Title :
Evaluation of cepstral analysis of EHG signals to prediction of preterm labor
Author :
Baghamoradi, S. Mohammad-Sina ; Naji, Mohsen ; Aryadoost, Hesam
Author_Institution :
Dept. of Biomed. Eng., Islamic Azad Univ., Dezful, Iran
Abstract :
The aim of this paper is to evaluate the application of cepstral analysis for classification of term and preterm labors. We used 20 electrohysterogram records from two groups according to the total length of gestation: term delivery records (pregnancy duration ≥37 weeks) and preterm delivery records (pregnancy duration ≤37 weeks). MLP neural network was employed to classify the two groups. An improved classification accuracy of 72.73% is obtained by using sequential forward feature selection scheme.
Keywords :
bioelectric potentials; feature extraction; medical signal processing; multilayer perceptrons; muscle; neural nets; obstetrics; patient diagnosis; EHG signals; MLP neural network; cepstral analysis; classification accuracy; electrohysterogram; gestation; multilayer perceptron; preterm labor prediction; sequential forward feature selection scheme; term delivery records; Band pass filters; Cepstral analysis; Electrodes; Electromyography; Entropy; Pregnancy;
Conference_Titel :
Biomedical Engineering (ICBME), 2011 18th Iranian Conference of
Conference_Location :
Tehran
Print_ISBN :
978-1-4673-1004-8
DOI :
10.1109/ICBME.2011.6168591
