Adaptive pattern recognition in the analysis of cardiotocographic records

• Published in: IEEE transactions on neural networks Vol. 12; no. 5; pp. 1188 - 1195
• Main Authors: Fontenla-Romero, O., Alonso-Betanzos, A., Guijarro-Berdinas, B.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2001
• Subjects: Acceleration; Artificial neural networks; Cardiography; Cardiology; Deceleration; Fetal heart rate; Heart rate; Information analysis; Multilayer perceptrons; Neural networks; Pattern analysis; Pattern recognition; Principal component analysis; Recognition; Signal resolution; Tasks
• ISSN: 1045-9227; 1941-0093
• DOI: 10.1109/72.950146

The recognition of accelerative and decelerative patterns in the fetal heart rate (FHR) is one of the tasks carried out manually by obstetricians when they analyze cardiotocograms for information respecting the fetal state. An approach based on artificial neural networks formed by a multilayer perceptron (MLP) is developed. However, since the system utilizes the FHR signal as direct input, an anterior stage must be incorporated that applies a principal component analysis (PCA) so as to make the system independent of the signal baseline. Furthermore, the introduction of multiresolution into the PCA has resolved other problems that were detected in the application of the system. Presented in this paper are the results of validation of these systems designated the PCA-MLP and multiresolutlon principal component analysis (MR-PCA) systems against three clinical experts.