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：

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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.	黃靖銳, 張衛社. 重視危重孕產婦管理早期識別孕產婦危象. 中華產科急救電子雜志, 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.	梁淑銀, 劉桂娟, 蔣社姣. 早期干預對妊高征高齡產婦的心臟結構及妊娠結局改善情況的探究. 中國醫藥指南, 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 動態心電圖和體外心電監測/遠程監測專家共識 (1): 動態心電監測方法, 高潔, 張煒鑫, 王新康, et al. 譯. 實用心電學雜志, 2019, 28(3): 153-160.
6.	李迎新, 黃河. 國家衛生與健康管理大數據平臺在醫藥衛生體制改革中的作用. 國際生物醫學工程雜志, 2018, 41(1): 1-10.
7.	田寧, 陳奕, 閆亭亭. 胎兒心電技術在胎兒監護中的研究和應用進展. 醫學綜述, 2018, 24(1): 101-106.
8.	張九賓, 張丕狀, 杜坤坤. 基于 GPS 的無線傳感器網絡時間同步技術. 傳感器與微系統, 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.	鮑文靜. 多通道混合心電信號建模及胎兒心電信號提取算法研究. 哈爾濱: 哈爾濱工業大學, 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, 吳水才, 袁麗, 等. 基于 BP 神經網絡的胎兒心電提取算法研究. 生物醫學工程與臨床, 2018, 22(3): 237-243.
13.	祁清. 研究胎心監護在待產孕婦中的應用. 實用婦科內分泌電子雜志, 2019, 6(12): 80.
14.	徐雯. 胎兒心電提取和胎心率變異分析算法研究. 杭州: 杭州電子科技大學, 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.	劉牮, 樓光海, 黃微. 結合雙樹復小波變換和滑動平均濾波的心電信號去噪方法. 電子測量技術, 2018, 41(19): 112-117.
17.	陸堯勝, 魏守一, 秦如意, 等. 一種胎心率基線自動識別算法: 中國, 201210383132.8. 2013-02-27.
18.	司菁菁, 許培, 程銀波. 自適應閾值的 1-bit 壓縮感知算法. 高技術通訊, 2019, 29(2): 134-141.
19.	彭向東, 張華, 劉繼忠. 基于過完備字典的體域網壓縮感知心電重構. 自動化學報, 2014, 40(7): 1421-1432.
20.	張屹, 陳立軍, 蔣慧勇. 基于大數據的無線傳感網絡數據采集的研究. 信息技術與網絡安全, 2019, 38(9): 39-43.
21.	吳連濤. 面向稀疏信號的物聯網高效傳輸體系及關鍵技術研究. 杭州: 浙江大學, 2017.
22.	李宏. 基于壓縮感知的低功耗體域網調度算法研究. 上海: 上海交通大學, 2014.
23.	李珂, 范永存, 鄧冬梅, 等. 一種面向微電網中無線傳感器網絡的高能效壓縮感知方法. 西南科技大學學報, 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, 郝創博. 無線傳感網絡中大數據量壓縮感知編解碼算法. 計測技術, 2019, 39(3): 28-33.
26.	葉昌榮. 微伏級 T 波交替多通道融合檢測與估計研究. 重慶: 重慶大學, 2018.
27.	韓亮, 蒲秀娟, 劉茜. 結合多路 v-SVR 與 TFBSS 的胎兒心電信號提取. 儀器儀表學報, 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.	孫紀光. 多導心電算法及應用系統的研制. 深圳: 深圳大學, 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.	陳耿鐸. 移動心電信息監護系統及心電監測算法的研究. 廣州: 南方醫科大學, 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. 心電信號智能分析關鍵技術研究. 長春: 吉林大學, 2012.
35.	查雪帆, 楊豐, 吳俁南, 等. 結合遷移學習與深度卷積網絡的心電分類研究. 中國醫學物理學雜志, 2018, 35(11): 1307-1312.
36.	許莉莉, 師煒, 郭學謙, 等. 基于最小二乘支持向量機的心音分類識別研究. 中國醫療設備, 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.	韓可都, 周禮杲. 應用改進的時序自適應噪聲消除法作胎兒心電信號的處理. 北京生物醫學工程, 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, 張子辰, 等. 基于小波變換和改進的瞬態獨立成分分析融合算法的心電信號降噪方法. 中國醫學物理學雜志, 2016, 33(4): 415-422.
41.	朱曉銘, 陳林海, 張帥, 等. 基于卷積神經網絡重建十二導聯心電圖. 微電子學與計算機, 2019(9): 12-15.

