Method on Abdominal Electrode-Sourced FECG Extraction Utilizing the Improved LightGBM with Signal-to-Noise Ratio Regularization

HAN Liang; CAI Wen-tao; PU Xiu-juan; LUO Tong-jun; HUANG Qian

doi:10.12263/DZXB.20211472

您当前的位置：

首页 >

文章列表页 >

Method on Abdominal Electrode-Sourced FECG Extraction Utilizing the Improved LightGBM with Signal-to-Noise Ratio Regularization

PAPERS | 更新时间：2025-12-08

- Method on Abdominal Electrode-Sourced FECG Extraction Utilizing the Improved LightGBM with Signal-to-Noise Ratio Regularization
- ACTA ELECTRONICA SINICA Vol. 50, Issue 8, Pages: 1793-1800(2022)
- 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：
- 基金信息：
- DOI：10.12263/DZXB.20211472
  CLC： TN911.72;TH701
- Received：01 November 2021，
  
  Revised：2022-01-25，
  
  Published：25 August 2022
- 稿件说明：
移动端阅览
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%，

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=44136013&type=

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=44136008&type=

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

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=44136032&type=

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

MARTINEK R , KAHANKOVA R , JEZEWSKI J , et al . Comparative effectiveness of ICA and PCA in extraction of fetal ECG from abdominal signals: Toward non-invasive fetal monitoring [J]. Frontiers in Physiology , 2018 , 9 : 648 .

BEHAR J , ANDREOTTI F , ZAUNSEDER S , et al . A practical Guide to non-invasive foetal electrocardiogram extraction and analysis [J]. Physiological Measurement , 2016 , 37 ( 5 ): R1 - R35 .

JEZEWSKI J , WROBEL J , MATONIA A , et al . Is abdominal fetal electrocardiography an alternative to Doppler ultrasound for FHR variability evaluation? [J]. Frontiers in Physiology , 2017 , 8 : 305 .

TAHA L Y , ABDEL-RAHEEM E . Fetal ECG extraction using input-mode and output-mode adaptive filters with blind source separation [J]. Canadian Journal of Electrical and Computer Engineering , 2020 , 43 ( 4 ): 295 - 304 .

WARMERDAM G J J , VULLINGS R , SCHMITT L , et al . Hierarchical probabilistic framework for fetal R-peak detection, using ECG waveform and heart rate information [J]. IEEE Transactions on Signal Processing , 2018 , 66 ( 16 ): 4388 - 4397 .

KAHANKOVA R , MARTINEK R , JAROS R , et al . A review of signal processing techniques for non-invasive fetal electrocardiography [J]. IEEE Reviews in Biomedical Engineering , 2020 , 13 : 51 - 73 .

KAHANKOVA R , MARTINEK R , BILIK P . Non-invasive fetal ECG extraction from maternal abdominal ECG using LMS and RLS adaptive algorithms [C]// Proceedings of the Third International Afro-European Conference for Industrial Advancement(AECIA 2016) . Marrakesh : Springer , 2016 . 258 - 271 .

ASSALEH K . Extraction of fetal electrocardiogram using adaptive neuro-fuzzy inference systems [J]. IEEE Transactions on Biomedical Engineering , 2007 , 54 ( 1 ): 59 - 68 .

HAN L , PU XIU-JUAN , CHEN XIAO-JUN . Method of fetal electrocardiogram extraction based on ν-support vector regression [J]. IET Signal Processing , 2015 , 9 ( 5 ): 430 - 439 .

TSUI S Y , LIU C S , LIN C W . Modified maternal ECG cancellation for portable fetal heart rate monitor [J]. Biomedical Signal Processing and Control , 2017 , 32 : 76 - 81 .

韩亮 , 蔡文涛 , 蒲秀娟 , 龙灵 . 结合FastICA与EKF的腹部源胎儿心电信号提取 [J]. 仪器仪表学报 , 2021 , 42 ( 7 ): 116 - 125 .

