Detection of perinatal hypoxia using time-frequency analysis of heart rate variability signals

Abstract

This paper presents a time-frequency approach to detect perinatal hypoxia by characterizing the nonstationary nature of heart rate variability (HRV) signals. Quadratic time-frequency distributions (TFDs) are used to represent the HRV signals. Six features based on the instantaneous frequency (IF) of the lower frequency components of HRV signals are selected to establish a classifier using support vector machine. The classifier is trained and tested using the signals recorded from a neonatal piglet model under a controlled hypoxic condition, which provides reliable annotations on the data. The method shows superior performance in the detection of hypoxic epochs with sensitivity (89.8%), specificity (100%) and total accuracy (94.9%) compared with that based on frequency domain features, indicating that nonstationarity should be taken into account for a more accurate assessment of the newborn status with possible hypoxia when analyzing HRV signals.