This Ph.D. dissertation processes and analyzes signals from the neonatal intensive care units (NICUs) for the study of maturity, systemic infection (sepsis) and the influence of immunization in the premature newborn. A special attention is payed to the electroencephalography and the breathing signal. The former is often contaminated by several sources of noise, thus methods based on the signals decomposition and optimal noise cancellation, adapted to the characteristics of the immature EEG, were proposed and evaluated objectively on real and simulated signals. By means of the EEG and delta burst analysis, detected automatically by a proposed classifier, infant's maturation and the effects of vaccination are studied. Concerning the second signal, breathing, non-linear and fractal methods are adapted to evaluate maturity and sepsis. A robustness study of estimation methods is also conducted, showing that the Hurst exponent, estimated on respiratory variability signals, is a good detector of infection.
