Respiratory Pattern Variability Research Articles

Analysis of the Cardiorespiratory Pattern of Patients Undergoing Weaning Using Artificial Intelligence

The optimal extubating moment is still a challenge in clinical practice. Respiratory pattern variability analysis in patients assisted through mechanical ventilation to identify this optimal moment could contribute to this process. This work proposes the analysis of this variability using several time series obtained from the respiratory flow and electrocardiogram signals, applying techniques based on artificial intelligence. 154 patients undergoing the extubating process were classified in three groups: successful group, patients who failed during weaning process, and patients who after extubating failed before 48 hours and need to reintubated. Power Spectral Density and time-frequency domain analysis were applied, computing Discrete Wavelet Transform. A new Q index was proposed to determine the most relevant parameters and the best decomposition level to discriminate between groups. Forward selection and bidirectional techniques were implemented to reduce dimensionality. Linear Discriminant Analysis and Neural Networks methods were implemented to classify these patients. The best results in terms of accuracy were, 84.61 ± 3.1% for successful versus failure groups, 86.90 ± 1.0% for successful versus reintubated groups, and 91.62 ± 4.9% comparing the failure and reintubated groups. Parameters related to Q index and Neural Networks classification presented the best performance in the classification of these patients.

Effect of nasal airflow on respiratory pattern variability in rats

Effect of nasal airflow on respiratory pattern variability in rats

Phase synchronization as a flexible definition of the respiratory pattern: Application to pontine‐dependent control of the respiratory pattern

A distinct pattern of inspiratory, post‐inspiratory and expiratory activities that are expressed in respiratory‐modulated neurons and motor outputs during stationary breathing are the foundation of most contemporary models for respiratory rhythm generation and pattern formation. Models and experiments show that impaired synaptic inhibition in the respiratory circuitry of the brainstem is associated with neurogenic breathing disorders in Rett syndrome (inspiratory efforts against a closed glottis) or with paradoxical vocal cord movement (glottal closure during inspiration). Previously, we reported that the pontine Kölliker‐Fuse nuclei (KFn) couple the spinal and cranial motor patterns and control respiratory pattern variability. Here, we test the hypothesis that the KFn is critical for expression of the phase relationships among respiratory motor activities. In arterially‐perfused brainstem preparations (n=7), we recorded respiratory activity from the hypoglossal, cervical vagal, phrenic and iliohypogastric nerves before and after bilateral bicuculline (a GABA(A)R antagonist, 50nl, 10mM) microinjections in the KFn. Disinhibition of KFn increased the variability of the respiratory rhythm (coefficient of variation (CV) of cycle duration (TTOT before, 0.10 ± 0.01 versus after bicuculline 0.50 ± 0.26, p<0.01). The increase in variability was associated with unpredictable timing of phase durations and transitions. Standard metrics such as TTOT failed to quantitatively describe the complexity in the changes of the respiratory motor pattern. Here, we introduce a quantitative definition of the respiratory motor pattern based on the phase synchronization of respiratory motor outputs or its underlying neuronal activities. Power spectral analysis revealed that the respiratory motor activities had a periodicity indicating that a coherent respiratory‐pattern persisted. Quantifying phase synchronization of respiratory motor outputs revealed a significant reduction in coupling strengths, without a change in their relative phase relationships (mean relative phase at baseline, 1.16 ± 0.07 rad versus after disinhibition, 1.17 ± 0.05 rad; and phase coherence at baseline, 0.87 ± 0.01 versus after disinhibition, 0.31 ± 0.03, p=3.68×10−4). Our data further support the concept that respiratory pattern formation requires reciprocal interaction between pontine and medullary respiratory groups. Focal pontine disinhibition resulted in reduced coupling strength between ponto‐medullary synaptic interactions that consequently disrupted the motor pattern, while the undisturbed medullary aspects are able to uphold the periodicity of respiration. Taken together, our results illustrate that medullary respiratory rhythm generating circuit require the pons to convert its periodicity into a viable and vital respiratory motor pattern.Support or Funding InformationSupported by a grant from the Australian Research Council.This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Nonlinear methods of analysis to examine respiratory waveform variability during oral feeding in preterm infants

In a novel exploratory study, nonlinear methods of analysis for respiratory patterns were used to examine respiratory waveform variability during oral feeding in preterm infants. Twelve healthy preterm infants were studied using a within-subject cross over design to compare semi-upright and the elevated sidelying position during bottle feeding. Outcome measures were physiologic indicators of cardiopulmonary status: heart rate, respiratory rate, oxygen saturation and respiratory pattern variability. Preliminary results suggest that analysis of respiratory waveform variability is feasible and may be a more sensitive measure of cardiopulmonary physiology than means for heart rate, respiratory rate and percent haemoglobin oxygen saturation. Nonlinear methods of analysis of the respiratory data provide a distinct perspective on the adaptability of infants' breathing patterns while feeding. The clinical application of monitoring respiratory waveforms using nonlinear methods may be helpful in identifying readiness to initiate and advance feeds orally in preterm infants.

