A summary of research progress on intelligent information processing methods for pregnant women's remote monitoring_Journal of Biomedical Engineering

Authors：

 LI Yongming ¹ , ZHANG Yuanfan ¹ , YE Changrong ² , WANG Pin ¹ , ZENG Xiaoping ¹

1. School of microelectronics and communication engineering, Chongqing University, Chongqing 400044, P.R.China;
2. College of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, P.R.China;

Corresponding author：

LI Yongming, Email: yongmingli@cqu.edu.cn

Keywords：

maternity monitoring; fetal electrocardiograph; intelligent processing; artificial intelligence

DOI：

10.7507/1001-5515.201912011

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

The monitoring of pregnant women is very important. It plays an important role in reducing fetal mortality, ensuring the safety of perinatal mother and fetus, preventing premature delivery and pregnancy accidents. At present, regular examination is the mainstream method for pregnant women's monitoring, but the means of examination out of hospital is scarce, and the equipment of hospital monitoring is expensive and the operation is complex. Using intelligent information technology (such as machine learning algorithm) can analyze the physiological signals of pregnant women, so as to realize the early detection and accident warning for mother and fetus, and achieve the purpose of high-quality monitoring out of hospital. However, at present, there are not enough public research reports related to the intelligent processing methods of out-of-hospital monitoring for pregnant women, so this paper takes the out-of-hospital monitoring for pregnant women as the research background, summarizes the public research reports of intelligent processing methods, analyzes the advantages and disadvantages of the existing research methods, points out the possible problems, and expounds the future development trend, which could provide reference for future related researches.

Citation： LI Yongming, ZHANG Yuanfan, YE Changrong, WANG Pin, ZENG Xiaoping. A summary of research progress on intelligent information processing methods for pregnant women's remote monitoring. Journal of Biomedical Engineering, 2020, 37(5): 910-917. doi: 10.7507/1001-5515.201912011 Copy

