NDLI: Automated evaluation of fetal cardiotocograms using neural network

Content Provider	IEEE Xplore Digital Library
Author	Johnson, B. Bennett, A. Myungjae Kwak Choi, A.
Copyright Year	2012
Description	Author affiliation: Biomedical Engineering, Mercer University, Macon, GA, USA (Bennett, A.) \|\| Information Technology, Macon State College, Macon, GA, USA (Myungjae Kwak) \|\| Electrical & Computer Engineering, Mercer University, Macon, GA, USA (Johnson, B.; Choi, A.)
Abstract	Cardiotocograms (CTG) monitor fetal heart rate and uterine contractions and are routinely used as a diagnostic tool by obstetricians to determine fetal state. Evaluation of CTG traces require an extensive visual interpretation subject to extensive inter and intra observer variability. A neural network implementation would automatically evaluate fetal state via CTG parametric inputs in a short period of time and reduce resource costs associated with electronic fetal monitoring. The optimal neural network designed included a topology of four hidden layers with 200 neurons per layer, a scaled conjugate gradient back propagation method, and a threshold of 1.915. The optimized network performed with an absolute accuracy of 84.26%, a positive error of 10.18%, and a negative error of 5.56%. Conceptually the network performs such that only 15.74% of patients are misclassified, and of that percentage, only 5.56% of those are incorrect decisions could potentially harm the patients. Overall, substantial costs in resources and medical expertise are obviated by preventing the unnecessary investigation of 69.9% of all patients. Despite its scholastic merit, the neural network may not be suitable for medical incorporation due to strict FDA regulations and political and public scrutiny over endangerment of 5.56% of all fetal patients. Use of a developmental parameter could potentially reduce error and make such an interactive decision support tool feasible and should be investigated.
Starting Page	408
Ending Page	413
File Size	922822
Page Count	6
File Format	PDF
ISBN	9781467317139
e-ISBN	9781467317146
e-ISBN	9781467317122
DOI	10.1109/ICSMC.2012.6377735
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-14
Publisher Place	Korea (South)
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Monitoring Fetal heart rate Accuracy Fetus Histograms Biological neural networks Uterine Contractions Cardiotocogram Fetal MATLAB Neural Networks
Content Type	Text
Resource Type	Article

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