Neonatal gender contributes to respiratory pattern variability re‐establishing stable rhythmogenesis following hypoxia

Gender affects many aspects of neuronal control and has been implicated in the etiology of SIDS because males are more likely to die of SIDS than females. This study tests the hypothesis that gender influences neonatal respiratory patterns following hypoxia. Experiments in CD1 mice (P10–13days) reveal that recovery from peak post‐hypoxia depression of respiratory rate is significantly delayed in males. Recordings from brainstem slices containing the preBötzinger complex (preBötC) show that male rhythmogenesis recovers slower from hypoxia compared to female rhythmogenesis. Recovery time in males correlated to baseline respiratory frequency before hypoxia, which was not the case in females. We employed computational modeling to investigate the underlying mechanisms. Based on multi‐array and intracellular recordings from spontaneously active respiratory networks, we modeled the preBötC as a sparsely connected network containing different types of respiratory neurons including, various pacemaker, tonically active and silent neurons. Our modeling indicates that different neuronal types have different recovery profiles, and that recovery time and frequency dependency are dependent on network composition. This approach allows us to make specific predictions which neurons or neuronal properties could explain observed gender differences. Our data indicate that even when intrinsic membrane properties of individual neurons of the population are conserved the observed dimorphic differences could be explained by differences in network composition. Ongoing experiments aim at testing these hypotheses in the respiratory network.

The variability of pulmonary gas exchange and respiratory pattern

Very-low-frequency (VLF) fluctuations, whose nature is probably determined by rhythms of energy processes, are known to determine the variability of respiratory and heart rates. It is still unclear to which type of wave processes (chaotic or regular) these rhythm patterns belong. The goal of this study was to investigate the rhythms of pulmonary gas exchange and the variability of the respiratory pattern, as well as to find their possible relation. To analyze the variability of ventilation indices in the VLF band, pneumograms were recorded for 30 min and then the pulmonary gas exchange indices (Ve, pulmonary ventilation; \(V_{O_2 }\), oxygen consumption; \(V_{CO_2 }\), carbon dioxide release) were recorded for 30 min using the breath-by-breath method in ten healthy subjects. Spectral analysis carried out using the fast Fourier transform revealed two groups of major peaks: the first one was in the range from 0.2 to 0.3 Hz (the time interval of 3–5 s), which was in good agreement with the respiratory rate varied from 12 to 20 per min in tested subjects; the second was from 0.002 to 0.0075 Hz, which corresponded to the VLF band. The data make it possible to draw a conclusion about the stability of the wave processes found. Apparently, the slow-wave pattern of the pulmonary gas exchange indices belongs to the quasi-periodic oscillation type, reflecting synchronization of oscillators with incommensurable frequencies when the two-frequency pattern dominates. The first oscillator is the chemoreceptor mechanism of the regulation of ventilation, the nature of the second one is still unclear. Taking into consideration that \(V_{O_2 }\) and \(V_{CO_2 }\) depend on energy demand, one can suppose that energy processes form (an)other oscillator(s) of periodic processes.

Vagal-dependent nonlinear variability in the respiratory pattern of anesthetized, spontaneously breathing rats

Physiological rhythms, including respiration, exhibit endogenous variability associated with health, and deviations from this are associated with disease. Specific changes in the linear and nonlinear sources of breathing variability have not been investigated. In this study, we used information theory-based techniques, combined with surrogate data testing, to quantify and characterize the vagal-dependent nonlinear pattern variability in urethane-anesthetized, spontaneously breathing adult rats. Surrogate data sets preserved the amplitude distribution and linear correlations of the original data set, but nonlinear correlation structure in the data was removed. Differences in mutual information and sample entropy between original and surrogate data sets indicated the presence of deterministic nonlinear or stochastic non-Gaussian variability. With vagi intact (n = 11), the respiratory cycle exhibited significant nonlinear behavior in templates of points separated by time delays ranging from one sample to one cycle length. After vagotomy (n = 6), even though nonlinear variability was reduced significantly, nonlinear properties were still evident at various time delays. Nonlinear deterministic variability did not change further after subsequent bilateral microinjection of MK-801, an N-methyl-D-aspartate receptor antagonist, in the Kölliker-Fuse nuclei. Reversing the sequence (n = 5), blocking N-methyl-D-aspartate receptors bilaterally in the dorsolateral pons significantly decreased nonlinear variability in the respiratory pattern, even with the vagi intact, and subsequent vagotomy did not change nonlinear variability. Thus both vagal and dorsolateral pontine influences contribute to nonlinear respiratory pattern variability. Furthermore, breathing dynamics of the intact system are mutually dependent on vagal and pontine sources of nonlinear complexity. Understanding the structure and modulation of variability provides insight into disease effects on respiratory patterning.