1.	黄靖锐, 张卫社. 重视危重孕产妇管理早期识别孕产妇危象. 中华产科急救电子杂志, 2018, 7(2): 65-67.
2.	Yahya M A M, Gan K B. Development of a two channel dynamic phantom for fetal heart rate and pulse oximetry application. Adv Sci Lett, 2017, 23(2): 1277-1280.
3.	梁淑银, 刘桂娟, 蒋社姣. 早期干预对妊高征高龄产妇的心脏结构及妊娠结局改善情况的探究. 中国医药指南, 2019, 17(16): 45.
4.	Story L, Hutter J, Zhang T, et al. The use of antenatal fetal magnetic resonance imaging in the assessment of patients at high risk of preterm birth. Eur J Obstet Gynecol Reprod Biol, 2018, 222: 134-141.
5.	Steinberg J S, Varma N, Cygankiewicz I, et al. 2017 ISHNE/HRS 动态心电图和体外心电监测/远程监测专家共识 (1): 动态心电监测方法, 高洁, 张炜鑫, 王新康, et al. 译. 实用心电学杂志, 2019, 28(3): 153-160.
6.	李迎新, 黄河. 国家卫生与健康管理大数据平台在医药卫生体制改革中的作用. 国际生物医学工程杂志, 2018, 41(1): 1-10.
7.	田宁, 陈奕, 闫亭亭. 胎儿心电技术在胎儿监护中的研究和应用进展. 医学综述, 2018, 24(1): 101-106.
8.	张九宾, 张丕状, 杜坤坤. 基于 GPS 的无线传感器网络时间同步技术. 传感器与微系统, 2009, 28(6): 31-33.
9.	Chen Mengyuan, Zhu Changan. Research on time synchronization algorithm of high precision and low power consumption based on IRBRS WSNs. Wirel Pers Commun, 2017, 95(3): 2255-2270.
10.	鲍文静. 多通道混合心电信号建模及胎儿心电信号提取算法研究. 哈尔滨: 哈尔滨工业大学, 2014.
11.	Sameni R, Clifford G D, Jutten C, et al. Multichannel ECG and noise modeling: application to maternal and fetal ECG signals. EURASIP J Adv Signal Process, 2007(1): 043407-043407.
12.	袁延超, 吴水才, 袁丽, 等. 基于 BP 神经网络的胎儿心电提取算法研究. 生物医学工程与临床, 2018, 22(3): 237-243.
13.	祁清. 研究胎心监护在待产孕妇中的应用. 实用妇科内分泌电子杂志, 2019, 6(12): 80.
14.	徐雯. 胎儿心电提取和胎心率变异分析算法研究. 杭州: 杭州电子科技大学, 2016.
15.	Yazdani S, Azghani M R, Sedaaghi M H. A new algorithm for ECG interference removal from single channel EMG recording. Australas Phys Eng Sci Med, 2017, 40(3): 575-584.
16.	刘牮, 楼光海, 黄微. 结合双树复小波变换和滑动平均滤波的心电信号去噪方法. 电子测量技术, 2018, 41(19): 112-117.
17.	陆尧胜, 魏守一, 秦如意, 等. 一种胎心率基线自动识别算法: 中国, 201210383132.8. 2013-02-27.
18.	司菁菁, 许培, 程银波. 自适应阈值的 1-bit 压缩感知算法. 高技术通讯, 2019, 29(2): 134-141.
19.	彭向东, 张华, 刘继忠. 基于过完备字典的体域网压缩感知心电重构. 自动化学报, 2014, 40(7): 1421-1432.
20.	张屹, 陈立军, 蒋慧勇. 基于大数据的无线传感网络数据采集的研究. 信息技术与网络安全, 2019, 38(9): 39-43.
21.	吴连涛. 面向稀疏信号的物联网高效传输体系及关键技术研究. 杭州: 浙江大学, 2017.
22.	李宏. 基于压缩感知的低功耗体域网调度算法研究. 上海: 上海交通大学, 2014.
23.	李珂, 范永存, 邓冬梅, 等. 一种面向微电网中无线传感器网络的高能效压缩感知方法. 西南科技大学学报, 2018, 33(2): 88-95.
24.	Wu Jian, Chen Zhigang, Wu Jia, et al. An energy efficient data transmission approach for low-duty-cycle wireless sensor networks. Peer Peer Netw Appl, 2020, 13(1): 255-268.
25.	郄有田, 宋萍, 郝创博. 无线传感网络中大数据量压缩感知编解码算法. 计测技术, 2019, 39(3): 28-33.
26.	叶昌荣. 微伏级 T 波交替多通道融合检测与估计研究. 重庆: 重庆大学, 2018.
27.	韩亮, 蒲秀娟, 刘茜. 结合多路 v-SVR 与 TFBSS 的胎儿心电信号提取. 仪器仪表学报, 2015, 36(6): 1381-1387.
28.	Nejad M R E, Rizi F Y. An adaptive FECG extraction and analysis method using ICA, ICEEMDAN and wavelet shrinkage// 2018 Iranian Conference on Electrical Engineering (ICEE), Mashhad: IEEE, 2018. DOI: 10.1109/ICEE.2018.8472478.
29.	Sahoo S, Kanungo B, Behera S, et al. Multiresolution wavelet transform based feature extraction and ECG classification to detect cardiac abnormalities. Measurement, 2017, 108: 55-66.
30.	孙纪光. 多导心电算法及应用系统的研制. 深圳: 深圳大学, 2017.
31.	Gokhale T. Machine learning based identification of pathological heart sounds//2016 Computing in Cardiology Conference (CinC), 2016, 43: 2325-887X. DOI: 10.22489/CinC.2016.159-485.
32.	陈耿铎. 移动心电信息监护系统及心电监测算法的研究. 广州: 南方医科大学, 2018.
33.	Jaafar H, Ramli N H, Nasir A S A., et al An improvement to the k-nearest neighbor classifier for ECG database. IOP Conference Series: Materials Science and Engineering, 2018, 318: 012046.
34.	姚成. 心电信号智能分析关键技术研究. 长春: 吉林大学, 2012.
35.	查雪帆, 杨丰, 吴俣南, 等. 结合迁移学习与深度卷积网络的心电分类研究. 中国医学物理学杂志, 2018, 35(11): 1307-1312.
36.	许莉莉, 师炜, 郭学谦, 等. 基于最小二乘支持向量机的心音分类识别研究. 中国医疗设备, 2017, 32(4): 38-41.
37.	Nagendra V, Gude H, Sampath D, et al. Evaluation of support vector machines and random forest classifiers in a real-time fetal monitoring system based on cardiotocography data//2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Manchester: IEEE, 2017. DOI: 10.1109/CIBCB.2017.8058546.
38.	韩可都, 周礼杲. 应用改进的时序自适应噪声消除法作胎儿心电信号的处理. 北京生物医学工程, 1985, 1: 9-16.
39.	Taralunga D, Wolf W, Strungaru R, et al. Abdominal signals: denoising by application of the event synchronuous canceller//2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon: IEEE, 2007: 566-569.
40.	袁野, 王夏天, 张子辰, 等. 基于小波变换和改进的瞬态独立成分分析融合算法的心电信号降噪方法. 中国医学物理学杂志, 2016, 33(4): 415-422.
41.	朱晓铭, 陈林海, 张帅, 等. 基于卷积神经网络重建十二导联心电图. 微电子学与计算机, 2019(9): 12-15.

