Abstract
Fetal Arrhythmia is an abnormal heart rhythm caused by a problem in the fetus's heart's electrical system. Monitoring fetal ECG is vital to delivering useful information regarding the fetus's condition. Acute fetal arrhythmia may result in cardiac failure or death. Thus the early detection of fetal arrhythmia is important. Current approaches use several electrodes to acquire abdomen ECG from the mother, which causes discomfort. Moreover, ECG signals acquired are extremely noisy and have artifacts from breathing and muscle contraction, which hardens ECG extraction. In this study, a machine learning framework for fetal arrhythmia detection. The proposed framework uses only a single abdomen ECG. It employs multiple filtering techniques to remove noise and artifacts. It also extracts 16 significant features from multiple domains, including (time, frequency, and time-frequency features. Finally, it utilizes four machine learning classifiers to detect arrhythmia. The highest accuracy of 93.12% is achieved using Boosted decision tree classifier. The performance of the proposed method shows its competing ability compared to other methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Thomas, K.: Arrhythmia in Children. Loyola Medicine, July 21 2015. https://loyolamedicine.org/pediatrics/arrhythmia-children. Accessed 09 Jul 2021
Arrhythmias in Children; Causes, Symptoms, Management & Treatment. Cleveland Clinic. https://my.clevelandclinic.org/health/diseases/14788-arrhythmias-in-children. Accessed 09 Jul 2021
Nijhuis, I.J.M., et al.: Fetal heart rate in relation to its variation in normal and growth retarded fetuses. Eur. J. Obstet. Gynecol. Reprod. Biol. 89(1), 27–33 (2000). https://doi.org/10.1016/S0301-2115(99)00162-1
Lamesgin, G., Kassaw, Y., Assefa, D.: Extraction of fetal ECG from abdominal ECG and heart rate variability analysis. In: Abraham, A., Krömer, P., Snasel, V. (eds.) Afro-European Conference for Industrial Advancement. AISC, vol. 334, pp. 65–76. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-13572-4_5
Rooijakkers, M., Rabotti, C., Bennebroek, M., van Meerbergen, J., Mischi, M.: Low-complexity R-peak detection in ECG signals: A preliminary step towards ambulatory fetal monitoring. In: Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1761–1764 (2011). https://doi.org/10.1109/IEMBS.2011.6090503
Moody, G.B., Mark, R.G.: The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 20(3), 45–50 (2001). https://doi.org/10.1109/51.932724
Apsana, S., Suresh, M.G., Aneesh, R.P.: A novel algorithm for early detection of fetal arrhythmia using ICA. In: Proceedings of the 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), July 2017, pp. 1277–1283 (2017). https://doi.org/10.1109/ICICICT1.2017.8342753
Devika, M.G., Gopakumar, C., Aneesh, R.P., Nayar, G.R.: Myocardial infarction detection using hybrid BSS method. In: Proceedings of the 2016 International Conference on Communication Systems and Networks (ComNet), July 2016, pp. 167–172 (2016). https://doi.org/10.1109/CSN.2016.7824008
Rodrigues, R.: Fetal beat detection in abdominal ECG recordings: Global and time adaptive approaches. Physiol. Meas. 35(8), 1699–1711 (2014). https://doi.org/10.1088/0967-3334/35/8/1699
La, F.W., Tsai, P.Y.: Deep learning for detection of fetal ECG from Multi-channel abdominal leads. In: Proceedings of the 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018, pp. 1397–1401 (March 2019). https://doi.org/10.23919/APSIPA.2018.8659503
Ganguly, B., Biswas, S., Ghosh, S., Maiti, S., Bodhak, S.: A deep learning framework for eye melanoma detection employing convolutional neural network. In: Proceedings of the 2019 International Conference on Computer, Electrical Communication Engineering (ICCECE), pp. 1–4 (2019). https://doi.org/10.1109/ICCECE44727.2019.9001858
Ganguly, B., et al.: Wavelet kernel-based convolutional neural network for localization of partial discharge sources within a power apparatus. IEEE Trans. Industr. Inf. 17(3), 1831–1841 (2021). https://doi.org/10.1109/TII.2020.2991686
Acharya, U.R., et al.: Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals. Appl. Intell. 49(1), 16–27 (2018). https://doi.org/10.1007/s10489-018-1179-1
Singh, S., Pandey, S., Pawar, U., Janghel, R.: Classification of ECG arrhythmia using recurrent neural networks. Procedia Comput. Sci. 132, 1290–1297 (2018). https://doi.org/10.1016/j.procs.2018.05.045
Ktata, S., Ouni, K., Ellouze, N.: ECG signal maxima detection using wavelet transform. In: Proceedings of the 2006 IEEE International Symposium on Industrial Electronics, vol. 1, pp. 700–703 (2006). https://doi.org/10.1109/ISIE.2006.295547
Finley, J.P., Nugent, S.T.: Heart rate variability in infants, children and young adults. J. Auton. Nerv. Syst. 51(2), 103–108 (1995). https://doi.org/10.1016/0165-1838(94)00117-3
Pavel, M.S.R., Islam, M.R., Siddiqee, A.M.: Fetal arrhythmia detection using fetal ECG signal. In: Proceedings of the 2019 IEEE International Conference on Telecommunications and Photonics (ICTP) December 2019, pp. 1–4 (2019). https://doi.org/10.1109/ICTP48844.2019.9041789
Surya, K., Abdul Majeed, K.K.: Multichannel probabilistic framework for prenatal diagnosis of fetal arrhythmia using ECG. In: Palesi, M., Trajkovic, L., Jayakumari, J., Jose, J. (eds.) Second International Conference on Networks and Advances in Computational Technologies. TCSCI, pp. 141–150. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-49500-8_13
Barnova, K., et al.: A novel algorithm based on ensemble empirical mode decomposition for non-invasive fetal ECG extraction. PLoS ONE 16(8), e0256154 (2021). https://doi.org/10.1371/journal.pone.0256154
Krupa, A.J.D., Dhanalakshmi, S., Kumar, R.: An improved parallel sub-filter adaptive noise canceler for the extraction of fetal ECG. Biomedical Engineering / Biomedizinische Technik 66(5), 503–514 (2021). https://doi.org/10.1515/bmt-2020-0313
Corino, V.D.A., Iozzia, L., Scarpini, G., Mainardi, L.T., Lombardi, F.: A simple model to detect atrial fibrillation via visual imaging. Biomed. Eng. / Biomed. Tech. 65(6), 721–728 (2020). https://doi.org/10.1515/bmt-2019-0153
Gowtham, A., Anirudh, L., Sreeja, B., Aakash, B., Adittya, S.: Detection of arrhythmia using ECG waves with deep convolutional neural networks. In: Proceedings of the 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), November 2020, pp. 1390–1396 (2020). https://doi.org/10.1109/ICECA49313.2020.9297467
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Al-Saadany, D., Attallah, O., Elzaafarany, K., Nasser, A. (2022). A Machine Learning Framework for Fetal Arrhythmia Detection via Single ECG Electrode. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13351. Springer, Cham. https://doi.org/10.1007/978-3-031-08754-7_60
Download citation
DOI: https://doi.org/10.1007/978-3-031-08754-7_60
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-08753-0
Online ISBN: 978-3-031-08754-7
eBook Packages: Computer ScienceComputer Science (R0)