1. 黃靖銳, 張衛社. 重視危重孕產婦管理早期識別孕產婦危象. 中華產科急救電子雜志, 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. 梁淑銀, 劉桂娟, 蔣社姣. 早期干預對妊高征高齡產婦的心臟結構及妊娠結局改善情況的探究. 中國醫藥指南, 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 動態心電圖和體外心電監測/遠程監測專家共識 (1): 動態心電監測方法, 高潔, 張煒鑫, 王新康, et al. 譯. 實用心電學雜志, 2019, 28(3): 153-160.
6. 李迎新, 黃河. 國家衛生與健康管理大數據平臺在醫藥衛生體制改革中的作用. 國際生物醫學工程雜志, 2018, 41(1): 1-10.
7. 田寧, 陳奕, 閆亭亭. 胎兒心電技術在胎兒監護中的研究和應用進展. 醫學綜述, 2018, 24(1): 101-106.
8. 張九賓, 張丕狀, 杜坤坤. 基于 GPS 的無線傳感器網絡時間同步技術. 傳感器與微系統, 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. 鮑文靜. 多通道混合心電信號建模及胎兒心電信號提取算法研究. 哈爾濱: 哈爾濱工業大學, 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, 吳水才, 袁麗, 等. 基于 BP 神經網絡的胎兒心電提取算法研究. 生物醫學工程與臨床, 2018, 22(3): 237-243.
13. 祁清. 研究胎心監護在待產孕婦中的應用. 實用婦科內分泌電子雜志, 2019, 6(12): 80.
14. 徐雯. 胎兒心電提取和胎心率變異分析算法研究. 杭州: 杭州電子科技大學, 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. 劉牮, 樓光海, 黃微. 結合雙樹復小波變換和滑動平均濾波的心電信號去噪方法. 電子測量技術, 2018, 41(19): 112-117.
17. 陸堯勝, 魏守一, 秦如意, 等. 一種胎心率基線自動識別算法: 中國, 201210383132.8. 2013-02-27.
18. 司菁菁, 許培, 程銀波. 自適應閾值的 1-bit 壓縮感知算法. 高技術通訊, 2019, 29(2): 134-141.
19. 彭向東, 張華, 劉繼忠. 基于過完備字典的體域網壓縮感知心電重構. 自動化學報, 2014, 40(7): 1421-1432.
20. 張屹, 陳立軍, 蔣慧勇. 基于大數據的無線傳感網絡數據采集的研究. 信息技術與網絡安全, 2019, 38(9): 39-43.
21. 吳連濤. 面向稀疏信號的物聯網高效傳輸體系及關鍵技術研究. 杭州: 浙江大學, 2017.
22. 李宏. 基于壓縮感知的低功耗體域網調度算法研究. 上海: 上海交通大學, 2014.
23. 李珂, 范永存, 鄧冬梅, 等. 一種面向微電網中無線傳感器網絡的高能效壓縮感知方法. 西南科技大學學報, 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, 郝創博. 無線傳感網絡中大數據量壓縮感知編解碼算法. 計測技術, 2019, 39(3): 28-33.
26. 葉昌榮. 微伏級 T 波交替多通道融合檢測與估計研究. 重慶: 重慶大學, 2018.
27. 韓亮, 蒲秀娟, 劉茜. 結合多路 v-SVR 與 TFBSS 的胎兒心電信號提取. 儀器儀表學報, 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. 孫紀光. 多導心電算法及應用系統的研制. 深圳: 深圳大學, 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. 陳耿鐸. 移動心電信息監護系統及心電監測算法的研究. 廣州: 南方醫科大學, 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. 心電信號智能分析關鍵技術研究. 長春: 吉林大學, 2012.
35. 查雪帆, 楊豐, 吳俁南, 等. 結合遷移學習與深度卷積網絡的心電分類研究. 中國醫學物理學雜志, 2018, 35(11): 1307-1312.
36. 許莉莉, 師煒, 郭學謙, 等. 基于最小二乘支持向量機的心音分類識別研究. 中國醫療設備, 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. 韓可都, 周禮杲. 應用改進的時序自適應噪聲消除法作胎兒心電信號的處理. 北京生物醫學工程, 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, 張子辰, 等. 基于小波變換和改進的瞬態獨立成分分析融合算法的心電信號降噪方法. 中國醫學物理學雜志, 2016, 33(4): 415-422.
41. 朱曉銘, 陳林海, 張帥, 等. 基于卷積神經網絡重建十二導聯心電圖. 微電子學與計算機, 2019(9): 12-15.

Journal of Biomedical Engineering

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

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