Fetal electrocardiogram signal extraction and analysis method combining fast independent component analysis algorithm and convolutional neural network_Journal of Biomedical Engineering

Authors：

YANG Yuyao , HAO Jingyu ,  WU Shuicai

Department of Biomedical Engineering, Beijing University of Technology, Beijing 100124, P. R. China;

Corresponding author：

WU Shuicai, Email: wushuicai@bjut.edu.cn

Keywords：

Fetal electrocardiogram; QRS complex waves; Fast independent component analysis; Singular value decomposition; Convolutional neural network

DOI：

10.7507/1001-5515.202210071

Video：

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Fetal electrocardiogram (ECG) signals provide important clinical information for early diagnosis and intervention of fetal abnormalities. In this paper, we propose a new method for fetal ECG signal extraction and analysis. Firstly, an improved fast independent component analysis method and singular value decomposition algorithm are combined to extract high-quality fetal ECG signals and solve the waveform missing problem. Secondly, a novel convolutional neural network model is applied to identify the QRS complex waves of fetal ECG signals and effectively solve the waveform overlap problem. Finally, high quality extraction of fetal ECG signals and intelligent recognition of fetal QRS complex waves are achieved. The method proposed in this paper was validated with the data from the PhysioNet computing in cardiology challenge 2013 database of the Complex Physiological Signals Research Resource Network. The results show that the average sensitivity and positive prediction values of the extraction algorithm are 98.21% and 99.52%, respectively, and the average sensitivity and positive prediction values of the QRS complex waves recognition algorithm are 94.14% and 95.80%, respectively, which are better than those of other research results. In conclusion, the algorithm and model proposed in this paper have some practical significance and may provide a theoretical basis for clinical medical decision making in the future.

Citation： YANG Yuyao, HAO Jingyu, WU Shuicai. Fetal electrocardiogram signal extraction and analysis method combining fast independent component analysis algorithm and convolutional neural network. Journal of Biomedical Engineering, 2023, 40(1): 51-59. doi: 10.7507/1001-5515.202210071 Copy

