چکیده (انگلیسی):

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.