Unsupervised Fetal Behavioral State Classification Using Non-Invasive Electrocardiographic Recordings

Understanding the Fetal Behavioral States (FBSes) is one of the ways to understand the fetal Autonomic Nervous System (ANS) maturation. This preliminary work aims to automatically classify FBSes using the unsupervised k-means clustering technique. Non-invasive electrocardiogram signals were recorded from 67 healthy fetuses with Gestational Age (GA) range of 20-40 weeks for a duration of 10 min. Features extracted from the original fetal Heart Rate (HR) and detrended HR are used to classify the FB-Ses. Results showed that during the early gestational period, the prominent state was 1F compared to other states and the least common state was 4F. The decrease in 1F frequency and the increase in 2F frequency in late gestation represent the coordination and overall maturation of the fetal ANS. Results showed that the k-means clustering algorithm had good overall performance and stronger classification ability with good Cohen's kappa score. Unsupervised classification of FBSes based on electrocardiography data is possible. It is achievable to incorporate this algorithm into future devices for in depth understanding of fetal brain maturation and well-being.