1.	Monson M, Heuser C, Einerson B D, et al. Evaluation of an external fetal electrocardiogram monitoring system:a randomized controlled trial. American Journal of Obstetrics and Gynecology, 2020, 223(2): 244.
2.	Zwanenburg F, Jongbloed M R M, van Geloven N, et al. Assessment of human fetal cardiac autonomic nervous system development using color tissue Doppler imaging. Echocardiography, 2021, 38(6): 974-981.
3.	Krupa A J D, Dhanalakshmi S, Kumar R. Joint time-frequency analysis and non-linear estimation for fetal ECG extraction. Biomedical Signal Processing and Control, 2022, 75: 103569.
4.	Kang T, Lee S, Lee N, et al. Baseflow separation using the digital filter method: review and sensitivity analysis. Water, 2022, 14(3): 485.
5.	Thunga S S, Muthu R K. Adaptive noise cancellation using improved LMS algorithm. Singapore: Advances in Intelligent Systems and Computing, 2020: 971-980.
6.	Yang C, Dai N, Wang Z, et al. Cardiopulmonary auscultation enhancement with a two-stage noise cancellation approach. Biomedical Signal Processing and Control, 2023, 79: 104175.
7.	Taha L Y, Abdel-Raheem E. Fetal ECG extraction using input-mode and output-mode adaptive filters with blind source separation. Canadian Journal of Electrical and Computer Engineering, 2020, 43(4): 295-304.
8.	Kahankova R, Mikolasova M, Martinek R. Optimization of adaptive filter control parameters for non-invasive fetal electrocardiogram extraction. PloS one, 2022, 17(4): e0266807.
9.	Nadila H, Danudirdjo D, Zakaria H. Fetal heart rate detection algorithm from noninvasive fetal electrocardiogram//International Biomedical Instrumentation and Technology Conference, Yogyakarta: IEEE, 2021: 71-76.
10.	Zhang D, Zhao H, Yang J. Signal denoising of double-beam and double-scattering laser doppler velocimetry based on wavelet layering. Optik, 2020, 202: 163545.
11.	Singh R, Rajpal N, Mehta R. Non-invasive single channel integration model for fetal ECG extraction and sustainable fetal healthcare using wavelet framework. Multimedia Tools and Applications, 2022. DOI: 10.1007/s11042-022-13534-3.
12.	Zhang Y, Yu S. Single-lead noninvasive fetal ECG extraction by means of combining clustering and principal components analysis. Medical & Biological Engineering & Computing, 2020, 58(2): 419-432.
13.	Mirza S, Bhole K, Singh P. Fetal ECG extraction and QRS detection using independent component analysis// International Colloquium on Signal Processing & Its Applications, Langkawi: IEEE, 2020: 157-161.
14.	陈森涛. 领域自适应算法研究. 广州: 华南理工大学, 2020.
15.	Liu H, Chen D, Sun G. Detection of fetal ECG R wave from single-lead abdominal ECG using a combination of RR time-series smoothing and template-matching approach. IEEE Access, 2019, 7: 66633-66643.
16.	Yuan L, Zhou Z, Yuan Y, et al. An improved FastICA method for fetal ECG extraction. Computational and Mathematical Methods in Medicine, 2018, 2018: 7061456.
17.	Keenan E, Karmakar C, Udhayakumar R K, et al. Detection of fetal arrhythmias in non-invasive fetal ECG recordings using data-driven entropy profiling. Physiological Measurement, 2022, 43(2): 025008.
18.	Silva I, Behar J, Sameni R, et al. Noninvasive fetal ECG: the PhysioNet/computing in cardiology challenge 2013//Computing in Cardiology 2013, Zaragoza: IEEE, 2013: 149-152.
19.	Jezewski J, Matonia A, Kupka T, et al. Determination of fetal heart rate from abdominal signals: evaluation of beat-to-beat accuracy in relation to the direct fetal electrocardiogram. Biomedizinische Technik/Biomedical Engineering, 2012, 57(5): 383-394.
20.	Anisha M, Kumar S S, Nithila E E, et al. Detection of fetal cardiac anomaly from composite abdominal electrocardiogram. Biomedical Signal Processing and Control, 2021, 65: 102308.
21.	Jallouli M, Arfaoui S, Ben Mabrouk A, et al. Clifford wavelet entropy for fetal ECG extraction. Entropy, 2021, 23(7): 844.
22.	吴嘉荣, 陈明, 谭晓林, 等. 胎儿心电图QRS波增宽的临床意义. 江苏实用心电学杂志, 2012, 21(5): 370-371.
23.	Wang H, Li H, Wei X, et al. Recognition of high-specificity hERG K+ channel inhibitor-induced arrhythmia in cardiomyocytes by automated template matching. Microsystems & Nanoengineering, 2021, 7: 24.
24.	Mollakazemi M J, Asadi F, Tajnesaei M, et al. Fetal QRS detection in noninvasive abdominal electrocardiograms using principal component analysis and discrete wavelet transforms with signal quality estimation. Journal of Biomedical Physics & Engineering, 2021, 11(2): 197-204.
25.	Mertes G, Long Y, Liu Z, et al. A deep learning approach for the assessment of signal quality of non-invasive foetal electrocardiography. Sensors, 2022, 22(9): 3303.
26.	Zhong W, Liao L, Guo X, et al. A deep learning approach for fetal QRS complex detection. Physiological Measurement, 2018, 39(4): 045004.
27.	Lee J S, Seo M, Kim S W, et al. Fetal QRS detection based on convolutional neural networks in noninvasive fetal electrocardiogram//International Conference on Frontiers of Signal Processing, Corfu: IEEE, 2018: 75-78.
28.	de Micheaux H L, Resendiz M, Rivet B, et al. Residual convolutional autoencoder combined with a non-negative matrix factorization to estimate fetal heart rate//Annual International Conference of the IEEE Engineering in Medicine & Biology Society, Glasgow: IEEE, 2022: 1292-1295.
29.	Vo K, Le T, Rahmani A M, et al. An efficient and robust deep learning method with 1-D octave convolution to extract fetal electrocardiogram. Sensors, 2020, 20(13): 3757.
30.	韩亮, 蔡文涛, 蒲秀娟, 等. 结合FastICA与EKF的腹部源胎儿心电信号提取. 仪器仪表学报, 2021, 42(7): 116-125.

