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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
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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:
AccelerometerArtefact removalClassificationFeature extractionFetal 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.