Feature selection for computerized fetal heart rate analysis using genetic algorithms
Journal
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Pages
445-448
Date Issued
2013
Author(s)
Abstract
During birth, timely and accurate diagnosis is needed in order to prevent severe conditions such as birth asphyxia. The fetal heart rate (FHR) is often monitored during labor to assess the condition of fetal health. Computerized FHR analysis is needed to help clinicians identify abnormal patterns and to intervene when necessary. The objective of this study is to apply Genetic Algorithms (GA) as a feature selection method to select a best feature subset from 64 FHR features and to integrate these best features to recognize unfavorable FHR patterns. The GA was trained on 408 cases and tested on 102 cases (both balanced datasets) using a linear SVM as classifier. 100 best feature subsets were selected according to different splits of data; a committee was formed using these best classifiers to test their classification performance. Fair classification performance was shown on the testing set (Cohen's kappa 0.47, proportion of agreement 73.58%). To our knowledge, this is the first time that a feature selection method has been tested for FHR analysis on a database of this size. ? 2013 IEEE.
Subjects
Abnormal patterns
Balanced datasets
Birth asphyxias
Classification performance
Cohen's kappas
Feature selection methods
Feature subset
Fetal heart rate
Genetic algorithms
Neonatal monitoring
Diagnosis
algorithm
devices
factual database
female
fetus
fetus heart rate
fetus monitoring
human
labor
pH
physiology
pregnancy
procedures
reproducibility
signal processing
support vector machine
Algorithms
Databases, Factual
Female
Fetal Monitoring
Fetus
Heart Rate, Fetal
Humans
Hydrogen-Ion Concentration
Labor, Obstetric
Pregnancy
Reproducibility of Results
Signal Processing, Computer-Assisted
Support Vector Machines
Type
conference paper