1. Monson M, Heuser C, Einerson B D, et al. Evaluation of an external fetal electrocardiogram monitoring system:a randomized controlled trial. American Journal of Obstetrics and Gynecology, 2020, 223(2): 244.
2. Zwanenburg F, Jongbloed M R M, van Geloven N, et al. Assessment of human fetal cardiac autonomic nervous system development using color tissue Doppler imaging. Echocardiography, 2021, 38(6): 974-981.
3. Krupa A J D, Dhanalakshmi S, Kumar R. Joint time-frequency analysis and non-linear estimation for fetal ECG extraction. Biomedical Signal Processing and Control, 2022, 75: 103569.
4. Kang T, Lee S, Lee N, et al. Baseflow separation using the digital filter method: review and sensitivity analysis. Water, 2022, 14(3): 485.
5. Thunga S S, Muthu R K. Adaptive noise cancellation using improved LMS algorithm. Singapore: Advances in Intelligent Systems and Computing, 2020: 971-980.
6. Yang C, Dai N, Wang Z, et al. Cardiopulmonary auscultation enhancement with a two-stage noise cancellation approach. Biomedical Signal Processing and Control, 2023, 79: 104175.
7. Taha L Y, Abdel-Raheem E. Fetal ECG extraction using input-mode and output-mode adaptive filters with blind source separation. Canadian Journal of Electrical and Computer Engineering, 2020, 43(4): 295-304.
8. Kahankova R, Mikolasova M, Martinek R. Optimization of adaptive filter control parameters for non-invasive fetal electrocardiogram extraction. PloS one, 2022, 17(4): e0266807.
9. Nadila H, Danudirdjo D, Zakaria H. Fetal heart rate detection algorithm from noninvasive fetal electrocardiogram//International Biomedical Instrumentation and Technology Conference, Yogyakarta: IEEE, 2021: 71-76.
10. Zhang D, Zhao H, Yang J. Signal denoising of double-beam and double-scattering laser doppler velocimetry based on wavelet layering. Optik, 2020, 202: 163545.
11. Singh R, Rajpal N, Mehta R. Non-invasive single channel integration model for fetal ECG extraction and sustainable fetal healthcare using wavelet framework. Multimedia Tools and Applications, 2022. DOI: 10.1007/s11042-022-13534-3.
12. Zhang Y, Yu S. Single-lead noninvasive fetal ECG extraction by means of combining clustering and principal components analysis. Medical & Biological Engineering & Computing, 2020, 58(2): 419-432.
13. Mirza S, Bhole K, Singh P. Fetal ECG extraction and QRS detection using independent component analysis// International Colloquium on Signal Processing & Its Applications, Langkawi: IEEE, 2020: 157-161.
14. 陈森涛. 领域自适应算法研究. 广州: 华南理工大学, 2020.
15. Liu H, Chen D, Sun G. Detection of fetal ECG R wave from single-lead abdominal ECG using a combination of RR time-series smoothing and template-matching approach. IEEE Access, 2019, 7: 66633-66643.
16. Yuan L, Zhou Z, Yuan Y, et al. An improved FastICA method for fetal ECG extraction. Computational and Mathematical Methods in Medicine, 2018, 2018: 7061456.
17. Keenan E, Karmakar C, Udhayakumar R K, et al. Detection of fetal arrhythmias in non-invasive fetal ECG recordings using data-driven entropy profiling. Physiological Measurement, 2022, 43(2): 025008.
18. Silva I, Behar J, Sameni R, et al. Noninvasive fetal ECG: the PhysioNet/computing in cardiology challenge 2013//Computing in Cardiology 2013, Zaragoza: IEEE, 2013: 149-152.
19. Jezewski J, Matonia A, Kupka T, et al. Determination of fetal heart rate from abdominal signals: evaluation of beat-to-beat accuracy in relation to the direct fetal electrocardiogram. Biomedizinische Technik/Biomedical Engineering, 2012, 57(5): 383-394.
20. Anisha M, Kumar S S, Nithila E E, et al. Detection of fetal cardiac anomaly from composite abdominal electrocardiogram. Biomedical Signal Processing and Control, 2021, 65: 102308.
21. Jallouli M, Arfaoui S, Ben Mabrouk A, et al. Clifford wavelet entropy for fetal ECG extraction. Entropy, 2021, 23(7): 844.
22. 吴嘉荣, 陈明, 谭晓林, 等. 胎儿心电图QRS波增宽的临床意义. 江苏实用心电学杂志, 2012, 21(5): 370-371.
23. Wang H, Li H, Wei X, et al. Recognition of high-specificity hERG K+ channel inhibitor-induced arrhythmia in cardiomyocytes by automated template matching. Microsystems & Nanoengineering, 2021, 7: 24.
24. Mollakazemi M J, Asadi F, Tajnesaei M, et al. Fetal QRS detection in noninvasive abdominal electrocardiograms using principal component analysis and discrete wavelet transforms with signal quality estimation. Journal of Biomedical Physics & Engineering, 2021, 11(2): 197-204.
25. Mertes G, Long Y, Liu Z, et al. A deep learning approach for the assessment of signal quality of non-invasive foetal electrocardiography. Sensors, 2022, 22(9): 3303.
26. Zhong W, Liao L, Guo X, et al. A deep learning approach for fetal QRS complex detection. Physiological Measurement, 2018, 39(4): 045004.
27. Lee J S, Seo M, Kim S W, et al. Fetal QRS detection based on convolutional neural networks in noninvasive fetal electrocardiogram//International Conference on Frontiers of Signal Processing, Corfu: IEEE, 2018: 75-78.
28. de Micheaux H L, Resendiz M, Rivet B, et al. Residual convolutional autoencoder combined with a non-negative matrix factorization to estimate fetal heart rate//Annual International Conference of the IEEE Engineering in Medicine & Biology Society, Glasgow: IEEE, 2022: 1292-1295.
29. Vo K, Le T, Rahmani A M, et al. An efficient and robust deep learning method with 1-D octave convolution to extract fetal electrocardiogram. Sensors, 2020, 20(13): 3757.
30. 韩亮, 蔡文涛, 蒲秀娟, 等. 结合FastICA与EKF的腹部源胎儿心电信号提取. 仪器仪表学报, 2021, 42(7): 116-125.

Journal of Biomedical Engineering

Fetal electrocardiogram signal extraction and analysis method combining fast independent component analysis algorithm and convolutional neural network

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