UNSUPERVISED CLUSTERING AND ANALYSIS OF CONTRACTION-DEPENDENT FETAL HEART RATE SEGMENTS

Abstract

The computer-aided interpretation of fetal heart rate (FHR) and uterine contraction (UC) has not been developed well enough for wide use in delivery rooms. The main challenges still lie in the lack of unclear and nonstandard labels for cardiotocography (CTG) recordings, and the timely prediction of fetal state during monitoring. Rather than traditional supervised approaches to FHR classification, this paper demonstrates a way to understand the UC-dependent FHR responses in an unsupervised manner. In this work, we provide a complete method for FHR-UC segment clustering and analysis via the Gaussian process latent variable model, and density-based spatial clustering. We map the UC-dependent FHR segments into a space with a visual dimension and propose a trajectory-based FHR interpretation method. Three metrics of FHR trajectory are defined and an open-access CTG database is used for testing the proposed method.

Keywords: cardiotocography; trajectory of contraction-dependent fetal heart rate segments; unsupervised clustering.

Fig. 1:

Flowchart of the proposed method.

Fig. 2:

Data pre-processing and segmentation.

Fig. 3:

Example of sample distributions of normal and abnormal classes.

Fig. 4:

Example of trajectory of a FHR recording.

Fig. 5:

Five representative trajectories of FHR recordings. The left three are in normal class and the right two are in abnormal class.

Fig. 6:

Comparison of three metrics of trajectories from normal and abnormal FHR recordings.