title: Neural networks for recognizing patterns in cardiotocograms.
abstract: The cardiotocogram (CTG) is commonly used for routine fetal monitoring in the delivery room. A major problem is that the interpretation of the CTG trace requires experienced specialists. In order to avoid long gaps between the detection of a suspicious pattern and the intervention, the CTG has to be checked in short intervals. An automated monitoring system at the obstetric site can reduce such delays. Therefore, an alarm system immediately reporting suspicious events has been built. The focus of our study was put on the question whether AI techniques such as neural networks are suited to the task of recognizing patterns in the CTG trace. In a comparative study, their performance was evaluated against that of conventional methods. The neural networks turned out to provide significantly better results than the tested conventional methods.
authors.affiliation: Austrian Research Institute for Artificial Intelligence, Vienna. claudia@ai.univie.ac.at
authors.forename: C
authors.initials: C
authors.lastname: Ulbricht
authors.forename: G
authors.initials: G
authors.lastname: Dorffner
authors.forename: A
authors.initials: A
authors.lastname: Lee
mesh_terms: D015148:Cardiotocography / Q000379:methods*; D005260:Female; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D011247:Pregnancy; D013997:Time Factors