Utilizing Deep Learning Methods for Heart Defect Identification via Electrocardiogram (ECG): A Literature Review

https://doi.org/10.48185/jaai.v5i1.1007

Authors

  • Darwan IAIN Syekh Nurjati Cirebon
  • Faiz Muqorrir Kaffah Department of Informatics, IAIN Syekh Nurjati Cirebon, West Java, Indonesia.
  • Heru Purnomo Kurniawan Department of Informatics, IAIN Syekh Nurjati Cirebon, West Java, Indonesia.
  • Lia Farhatuaini Department of Informatics, IAIN Syekh Nurjati Cirebon, West Java, Indonesia.
  • Ibnu Rusydi Department of Software Engineering, Universitas Dharmawangsa, Medan, Indonesia.

Keywords:

Electrocardiogram, deep learning, dataset, pre-processing, identification

Abstract

In recent years the application of Deep Learning is widely used in various fields of science, such as in the military, agriculture, health and even in other fields. In the field of health many studies use deep learning, especially related to heart disorders. Identification and detection of heart defects that are widely used are by using an electrocardiogram (ECG). Research related to ECG and the application of deep learning methods is very interesting for researchers, because researchers trace that there are still few studies that focus on researching related to it. This article aims to explain the trend of ECG research using deep learning approaches in recent years. We reviewed journals with the keyword title "ECG Deep Learning" and published from 2016 to October 2023. The articles that have been obtained are then classified based on the most frequently discussed topics including: data sets, pre-processing, feature extraction, and classification/identification methods. The approach used by some researchers is mostly to get the best results from the use of deep learning methods. This article will provide further explanation of the most widely used algorithms for ECG research with a deep learning approach. Of the deep learning methods used, almost 84% use the Convolutional Neural Network method. In this article, critical aspects of ECG research can be carried out in the future, namely the use of data in the form of other data from ECG, and the use of deep learning is a very big opportunity for researchers in the future.

Downloads

Download data is not yet available.

References

https://sehatnegeriku.kemkes.go.id/baca/rilis-media/20220929/0541166/penyakit-jantung-penyebab-utama-kematian-kemenkes-perkuat-layanan-primer/

H. A. Poonja, M. Soleman Ali Shah, M. A. Shirazi and R. Uddin, "Evaluation of ECG based Recognition of Cardiac Abnormalities using Machine Learning and Deep Learning," 2021 International Conference on Robotics and Automation in Industry (ICRAI), Rawalpindi, Pakistan, pp. 1-4, 2021, doi: 10.1109/ICRAI54018.2021.9651457.

L. C. N. Barbosa, A. Real, A. H. J. Moreira, V. Carvalho, J. L. Vilaça and P. Morais, "ECG Classification with Deep Learning Models – A Comparative Study," 2022 E-Health and Bioengineering Conference (EHB), Iasi, Romania, pp. 01-04, 2022, doi: 10.1109/EHB55594.2022.9991600.

M. Wu, Y. Lu, W. Yang and S.Y. Wong, “A Study on Arrhythmia via ECG Signal Classification Using the Convolutional Neural Network”, Front. Comput. Neurosci, 14:564015, 2021, doi: 10.3389/fncom.2020.564015

P. Zhao, D. Quan, W. Yu, X. Yang and X. Fu, "Towards Deep Learning-Based Detection Scheme with Raw ECG Signal for Wearable Telehealth Systems," 2019 28th International Conference on Computer Communication and Networks (ICCCN), Valencia, Spain, pp. 1-9, 2019, doi: 10.1109/ICCCN.2019.8847069.

R. Islam, M. Rahman, S. M. Ismail and S. Akter, "Transfer Learning in Deep Neural Network Model of ECG Signal Classification," 2022 International Conference on Recent Progresses in Science, Engineering and Technology (ICRPSET), Rajshahi, Bangladesh, pp. 1-4, 2022, doi: 10.1109/ICRPSET57982.2022.10188563.

https://a-fib.com/treatments-for-atrial-fibrillation/diagnostic-tests-2/the-ekg-signal/

S. Khan, W. H. Bhatti, F. Chaudhary, A, Shafique, M. Irfan, M. A. Teevno, and R. Qureshi, "A Deep Learning Framework for the Classification of ECG Signals," 2022 International Conference on Engineering and Emerging Technologies (ICEET), Kuala Lumpur, Malaysia, pp. 1-5, 2022, doi: 10.1109/ICEET56468.2022.10007143.

E. İzci, M. Değirmenci, M. A. Özdemir and A. Akan, "ECG Arrhythmia Detection with Deep Learning," 2020 28th Signal Processing and Communications Applications Conference (SIU), Gaziantep, Turkey, pp. 1-4, 2020, doi: 10.1109/SIU49456.2020.9302219.

