Pascale Calabrese

Comparison Between the Respiratory Inductance Plethysmography Signal Derivative and the Airflow Signal

The use of respiratory inductance plethysmography (RIP) for measurement of breathing is appealing not only because of its noninvasive nature but also because it provides rib cage and abdomen cross sectional area changes.

Enhanced cardiac vagal efferent activity does not explain training-induced bradycardia.

Studies of heart rate variability (HRV) have so far produced contradictory evidence to support the common belief that endurance training enhances cardiac parasympathetic tone. This may be related to the fact that most studies failed to specifically isolate the vagally mediated influence of respiration. This study used a cross-sectional comparison of endurance athletes (n=20; ATHL) exhibiting resting bradycardia and age-matched nonathletes (n=12; CRTL) to indirectly assess training effects on amplitude and timing characteristics of respiratory sinus arrhythmia (RSA). Continuous electrocardiogram (ECG) and ventilatory flows were recorded during spontaneous breathing (SP), as well as during breathing at four cycles less than (M4) or more (P4) than SP, to also examine potential repercussions of training on the sensitivity of the cardiac vagal responses to breathing. A fast Fourier transform procedure was used to quantify the standard spectral high-frequency (HF) and low-frequency (LF) components and a respiratory-centered frequency (RCF) component of HRV. RSA was assessed using a breath-by-breath quantification of the amplitude and timing of the maximum change in instantaneous heart rate. Under baseline SP conditions, heart rate was lower in ATHL (62.6+/-6.5 vs. 75.2+/-9 beats/min; p<0.05) while blood pressure (BP), breath cycle duration, tidal volume, and ventilatory drive were similar in both groups. HRV total spectral power density, LF, HF, or RCF was not different between groups at either the SP, M4, or P4 conditions. Changes in total breath duration similarly affected RSA amplitude in all groups, while HR and BP remained unchanged from SP. RSA phase was not affected by training status or by changes in total breath duration. RSA amplitude was negatively related to breathing frequency in all groups (p<0.05), while the mean slope of the relationship (sensitivity) was not different between groups. In as much as RSA is an adequate marker of cardiac vagal efferent activity, these results add support to a contribution of a decrease in intrinsic heart rate to explain training-induced bradycardia.

Effects of resistive loading on the pattern of breathing

In order to determine changes in breathing patterns brought about by resistive loading, ventilation was recorded in 11 healthy subjects with four linear resistances (3.57, 5.75, 8.76 and 13.13 cmH2O L(-1) sec) added in a random order throughout the entire breath. At steady state, a breath-by-breath analysis of airflow was used to quantify the pattern of breathing in terms of respiratory variables: TI, TE, Tt, VT, VT/TI, TI/Tt, and by taking TI, TE, VT all together (TRIAD) and also the shape of the entire airflow profile quantified by harmonic analysis (ASTER). Group analysis using ANOVA showed significant changes in all variables. There were increasing changes with increasing loads in all variables, the smallest changes being in TI/Tt. Within to between-individual comparisons between two loads showed that only TI/Tt and the ASTER were more similar within than between-individuals for all comparisons. It was concluded that at steady state mechanisms of load compensation come into play inducing changes in the pattern of breathing proportional to the loads while maintaining some of the individual characteristics.

A model of mechanical interactions between heart and lungs

To study the mechanical interactions between heart, lungs and thorax, we propose a mathematical model combining a ventilatory neuromuscular model and a model of the cardiovascular system, as described by Smith et al. (Smith, Chase, Nokes, Shaw & Wake 2004 Med. Eng. Phys.26, 131–139. (doi:10.1016/j.medengphy.2003.10.001)). The respiratory model has been adapted from Thibault et al. (Thibault, Heyer, Benchetrit & Baconnier 2002 Acta Biotheor. 50, 269–279. (doi:10.1023/A:1022616701863)); using a Liénard oscillator, it allows the activity of the respiratory centres, the respiratory muscles and rib cage internal mechanics to be simulated. The minimal haemodynamic system model of Smith includes the heart, as well as the pulmonary and systemic circulation systems. These two modules interact mechanically by means of the pleural pressure, calculated in the mechanical respiratory system, and the intrathoracic blood volume, calculated in the cardiovascular model. The simulation by the proposed model provides results, first, close to experimental data, second, in agreement with the literature results and, finally, highlighting the presence of mechanical cardiorespiratory interactions.

Variability of end-expiratory lung volume in premature infants.

Background: Analysis of breath-to-breath variability of respiratory characteristics provides information on the respiratory control. In infants, the control of end-expiratory lung volume (EELV) is active and complex, and it can be altered by respiratory disease. The pattern of EELV variability may reflect the behavior of this regulatory system. Objectives: We aimed to characterize EELV variability in premature infants, and to evaluate variability pattern changes associated with respiratory distress and ventilatory support. Methods: EELV variations were recorded using inductance plethysmography in 18 infants (gestational age 30–33 weeks) during the first 10 days of life. An autocorrelation analysis was conducted to evaluate the ‘EELV memory’, i.e. the impact of the characteristics of one breath on the following breaths. Results: In infants without respiratory symptoms, EELV variability was high, with large standard deviations of EELV. Autocorrelation was found to be significant until a median lag of 7 (interquartiles: 4–8) breaths. Autocorrelation was markedly prolonged in patients with respiratory distress or ventilatory support, with a higher number of breath lags with significant autocorrelation (p < 0.01) and higher autocorrelation coefficients (p < 0.05). Conventional assisted ventilation does not re-establish a healthy EELV profile and is associated with lower respiratory variability. Conclusions: In premature infants, EELV variability pattern is modified by respiratory distress with a prolonged ‘EELV memory’, which suggests a greater instability of the control of EELV.