1. 黄靖锐, 张卫社. 重视危重孕产妇管理早期识别孕产妇危象. 中华产科急救电子杂志, 2018, 7(2): 65-67.
2. Yahya M A M, Gan K B. Development of a two channel dynamic phantom for fetal heart rate and pulse oximetry application. Adv Sci Lett, 2017, 23(2): 1277-1280.
3. 梁淑银, 刘桂娟, 蒋社姣. 早期干预对妊高征高龄产妇的心脏结构及妊娠结局改善情况的探究. 中国医药指南, 2019, 17(16): 45.
4. Story L, Hutter J, Zhang T, et al. The use of antenatal fetal magnetic resonance imaging in the assessment of patients at high risk of preterm birth. Eur J Obstet Gynecol Reprod Biol, 2018, 222: 134-141.
5. Steinberg J S, Varma N, Cygankiewicz I, et al. 2017 ISHNE/HRS 动态心电图和体外心电监测/远程监测专家共识 (1): 动态心电监测方法, 高洁, 张炜鑫, 王新康, et al. 译. 实用心电学杂志, 2019, 28(3): 153-160.
6. 李迎新, 黄河. 国家卫生与健康管理大数据平台在医药卫生体制改革中的作用. 国际生物医学工程杂志, 2018, 41(1): 1-10.
7. 田宁, 陈奕, 闫亭亭. 胎儿心电技术在胎儿监护中的研究和应用进展. 医学综述, 2018, 24(1): 101-106.
8. 张九宾, 张丕状, 杜坤坤. 基于 GPS 的无线传感器网络时间同步技术. 传感器与微系统, 2009, 28(6): 31-33.
9. Chen Mengyuan, Zhu Changan. Research on time synchronization algorithm of high precision and low power consumption based on IRBRS WSNs. Wirel Pers Commun, 2017, 95(3): 2255-2270.
10. 鲍文静. 多通道混合心电信号建模及胎儿心电信号提取算法研究. 哈尔滨: 哈尔滨工业大学, 2014.
11. Sameni R, Clifford G D, Jutten C, et al. Multichannel ECG and noise modeling: application to maternal and fetal ECG signals. EURASIP J Adv Signal Process, 2007(1): 043407-043407.
12. 袁延超, 吴水才, 袁丽, 等. 基于 BP 神经网络的胎儿心电提取算法研究. 生物医学工程与临床, 2018, 22(3): 237-243.
13. 祁清. 研究胎心监护在待产孕妇中的应用. 实用妇科内分泌电子杂志, 2019, 6(12): 80.
14. 徐雯. 胎儿心电提取和胎心率变异分析算法研究. 杭州: 杭州电子科技大学, 2016.
15. Yazdani S, Azghani M R, Sedaaghi M H. A new algorithm for ECG interference removal from single channel EMG recording. Australas Phys Eng Sci Med, 2017, 40(3): 575-584.
16. 刘牮, 楼光海, 黄微. 结合双树复小波变换和滑动平均滤波的心电信号去噪方法. 电子测量技术, 2018, 41(19): 112-117.
17. 陆尧胜, 魏守一, 秦如意, 等. 一种胎心率基线自动识别算法: 中国, 201210383132.8. 2013-02-27.
18. 司菁菁, 许培, 程银波. 自适应阈值的 1-bit 压缩感知算法. 高技术通讯, 2019, 29(2): 134-141.
19. 彭向东, 张华, 刘继忠. 基于过完备字典的体域网压缩感知心电重构. 自动化学报, 2014, 40(7): 1421-1432.
20. 张屹, 陈立军, 蒋慧勇. 基于大数据的无线传感网络数据采集的研究. 信息技术与网络安全, 2019, 38(9): 39-43.
21. 吴连涛. 面向稀疏信号的物联网高效传输体系及关键技术研究. 杭州: 浙江大学, 2017.
22. 李宏. 基于压缩感知的低功耗体域网调度算法研究. 上海: 上海交通大学, 2014.
23. 李珂, 范永存, 邓冬梅, 等. 一种面向微电网中无线传感器网络的高能效压缩感知方法. 西南科技大学学报, 2018, 33(2): 88-95.
24. Wu Jian, Chen Zhigang, Wu Jia, et al. An energy efficient data transmission approach for low-duty-cycle wireless sensor networks. Peer Peer Netw Appl, 2020, 13(1): 255-268.
