Understanding the Dynamics of State-Respiratory Interaction During Sleep

A Model-Based Approach
  • Michael C. K. Khoo


Over past decade, there has been a tremendous increase in the number of pulmonary clinics dedicated to diagnosing and treating primary sleep disorders. An important contributing factor to this phenomenal expansion of the field of sleep medicine is the recognition of the need to better understand the effects of sleep on respiratory control, since it is during sleep that potentially harmful apneas or hypopneas occur in apparently normal individuals. Previous studies have shown that sleep onset leads to a reduction in metabolic rate and the baseline level of ventilation, and an elevation of circulatory delay and average Paco2 [1],[2]. These changes work together with the accompanying depression of upper airway muscle tone and chemoresponsiveness to alter the operating set-point of the respiratory control system. In the light stages of sleep, the decrease in damping may be large enough to offset the reduction in controller gain leading to a system that is more susceptible to oscillation[3],[4],[5]. Under conditions of high loop gain, spontaneous oscillations in ventilation can develop as a result of feedback instability [6]. Alternatively, because of decreased damping, continual random excitation from a variety of sources, including influences from other physiological control systems, can also produce significant periodic and nonperiodic variability in breathing 7. A number of studies have reported a close association between ventilatory oscillations and fluctuations in sleep states[8],[9],[10]. These observations suggest a third possibility: that respiratory variability during sleep may be due to primary fluctuations in state[11]. It is likely, however, that more than one of the above three mechanisms are at play in any given instance of sleep-disordered breathing (SDB). Moreover, it is probably reasonable to expect that there will be considerable interplay among the multiple factors and that these relationships will likely be complex and dynamic. Physical intuition and conventional physiological methodology are not the best tools for unraveling the mechanisms underlying such complex interactions. Under these circumstances, the rigorous framework provided by a quantitative dynamic model becomes not only useful but, as one could argue, necessary. The theoretical study of Khoo et al.[12] represented a starting point for this kind of model-based approach. However, a number of assumptions were made that did not have firm empirical bases. In this chapter, we consider potential extensions of the model, incorporating data that have been obtained in recent experimental studies. While the specific goal of this study is to enhance our current understanding of the control of breathing during sleep, an equally important objective is to highlight the utility of modeling as a means for hypothesis testing as well as a tool for extracting relevant information from highly variable and dynamic data.


Impulse Response Sleep Onset Ventilatory Response Periodic Breathing Ventilatory Oscillation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Plenum Press 1996

Authors and Affiliations

  • Michael C. K. Khoo
    • 1
  1. 1.Biomedical Engineering DepartmentUniversity of Southern CaliforniaUniversity Park, Los Angeles

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