Postural breathing pattern changes in patients with myotonic dystrophy.

We recorded by pneumotachography the breathing in nine patients with myotonic dystrophy (MD), both seated and supine and with eyes open in both positions. Irregular breathing (coefficient of variation >20% for VT and TTOT) was observed in six of the patients, two of whom showed irregularity in both positions whilst the remaining four had irregular breathing only when supine. In addition, in this latter group, irregularities first appeared in VT and only after a few minutes in TTOT. Whereas in the group exhibiting irregular breathing in both seated and supine positions, irregularities were observed throughout the recording. However, no significant difference in any ventilatory variable was observed as between the two postures. Rib cage (RC) and abdomen (AB) motions were recorded by uncalibrated respiratory inductance plethysmography. Although for MD patients the mean values of the RC/AB ratio lay within the normal range the relative decrease in value as between seated (0.78+/-0.52) and supine (0.31+/-0.13) position was less than in healthy subjects. These observations suggest that MD may cause deficiencies in several mechanisms. Analyses of the respiratory pattern in each patient may provide information leading to the identification of the impaired respiratory mechanisms.

Respiratory Inductance Plethysmography is suitable for voluntary hyperventilation test

The aim of this work was to evaluate the goodness of fit of a signal issued of the respiratory inductance plethysmography (RIP) derivative to the airflow signal during rest, voluntary hyperventilation, and recovery. RIP derivative signal was filtered with an adjusted filter based on each subject airflow signal (pneumotachography). For each subject and for each condition (rest, voluntary hyperventilation, and recovery) comparisons were performed between the airflow signal and the RIP derivative signal filtered with an adjusted filter obtained either on rest signal or on the studied part of the signals (voluntary hyperventilation or recovery). Results show that the goodness of fit was : (1) higher than 90 % at almost all comparisons (122 on 132), (2) not improved by applying an adjusted filter obtained on the studied part of the signals. These results suggest that RIP could be used for studying breathing during voluntary hyperventilation and recovery using adjusted filters obtained from comparison to airflow signal at rest.

Can Cardiogenic Oscillations Provide an Estimate of Chest Wall Mechanics

Every time the heart beats, it produces a mechanical deformation of the lungs causing small fluctuations of airway pressure and flow called cardiogenic oscillations (CO). CO have been observed on respiratory signals during pulmonary function tests, during relaxed expiration as well as during apnea, as a mean for differentiating central and obstructive apneas1. Finally, we have recently shown that the processing of CO in mouth pressure and airflow can be used as a non-invasive measurement of airway resistance2.

Effects of Resistive Loading on Breathing Variability

The ventilatory system as many biological systems is a complex dynamical one involving many controls and regulations. More over, in a reconstructed phase space respiratory data present a structure similar to a strange attractor (cf. Figure 1). In this way, considering the respiratory system as a deterministic chaotic one seems to be a good hypothesis to understand breathing and its variability. This hypothesis remains controversial. Donaldson (1992) shows that respiratory trajectories of resting human are not random but chaotic using the Largest Lyapunov Exponent (LLE). Unfortunately, Hughson et al. (1995) said that previous suggestion of Donaldson (1992) that human respiratory pattern was chaotic appears from the outcome of surrogate data analysis in their study to have been premature. Similarly, Fortrat et al. (1997), in human resting breathing could not conclude that ventilation derive from a deterministic chaotic system. On the contrary, Sammon et al. (1991, 1993a, 1993b) show that irregular inspiratory-expiratory phase switching and central respiratory pattern generator (CRPG) output in rats are consistent with low-dimensional chaos. Small et al. (1999) bring evidence that respiratory variability in infants during quiet sleep is deterministic and not random. Starting from the hypothesis that controlling the presence of chaos by changing a parameter of a system brings further evidence of the chaotic nature of the system we tried to modify, experimentally and on simulations, the chaotic dimension of the ventilatory system by changing the resistive load in resting breathing human.

Thorax and Abdomen Motion Analysis in Patients with Obstructive Diseases

Objective: We evaluated changes in bronchoconstriction by a new approach based on respiratory inductive plethysmography (RIP) signal analysis. Methods: Thoracic and abdominal motions were recorded (5 min) by uncalibrated RIP in 44 adult subjects with a diagnosis of moderate bronchial obstruction (Obstructive group) and 50 healthy adult controls (Healthy group). In the Obstructive group, two series of measurements were performed before (Obstructive PRE) and after (Obstructive POST) a bronchodilation protocol. Airway resistance (Raw) and lung function data (forced vital capacity (FVC), forced expiratory volume in one second (FEV1 ) and FEV1 /FVC) were measured with a body plethysmograph. A breath-bybreath analysis was performed to calculate distances between normalized thorax and abdomen RIP signals and a mean distance (D) was calculated for each recording. Results: D and Raw were higher in the Obstructive group than in the Healthy group in both PRE and POST conditions. Both D and Raw significantly decreased after bronchodilation in the Obstructive group. D and Raw were also positively and significantly correlated in the Obstructive group in both PRE and POST conditions. Conclusion: D, as calculated from signals recorded by RIP, appears to be a useful non-invasive parameter for continuous monitoring of changes in bronchoconstriction.