X. Zhang, K. Gu, S. Miao, X. Zhang, Y. Yin, C. Wan, Y. Yu, J. Hu, Z. Wang, T. Shan, S. Jing, W. Wang, Y. Ge, Y. Chen, J. Guo, and Y. Liu, “Automated detection of cardiovascular disease by electrocardiogram signal analysis: a deep learning system”, Cardiovasc Diagn Ther, 10(2):227-235, 2020, doi: 10.21037/cdt.2019.12.

M. Cao, T. Zhao, Y. Li, W. Zhang, P. Benharash, and R. Ramezani, “ECG Heartbeat classification using deep transfer learning with Convolutional Neural Network and STFT technique”, The 4th International Conference on Computing and Data Science (CONF-CDS 2022), Journal of Physics: Conference Series, 2547, 01203, 2023, doi:10.1088/1742-6596/2547/1/0120.

F. Bozyigit, F. Erdemir, M. Sahin and D. Kilinc, "Classification of electrocardiogram (ECG) data using deep learning methods," 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Istanbul, Turkey, pp. 1-5, 2020, doi: 10.1109/ISMSIT50672.2020.9255000.

C. T. C. Arsene, R. Hankins and H. Yin, "Deep Learning Models for Denoising ECG Signals," 2019 27th European Signal Processing Conference (EUSIPCO), A Coruna, Spain, pp. 1-5, 2019, doi: 10.23919/EUSIPCO.2019.8902833.

Darwan and H. Mustafidah, “Use of Wavelets in Electrocardiogram Research: a Literature Review”, JUITA: Jurnal Informatika, Vol.9, No.1, pp.49-56, May, 2021.

J. Pan, and W. J. Tompkins, “A Real-Time QRS Detection Algorithm”, IEEE Transactions On Biomedical Engineering, Vol. BME-32, No. 3, March 1985.

X. Lei, Y. Zhang and Z. Lu, "Deep learning feature representation for electrocardiogram identification," 2016 IEEE International Conference on Digital Signal Processing (DSP), Beijing, China, pp. 11-14, 2016, doi: 10.1109/ICDSP.2016.7868505.

B. Pyakillya, N. Kazachenko and N. Mikhailovsky, “Deep Learning for ECG Classification”, Journal of Physics: Conference Series, Vol. 913, 2017, DOI 10.1088/1742-6596/913/1/012004

https://physionet.org/

http://2018.icbeb.org/Challenge.html

https://www.ukbiobank.ac.uk/

https://ascertain-dataset.github.io/

S. Katsigiannis and N. Ramzan, "DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals From Wireless Low-cost Off-the-Shelf Devices," in IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 1, pp. 98-107, Jan, 2018, doi: 10.1109/JBHI.2017.2688239.

T. Sweeney-Fanelli, and M. Imtiaz, “Automated Emotion Recognition Employing Wearable ECG Sensor and Deep-Learning”, TechRxiv, Preprint, 2023, https://doi.org/10.36227/techrxiv.23636304.v2

A. Scagnetto, G. Barbati, I. Gandin, C. Cappelletto, G. Baj, A. Cazzaniga, F. Cuturello, A. Ansuini, L. Bortolussi, and A. Di Lenarda, “Deep artificial neural network for prediction of atrial fibrillation through the analysis of 12-leads standard ECG”, Electrical Engineering and Systems Science, Signal Processing. 2022.

B. T. Lee, J. M. Kwon, J. Cho, W. Bae, H. Park, W. W. Seo, I. Cho, Y. Lee, J. Park, B. H. Oh, and K. H. Jeon, “Usefulness of Deep-Learning Algorithm for Detecting Acute Myocardial Infarction Using Electrocardiogram Alone in Patients With Chest Pain at Emergency Department: DAMI-ECG Study”, J Cardiovasc Interv., Apr;2(2):100-112, 2023, https://doi.org/10.54912/jci.2022.0028

R. Ao and G. He, “Image based deep learning in 12-lead ECG diagnosis”, Front. Artif. Intell, 5:1087370, 2023, doi: 10.3389/frai.2022.1087370

K. Miura, R. Yagi, H. Miyama, M. Kimura, H. Kanazawa, M. Hashimoto, S. Kobayashi, S. Nakahara, T. Ishikawa, I. Taguchi, M. Sano, K. Sato, K. Fukuda, R. C. Deo, C. A. MacRae, Y. Itabashi, Y. Katsumata, and S. Goto, “Deep learning-based model detects atrial septal defects from electrocardiography: a cross-sectional multicenter hospital-based study”, eClinicalMedicine, Volume. 63, 102141, September, 2023, doi:https://doi.org/10.1016/j.eclinm.2023.102141

J. Li, Y. Si, T. Xu, and S. Jiang, "Deep Convolutional Neural Network Based ECG Classification System Using Information Fusion and One-Hot Encoding Techniques", Mathematical Problems in Engineering, Article ID 7354081, 10 pages, 2018. https://doi.org/10.1155/2018/7354081

A. Peimankar and S. Puthusserypady, “DENS-ECG: A Deep Learning Approach for ECG Signal Delineation”, A preprint, May 19, 2020.

