Variability analysis of the respiratory volume based on non-linear prediction methods

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.

Nonlinear dynamic analysis of the cardiorespiratory system in patients undergoing the weaning process

In this work, the cardiorespiratory pattern of patients undergoing extubation process is studied. First, the respiratory and cardiac signals were resampled, next the Symbolic Dynamics (SD) technique was implemented, followed of a dimensionality reduction applying Forward Selection (FS) and Moving Window with Variance Analysis (MWVA) methods. Finally, the Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM) classifiers were used. The study analyzed 153 patients undergoing weaning process, classified into 3 groups: Successful Group (SG: 94 patients), Failed Group (FG: 39 patients), and patients who had been successful during the extubation and had to be reintubated before 48 hours, Reintubated Group (RG: 21 patients). According to the results, the best classification present an accuracy higher than 88.98 ± 0.013% in all proposed combinations.

Significance of using a nonlinear analysis technique, the Lyapunov exponent, on the understanding of the dynamics of the cardiorespiratory system in rats

BACKGROUND/AIM

Pneumocardiography (PNCG) is the recording method of cardiac-induced tracheal air flow and pressure pulsations in the respiratory airways. PNCG signals reflect both the lung and heart actions and could be accurately recorded in spontaneously breathing anesthetized rats. Nonlinear analysis methods, including the Lyapunov exponent, can be used to explain the biological dynamics of systems such as the cardiorespiratory system.

MATERIALS AND METHODS

In this study, we recorded tracheal air flow signals, including PNCG signals, from 3 representative anesthetized rats and analyzed the nonlinear behavior of these complex signals using Lyapunov exponents.

RESULTS

Lyapunov exponents may also be used to determine the normal and pathological structure of biological systems. If the signals have at least one positive Lyapunov exponent, the signals reflect chaotic activity, as seen in PNCG signals in rats; the largest Lyapunov exponents of the signals of the healthy rats were greater than zero in this study.

CONCLUSION

A method was proposed to determine the diagnostic and prognostic values of the cardiorespiratory system of rats using the arrangement of the PNCG and Lyapunov exponents, which may be monitored as vitality indicators.

Complex dynamics of semantic memory access in reading

Understanding a word in context relies on a cascade of perceptual and conceptual processes, starting with modality-specific input decoding, and leading to the unification of the word's meaning into a discourse model. One critical cognitive event, turning a sensory stimulus into a meaningful linguistic sign, is the access of a semantic representation from memory. Little is known about the changes that activating a word's meaning brings about in cortical dynamics. We recorded the electroencephalogram (EEG) while participants read sentences that could contain a contextually unexpected word, such as 'cold' in 'In July it is very cold outside'. We reconstructed trajectories in phase space from single-trial EEG time series, and we applied three nonlinear measures of predictability and complexity to each side of the semantic access boundary, estimated as the onset time of the N400 effect evoked by critical words. Relative to controls, unexpected words were associated with larger prediction errors preceding the onset of the N400. Accessing the meaning of such words produced a phase transition to lower entropy states, in which cortical processing becomes more predictable and more regular. Our study sheds new light on the dynamics of information flow through interfaces between sensory and memory systems during language processing.

Coordination of respiratory muscles assessed by means of nonlinear forecasting of demodulated myographic signals

Pulmonary diseases such as obstructive sleep apnea syndrome (OSAS) affect function of respiratory muscles. Individuals with OSAS suffer intermittent collapse of the upper airways during sleep due to unbalanced forces generated by the contraction of the diaphragm and upper airway dilator muscles. Respiratory rhythm and pattern generation can be described via nonlinear or coupled oscillators; therefore, the resulting activation of different respiratory muscles may be related to complex nonlinear interactions. The aims of this work were: to evaluate locally linear models for fitting and prediction of demodulated myographic signals from respiratory muscles; and to analyze quantitatively the influence of a pulmonary disease on this nonlinear forecasting related to low and moderate levels of respiratory effort. Electromyographic and mechanomyographic signals from three respiratory muscles (genioglossus, sternomastoid and diaphragm) were recorded in OSAS patients and controls while awake during an increased respiratory effort. Variables related to auto and cross prediction between muscles were calculated from the r(2) coefficient and the estimation of residuals, as functions of prediction horizon. In general, prediction improved linearly with higher levels of effort. A better prediction between muscle activities was obtained in OSAS patients when using genioglossus as the predictor signal. The prediction was significant for more than two respiratory cycles in OSAS patients compared to only a half cycle in controls. It could be concluded that nonlinear forecasting applied to genioglossus coupling with other muscles provides a promising assessment to monitor pulmonary diseases.