Respiratory variability in mechanically ventilated patients

Increased respiratory pattern variability is associated with improved oxygenation. Pressure support (PS) is a widely used partial-assist mechanical ventilation (MV) mode, in which each breathing cycle is initiated by flow or pressure variation at the airway due to patient inspiratory effort. Neurally adjusted ventilatory assist (NAVA) is relatively new and uses the electrical activity of the diaphragm (Eadi) to deliver ventilatory support proportional to the patient's inspiratory demand. We hypothesize that respiratory variability should be greater with NAVA compared with PS.

An optical flow based method for improved reconstruction of 4D CT data sets acquired during free breathing

Respiratory motion degrades anatomic position reproducibility and leads to issues affecting image acquisition, treatment planning, and radiation delivery. Four-dimensional (4D) computer tomography (CT) image acquisition can be used to measure the impact of organ motion and to explicitly account for respiratory motion during treatment planning and radiation delivery. Modern CT scanners can only scan a limited region of the body simultaneously and patients have to be scanned in segments consisting of multiple slices. A respiratory signal (spirometer signal or surface tracking) is used to reconstruct a 4D data set by sorting the CT scans according to the couch position and signal coherence with predefined respiratory phases. But artifacts can occur if there are no acquired data segments for exactly the same respiratory state for all couch positions. These artifacts are caused by device-dependent limitations of gantry rotation, image reconstruction times and by the variability of the patient's respiratory pattern. In this paper an optical flow based method for improved reconstruction of 4D CT data sets from multislice CT scans is presented. The optical flow between scans at neighboring respiratory states is estimated by a non-linear registration method. The calculated velocity field is then used to reconstruct a 4D CT data set by interpolating data at exactly the predefined respiratory phase. Our reconstruction method is compared with the usually used reconstruction based on amplitude sorting. The procedures described were applied to reconstruct 4D CT data sets for four cancer patients and a qualitative and quantitative evaluation of the optical flow based reconstruction method was performed. Evaluation results show a relevant reduction of reconstruction artifacts by our technique. The reconstructed 4D data sets were used to quantify organ displacements and to visualize the abdominothoracic organ motion.

Weaning From Mechanical Ventilation: A Retrospective Analysis Leading to a Multimodal Perspective

Practitioners' decision for mechanical aid discontinuation is a challenging task that involves a complete knowledge of a great number of clinical parameters, as well as its evolution in time. Recently, an increasing interest on respiratory pattern variability as an extubation readiness indicator has appeared. Reliable assessment of this variability involves a set of signal processing and pattern recognition techniques. This paper presents a suitability analysis of different methods used for breathing pattern complexity assessment. The contribution of this analysis is threefold: 1) to serve as a review of the state of the art on the so-called weaning problem from a signal processing point of view; 2) to provide insight into the applied processing techniques and how they fit into the problem; 3) to propose additional methods and further processing in order to improve breathing pattern regularity assessment and weaning readiness decision. Results on experimental data show that sample entropy outperforms other complexity assessment methods and that multidimensional classification does improve weaning prediction. However, the obtained performance may be objectionable for real clinical practice, a fact that paves the way for a multimodal signal processing framework, including additional high-quality signals and more reliable statistical methods.

Optimized Symbolic Dynamics Approach for the Analysis of the Respiratory Pattern

Traditional time domain techniques of data analysis are often not sufficient to characterize the complex dynamics of respiration. In this paper, the respiratory pattern variability is analyzed using symbolic dynamics. A group of 20 patients on weaning trials from mechanical ventilation are studied at two different pressure support ventilation levels, in order to obtain respiratory volume signals with different variability. Time series of inspiratory time, expiratory time, breathing duration, fractional inspiratory time, tidal volume and mean inspiratory flow are analyzed. Two different symbol alphabets, with three and four symbols, are considered to characterize the respiratory pattern variability. Assessment of the method is made using the 40 respiratory volume signals classified using clinical criteria into two classes: low variability (LV) or high variability (HV). A discriminant analysis using single indexes from symbolic dynamics has been able to classify the respiratory volume signals with an out-of-sample accuracy of 100%.

