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

fYear

2011

fDate

14-16 Dec. 2011

Firstpage

81

Lastpage

83

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Engineering (ICBME), 2011 18th Iranian Conference of

Conference_Location

Tehran

Print_ISBN

978-1-4673-1004-8

Type

conf

DOI

10.1109/ICBME.2011.6168591

Filename

6168591