Discovering hidden relationships in physiological signals for prediction of Periventricular Leukomalacia

This paper is concerned with predicting the occurrence of Periventricular Leukomalacia (PVL) using vital data which are collected over a period of twelve hours after neonatal cardiac surgery. The vital data contain heart rate (HR), mean arterial pressure (MAP), right atrium pressure (RAP), and oxygen saturation (SpO₂). Various features are extracted from the data and are then ranked so that an optimal subset of features that have the highest discriminative capabilities can be selected. A decision tree (DT) is then developed for the vital data in order to identify the most important vital measurements. The DT result shows that high amplitude 20 minutes variations and low sample entropy in the data is an important factor for prediction of PVL. Low sample entropy represents lack of variability in hemodynamic measurement, and constant blood pressure with small fluctuations is an important indicator of PVL occurrence. Finally, using the different time frames of the collected data, we show that the first six hours of data contain sufficient information for PVL occurrence prediction.