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Updated: May 15, 2025

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

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Cardiotocography data analysis for foetal health classification using Spatial Bayesian Neural Network Optimized with

P Solainayagi¹, G Sivagaminathan², Sabenabanu Abdulkadhar³

¹Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, Tamil Nadu, India.

Computer Methods in Biomechanics and Biomedical Engineering

|April 11, 2025

Summary

This summary is machine-generated.

This study introduces an automated Cardiotocography Data Analysis for Foetal Health Classification (CDA-FHC) system. The novel approach enhances early detection of pregnancy complications, improving accuracy and reliability in foetal health assessment.

Keywords:

Cardiotocogram Dwarf mongoose optimizer Humboldt Squid optimization algorithm Spatial Bayesian Neural Network foetal health pregnancy

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Last Updated: May 15, 2025

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A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

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Published on: March 3, 2018

Area of Science:

Medical Informatics
Computational Biology
Artificial Intelligence in Healthcare

Background:

Traditional Cardiotocography (CTG) analysis for detecting pregnancy complications is time-consuming and prone to errors.
Early identification of foetal distress is crucial for improving pregnancy outcomes.

Purpose of the Study:

To develop and validate an automated system for Cardiotocography Data Analysis for Foetal Health Classification (CDA-FHC).
To enhance the accuracy and efficiency of foetal health assessment using advanced computational methods.

Main Methods:

Utilized Spatial Bayesian Neural Network (SBNN) for foetal health classification.
Optimized feature selection using the Humboldt Squid Optimization Algorithm (HSOA).
Employed the Dwarf Mongoose Optimizer (DMO) to fine-tune the SBNN model.

Main Results:

The proposed CDA-FHC-SBNN-DMO method demonstrated superior performance compared to existing techniques.
Achieved significant improvements in accuracy (20.89%), precision, and recall for foetal health classification.
The system was implemented effectively using Python.

Conclusions:

The CDA-FHC-SBNN-DMO approach offers a more accurate and efficient alternative for analysing CTG data.
This automated method holds potential for earlier and more reliable detection of pregnancy complications.
Further research can explore broader clinical integration of this AI-driven tool.