HAN L , CAI W T , PU X J , LONG L . Abdominal electrode-sourced FECG extraction utilizing EKF combined with FastICA [J]. Chinese Journal of Scientific Instrument , 2021 , 42 ( 7 ): 116 - 125 . (in Chinese)

LIMA-HERRERA S L , ALVARADO-SERRANO C , HERNÁNDEZ-RODRÍGUEZ P R . Fetal ECG extraction based on adaptive filters and Wavelet Transform: Validation and application in fetal heart rate variability analysis [C]// 2016 13th International Conference on Electrical Engineering, Computing Science and Automatic Control(CCE) . Mexico City : IEEE , 2016 : 1 - 6 .

ANDREOTTI F , RIEDL M , HIMMELSBACH T , et al . Robust fetal ECG extraction and detection from abdominal leads [J]. Physiological Measurement , 2014 , 35 ( 8 ): 1551 - 1567 .

KE G , MENG Q , FINLEY T , et al . Lightgbm: A highly efficient gradient boosting decision tree [C]// Advances in Neural Information Processing Systems 30(NIPS 2017) . Long Beach : Curran Associates, Inc. , 2017 : 1 - 9 .

孙全明 , 曲志坚 , 任崇广 . 基于粒子群优化和LightGBM的情景感知多式联运推荐 [J]. 电子学报 , 2021 , 49 ( 5 ): 894 - 903 .

SUN Q M , QU Z J , REN C G . Context-aware multi-modal transportation recommendation based on particle swarm optimization and LightGBM [J]. Acta Electronica Sinica , 2021 , 49 ( 5 ): 894 - 903 . (in Chinese)

Outram N J . Intelligent Pattern Analysis of the Foetal Electrocardiogram [D]. Plymouth : University of Plymouth , 1997 .

VARANINI M , TARTARISCO G , BILLECI L , et al . A multi-step approach for non-invasive fetal ECG analysis [C]// Computing in Cardiology Conference . Zaragoza : IEEE , 2013 : 281 - 284 .

MARTINEZ M , CALPE J , SORIA E , et al . Methods to evaluate the performance of fetal electrocardiogram extraction algorithms [C]// Computers in Cardiology . Rotterdam : IEEE , 2001 : 253 - 256 .

BEHAR J , OSTER J , CLIFFORD G D . Combining and benchmarking methods of foetal ECG extraction without maternal or scalp electrode data [J]. Physiological Measurement , 2014 , 35 ( 8 ): 1569 - 1589 .

GOLDBERGER A L , AMARAL L A , GLASS L , et al . PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals [J]. Circulation , 2000 , 101 ( 23 ): E215 - E220 .

JOHNSON A E W , BEHAR J , ANDREOTTI F , et al . Multimodal heart beat detection using signal quality indices [J]. Physiological Measurement , 2015 , 36 ( 8 ): 1665 - 1677 .

张昱 , 刘开峰 , 张全新 , 王艳歌 , 高凯龙 . 基于组合-卷积神经网络的中文新闻文本分类 [J]. 电子学报 , 2021 , 49 ( 6 ): 1059 - 1067 .

ZHANG Y , LIU K F , ZHANG Q X , WANG K G , GAO K L . A combined-convolutional neural network for Chinese news text classification [J]. Acta Electronica Sinica , 2021 , 49 ( 6 ): 1059 - 1067 . (in Chinese)

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on Multi-Granularity Neural Network Pruning Method with Regularization Mechanism

Deep Prior-Based Blind Image Deblurring

Imaging Algorithm of Synthetic Aperture Interferometric Radiometers Based on Laplace Operator

The Application of Improved Iterative Algorithm to Regularization Model of Image Restoration

Related Author

Qian HUANG

Tong-jun LUO

Xiu-juan PU

Wen-tao CAI

Liang HAN

CHEN Ying

LIU Qi

XIAO Chuang-bai

Related Institution

School of Microelectronics and Communication Engineering， Chongqing University

The Key Laboratory of Advanced Process Control for Light Industry （Ministry of Education）， Jiangnan University

Faculty of Information Technology, Beijing University of Technology

School of Information Science and Technology, Zhejiang Sci-Tech University

National Space Science Center, Chinese Academy of Sciences

⁰