A fetal electrocardiogram signal extraction method based on long short term memory network optimized by genetic algorithm_Journal of Biomedical Engineering

Authors：

QIAN Long ,  WANG Wenbo , CHEN Guici , YU Min

Department of Mathematical Statistics, College of Science, Wuhan University of Science and Technology, Wuhan 430065, P.R.China;

Corresponding author：

WANG Wenbo, Email: wangwenbo@wust.edu.cn

Keywords：

fetal electrocardiogram signals; genetic algorithm; long short term memory network; nonlinear estimation

DOI：

10.7507/1001-5515.202004063

Video：

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Fetal electrocardiogram signal extraction is of great significance for perinatal fetal monitoring. In order to improve the prediction accuracy of fetal electrocardiogram signal, this paper proposes a fetal electrocardiogram signal extraction method (GA-LSTM) based on genetic algorithm (GA) optimization with long and short term memory (LSTM) network. Firstly, according to the characteristics of the mixed electrocardiogram signal of the maternal abdominal wall, the global search ability of the GA is used to optimize the number of hidden layer neurons, learning rate and training times of the LSTM network, and the optimal combination of parameters is calculated to make the network topology and the mother body match the characteristics of the mixed signals of the abdominal wall. Then, the LSTM network model is constructed using the optimal network parameters obtained by the GA, and the nonlinear transformation of the maternal chest electrocardiogram signals to the abdominal wall is estimated by the GA-LSTM network. Finally, using the non-linear transformation obtained from the maternal chest electrocardiogram signal and the GA-LSTM network model, the maternal electrocardiogram signal contained in the abdominal wall signal is estimated, and the estimated maternal electrocardiogram signal is subtracted from the mixed abdominal wall signal to obtain a pure fetal electrocardiogram signal. This article uses clinical electrocardiogram signals from two databases for experimental analysis. The final results show that compared with the traditional normalized minimum mean square error (NLMS), genetic algorithm-support vector machine method (GA-SVM) and LSTM network methods, the method proposed in this paper can extract a clearer fetal electrocardiogram signal, and its accuracy, sensitivity, accuracy and overall probability have been better improved. Therefore, the method could extract relatively pure fetal electrocardiogram signals, which has certain application value for perinatal fetal health monitoring.

Citation： QIAN Long, WANG Wenbo, CHEN Guici, YU Min. A fetal electrocardiogram signal extraction method based on long short term memory network optimized by genetic algorithm. Journal of Biomedical Engineering, 2021, 38(2): 257-267. doi: 10.7507/1001-5515.202004063 Copy

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19.	蒲秀娟, 曾孝平, 韩亮, 等. 基于经验模式分解和最小二乘支持向量机的胎儿心电信号提取. 四川大学学报: 工程科学版, 2010, 42(6): 158-165.
20.	Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput, 1997, 9(8): 1735-1780.
21.	Deihimi A, Orang O, Showkati H. Short-term electric load and temperature forecasting using wavelet echo state networks with neural reconstruction. Energy, 2013, 57(3): 382-401.
22.	Cai M, Liu J. Maxout neurons for deep convolutional and LSTM neural networks in speech recognition. Speech Commun, 2016, 77(3): 53-64.
23.	Zhou C, Sun C, Liu Z, et al. A C-LSTM neural network for text classification. Comput Sci, 2015, 1(4): 39-44.
24.	Fischer T, Krauss C. Deep learning with long short-term memory networks for financial market predictions. Eur J Oper Res, 2018, 270(2): 654-669.
25.	Assaleh K. Extraction of fetal electrocardiogram using adaptive neuro-fuzzy inference systems. IEEE Trans Biomed Eng, 2007, 54(1): 59-68.
26.	Holland J H, Reitman J S. Cognitive systems based on adaptive algorithms. ACM SIGART Bull, 1977(63): 49.

