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. 2017:2017:7949507.
doi: 10.1155/2017/7949507. Epub 2017 Feb 19.

Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine

Lili Chen  1 , Yaru Hao  1
Affiliations

Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine

Lili Chen et al. Comput Math Methods Med. 2017.

Abstract

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.

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Conflict of interest statement

The authors declare that there is no conflict of interests regarding the publication of this paper.

Figures

Figure 1
Figure 1
Samples of raw EHG segment. (a) Labour EHG segment and (b) pregnancy EHG segment.
Figure 2
Figure 2
The denoising result of a labour EHG channel and a pregnancy EHG channel.
Figure 3
Figure 3
(a) Uterine contraction tracing obtained by tocographic measurement (each small square represents 30 seconds). (b) The channel one of an EHG recording simultaneously recorded when filtered in the 0.1–3 Hz bandwidth.
Figure 4
Figure 4
The results of decomposition performed by EMD. (a) The results of decomposition performed by EMD for a labour EHG channel. (b) The results of decomposition performed by EMD for a pregnancy EHG channel.
Figure 5
Figure 5
The feature values extracted from IMF1 of channel 1 over all the 150 labour EHG samples and 150 pregnancy EHG samples.
Figure 6
Figure 6
The box plot about maximum amplitudes of analytic function from IMF1 of channel 1 over all 150 labour EHG samples and 150 pregnancy EHG samples.
Figure 7
Figure 7
The identification result of test set for IMF1 by using the obtained model.
Figure 8
Figure 8
The ROC curve of ELM classifier.
See this image and copyright information in PMC

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