On the Early Detection of Perinatal Hypoxia with Information-Theory based Methods

Abstract

Perinatal hypoxia is a severe condition that may harm fetus organs permanently. When the fetus brain is partially deprived from oxygen, the control of the fetal heart rate (FHR) is affected. We hypothesized that advanced processing of the FHR can reveal whether the fetus is under perinatal hypoxia.

We analyzed FHR morphology with normalized compression distance (NCD) that compares two arbitrary sequences and outputs their dissimilarity. This parameter-free measure exploits linear and non-linear relations in the data and allows the comparison of sequences of different sizes. It was applied to raw FHR sequences and to a set of statistics computed from them (e.g. moments on 5 minutes signal windows). We classified the cases from the NCD dissimilarity matrix by using a simple nearest neighbor classifier and leave-one-out cross-validation.

Best results in a database with 26 FHR recordings (13 controls and 13 cases) were provided by the central moment of order 3 calculated over sliding windows of 5 minutes on the interval from 4 to 3 hours to delivery. The resulting accuracy was 0.88 with sensitivity 0.92 and specificity 0.85.