1. 贾文娟. 胎儿心电信号检测方法及监护系统的研究. 北京: 北京工业大学, 2011.
2. 祖秋雨, 林勇. 基于平稳小波变换的胎儿心电提取方法. 中国医学物理学杂志, 2017, 34(12): 1249-1253.
3. Furrukh S, Tarig B, Maha S, et al. Fetal ECG extraction exploiting joint sparse supports in a dual dictionary framework. Biomed Signal Process Control, 2019, 48(2): 46-60.
4. Gurve D, Pant J K, Krishnan S. Real-time fetal ECG extraction from multichannel abdominal ECG using compressive sensing and ICA. Annu Int Conf IEEE Eng Med Biol Soc, 2017: 2794-2797.
5. 李朝兰, 方滨, 李慧杰, 等. 应用自相关和独立分量分析的胎儿心电信号提取. 中国医学物理学杂志, 2016, 33(8): 838-843.
6. Kim C M, Park H M, Kim T, et al. FPGA implementation of ICA algorithm for blind signal separation and adaptive noise canceling. IEEE Trans Neural Netw, 2003, 14(5): 1038-1046.
7. 邵文婷, 方滨, 王普, 等. 稀疏信号的盲源分离求解方法提取胎儿心电信号. 生物医学工程学杂志, 2009, 26(6): 1206-1210.
8. Vasudeva B, Deora P, Pradhan P M, et al. Efficient implementation of LMS adaptive filter-based FECG extraction on an FPGA. Healthc Technol Lett, 2020, 7(5): 125-131.
9. 袁丽, 吴水才, 袁延超, 等. 一种变步长 LMS 算法提取胎儿心电. 中国医疗设备, 2018, 33(3): 15-17, 21.
10. 宋盟春, 熊念. 基于匹配滤波的胎儿心电信号检测系统的研究与设计. 中国医疗器械信息, 2014, 20(3): 42-46.
11. Dessì A, Pani D, Raffo L. An advanced algorithm for fetal heart rate estimation from non-invasive low electrode density recordings. Physiol Meas, 2014, 35(8): 1621-1636.
12. Sutha P, Jayanthi V E. Fetal electrocardiogram extraction and analysis using adaptive noise cancellation and wavelet transformation techniques. J Med Syst, 2017, 42(1): 21.
13. Wu Shuicai, Shen Yanni, Zhou Zhuhuang, et al. Research of fetal ECG extraction using wavelet analysis and adaptive filtering. Comput Biol Med, 2013, 43(10): 1622-1627.
14. Zhang N, Zhang J, Li H, et al. A novel technique for fetal ECG extraction using single-channel abdominal recording. Sensors (Basel), 2017, 17(3): 457.
15. 曾兆山, 沈海斌, 陆昊俊. 基于 SVD-LSSVM 的单导联胎儿心电提取算法. 电子技术, 2016, 45(4): 4-8.
16. de Lathauwerl, de Moor B, Vandewalle J. Fetal electrocardiogram extraction by blind source subspace separation. IEEE Trans Biomed Eng, 2000, 47(5): 567-572.
17. 贾文娟, 杨春兰, 钟果程, 等. 基于自适应线性神经网络的胎儿心电信号提取. 北京生物医学工程, 2010, 29(6): 575-580.
18. Panigrahy D, Sahu P K. Extraction of fetal electrocardiogram (ECG) by extended state Kalman filtering and adaptive neuro-fuzzy inference system (ANFIS) based on single channel abdominal recording. Sadhana, 2015, 40(4): 1091-1104.
19. 蒲秀娟, 曾孝平, 韩亮, 等. 基于经验模式分解和最小二乘支持向量机的胎儿心电信号提取. 四川大学学报: 工程科学版, 2010, 42(6): 158-165.
20. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput, 1997, 9(8): 1735-1780.
21. Deihimi A, Orang O, Showkati H. Short-term electric load and temperature forecasting using wavelet echo state networks with neural reconstruction. Energy, 2013, 57(3): 382-401.
22. Cai M, Liu J. Maxout neurons for deep convolutional and LSTM neural networks in speech recognition. Speech Commun, 2016, 77(3): 53-64.
23. Zhou C, Sun C, Liu Z, et al. A C-LSTM neural network for text classification. Comput Sci, 2015, 1(4): 39-44.
24. Fischer T, Krauss C. Deep learning with long short-term memory networks for financial market predictions. Eur J Oper Res, 2018, 270(2): 654-669.
25. Assaleh K. Extraction of fetal electrocardiogram using adaptive neuro-fuzzy inference systems. IEEE Trans Biomed Eng, 2007, 54(1): 59-68.
26. Holland J H, Reitman J S. Cognitive systems based on adaptive algorithms. ACM SIGART Bull, 1977(63): 49.

Journal of Biomedical Engineering

A fetal electrocardiogram signal extraction method based on long short term memory network optimized by genetic algorithm

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