Study of multiparameter respiratory pattern complexity in surgical critically ill patients during weaning trials

Background

Separation from mechanical ventilation is a difficult task, whereas conventional predictive indices have not been proven accurate enough, so far. A few studies have explored changes of breathing pattern variability for weaning outcome prediction, with conflicting results. In this study, we tried to assess respiratory complexity during weaning trials, using different non-linear methods derived from theory of complex systems, in a cohort of surgical critically ill patients.

Results

Thirty two patients were enrolled in the study. There were 22 who passed and 10 who failed a weaning trial. Tidal volume and mean inspiratory flow were analyzed for 10 minutes during two phases: 1. pressure support (PS) ventilation (15-20 cm H₂O) and 2. weaning trials with PS: 5 cm H₂O. Sample entropy (SampEn), detrended fluctuation analysis (DFA) exponent, fractal dimension (FD) and largest lyapunov exponents (LLE) of the two respiratory parameters were computed in all patients and during the two phases of PS. Weaning failure patients exhibited significantly decreased respiratory pattern complexity, reflected in reduced sample entropy and lyapunov exponents and increased DFA exponents of respiratory flow time series, compared to weaning success subjects (p < 0.001). In addition, their changes were opposite between the two phases of the weaning trials. A new model including rapid shallow breathing index (RSBI), its product with airway occlusion pressure at 0.1 sec (P_0.1), SampEn and LLE predicted better weaning outcome compared with RSBI, P_0.1 and RSBI* P_0.1 (conventional model, R² = 0.874 vs 0.643, p < 0.001). Areas under the curve were 0.916 vs 0.831, respectively (p < 0.05).

Conclusions

We suggest that complexity analysis of respiratory signals can assess inherent breathing pattern dynamics and has increased prognostic impact upon weaning outcome in surgical patients.

Lower interbreath interval complexity is associated with extubation failure in mechanically ventilated patients during spontaneous breathing trials

OBJECTIVE

To determine whether lower complexity of interbreath interval as measured with nonlinear analysis techniques will identify patients who fail to separate from mechanical ventilation after 30-minute spontaneous breathing trials (SBTs).

METHODS

Respiratory waveforms from SBT of patients in surgical or burn intensive care units were recorded for later analysis. The decision to extubate was made by attending physician. Extubated patients were observed for 48 hours; during this time, reintubation or noninvasive positive pressure ventilation was considered as a failure. Analysis of waveform data by software was performed post hoc. Sample entropy (SampEn) and other nonlinear measures were 48 hours of extubation.

RESULTS

Thirty-two patients (24 burn, 8 trauma/surgical admissions; mean age, 40.2 +/- 16.9 years; 26 men and 6 women) who were intubated >24 hours were extubated after SBT. Twenty-four patients were successfully separated from mechanical ventilation and eight failed. Age, gender, and mechanism of injury did not influence outcome. SampEn calculated for the two groups presented in this study was different with the cohort that failed extubation having a lower mean value (1.35 +/- 0.39 vs. 1.87 +/- 0.27; p < 0.001). Other nonlinear metrics were moved in concert with SampEn. The stationarity in the respiratory signal was not different between groups.

CONCLUSION

In intubated patients, the interbreath interval in those who were successfully separated from mechanical ventilation was more irregular than those who failed, as measured by nonlinear techniques. When available at bedside, these metrics may be useful markers of pulmonary health and assist in clinical decision making.

Biosignal analysis techniques for weaning outcome assessment

Discontinuation of mechanical ventilation in critically ill patients is a challenging task and involves a careful weighting of the benefits of early extubation and the risks of premature spontaneous breathing trial. Recently, apart from studying different physiological variables by means of descriptive statistical tests, breathing pattern variability analysis has been performed for the assessment of weaning readiness. A limited number of clinical studies implementing different weaning protocols in heterogeneous groups of patients and using a variable set of signal processing techniques have appeared in the critical care literature, with varying results. The purpose of this review article is 3-fold: (1) to describe the different signal processing techniques being implemented for the assessment of weaning readiness, (2) to provide insight into the pathophysiological mechanisms that may govern breath-to-breath variability/complexity in health and disease, and (3) to present results from the critical care literature derived from the application of biosignal analysis tools for the identification of possible weaning indices.

Data mining of patients on weaning trials from mechanical ventilation using cluster analysis and neural networks

The process of weaning from mechanical ventilation is one of the challenges in intensive care. 149 patients under extubation process (T-tube test) were studied: 88 patients with successful trials (group S), 38 patients who failed to maintain spontaneous breathing and were reconnected (group F), and 23 patients with successful test but that had to be reintubated before 48 hours (group R). Each patient was characterized using 8 time series and 6 statistics extracted from respiratory and cardiac signals. A moving window statistical analysis was applied obtaining for each patient a sequence of patterns of 48 features. Applying a cluster analysis two groups with the majority dataset were obtained. Neural networks were applied to discriminate between patients from groups S, F and R. The best performance obtained was 84.0% of well classified patients using a linear perceptron trained with a feature selection procedure (that selected 19 of the 48 features) and taking as input the main cluster centroid. However, the classification baseline 69.8% could not be improved when using the original set of patterns instead of the centroids to classify the patients.

