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Updated: Aug 12, 2025

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

Cardiotocography analysis by empirical dynamic modeling and Gaussian processes.

Guanchao Feng¹, Cassandra Heiselman², J Gerald Quirk²

¹Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, United States.

Frontiers in Bioengineering and Biotechnology

|January 30, 2023

Summary

This summary is machine-generated.

This study models fetal heart rate (FHR) and uterine activity (UA) signals using Gaussian processes, revealing their causal relationship for improved electronic fetal monitoring analysis.

Keywords:

attractor manifold cardiotocography empirical dynamic modelling fetal heart rate uterine activity

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Area of Science:

Medical Informatics
Dynamical Systems Theory
Machine Learning

Background:

Cardiotocography (CTG) is standard for electronic fetal monitoring, but interpretation has high variability.
Existing machine learning for CTG analysis often overlooks uterine activity (UA) signals.
Obstetricians evaluate both fetal heart rate (FHR) and UA for fetal well-being assessment.

Purpose of the Study:

To model intrapartum CTG recordings using empirical dynamic modeling with Gaussian processes.
To investigate the causal relationship between FHR and UA signals.
To leverage these models for enhanced computerized analysis of CTG data.

Main Methods:

Utilized Gaussian processes, a Bayesian nonparametric approach, for function estimation.
Modeled CTG recordings from a dynamical system perspective.
Applied empirical dynamic modeling to time series data of FHR and UA.

Main Results:

Gaussian processes enabled simultaneous estimation of attractor manifold dimensionality and reconstruction.
Demonstrated a causal relationship between FHR and UA signals in real CTG recordings.
Developed models capable of estimating missing FHR samples and recovering burst errors.

Conclusions:

The causal relationship between FHR and UA is significant for fetal well-being assessment.
Gaussian process modeling offers a robust framework for analyzing complex CTG data.
This approach has potential applications in improving the accuracy and reliability of electronic fetal monitoring.