Extraction of fetal electrocardiogram signal based on K-means Clustering
DOI:
https://doi.org/10.56294/dm202384Keywords:
ECG, Machine Learning, K-means, Fetal electrocardiogramsAbstract
Fetal electrocardiograms (ECG) provide crucial information for the interventions and diagnoses of pregnant women at the clinical level. Maternal signals are robust, making retrieval and detection of Fetal ECGs difficult. In this article, we propose a solution based on Machine Learning by adapting the k-means clustering to detect the fetal ECG by recording the ECGs. In our first preprocessing part, we tried normalized and segmented ECG waveform. Next, we used the Euclidean distance to measure similarity. To identify a certain number of centroids in our data, the results classified into two classes are represented in the last part through graphs and compared with other algorithms, such as the CNN classifier, to demonstrate the effectiveness of this innovative approach, which can be deployed in real-time
References
1. Moutaib, M., Ahajjam, T., Fattah, M., Farhaoui, Y., Aghoutane, B., & el Bekkali, M. (2022). Reduce the Energy Consumption of IOTs in the Medical Field. Digital Technologies and Applications, 259 268. https://doi.org/10.1007/978-3-031-02447-4_27.
2. Moutaib, M., Ahajjam, T., Fattah, M., Farhaoui, Y., & Aghoutane, B. (2021). Reduce the Energy Consumption of Connected Objects. Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning. https://doi.org/10.5220/0010728900003101
3. M. Moutaib, M. Fattah, Y. Farhaoui, Internet of things: Energy Consumption and Data Storage, Procedia Computer Science, Volume 175, 2020, Pages 609-614.
4. Monson, M.; Heuser, C.; Einerson, B.D.; Esplin, I.; Snow, G.; Varner, M.; Esplin, M.S. Evaluation of an external fetal electrocardiogram monitoring system: A randomized controlled trial. Am. J. Obstet. Gynecol. 2020, 223, e1–e244.
5. Zwanenburg, F.; Jongbloed, M.R.M.; Van Geloven, N.; Ten Harkel, A.D.J.; Van Lith, JMM; Haak, M.C. Assessment of human fetal cardiac autonomic nervous system development using color tissue Doppler imaging. Echocardiography 2021, 38, 974–981.
6. Fotiadou, E.; Xu, M.; Van Erp, B.; Van Sloun, R.J.G.; Vullings, R. Deep Convolutional Long Short-Term Memory Network for Fetal Heart Rate Extraction. In Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Montreal, QC, Canada, 20–24 July 2020; pp. 608–611.
7. M. Moutaib, T. Ahajjam, M. Fattah, Y. Farhaoui, B. Aghoutane, and M. el Bekkali (2021). Optimization of the Energy Consumption of Connected Objects. International Journal of Interactive Mobile Technologies (iJIM), 15(24), 176 190. https://doi.org/10.3991/ijim.v15i24.26985
8. Sulas, E.; Urru, M.; Tumbarello, R.; Raffo, L.; Pani, D. Systematic analysis of single-and multi-reference adaptive filters for non-invasive fetal electrocardiography. Math. Biosci. Eng. 2019, 17, 286–308.
9. Taha, L.; Abdel-Raheem, E. A null space-based blind source separation for fetal electrocardiogram signals. Sensors 2020, 20, 3536.
10. Barnova, K.; Martinek, R.; Jaros, R.; Kahankova, R.; Matonia, A.; Jezewski, M.; Czabanski, R.; Horoba, K.; Jezewski, J. A novel algorithm based on ensemble empirical mode decomposition for non-invasive fetal ECG extraction. PLoS ONE 2021, 16, e0256154
11. Wu, S.; Shen, Y.; Zhou, Z.; Lin, L.; Zeng, Y.; Gao, X. Research of fetal ECG extraction using wavelet analysis and adaptive filtering.Comput. Biol. Med. 2013, 43, 1622–1627.
12. Vasudeva, B.; Deora, P.; Pradhan, P.M.; Dasgupta, S. Efficient implementation of LMS adaptive filter-based FECG extraction on an FPGA. Healthc. Technol. Lett. 2020, 7, 125–131.
13. Ferranti, M.; Le, T.H.; Vandebril, R. A comparison between the complex symmetric based and classical computation of the singular value decomposition of normal matrices. Numer. Algorithms 2021, 67, 109–120.
