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Preterm infants with very low birth weight suffer from a high risk ofintra-ventricular hemorrhage(IVH) and other serious diseases. To improve theclinical risk assessment of preterm infants and develop potential clinicallymakers for the adverse outcome, the first part of the paper develops thefrequency spectral analysis on the non-invasively measured heart ratevariability, blood pressure variability and cerebral near-infrared spectroscopymeasures. Moderate and high correlations with the clinical risk index forbabies were identified from various spectral measures of arterial baroreflexand cerebral autoregulation functions. It was also observed that thecross-spectral transfer function analysis of cerebral NIRS and arterial bloodpressure was able to provide a number of parameters that were potentiallyuseful for distinguishing between preterm infants with or without IVH.Furthermore, the detrended fluctuation analysis that quantifies the fractalcorrelation properties of physiological signals has been examined, to determinewhether it could derive markers for the identification of preterm infants withIVH. Cardiac output(CO) and total peripheral resistance(TPR) are two importantparameters of the cardiovascular system. Measurement of these two parameterscan provide valuable information for the assessment and management of patientsneeding intensive care, including preterm infants in the neonatal intensivecare unit. To further assess the changes in CO and TPR in the preterm infants,the multivariate regression model based on the useful features from ABP methodwas used to improve the accuracy and robustness of the estimation. Thecombination of signal analysis and multivariate regression model in estimationof CO has produced some outcomes, and in the future, more effort should beinvolved in this kind of research to improve the prediction of serious diseasesin preterm infants.

Application of signal processing techniques in the assessment of clinical risks in preterm infants