![](/images/graphics-bg.png)
Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine
Joint Authors
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-02-19
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Preterm birth (PTB) is the leading cause of perinatal mortality and long-term morbidity, which results in significant health and economic problems.
The early detection of PTB has great significance for its prevention.
The electrohysterogram (EHG) related to uterine contraction is a noninvasive, real-time, and automatic novel technology which can be used to detect, diagnose, or predict PTB.
This paper presents a method for feature extraction and classification of EHG between pregnancy and labour group, based on Hilbert-Huang transform (HHT) and extreme learning machine (ELM).
For each sample, each channel was decomposed into a set of intrinsic mode functions (IMFs) using empirical mode decomposition (EMD).
Then, the Hilbert transform was applied to IMF to obtain analytic function.
The maximum amplitude of analytic function was extracted as feature.
The identification model was constructed based on ELM.
Experimental results reveal that the best classification performance of the proposed method can reach an accuracy of 88.00%, a sensitivity of 91.30%, and a specificity of 85.19%.
The area under receiver operating characteristic (ROC) curve is 0.88.
Finally, experimental results indicate that the method developed in this work could be effective in the classification of EHG between pregnancy and labour group.
American Psychological Association (APA)
Chen, Lili& Hao, Yaru. 2017. Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine. Computational and Mathematical Methods in Medicine،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1142337
Modern Language Association (MLA)
Chen, Lili& Hao, Yaru. Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine. Computational and Mathematical Methods in Medicine No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1142337
American Medical Association (AMA)
Chen, Lili& Hao, Yaru. Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine. Computational and Mathematical Methods in Medicine. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1142337
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1142337