Feature selection using genetic algorithms for fetal heart rate analysis
Journal
Physiological Measurement
Journal Volume
35
Journal Issue
7
Pages
1357-1371
Date Issued
2014
Author(s)
Abstract
The fetal heart rate (FHR) is monitored on a paper strip (cardiotocogram) during labour to assess fetal health. If necessary, clinicians can intervene and assist with a prompt delivery of the baby. Data-driven computerized FHR analysis could help clinicians in the decision-making process. However, selecting the best computerized FHR features that relate to labour outcome is a pressing research problem. The objective of this study is to apply genetic algorithms (GA) as a feature selection method to select the best feature subset from 64 FHR features and to integrate these best features to recognize unfavourable FHR patterns. The GA was trained on 404 cases and tested on 106 cases (both balanced datasets) using three classifiers, respectively. Regularization methods and backward selection were used to optimize the GA. Reasonable classification performance is shown on the testing set for the best feature subset (Cohen's kappa values of 0.45 to 0.49 using different classifiers). This is, to our knowledge, the first time that a feature selection method for FHR analysis has been developed on a database of this size. This study indicates that different FHR features, when integrated, can show good performance in predicting labour outcome. It also gives the importance of each feature, which will be a valuable reference point for further studies. ? 2014 Institute of Physics and Engineering in Medicine.
Subjects
Classification (of information)
Decision making
Feature extraction
Heart
Neonatal monitoring
Support vector machines
Cardiotocogram
Data driven
Decision-making process
Feature selection methods
Feature subset
Features selection
Foetal heart rates
Heart rate analysis
Pressung
Support vectors machine
Genetic algorithms
acidosis
algorithm
article
cardiotocography
factual database
female
fetus disease
fetus heart rate
fetus monitoring
human
labor
methodology
pathophysiology
pregnancy
receiver operating characteristic
signal processing
statistical model
Acidosis
Algorithms
Cardiotocography
Databases, Factual
Female
Fetal Diseases
Fetal Monitoring
Heart Rate, Fetal
Humans
Labor, Obstetric
Linear Models
Pregnancy
ROC Curve
Signal Processing, Computer-Assisted
SDGs
Type
journal article