Modulation of reflex and sleep related apnea by pedunculopontine tegmental and intertrigeminal neurons

We describe and summarize here our recent findings about the role in respiration of two pontine structures that are not classically included in the pontine respiratory group: the pedunculopontine tegmental nucleus (PPT) and the intertrigeminal region (ITR). We also discuss significant contributions of other workers in the field, especially, S. Datta [Cell. Mol. Neurobiol. 17: 341-365, 1997], R. Lydic and H. Baghdoyan [Sleep, 25: 617-622, 2002], and N. Chamberlin and C. Saper [J. Neurosci. 18: 6048-6056, 1998], who postulated a role for the ITR in modulating reflex apnea. In anesthetized and freely moving rats we have consistently documented that PPT and ITR have a role in respiration. Neurochemical manipulations of each area affected the brainstem respiratory pattern generator and respiratory pattern variability,observed as spontaneous disturbances during sleep or as induced reflex apnea. Although the exact central mechanisms of apnea cannot be determined from our studies to date, we postulate that reflex and sleep-related apneas in rats share some common brainstem pathways, which may include PPT and ITR.

Estimating Respiratory Pattern Variability by Symbolic Dynamics

The traditional techniques of data analysis are often not sufficient to characterize the complex dynamics of respiration. In this study the respiratory pattern variability was analyzed using symbolic dynamics. A group of 20 patients on weaning trials from mechanical ventilation were studied at two different pressure support ventilation levels. Breath duration (T(TOT)) time series and the relation T(I)/T(TOT), that contains the influence of inspiratory time (T(I)), were considered. Length-3 words and 3 different symbols were proposed. The incidence of the overlapping tau and the parameter alpha were analyzed. From the breath duration time series, the distribution of words with probability of occurrence higher than 6% was concentrated on one word for low respiratory variability, whereas high variability was characterized by 4 words, presenting a statistically significant difference (p </= 0.0005). The probability occurrence of words "110" and "111" was also significantly different (p</= 0.0005) when comparing both variabilities. The analysis carried out obtained discriminant functions able to correctly classify all the testing set series. These results permit the consideration of symbolic dynamics as a promising methodology to study the respiratory pattern variability.

Chemoreflex and vagal afferent mechanisms enhance breath to breath variability of breathing

In anesthetized rats, vagal afferent activities activate slow central mechanisms which modulate the pattern of breathing over several breaths, giving rise to increased breath to breath variability of respiratory pattern. We hypothesized that variability in breathing pattern would produce variability in blood gases and further enhance breath to breath variability of inspired ventilation. Anesthetized rats were placed in a head-out plethysmograph and spontaneous breathing recorded during inhalation of room air and 100% oxygen. The standard deviations and coefficients of variation of ventilation were similar for both inspired gases, but the shapes of the power spectra of ventilation differed, indicating a relative increase in low-frequency power on room air in those animals exhibiting little low-frequency power on oxygen. Simple indices of variability cannot discriminate these temporal changes in breathing pattern variability.

Repeatability of measurements and sources of variability in tests of cardiovascular autonomic function.

To determine the repeatability and sources of variability of clinical tests of cardiovascular autonomic function. The commonly used electrocardiographic related tests of autonomic function were studied. Two repeat measurements of all tests were made on all subjects on four separate days over a four week period. Ten normal subjects with no known autonomic dysfunction were investigated. These were deep breathing (subject seated and supine), Valsalva manoeuvre, standing up from lying position, and normal relaxed breathing (subject supine). During the tests the electrocardiogram and respiratory pattern were recorded by computer. Beat to beat RR intervals were measured automatically from the electrocardiogram, and from these the results of the tests were calculated. Variance analysis showed significant between subject variability for all tests (p < 0.005), but some tests showed a much smaller relative within subject variability than others. Average repeatability data (within subject SD) for each test were calculated, and included deep breathing sitting (max-min) RR (46 ms), Valsalva ratio (0.17), and lying to standing RR ratio (0.11). These compare with between subject SDs of 65 ms, 0.38, and 0.13 respectively, at mean values of 305 ms, 1.92, and 1.15 respectively. The data highlighted one subject with the poorest repeatability, whose electrocardiogram turned out on closer inspection to be under atrial rather than sinus control at times. Poor repeatability in the other subjects was related to variability in the respiratory pattern, and in the deep breathing test, repeat variability was significantly correlated (r = 0.79) with variability in the respiratory amplitude (p < 0.05). Repeatability data should be available to each laboratory carrying out autonomic function tests. The data provided in this study could be used as a baseline. Poor repeatability highlights the need to re-examine the test procedures, or the test data from specific subjects. Variability of respiratory pattern is associated with poor repeatability, and so careful instructions on respiration should be given to each subject before the tests.