Patients on weaning trials from mechanical ventilation classified with neural networks and feature selection

One of the challenges in intensive care is the process of weaning from mechanical ventilation. We studied the differences in respiratory pattern variability between patients capable of maintaining spontaneous breathing during weaning trials and patients that fail to maintain spontaneous breathing. In this work, neural networks were applied to study these differences. 64 patients from mechanical ventilation are studied: Group S with 32 patients with Successful trials and Group F with 32 patients that Failed to maintain spontaneous breathing and were reconnected. A performance of 64.56% of well classified patients was obtained using a neural network trained with the whole set of 35 features. After the application of a feature selection procedure (backward selection) 84.56% was obtained using only 8 of the 35 features.

Study of the respiratory pattern variability in patients during weaning trials

Mechanical ventilators are used to provide life support in patients with respiratory failure. One of the challenges in intensive care is the process of weaning from mechanical ventilation. We studied the differences in respiratory pattern variability between patients capable of maintaining spontaneous breathing during weaning trials and patients that fail to maintain spontaneous breathing. The respiratory pattern was characterized by the following time series: inspiratory time (T(I)), expiratory time (T(E)), breath duration (T(Tot)), tidal volume (V(T)), fractional inspiratory time (T(I)/T(Tot)), mean inspiratory flow (V(T)/T(I)), respiratory frequency (f), and rapid shallow breathing index (f/V(T)). The variational activity of breathing was partitioned into autoregressive, periodic and white noise fractions. Patients with unsuccessful trial presented a tendency to higher values of gross variability of V(T)/T(I) and f/V(T), and lower values of T(I). The autocorrelation coefficients tended to present higher values for T(I), T(I)/T(Tot) and V(T)/T(I). During both successful and unsuccessful T-tube test uncorrelated random behavior constituted > 75% of the variance of each time breath components and represented 50 to 70% in the breath component related to V(T). Correlated behavior represented 6 to 21% in time components and 28 to 50% in component related to V(T).

Sensitively recorded breathing signals of rats and their nonlinear dynamics

Nonlinear dynamical properties of sensitively recorded breathing signals (SRBS), which include cardiac induced air flow pulsations so-called pneumocardiogram (PNCG) signals, are investigated, in this methodological study. For this purpose, we assessed the SRBS of laboratory rat. The nonlinear behaviors of SRBS were investigated by the reconstructing phase space, using the autocorrelation function and the false nearest neighbor method. The chaotic SRBS attractors were discussed from the point of view of the cardiopulmonary system. This method can be used to assess the heart performance and respiratory mechanics, and might be useful to design for the physiological studies of cardiorespiratory system in small laboratory animals.

Reduced breathing variability as a predictor of unsuccessful patient separation from mechanical ventilation

OBJECTIVES

To compare descriptors of the breath-to-breath respiratory variability during a 60-min spontaneous breathing trial in patients successfully and unsuccessfully separated from the ventilator and the endotracheal tube and to assess the usefulness of these predictors in discriminating these two categories of patients.

DESIGN

Prospective observational study.

SETTING

Four general intensive care units in university hospitals.

PATIENTS

A total of 51 consecutive patients mechanically ventilated for >24 hrs.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Tidal volume, respiratory period, inspiratory time, expiratory time, mean inspiratory flow (tidal volume/inspiratory time), and duty cycle (inspiratory time/respiratory period) were obtained from the flow signal. Breath-by-breath variability was expressed in terms of their coefficients of variation (CV), the number of breaths among which a significant correlation was found (lag), and the autocorrelation coefficient between one breath and the following one. Five patients were excluded because of nonstationarity of the data, leaving 46 cases for analysis. Between-group comparison was conducted with the Mann-Whitney test, and a nonparametric classification and regression tree was used to identify variables discriminating "success" (n = 32) and "failure" patients (n = 14). All coefficients of variation were significantly higher in success patients, who also exhibited significantly less respiratory autocorrelation (shorter "short memory"). The classification and regression tree analysis allocated all success patients to a group defined by a coefficient of variation of tidal volume/inspiratory time of > or =19% and a coefficient of variation of inspiratory time/respiratory period of > or =10% that did not contain any failure patient. All failure patients belonged to a group with coefficient of variation of tidal volume/inspiratory time of <19%, a lag tidal volume of > or =11, and that contained no success patient.

CONCLUSIONS

In intensive care unit patients undergoing a spontaneous breathing trial, breathing variability is greater in patients successfully separated from the ventilator and the endotracheal tube. Variability indices are sufficient to separate success from failure cases.