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Nonlinear dynamics in infant respiration

By using inductance plethysmography it is possible to obtain a non-invasive measurement of the chest and abdominal cross-sectional area. These measurements are "representative" of the instantaneous lung volume. This thesis describes an analysis of the breathing patterns of human infants during quiet sleep using techniques of nonlinear dynamical systems theory [1, 6, 7]. The purpose of this study is to determine if these techniques may be used to extend our understanding of the human respiratory system and its development during the first few months of life. Ultimately, we wish to use these techniques to detect and diagnose abnormalities and illness (such as apnea and sudden infant death syndrome) from recordings of respiratory effort during natural sleep. Previous applications of dynamical systems theory to biological systems have been primarily concerned with the estimation of dynamic invariants: correlation dimension, Lyapunov exponents, entropy and algorithmic complexity. However, estimating these numbers has not proven useful in general. The study described in this thesis focuses on building models from time-series recordings and using these models to deduce properties of the underlying dynamical system. We apply a correlation dimension estimation algorithm [2, 3] in conjunction with well known surrogate data techniques [16, 17, 18] and conclude that the respiratory system is not linear [10, 13]. To elucidate the nature of the nonlinearity within this complex system we apply a new type of radial basis modelling algorithm (cylindrical basis modelling) [4, 5, 9] and generate new nonlinear surrogate data [10, 11].