IMPROVING PHASE-RECTIFIED SIGNAL AVERAGING FOR FETAL HEART RATE ANALYSIS

Abstract

Low umbilical artery pH is a marker for neonatal acidosis and is associated with an increased risk for neonatal complications. The phase-rectified signal averaging (PRSA) features have demonstrated superior discriminatory or diagnostic ability and good interpretability in many biomedical applications including fetal heart rate analysis. However, the performance of PRSA method is sensitive to values of the selected parameters which are usually either chosen based on a grid search or empirically in the literature. In this paper, we examine PRSA method through the lens of dynamical systems theory and reveal the intrinsic connection between state space reconstruction and PRSA. From this perspective, we then introduce a new feature that can better characterize dynamical systems comparing with PRSA. Our experimental results on an open-access intrapartum Cardiotocography database demonstrate that the proposed feature outperforms state-of-the-art PRSA features in pH-based fetal heart rate analysis.

Keywords: dynamical system; fetal heart rate; phase-rectified signal averaging; state space reconstruction.

Fig. 1:. Comparison between PRSA and proposed approach using simulated data.

(a) DC and AC anchor points selected in x_t with T = 5 and L = 20 (top) and their corresponding states vectors in shadow manifold (bottom) where the state vectors were colored based on the type of their corresponding anchor points selected using PRSA. (b) Different from PRSA, in our method, we first applied K-mean clustering of state vectors in the shadow manifold and colored them based on which cluster they belong to (bottom). Then the observations in x_t were colored based on the clustering results (top).

Fig. 2:

Histogram of AUC-ROC of ASD over 100 runs on the open access intrapartum CTG database.