By Topic

Predicting the Risk of Low-Fetal Birth Weight From Cardiotocographic Signals Using ANBLIR System With Deterministic Annealing and {bm \varepsilon } -Insensitive Learning

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Czabanski, R. ; Div. of Biomed. Electron., Silesian Univ. of Technol., Gliwice, Poland ; Jezewski, J. ; Wrobel, J. ; Jezewski, J.
more authors

Cardiotocography (CTG) is a biophysical method of fetal condition assessment based mainly on recording and automated analysis of fetal heart activity. The computerized fetal monitoring systems provide the quantitative description of the CTG signals, but the effective conclusion generation methods for decision process support are still needed. Assessment of the fetal state can be verified only after delivery using the fetal (newborn) outcome data. One of the most important features defining the abnormal fetal outcome is low birth weight. This paper describes an application of the artificial neural network based on logical interpretation of fuzzy if-then rules neurofuzzy system to evaluate the risk of low-fetal birth weight using the quantitative description of CTG signals. We applied different learning procedures integrating least squares method, deterministic annealing (DA) algorithm, and ε-insensitive learning, as well as various methods of input dataset modification. The performance was evaluated with the number of correctly classified cases (CC) expressed as the percentage of the testing set size, and with overall index (OI) being the function of predictive indexes. The best classification efficiency (CC = 97.5% and OI = 82.7%), was achieved for integrated DA with ε-insensitive learning and dataset comprising of the CTG traces recorded as earliest for a given patient. The obtained results confirm efficiency for supporting the fetal outcome prediction using the proposed methods.

Published in:

Information Technology in Biomedicine, IEEE Transactions on  (Volume:14 ,  Issue: 4 )