**7. Conclusions**

According to Macklem [90], there is a continuum of thermodynamic systems that do not follow the 2nd thermodynamic axiom (increase in entropy), since they exchange energy with their environment (open systems): from near to equilibrium, like crystals to far from equilibrium systems, like the weather. 'The amount of energy dissipated determines where the system is situated along the continuum. Between the crystals and weather a sudden phase transition occurs over a small range of energy consumption. It is only there where life can flourish'. From the above statement it is concluded that either a decrease in energy consumption, i.e., during myocardial ischemia, or an increase may both contribute to a shift away from stable state. It has been shown by Que [60] that during asthma, the local increase in metabolic rate is associated with increased variability of respiratory impedance to flow. The same author has proposed the term 'homeokinesis' instead of homeostasis [60], as a basic property of living systems that 'describes the ability of an organism to utilize external energy sources to maintain a highly organized internal environment fluctuating within acceptable limits in a far from equilibrium state'.

72 Practical Applications in Biomedical Engineering

occurred to the FW group.

weaning success patients.

**7. Conclusions** 

been shown by Sammon and Bruce [40].

**Figure 5.** Distribution of LLE between the two groups of patients (S: successful weaning and U: unsuccessful), during high (H) and low (L) pressure support ventilation. It seems that SW group demonstrated an increase in LLE after shifting to spontaneous breathing, contrary to what have

In another study, Mangin and colleagues [88] investigated ventilatory chaotic dynamics in 17 mechanically ventilated patients during switching the ventilator from the assist-control mode to pressure support mode. They were able to show that both fractal dimension and LLE were increased, particularly in 5 patients who were successfully extubated. Furthermore, the authors supposed that increased breathing complexity may also be attributed to higher vagal afferent feedback during unassisted breathing, as has already

These studies support our findings that transition between mechanical and spontaneous ventilation is associated with increased complexity of respiratory signals in weaning success patients, since duration of ventilation before the SBTs was similar between groups with different weaning outcome. Moreover, in a study of Burykin and Buchman [89] investigating cardiorespiratory dynamics and synchronization during controlled and unassisted breathing in 13 surgical patients, it was demonstrated that mechanical ventilation reduces significantly both heart and respiratory rate complexity whereas spontaneous respiration is more irregular with increased uncoupling of cardiorespiratory rhythms in

According to Macklem [90], there is a continuum of thermodynamic systems that do not follow the 2nd thermodynamic axiom (increase in entropy), since they exchange energy with There is good basic science describing the physiological and fractal variability observed in respiratory dynamics and substantial evidence that conditions prevalent in the ICU impacts breath-to-breath variability. Different clinical studies have assessed the ability of markers of respiratory variability as predictors of successful separation from mechanical ventilation. However, these studies are not comparable since the authors implemented different weaning protocols in heterogeneous groups of patients and examined various indices of both breathing pattern variability (linear) and complexity (nonlinear) that by their nature capture different aspects of signal dynamics. It seems that the study of Wysocki [78] performed better, since it was conducted in 5 medical centres, stationarity tests were done and possible bias in the time series analysis was significantly controlled. Furthermore, lack of ventilatory support during weaning trial could have eliminated any possible impact of positive pressure ventilation upon breathing variability and could be responsible for the different findings between this and the first two studies. Unfortunately, the authors did not perform complexity nonlinear analysis. Since neither ApEn, nor any other complexity index was calculated, we cannot make assumptions regarding inherent breathing dynamics during discontinuation from mechanical ventilation, because variability and complexity may change in opposite directions.

We suggest that new studies are needed, for estimating both linear and non-linear indices of breathing variability and complexity for weaning outcome assessment, in different and homogeneous groups of patients, and comparing their diagnostic accuracy, along with other traditional and already used physiological predictors. Their association, in terms of 'coupling' or 'uncoupling' with other biosignals such as the electroencephalogram (EEG), with the aid of robust methods from signal processing theory (i.e., cross-ApEn, coherence etc) could uncover possible inter-relations between breathing and central nervous system dynamics during weaning trials.

However, we think that the development of a consensus between clinicians and modelers is urgently needed for the assessment of different methods used for breathing pattern variability prior to the performance of other studies. In the cardiology literature, the Task Force on HRV has standardized the use of a set of signal processing techniques, including target variables, frequency and duration of measurements and signal quality assessment,

aiding in the development of better and more accurate diagnostic or prognostic indices in cardiovascular diseases [16]. Furthermore, many important questions remain unanswered such as the value of single or longitudinal variability measurements for clinically useful information or its prognostic capability for risk stratification between patients or within the same patient (trend analysis).

Fractal Physiology, Breath-to-Breath Variability and Respiratory Diseases:

An Introduction to Complex Systems Theory Application in Pulmonary and Critical Care Medicine 75

Pimax: maximal inspiratory pressure P0.1: airway occlusion pressure at 0.1 sec

RSBI: rapid shallow breathing index

SIMV: synchronized intermittent mandatory ventilation SIRS: systemic inflammatory response syndrome

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[3] Godin PJ, Buchman TG. Uncoupling of biological oscillators: A complementary hypothesis concerning the pathogenesis of multiple organ dysfunction syndrome. Crit

[4] Kennedy H. Heart rate variability - a potential, noninvasive prognostic index in the

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SBT: spontaneous breathing trial

Pre-Botz: pre-Botzinger complex PSD: power spectrum density PSV: pressure support ventilation ROC: receiver operating characteristic

RR: respiratory rate

SampEn: sample entropy

SD: standard deviation

SW: successful weaning Te: expiratory time Ti: inspiratory time Ttot: total breath duration

VT: tidal volume

**Author details** 

**8. References** 

 \*

Corresponding Author

Vasilios Papaioannou\*

In conclusion, we believe that the most important aspect of research on breathing pattern dynamics remains the development of international databases of various respiratory signals, with free access from different investigators, such as the Web site Physionet (www.physionet.org), which has boosted research on heart rate dynamics in cardio-vascular medicine [91]. We also think that in addition, such efforts must also be undertaken by pulmonary physicians and those who treat patients with severe respiratory diseases. In conclusion, we suggest that implementation of fractal analysis and breathing pattern variability and complexity measurements in respiratory physiology will result in better understanding of different and dynamic changes during various pathologic states, whereas its impact upon prediction of severe complications will add value to such methods. Clinicians must begin to understand basic principles of complex systems theory and support an interdisciplinary approach for understanding pulmonary diseases, in order to treat their patients more effectively.
