Individual differences in respiratory sinus arrhythmia

Abstract

To investigate the interindividual differences in respiratory sinus arrhythmia (RSA), recordings of ventilation and electrocardiogram were obtained from 12 healthy subjects for five imposed breathing periods (T(TOT)) surrounding each individual's spontaneous breathing period. In addition to the spectral analysis of the R-R interval signal at each breathing period, RSA characteristics were quantified by using a breath-by-breath analysis where a sinusoid was fitted to the changes in instantaneous heart rate in each breath. The amplitude and phase (or delay = phase x T(TOT)) of this sinusoid were taken as the RSA characteristics for each breath. It was found that for each subject the RSA amplitude-T(TOT) relationship was linear, whereas the delay-T(TOT) relationship was parabolic. However, the parameters of these relationships differed between individuals. Linear correlation between the slopes of RSA amplitude versus T(TOT) regression lines and 1) mean breathing period and 2) mean R-R interval during spontaneous breathing were calculated. Only the correlation coefficient with breathing period was significantly different from zero, indicating that the longer the spontaneous breathing period the lesser the increase in RSA amplitude with increasing breathing period. Similarly, only the correlation coefficient between the curvature of the RSA delay-T(TOT) parabola and mean breathing period was significantly different from zero; the longer the spontaneous breathing period the larger the curvature of RSA delay. These results suggest that the changes in RSA characteristics induced by changing the breathing period may be explained partly by the spontaneous breathing period of each individual. Furthermore, a transfer function analysis performed on these data suggested interindividual differences in the autonomic modulation of the heart rate.

Effects of ovarian hormones and aging on respiratory sinus arrhythmia and breathing patterns in women

“…While baseline-hypoxia and baseline-hypercapnia RSA amplitude increased in some individuals, these changes were increased with hypercapnia and decreased with hypoxia compared to baseline, and vice versa in others. This is consistent with others [2] who reported great variability in the individual changes of RSA with respect to changes in breathing patterns, thus making interpretation of RSA difficult [2].…”

“…For each recording, a restricted respiratory frequency component identified as the respiratory centered frequency component was calculated using the frequency range corresponding to ±10% of the respiratory rate averaged over the entire recording. Nevertheless, our approach, similar to that used by others [2], did not reveal any significant differences in RSA amplitude between the three phases of the menstrual cycle. The influence of ovarian hormones changes over the menstrual cycle on RSA was minimal with an g 2 of 0.067 during baseline, 0.035 during IH, and 0.017 during EH.…”

Consciously Controlled Breathing Decreases the High-Frequency Component of Heart Rate Variability by Inhibiting Cardiac Parasympathetic Nerve Activity

“…Moreover, RSA was found to differ depending on the spontaneous breathing period of each individual (Ben Lamine et al 2004), since some individuality in respiration pattern exists among humans (Shea et al 1987;Benchetrit 2000). Considering these facts, evaluation of HRV using spectral analysis necessitates assessment of the participant's respiratory style.…”

Liénard systems and potential-Hamiltonian decomposition III – applications

“…To represent the vegetative control system of the cardiac and respiratory functions, let's consider two regulon-type coupled oscillators in interaction [2,11]. Indeed to simplify, we consider I, a set of inspiratory neurons (a center firing synchronously with the phrenic nerve) having a self-activatory loop and interacting with E, a set of expiratory neurons (a center firing during the phrenic silence); E is activated by I (via the pleural stretch receptors) and E inhibits I (through intra-bulbar connections) (Fig.…”

“…k(y) is obtained by measuring the instantaneous cardiac period T (inter-beats duration) and calculating the slope of the regression line between T and the inspiratory activity y 2 (y being represented by the local inspiratory time counted from the beginning of an inspiration, when the cardiac beat of period T is occurring). This slope is approximated by −π(ηk(y)) 2 /2, if η and k(y) are small, and is estimated by the correlation coefficient between T and y 2 [2,11] multiplied by the ratio between standard deviations of T and y 2 . The integrity of the coupling between the 2 Liénard systems [17] allows the bulbar vegetative control system to adapt to the effort: first the breathing is entrained by a muscular activity and secondarily entrains the heart.…”