14. Kumar, A.; Tomar, H.; Mehla, V.K.; Komaragiri, R.; Kumar, M. Stationary wavelet transform based ECG signal denoising method.ISA Trans. 2021, 114, 251–262.
15. Martinek, R.; Kahankova, R.; Jezewski, J.; Jaros, R.; Mohylova, J.; Fajkus, M.; Nedoma, J.; Janku, P.; Nazeran, H. Comparative effectiveness of ICA and PCA in extraction of fetal ECG from abdominal signals: Toward non-invasive fetal monitoring. Front. Physiol. 2018, 9, 648.
16. Sarafan, S.; Le, T.; Naderi, A.M.; Nguyen, Q.D.; Kuo, B.T.Y.; Ghirmai, T.; Han, H.D.; Lau, M.P.H.; Cao, H. Investigation of methods to extract fetal electrocardiogram from the mother's abdominal signal in practical scenarios. Technologies 2020, 8, 33.
17. Hyvärinen, A. Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Netw. 1999, 10, 626–634.
18. Yuan, L.; Zhou, Z.; Yuan, Y.;Wu, S. An improved FastICA method for fetal ECG extraction. Comput. Math. Methods Med. 2018,2018, 7061456
19. Kaleem, A.M.; Kokate, R.D. A survey on FECG extraction using neural network and adaptive filter. Soft Comput. 2021, 25,4379–4392.
20. Panigrahy, D.; Sahu, PK Extraction of fetal ECG signal by an improved method using extended Kalman smoother framework from single channel abdominal ECG signal. Australas. Phys. Eng. Sci. Med. 2017, 40, 191–207.
21. Vijayakumar, V.; Ummar, S.; Varghese, T.J.; Shibu, A.E. ECG noise classification using deep learning with feature extraction.Signal. Image Video P. 2022, 1–7.
22. Tseng, K.K.; Wang, C.; Xiao, T.J.; Chen, C.M.; Hassan, M.M.; Albuquerque, V.H.C. Sliding large kernel of deep learning algorithm for mobile electrocardiogram diagnosis. Comput. Electr. Eng. 2021, 96, 107521
23. Vo, K.; Le, T.; Rahmani, A.M.; Dutt, N.; Cao, H. An efficient and robust deep learning method with 1-D octave convolution to extract fetal electrocardiogram. Sensors 2020, 20, 3757.
24. Ting, Y.C.; Lo, F.W.; Tsai, P.Y. Implementation for fetal ECG detection from multi-channel abdominal recordings with 2D convolutional neural network. J. Signal. Process. Syst. 2021, 93, 1101–1113.
25. Mohebbian, M.R.; Vedaei, S.S.;Wahid, K.A.; Dinh, A.; Marateb, H.R.; Tavakolian, K. Fetal ECG extraction from maternal ECG using attention-based CycleGAN. IEEE. J. Biomed. Health 2022, 26, 515–526.
26. Gurve, D.; Krishnan, S. Separation of fetal-ECG from single-channel abdominal ECG using activation scaled non-negative matrix factorization. IEEE. J. Biomed. Health Inform. 2020, 24, 669–680.
27. Zhang, Y.; Yu, S. Single-lead noninvasive fetal ECG extraction by means of combining clustering and principal components analysis. Med. Biol. Eng. Comput. 2020, 58, 419–432.
28. Jaba, D.K.A.; Dhanalakshmi, S.R.K. An improved parallel sub-filter adaptive noise canceler for the extraction of fetal ECG. Biomed. Tech. 2021, 66, 503–514.
29. Liu, C.; Li, P.; Di, MC; Zhao, L.; Zhang, H.; Chen, Z. A multi-step method with signal quality assessment and fine-tuning procedure to locate maternal and fetal QRS complexes from abdominal ECG recordings. Physiol. Meas. 2014, 35, 1665–1683.
30. Mollakazemi, M.J.; Asadi, F.; Tajnesaei, M.; Ghaffari, A. Fetal QRS Detection in Noninvasive Abdominal Electrocardiograms Using Principal Component Analysis and DiscreteWavelet Transforms with Signal Quality Estimation. J. Biomed. Phys. Eng. 2021,11, 197–204.
