AN OVERVIEW OF RESEARCH CHALLENGES FOR CLASSIFICATION OF CARDIOTOCOGRAM DATA

Abstract

Cardiotocography (CTG) is a simultaneous recording of Fetal Heart Rate (FHR) and Uterine Contractions (UC). The most common diagnostic techniques to evaluate maternal and fetal well-being during pregnancy and before delivery. By observing the Cardiotocography trace patterns doctors can understand the state of the fetus. There are several signal processing and computer programming based techniques for interpreting a typical Cardiotocography data. A model based CTG data classification system using a supervised Artificial Neural Network (ANN) which can classify the CTG data based on its training data. The performance neural network based classification model has been compared with the most commonly used unsupervised clustering methods Fuzzy C-mean and k-mean clustering. The arrived results show that the performance of the supervised machine learning based classification approach provided significant performance than other compared unsupervised clustering methods. The traditional clustering methods can identify the Normal CTG patterns; they were incapable of finding Suspicious and Pathologic patterns. The ANN based classifier was capable of identifying Normal, Suspicious and Pathologic condition, from the nature of CTG data with very good accuracy.