Novel Multichannel Entropy Features and Machine Learning for Early Assessment of Pregnancy Progression Using Electrohysterography

doi:10.1109/TBME.2022.3176668

	Novel Multichannel Entropy Features and Machine Learning for Early Assessment of Pregnancy Progression Using Electrohysterography
	Anyi Cheng1,2 ; Yang Yao1 ; Yibin Jin1 ; Chuan Chen 3; Rik Vullings 3; Lin Xu4,5 ; Massimo Mischi 3
	2022-12-01
发表期刊	IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (IF:4.4[JCR-2023],4.8[5-Year])
ISSN	1558-2531
卷号	69 期号:12 页码:3728-3738
发表状态	已发表
DOI	10.1109/TBME.2022.3176668
摘要	Objective: Preterm birth is the leading cause of morbidity and mortality involving over 10% of infants. Tools for timely diagnosis of preterm birth are lacking and the underlying physiological mechanisms are unclear. The aim of the present study is to improve early assessment of pregnancy progression by combining and optimizing a large number of electrohysterography (EHG) features with a dedicated machine learning framework. Methods: A set of reported EHG features are extracted. In addition, novel cross and multichannel entropy and mutual information are employed. The optimal feature set is selected using a wrapper method according to the accuracy of the leave-one-out cross validation. An annotated database of 74 EHG recordings in women with preterm contractions was employed to test the ability of the proposed method to recognize the onset of labor and the risk of preterm birth. Difference between using the contractile segments only and the whole EHG signal was compared. Results: The proposed method produces an accuracy of 96.4% and 90.5% for labor and preterm prediction, respectively, much higher than that reported in previous studies. The best labor prediction was observed with the contraction segments and the best preterm prediction achieved with the whole EHG signal. Entropy features, particularly the newly-employed cross entropy contribute significantly to the optimal feature set for both labor and preterm prediction. Significance: Our results suggest that changes in the EHG, particularly the regularity, might manifest early in pregnancy. Single-channel and cross entropy may therefore provide relevant prognostic opportunities for pregnancy monitoring.
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收录类别	SCI ; EI
来源库	IEEE
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/187911
专题	信息科学与技术学院_硕士生信息科学与技术学院_博士生信息科学与技术学院_PI研究组_徐林组
作者单位	1.School of Information Science and Technology, ShanghaiTech University, China 2.Shanghai Advanced Research Institute, Chinese Academy of Sciences, China 3.Department of Electrical Engineering, Eindhoven University of Technology, Netherlands 4.School of Information Science and Technology, ShanghaiTech University, Shanghai, China 5.Shanghai Engineering Research Center of Energy Efficient and Custom AI IC, Shanghai, China
第一作者单位	信息科学与技术学院
第一作者的第一单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Anyi Cheng,Yang Yao,Yibin Jin,et al. Novel Multichannel Entropy Features and Machine Learning for Early Assessment of Pregnancy Progression Using Electrohysterography[J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING,2022,69(12):3728-3738.
APA	Anyi Cheng.,Yang Yao.,Yibin Jin.,Chuan Chen.,Rik Vullings.,...&Massimo Mischi.(2022).Novel Multichannel Entropy Features and Machine Learning for Early Assessment of Pregnancy Progression Using Electrohysterography.IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING,69(12),3728-3738.
MLA	Anyi Cheng,et al."Novel Multichannel Entropy Features and Machine Learning for Early Assessment of Pregnancy Progression Using Electrohysterography".IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 69.12(2022):3728-3738.