Inference about Causality from Cardiotocography Signals Using Gaussian Processes

Abstract

In this paper, we propose a novel and simple method for discovery of Granger causality from noisy time series using Gaussian processes. More specifically, we adopt the concept of Granger causality, but instead of using autoregressive models for establishing it, we work with Gaussian processes. We show that information about the Granger causality is encoded in the hyperparameters of the used Gaussian processes. The proposed approach is first validated on simulated data, and then used for understanding the interaction between fetal heart rate and uterine activity in the last two hours before delivery and of interest in obstetrics. Our results indicate that uterine activity affects fetal heart rate, which agrees with recent clinical studies.

Data-Driven Insights into Labor Progression with Gaussian Processes

“…The literature is rapidly expanding with applications of GPs in various domains, reflecting its flexibility and robustness in handling various kinds of data [7][8][9]. In particular in healthcare care, GP models have been noted for their ability to provide probabilistic predictions and naturally adapt to uncertainty and noise within individual data [10][11][12][13][14]. GPs have been effectively used for probabilistic predictions in Alzheimer's disease [13], modelling intrapartum uterine pressure and fetal heart rate [14], and critical care monitoring, where they have been shown to predict clinical interventions [11].…”

“…In particular in healthcare care, GP models have been noted for their ability to provide probabilistic predictions and naturally adapt to uncertainty and noise within individual data [10][11][12][13][14]. GPs have been effectively used for probabilistic predictions in Alzheimer's disease [13], modelling intrapartum uterine pressure and fetal heart rate [14], and critical care monitoring, where they have been shown to predict clinical interventions [11]. Furthermore, GP models have also shown promise in the field of epidemiology, particularly in the predictive modeling of infectious diseases [15].…”

Independent Analysis of Decelerations and Resting Periods through CEEMDAN and Spectral-Based Feature Extraction Improves Cardiotocographic Assessment

“…On the other hand, according to the literature [14], the CTG interpretation problems are mainly associated with the assessment of FHR decelerations, which are certainly fetal cardiac responses modulated by the ANS [6]. Likewise, recent research in biomedical engineering indicates that the UC activity has a graded effect on the FHR response [15,16].…”