A. Darmawahyuni, S. Nurmaini, M. N. Rachmatullah, B. Tutuko, A. I. Sapitri, F. Firdaus, A. Fansyuri, and A. Predyansyah, “Deep learning-based electrocardiogram rhythm and beat features for heart abnormality classification”, PeerJ Comput. Sci, 8:e825, 2022, http://doi.org/10.7717/peerj-cs.825.

M. Naz, J. H. Shah, M. A. Khan, M. Sharif, M. Raza, and R. Damaševičius, “From ECG signals to images: a transformation based approach for deep learning”, PeerJ Comput. Sci, 7:e386, 2021, doi:10.7717/peerj-cs.386

S. C. Mohonta, M. A. Motin, and D. K. Kumar, “Electrocardiogram based arrhythmia classification using wavelet transform with deep learning model”, Sensing and Bio-Sensing Research, Volume 37, 100502, 2022, https://doi.org/10.1016/j.sbsr.2022.100502.

A. R. Yuniarti, F. A. Manurung, and S. Rizal, “Application of Neural Network for ECG-based Biometrics System Using QRS Features”, Journal of Computer Engineering, Electronics and Information Technology (COELITE), Volume 1, Issue 1, April, pp.22-31, 2022.

A. Dourson, R. Santilli, F. Marchesotti, J. Schneiderman, O. R. Stiel, F. Junior, M. Fitzke, N. Sithirangathan, E. Walleser, X. Qiao, and M. Parkinson, “PulseNet: Deep Learning ECG-signal classification using random augmentation policy and continous wavelet transform for canines”, Electrical Engineering and Systems Science, 2023, https://doi.org/10.48550/arXiv.230515454

F. Khan, X. Yu, Z. Yuan, and R. Au, “ECG classification using 1-D convolutional deep residual neural network”, PLoS ONE, 18(4), e0284791, 2023, https://doi.org/10.1371/journal.pone.0284791

O. Kovalchuk, P. Radiuk, O. Barmak, S. Petrovskyi, and I. Krak, “A Novel Feature Vector for ECG Classification using Deep Learning”, IntelITSIS’2023: 4th International Workshop on Intelligent Information Technologies & Systems of Information Security, March 22–24, Khmelnytskyi, Ukraine, 2023.

H. Ansaf, H. Najm, J. M. Atiyah, and O. A. Hassen, Improved Approach for Identification of Real and Fake Smile using Chaos Theory and Principal Component Analysis. Journal of Southwest Jiaotong University. 54. 1-11. 2019, doi:10.35741/issn.0258-2724.54.5.20.

Y. -H. Byeon, S. -B. Pan and K. -C. Kwak, "Ensemble Deep Learning Models for ECG-based Biometrics," 2020 Cybernetics & Informatics (K&I), Velke Karlovice, Czech Republic, pp. 1-5, 2020, doi: 10.1109/KI48306.2020.9039871.

N. Rahuja and S. K. Valluru, "A Comparative Analysis of Deep Neural Network Models using Transfer Learning for Electrocardiogram Signal Classification," 2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), Bangalore, India, pp. 285-290, 2021, doi: 10.1109/RTEICT52294.2021.9573692.

A. Adib, W. -P. Zhu and M. O. Ahmad, "Age Classification Based on ECG QRS Wave Using Deep Learning," 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET), Prague, Czech Republic, pp. 1-6, 2022, doi: 10.1109/ICECET55527.2022.9872674.

Han, J., Kamber, M., & Pei, J, Data Mining Concepts and Techniques (Third), Morgan Kaufmann Publishers, 2012.

M. Bramer, Principles of Data Mining, Second, Springer, 2013.

H. Abrishami, M. Campbell, C. Han, R. Czosek, and X. Zhou, "P-QRS-T localization in ECG using deep learning," 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Las Vegas, NV, USA, pp. 210-213, 2018, doi: 10.1109/BHI.2018.8333406.

E. K. Wang, L. Xi, R. Sun, F. Wang, L. Pan, C. Cheng, A. Dimitrakopoulou-Srauss, N. Zhe, and Y. Li, “A new deep learning model for assisted diagnosis on electrocardiogram”, Mathematical Biosciences and Engineering, 16(4): 2481-2491, 2019, doi: 10.3934/mbe.2019124

X. Gao, “Diagnosing Abnormal Electrocardiogram (ECG) via Deep Learning”, Practical Applications of Electrocardiogram, Edited by Umashankar Lakshmanadoss, 2019, DOI: 10.5772/intechopen.85509.

