AbstractsMedical & Health Science

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

by Ying Zhang

Institution: University of New South Wales
Department: UNSW
Year: 2014
Keywords: signal processing; Preterm infants; Clinical risks
Record ID: 1059511
Full text PDF: http://handle.unsw.edu.au/1959.4/53947


Preterm infants with very low birth weight (VLBW; birth weight ≤1500 g) suffer from a high risk of intra-ventricular hemorrhage (IVH) and other serious diseases. Even for those VLBW infants who survive, they still have a strong possibility of developing long-term neurodevelopmental disabilities. To improve the clinical risk assessment of preterm infants and develop potential clinical makers for the adverse outcome in preterm infants, the first part of the thesis develops the frequency spectral analysis on the non-invasively measured heart rate variability (HRV), blood pressure variability (BPV) and cerebral near-infrared spectroscopy (NIRS) measures. Moderate and high correlations with the clinical risk index for babies (CRIB II) were identified from various spectral measures of arterial baroreflex and cerebral autoregulation functions which provided further justification for these measures as possible markers of clinical risks and predictors of adverse outcome in preterm infants. It was also observed that the cross-spectral transfer function analysis of cerebral NIRS and arterial blood pressure (ABP) was able to provide a number of parameters that were potentially useful for distinguishing preterm infants with IVH from without IVH. Furthermore, the detrended fluctuation analysis (DFA) that quantifies the fractal correlation properties of physiological signals has been examined, to determine whether it could derive markers for the identification of preterm infants who developed IVH. The results have demonstrated that fractal dynamics embedded in the ABP waveform could provide useful information that facilitates early identification of IVH in preterm infants. Cardiac output (CO) and total peripheral resistance (TPR) are two important parameters of the cardiovascular system. Measurement of these two II parameters can provide valuable information for the assessment and management of patients needing intensive care, including preterm infants in the neonatal intensive care unit (NICU). The results of arterial blood pressure waveform analysis demonstrate that the diastolic decay rate had a significant positive relationship with CO and negative relationship with TPR. To further assess the changes in CO and TPR in the preterm infants, the multivariate regression model based on the useful features from arterial blood pressure waveform analysis was used to improve the accuracy and robustness of the estimation. The combination of signal analysis and multivariate regression model in estimation of CO has produced some outcomes, and in the future, more effort should be involved in this kind of research to improve the prediction of serious diseases in preterm infants. Overall, this PhD research project has demonstrated that the features extracted from the cardiovascular waveforms by different signal processing methods, including frequency spectrum analysis, DFA and ABP waveform analysis, could provide potentially useful information for assessing clinical risks in the preterm infants. This represents an important step towards…