25. 郄有田, 宋萍, 郝创博. 无线传感网络中大数据量压缩感知编解码算法. 计测技术, 2019, 39(3): 28-33.
26. 叶昌荣. 微伏级 T 波交替多通道融合检测与估计研究. 重庆: 重庆大学, 2018.
27. 韩亮, 蒲秀娟, 刘茜. 结合多路 v-SVR 与 TFBSS 的胎儿心电信号提取. 仪器仪表学报, 2015, 36(6): 1381-1387.
28. Nejad M R E, Rizi F Y. An adaptive FECG extraction and analysis method using ICA, ICEEMDAN and wavelet shrinkage// 2018 Iranian Conference on Electrical Engineering (ICEE), Mashhad: IEEE, 2018. DOI: 10.1109/ICEE.2018.8472478.
29. Sahoo S, Kanungo B, Behera S, et al. Multiresolution wavelet transform based feature extraction and ECG classification to detect cardiac abnormalities. Measurement, 2017, 108: 55-66.
30. 孙纪光. 多导心电算法及应用系统的研制. 深圳: 深圳大学, 2017.
31. Gokhale T. Machine learning based identification of pathological heart sounds//2016 Computing in Cardiology Conference (CinC), 2016, 43: 2325-887X. DOI: 10.22489/CinC.2016.159-485.
32. 陈耿铎. 移动心电信息监护系统及心电监测算法的研究. 广州: 南方医科大学, 2018.
33. Jaafar H, Ramli N H, Nasir A S A., et al An improvement to the k-nearest neighbor classifier for ECG database. IOP Conference Series: Materials Science and Engineering, 2018, 318: 012046.
34. 姚成. 心电信号智能分析关键技术研究. 长春: 吉林大学, 2012.
35. 查雪帆, 杨丰, 吴俣南, 等. 结合迁移学习与深度卷积网络的心电分类研究. 中国医学物理学杂志, 2018, 35(11): 1307-1312.
36. 许莉莉, 师炜, 郭学谦, 等. 基于最小二乘支持向量机的心音分类识别研究. 中国医疗设备, 2017, 32(4): 38-41.
37. Nagendra V, Gude H, Sampath D, et al. Evaluation of support vector machines and random forest classifiers in a real-time fetal monitoring system based on cardiotocography data//2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Manchester: IEEE, 2017. DOI: 10.1109/CIBCB.2017.8058546.
38. 韩可都, 周礼杲. 应用改进的时序自适应噪声消除法作胎儿心电信号的处理. 北京生物医学工程, 1985, 1: 9-16.
39. Taralunga D, Wolf W, Strungaru R, et al. Abdominal signals: denoising by application of the event synchronuous canceller//2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon: IEEE, 2007: 566-569.
40. 袁野, 王夏天, 张子辰, 等. 基于小波变换和改进的瞬态独立成分分析融合算法的心电信号降噪方法. 中国医学物理学杂志, 2016, 33(4): 415-422.
41. 朱晓铭, 陈林海, 张帅, 等. 基于卷积神经网络重建十二导联心电图. 微电子学与计算机, 2019(9): 12-15.

Journal of Biomedical Engineering

A summary of research progress on intelligent information processing methods for pregnant women's remote monitoring

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content