PSORR - An unsupervised feature selection technique for fetal heart rate

摘要

Fetal heart activity is generally monitored using a CardioTocoGraph (CTG) which estimates the fetal tachogram based on the evaluation of ultrasound pulses reflected from the fetal heart. It consists in a simultaneous recording and analysis of Fetal Heart Rate (FHR) signal, uterine contraction activity and fetal movements. Generally cardiotocograph comprises more number of features. This paper aims to identify the important features, consequently reducing the number of features to assess the fetal heart rate. The features are selected by using Unsupervised Particle Swarm Optimization (PSO) based Relative Reduct and are tested by using various measures of diagnostic accuracy.