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[Intelligent fetal state assessment based on genetic algorithm and least square support vector machine].

Yang Zhang¹, Zhidong Zhao², Haihui Ye³

¹The School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, P.R.China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi

|March 20, 2019

Summary

This study introduces an intelligent approach for fetal well-being assessment using fetal heart rate (FHR) signals, improving diagnostic accuracy in cardiotocography (CTG). The method enhances the reliability of electronic fetal monitoring (EFM) for better clinical decisions.

Keywords:

cardiotocography feature extraction fetal heart rate genetic algorithm least square support vector machine

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Area of Science:

Obstetrics and Gynecology
Biomedical Engineering
Signal Processing

Context:

Cardiotocography (CTG) is a standard electronic fetal monitoring (EFM) technique.
Subjective interpretation of CTG leads to diagnostic inaccuracies.
Accurate fetal well-being assessment is crucial for timely clinical intervention.

Purpose:

To develop an intelligent system for objective fetal state analysis using FHR signals.
To reduce misdiagnosis rates in electronic fetal monitoring.
To provide obstetricians with a reliable tool for fetal well-being assessment.

Summary:

Fetal heart rate (FHR) signals were preprocessed and features extracted from the CTU-UHB database.
An optimal feature subset was selected using a k-nearest neighbor (KNN) genetic algorithm (GA).
Classification was performed using a least square support vector machine (LS-SVM), achieving 91% accuracy.

Impact:

The proposed intelligent assessment method significantly improves the accuracy of fetal state classification.
Achieved high performance metrics: 91% accuracy, 89% sensitivity, 94% specificity, 92% quality index, and 92% AUC.
Provides effective clinical support for obstetricians in assessing fetal well-being, potentially reducing adverse outcomes.