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

Citations Over TimeTop 10% of 2017 papers

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.

Related Papers

• → R peak detection in electrocardiogram signal based on a combination between empirical mode decomposition and Hilbert transform(2014)19 cited
• → Instantaneous frequency and wave mode identification in a magnetosheath using few spatial points(2013)4 cited
• → Application and Improvement of Hilbert-Huang Transform in Subsynchronous Oscillation Mode Parameter Identification(2021)3 cited
• New Process Method for End Effects of HILBERT-HUANG Transform(2008)
• End effect restraint of Hilbert-Huang transform and its application in power quality monitoring(2008)