of RRI and Respiratory Signal Complexity and Cardiorespiratory Coupling

et al. 2020

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Objective: We explored the physiological background of the non-linear operating mode of cardiorespiratory oscillators as the fundamental question of cardiorespiratory homeodynamics and as a prerequisite for the understanding of neurocardiovascular diseases. We investigated 20 healthy human subjects for changes using electrocardiac RR interval (RRI) and respiratory signal (Resp) Detrended Fluctuation Analysis (DFA, α 1RRI , α 2RRI , α 1Resp , α 2Resp), Multiple Scaling Entropy (MSE RRI1−4 , MSE RRI5−10 , MSE Resp1−4 , MSE Resp5−10), spectral coherence (Coh RRI−Resp), cross DFA (ρ 1 and ρ 2) and cross MSE (X MSE1−4 and X MSE5−10) indices in four physiological conditions: supine with spontaneous breathing, standing with spontaneous breathing, supine with 0.1 Hz breathing and standing with 0.1 Hz breathing. Main results: Standing is primarily characterized by the change of RRI parameters, insensitivity to change with respiratory parameters, decrease of Coh RRI−Resp and insensitivity to change of in ρ 1 , ρ 2 , X MSE1−4 , and X MSE5−10. Slow breathing in supine position was characterized by the change of the linear and non-linear parameters of both signals, reflecting the dominant vagal RRI modulation and the impact of slow 0.1 Hz breathing on Resp parameters. Coh RRI−Resp did not change with respect to supine position, while ρ 1 increased. Slow breathing in standing reflected the qualitatively specific state of autonomic regulation with striking impact on both cardiac and respiratory parameters, with specific patterns of cardiorespiratory coupling. Significance: Our results show that cardiac and respiratory short term and long term complexity parameters have different, state dependent patterns. Sympathovagal non-linear interactions are dependent on the pattern of their activation, having different scaling properties when individually activated with respect to the state of their joint activation. All investigated states induced a change of α 1 vs. α 2 relationship, which can be accurately expressed by the proposed measure-inter-fractal angle θ. Short scale (α 1 vs. MSE 1−4) and long scale (α 2 vs. MSE 5−10) complexity measures had reciprocal interrelation in standing with 0.1 Hz breathing, with specific cardiorespiratory coupling pattern (ρ 1 vs. X MSE1−4). These results support the hypothesis of hierarchical organization Matić et al. RRI-Respiratory Complexity and Cardiorespiratory Coupling of cardiorespiratory complexity mechanisms and their recruitment in ascendant manner with respect to the increase of behavioral challenge complexity. Specific and comprehensive cardiorespiratory regulation in standing with 0.1 Hz breathing suggests this state as the potentially most beneficial maneuver for cardiorespiratory conditioning.

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“…The limb ECG leads I, II and III were sampled at 1 kHz and the respiration signal, r(t), at 125 Hz. The distribution of male (12) and female (13) were: four men and five women in the age range [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] years, four men and four women in the age range [36][37][38][39][40][41][42][43][44][45][46][47][48][49][50] years and four men and four women over 50 years.…”

Section: A Emotion Databasementioning

confidence: 99%

“…In [30], an HF bandwidth dependent on respiration stability was used to analyze HRV in critically ill patients. Recently, spectral coherence between respiration and HRV has been used to define the HF band [31], [32].…”

Section: Introductionmentioning

confidence: 99%

Human Emotion Characterization by Heart Rate Variability Analysis Guided by Respiration

Yamuza

Bolea

Orini

et al. 2019

IEEE J. Biomed. Health Inform.

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Developing 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 psychoneural 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 classic 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.

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Use of cardiorespiratory coherence to separate spectral bands of the heart rate variability

Cited by 5 publications

References 18 publications

Low-frequency ventilatory oscillations in hypoxia are a major contributor to the low-frequency component of heart rate variability

Low-frequency ventilatory oscillations in hypoxia are a major contributor to the low-frequency component of heart rate variability

Slow 0.1 Hz Breathing and Body Posture Induced Perturbations of RRI and Respiratory Signal Complexity and Cardiorespiratory Coupling

Human Emotion Characterization by Heart Rate Variability Analysis Guided by Respiration

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