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.
Highlights
Fetal Heart Rate (FHR) monitoring remains widely used as a method for detecting changes in fetal oxygenation that can occur during labor (Costa et al, 2009)
Cardiotocography (CTG), consisting of Fetal Heart Rate (FHR) and Tocographic (TOCO) measurements, is used to evaluate fetal well-being during the delivery
Synchronous with uterine contraction is the nadir of the heart rate trace, it occurs after the peak of the uterine contraction
Summary
The field of data mining and its of Fetal Heart Rate (FHR) and Uterine Contractions (UC). FHR patterns are observed manually extraction of comprehensible knowledge from medical by obstetricians during the process of CTG analyses. C. et al / Journal of Computer Science 9 (2): 198-206, 2013 the last three decades, great interest has been paid to the fetal heart rate baseline and its frequency analysis (Nidhal et al, 2010). Fetal Heart Rate (FHR) monitoring remains widely used as a method for detecting changes in fetal oxygenation that can occur during labor (Costa et al, 2009). Computation and other data mining techniques can be used to analyze and classify the CTG data to avoid human mistakes and to assist doctors to take a decision
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