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Fetal Circulation

Fetal Circulation

Fetal circulation is a unique system that facilitates the exchange of gases, nutrients, and waste products between the developing fetus and the mother. This intricate process takes place through a special organ called the placenta.
Two umbilical arteries transport blood from the fetus to the placenta. At the placenta, the blood absorbs oxygen and nutrients while simultaneously eliminating waste products. This oxygen-enriched and nutrient-rich blood then returns to the fetus through one...

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Related Experiment Video

Updated: Oct 20, 2025

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Published on: January 7, 2021

Automatic fetal movement recognition from multi-channel accelerometry data.

Mostefa Mesbah¹, Mohamed S Khlif², Siamak Layeghy³

¹Department of Electrical and Computer Engineering, Sultan Qaboos University, Muscat, Oman.

Computer Methods and Programs in Biomedicine

|September 13, 2021

Summary

This summary is machine-generated.

A novel algorithm using wearable accelerometers accurately detects fetal movements, improving at-home monitoring for high-risk pregnancies and reducing poor birth outcomes. This system offers a reliable method for assessing fetal health outside clinical settings.

Keywords:

Accelerometer Artefact removal Classification Feature extraction Fetal movement

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Area of Science:

Biomedical Engineering
Maternal-Fetal Medicine
Signal Processing

Background:

High-risk pregnancies require monitoring to prevent stillbirth and fetal growth compromise.
Maternal perception of fetal movement is subjective and unreliable.
Current methods for fetal movement detection are limited, necessitating advanced at-home monitoring solutions.

Purpose of the Study:

To develop a non-invasive, at-home system for accurate fetal movement detection.
To differentiate genuine fetal movements from artifacts using advanced signal processing.

Main Methods:

Utilized wearable tri-axial accelerometers placed on the maternal abdomen.
Applied Independent Component Analysis (ICA) and Discrete Wavelet Transform (DWT) for artifact removal and dimensionality reduction.
Extracted 31 features from acceleration data for movement recognition.

Main Results:

The Bagging classifier, based on Random Forest, achieved the highest accuracy (87.6%–95.8%) in distinguishing fetal movements.
Classifier performance was robust across varying levels of artifact concentration.
The system demonstrated high performance in detecting fetal movements.

Conclusions:

Accelerometer-based systems are suitable for long-term fetal movement monitoring.
High-performance fetal movement detection can be achieved through advanced signal processing algorithms.
This technology offers a promising advancement in remote monitoring of at-risk pregnancies.