使用信噪比正则化LightGBM的腹部源胎儿心电信号提取方法

韩亮; 蔡文涛; 蒲秀娟; 罗统军; 黄谦

doi:10.12263/DZXB.20211472

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使用信噪比正则化LightGBM的腹部源胎儿心电信号提取方法

学术论文 | 更新时间：2025-12-08

- 使用信噪比正则化LightGBM的腹部源胎儿心电信号提取方法
- Method on Abdominal Electrode-Sourced FECG Extraction Utilizing the Improved LightGBM with Signal-to-Noise Ratio Regularization
- 电子学报 2022年50卷第8期页码：1793-1800
- 作者机构：
  
  1.重庆大学微电子与通信工程学院，重庆 400044
  2.生物感知与智能信息处理重庆市重点实验室，重庆 400044
- 作者简介：
  
  [ "韩亮男，1975年生于陕西西乡.生物感知与智能信息处理重庆市重点实验室副主任，重庆大学微电子与通信工程学院副教授.主要研究方向为信号处理和图像处理.E-mail: hanliangaa@cqu.edu.cn" ]
  [ "蔡文涛男，1994年生于四川都江堰.重庆大学微电子与通信工程学院硕士研究生.主要研究方向为生物医学信号处理.E-mail: caiwentao5@cqu.edu.cn" ]
  [ "蒲秀娟女，1979年生于四川隆昌.重庆大学微电子与通信工程学院讲师.主要研究方向为生物医学信号处理.E-mail: puxj@cqu.edu.cn" ]
  [ "罗统军男，1998年生于广西北流.重庆大学微电子与通信工程学院硕士研究生.主要研究方向为生物医学信号处理.E-mail: luotongjun@cqu.edu.cn" ]
  [ "黄谦男，1998年生于四川成都.重庆大学微电子与通信工程学院硕士研究生.主要研究方向为生物医学信号处理.E-mail: Hqian@cqu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(62171066);重庆市基础与前沿研究计划项目(cstc2016jcyjA0376)
- DOI：10.12263/DZXB.20211472
  中图分类号： TN911.72;TH701
- 收稿：2021-11-01，
  
  修回：2022-01-25，
  
  纸质出版：2022-08-25
- 稿件说明：
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韩亮,蔡文涛,蒲秀娟等.使用信噪比正则化LightGBM的腹部源胎儿心电信号提取方法[J].电子学报,2022,50(08):1793-1800.

HAN Liang,CAI Wen-tao,PU Xiu-juan,et al.Method on Abdominal Electrode-Sourced FECG Extraction Utilizing the Improved LightGBM with Signal-to-Noise Ratio Regularization[J].ACTA ELECTRONICA SINICA,2022,50(08):1793-1800.
韩亮,蔡文涛,蒲秀娟等.使用信噪比正则化LightGBM的腹部源胎儿心电信号提取方法[J].电子学报,2022,50(08):1793-1800. DOI： 10.12263/DZXB.20211472.

HAN Liang,CAI Wen-tao,PU Xiu-juan,et al.Method on Abdominal Electrode-Sourced FECG Extraction Utilizing the Improved LightGBM with Signal-to-Noise Ratio Regularization[J].ACTA ELECTRONICA SINICA,2022,50(08):1793-1800. DOI： 10.12263/DZXB.20211472.

摘要

胎儿心电信号（Fetal ElectroCardioGram，FECG）能反映胎儿健康状况.但是，由于其信噪比相对较低，FECG仍未能在临床上得到广泛应用.如何有效提取高质量的FECG仍是一个巨大挑战.为此，本文提出一种使用信噪比正则化LightGBM（Light Gradient Boosting Machine）模型的FECG提取方法.针对原始母体腹壁混合信号，首先使用传统滤波方法进行噪声抑制，然后再使用快速独立成分分析（Fast Independent Component Analysis，FastICA）从中分离得到母体心电信号（Maternal ElectroCardioGram，MECG）估计和FECG估计，FECG估计中残留的MECG成分是MECG的一种非线性变换.改进传统LightGBM模型，在目标函数中增加FECG的基于互相关系数的信噪比作为正则项，构建信噪比正则化LightGBM模型，并使用该模型拟合这一非线性变换.将MECG估计经由所拟合的非线性变换得到MECG成分的最优估计，并将其抑制，提取得到高质量的FECG.采用真实腹部源心电信号数据集进行实验，结果显示本文提出的方法的灵敏度为99.9%，阳性预测值为99.1%，

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2.96333337

3.21733332

分数为99.5%，基于互相关系数与基于特征值分析的信噪比分别为6.0 dB和6.1 dB.实验结果表明，本文提出的方法是有效的且具有更好的性能.

Abstract

The fetal electrocardiogram(FECG) reflects health status of fetus. However

the FECG has not been widely used in clinical practice due to its relatively low signal-to-noise ratio(SNR). How to effectively extract high-quality fetal ECG signal remains a great challenge. In this paper

FECG extraction method utilizing improved LightGBM(light gradient boosting machine) with SNR regularization is proposed. Firstly

the raw maternal abdominal mixed signals are denoised by conventional filtering method. Then

FastICA(fast independent component analysis) is used to separate the maternal electrocardiogram(MECG) estimation and FECG estimation. The residual MECG component in FECG estimation is non-linear transform of MECG and the non-linear transform is fitted by the improved LightGBM with SNR regularization

which is constructed by adding a regularization term

the SNR of extracted FECG

to the objective function of conventional LightGBM. The SNR is estimated using cross correlation. By MECG estimation undergoing the fitted non-linear transform

the residual MECG component in FECG estimation is obtained. At last

the high-quality FECG is extracted by suppressing the estimated MECG component. The real data are adopted to verify the proposed FECG extraction method. The sensitivity

positive predictive value and

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https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=44136016&type=

2.96333337

3.21733332

score of the proposed FECG extraction method are 99.9%

99.1%

and 99.5%

respectively

and the SNR based on cross correlation and singular value decomposition are 6.0 dB and 6.1 dB

respectively. The experiment results indicate that the proposed method is effective and has better performance.

关键词

Keywords

references

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