31. Chafi, Saad-Eddine, et al. “Resource placement strategy optimization for smart grid application using 5G wireless networks.” International Journal of Electrical and Computer Engineering, Volume 12, Issue 4, Pages 3932 – 3942, 2022. https://doi.org/10.11591/ijece.v12i4.pp3932-3942
32. Chafi, Saad-Eddine, et al. “Cloud computing services, models and simulation tools.” International Journal of Cloud Computing, vol. 10, no. 5–6, pp. 533–547, 2021. https://doi.org/10.1504/IJCC.2021.120392
33. D. Daghouj, M. Fattah, S. Mazer, Y. Balboul, and M. El Bekkali, “UWB waveform for automotive short range radar,” International Journal on Engineering Applications, vol. 8, no. 4, pp. 158–164, Jul. 2020. https://doi.org/10.15866/irea.v8i4.18997
34. M. Abdellaoui, M. Fattah, “Characterization of Ultra Wide Band indoor propagation In 7th Mediterranean Congress of Telecommunications (CMT). IEEE, 2019, https://doi.org/10.1109/CMT.2019.8931367
35. Mamane, M. Fattah, M. el Ghazi, Y. Balboul, M. el Bekkali, and S. Mazer, “The impact of scheduling algorithms for real-time traffic in the 5G femto-cells network,” 9th International Symposium on Signal, Image, Video and Communications, ISIVC 2018 , Pages 147 – 1512, July 2018, https://doi.org/10.1109/ISIVC.2018.8709175
36. Farhaoui, Y., “Design and implementation of an intrusion prevention system” International Journal of Network Security, vol.19(5), pp. 675–683, 2017. DOI： 10.6633/IJNS.201709.19(5).04
37. Farhaoui, Y.and All, Big Data Mining and Analytics, 2023, 6(3), pp. I–II, DOI: 10.26599/BDMA.2022.9020045
38. Farhaoui, Y., “Intrusion prevention system inspired immune systems” Indonesian Journal of Electrical Engineering and Computer Science, vol. 2(1), pp. 168–179, 2016.
39. Farhaoui, Y. , "Big data analytics applied for control systems" Lecture Notes in Networks and Systems, 2018, 25, pp. 408–415. https://doi.org/10.1007/978-3-319-69137-4_36
40. Farhaoui, Y. and All, Big Data Mining and Analytics, 2022, 5(4), pp. I IIDOI: 10.26599/BDMA.2022.9020004
41. Alaoui, S.S., and all. "Hate Speech Detection Using Text Mining and Machine Learning", International Journal of Decision Support System Technology, 2022, 14(1), 80. DOI: 10.4018/IJDSST.286680
42. Alaoui, S.S., and all. ,"Data openness for efficient e-governance in the age of big data", International Journal of Cloud Computing, 2021, 10(5-6), pp. 522–532, https://doi.org/10.1504/IJCC.2021.120391
43. El Mouatasim, A., and all. "Nesterov Step Reduced Gradient Algorithm for Convex Programming Problems", Lecture Notes in Networks and Systems, 2020, 81, pp. 140–148. https://doi.org/10.1007/978-3-030-23672-4_11
44. Tarik, A., and all."Recommender System for Orientation Student" Lecture Notes in Networks and Systems, 2020, 81, pp. 367–370. https://doi.org/10.1007/978-3-030-23672-4_27
45. Sossi Alaoui, S., and all. "A comparative study of the four well-known classification algorithms in data mining", Lecture Notes in Networks and Systems, 2018, 25, pp. 362–373. https://doi.org/10.1007/978-3-319-69137-4_32
46. Farhaoui, Y."Teaching Computer Sciences in Morocco: An Overview", IT Professional, 2017, 19(4), pp. 12–15, 8012307. DOI: 10.1109/MITP.2017.3051325
47. Farhaoui, Y., "Securing a Local Area Network by IDPS Open Source", Procedia Computer Science, 2017, 110, pp. 416–421. https://doi.org/10.1016/j.procs.2017.06.106
Published
Issue
Section
License
Copyright (c) 2023 Mohammed Moutaib, Mohammed Fattah, Yousef Farhaoui, Badraddine Aghoutane, Moulhime El Bekkali (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
