Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine.
Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine.
复制标题
使用 Hilbert-Huang 变换和极限学习机对妊娠和分娩组之间的 EHG 进行特征提取和分类
DOI：
10.1155/2017/7949507
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发表时间：
2017
影响因子：
--
通讯作者：
Hao Y
中科院分区：
文献类型：
--
作者：
Chen L;Hao Y
早产（PTB）是围产期死亡和长期发病的主要原因，会导致严重的健康和经济问题。 PTB的早期发现对其预防具有重要意义。与子宫收缩相关的子宫电图（EHG）是一种无创、实时、自动的新技术，可用于检测、诊断或预测PTB。本文提出了一种基于希尔伯特-黄变换（HHT）和极限学习机（ELM）的妊娠组和临产组EHG特征提取和分类方法。对于每个样本，每个通道都使用经验模态分解 (EMD) 分解为一组固有模态函数 (IMF)。然后，将希尔伯特变换应用于IMF以获得解析函数。提取解析函数的最大幅度作为特征。基于ELM构建识别模型。实验结果表明，该方法的最佳分类性能可以达到88.00%的准确率、91.30%的灵敏度和85.19%的特异度。受试者工作特征 (ROC) 曲线下面积为 0.88。最后，实验结果表明，这项工作中开发的方法可以有效地对妊娠组和分娩组之间的 EHG 进行分类。
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.