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.

Opposition Based Firefly Algorithm Optimized Feature Subset Selection Approach for Fetal Risk Anticipation

“…A classifier which classifies the data into three classes by applying modular neural network is proposed in [8]. A neural network based classifier is proposed in [9] to improve the performance of other clustering algorithms in CTG classification. Naïve Bayes Classifier has been used in [10] to classify the CTG data in to three classes.…”

Genetic Algorithm Based Feature Subset Selection for Fetal State Classification

“…In [8], a classifier has been proposed which classifies the data into three classes by applying modular neural network. A neural network based classifier has been presented in [9], to improve the performance of clustering algorithms in CTG classification. Naïve Bayes Classifier has been used in [10] to classify the CTG data in to three classes.…”

Analysis of Cardiotocogram Data for Fetal Distress Determination by Decision Tree Based Adaptive Boosting Approach

“…Sundar et al [3] implemented a supervised ANN which can classify the CTG data, the results are evaluated with respect to rand index, precision, recall and f-Score. The authors presented another related work in which neural network based classification model has been compared with the most commonly used unsupervised clustering methods; Fuzzy C-mean and k-mean clustering [4]. The arrived results show that the performance of the supervised ANN approach provided outperformed the other compared unsupervised clustering methods significantly.…”