ArticleComputational and mathematical methods in medicine2017
Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine.
Article in Computational and mathematical methods in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Analysis of electrohysterogram signals for predicting obstetric outcome using machine learning methods: a scoping review.BMC pregnancy and childbirth · 2026Article
- Establishment of a model for predicting preterm birth based on the machine learning algorithm.BMC pregnancy and childbirth · 2023Article
- Novel DERMA Fusion Technique for ECG Heartbeat Classification.Life (Basel, Switzerland) · 2022Article
- Optimization of Imminent Labor Prediction Systems in Women with Threatened Preterm Labor Based on Electrohysterography.Sensors (Basel, Switzerland) · 2021Article
- Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis.JMIR medical informatics · 2020Review
- Acquisition and Analysis of Electrohysterogram Signal.Journal of medical systems · 2020Article
- Diagnosis of mesothelioma with deep learning.Oncology letters · 2019Article
- Detection of preterm birth in electrohysterogram signals based on wavelet transform and stacked sparse autoencoder.PloS one · 2019Article
- Preliminary Study on the Efficient Electrohysterogram Segments for Recognizing Uterine Contractions with Convolutional Neural Networks.BioMed research international · 2019Article
- Article
- Relevant Features Selection for Automatic Prediction of Preterm Deliveries from Pregnancy ElectroHysterograhic (EHG) records.Journal of medical systems · 2017Article
- Evaluation of electrohysterogram measured from different gestational weeks for recognizing preterm delivery: a preliminary study using random Forest.Biocybernetics and biomedical engineeringArticle
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2 authors.
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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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