Z. Ebrahimi, M. Loni, M. Daneshtalab, and A. Gharehbaghi, “A review on deep learning methods for ECG arrhythmia classification”, Expert Systems with Applications: X, Volume 7, ISSN 2590-1885, 2020, https://doi.org/10.1016/j.eswax.2020.100033.

S. M. Rafi and S. Akthar, "ECG Classification using a Hybrid Deeplearning Approach," 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), Coimbatore, India, pp. 302-305, 2021,doi: 10.1109/ICAIS50930.2021.9395897.

E. Essa and X. Xie, "Multi-model Deep Learning Ensemble for ECG Heartbeat Arrhythmia Classification," 2020 28th European Signal Processing Conference (EUSIPCO), Amsterdam, Netherlands, pp. 1085-1089, 2021, doi: 10.23919/Eusipco47968.2020.9287520.

W. Liang, "Deep Learning-Based ECG Abnormality Identification Prediction and Analysis", Journal of Sensors, Article ID 3466787, 9 pages, 2022. https://doi.org/10.1155/2022/3466787

F. S. Butt, M. F. Wagner, J. Schäfer and D. G. Ullate, "Toward Automated Feature Extraction for Deep Learning Classification of Electrocardiogram Signals," in IEEE Access, vol. 10, pp. 118601-118616, 2022, doi: 10.1109/ACCESS.2022.3220670.

N. Diamant, P. D. Achille, L-C. Weng, E. S. Lau, S. Khurshid, S. Friedman, C. Reeder, P. Singh, X. Wang, G. Sarma, M. Ghadessi, J. Mielke, E. Elci, I. Kryukov, H. M. Eilken, A. Derix, P. T. Ellinor, C. D. Anderson, A. A. Philippakis, P. Batra, S. A. Lubitz, and J. E. Ho, “Deep learning on resting electrocardiogram to identify impaired heart rate recovery”, Cardiovascular Digital Health Journal, Volume 3, Issue 4, pp.161-170, 2022, ISSN 2666-6936, https://doi.org/10.1016/j.cvdhj.2022.06.001.

P. Elias, T. Poterucha, and V. Rajaram, L. M. Moller , V. Rodriguez, S. Bhave, R. T. Hahn, G. Tison, S. A. Abreau, J. Barrios, J. N. Torres, J. W. Hughes, M. V. Perez, J. Finer, S. Kodali, O. Khalique, N. Hamid, A. Schwartz, S. Homma, D. Kumaraiah, D. J. Cohen, M. S. Maurer, A. J. Einstein, T. Nazif, M. B. Leon, and A. J. Perotte, “Deep Learning Electrocardiographic Analysis for Detection of Left-Sided Valvular Heart Disease”, J Am Coll Cardiol, Aug, 80 (6) 613–626, 2022, https://doi.org/10.1016/j.jacc.2022.05.029

C. Vinod and S. P. Mathew, "Classification and Segmentation of Electrocardiogram Signals Using Machine Learning Approach," 2022 International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS), Kochi, India, pp. 1-6, 2022, doi: 10.1109/IC3SIS54991.2022.9885654.

S. H. Choi, H-G. Lee, S-D. Park, J-W. Bae, W. Lee, M-S. Kim, T-H. Kim, and W. K. Lee, “Electrocardiogram-based deep learning algorithm for the screening of obstructive coronary artery disease”, BMC Cardiovascular Disorders, 23:287, 2023, https://doi.org/10.1186/s12872-023-03326-4.

S. Dey, R. Pal, and S. Biswas, ‘Deep Learning Algorithms for Efficient Analysis of ECG Signals to Detect Heart Disorders’, Biomedical Engineering. IntechOpen, Dec. 21, 2022. doi: 10.5772/intechopen.103075.

M. Ganeshkumar,V. Ravi, V. Sowmya, E. A. Gopalakrishnan, and K. P. Soman, "Explainable Deep Learning-Based Approach for Multilabel Classification of Electrocardiogram", IEEE Transactions on Engineering Management, vol. 70, no. 8, pp. 2787-2799, Aug. 2023, doi: 10.1109/TEM.2021.3104751.

J. Irungu, T. Oladunni, M. Denis, E. Ososanya, and R. Muriithi, "A CNN Transfer Learning -Electrocardiogram (ECG) Signal Approach to Predict COVID-19," 2023 15th International Conference on Computer and Automation Engineering (ICCAE), Sydney, Australia, pp. 367-371, 2023, doi: 10.1109/ICCAE56788.2023.10111114.

H. Nursalim, A. Bustamam, Hermawan, and D. Sarwinda, "Classification of Electrocardiogram Signal Using Deep Learning Models," 2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE), Jakarta, Indonesia, pp. 767-772, 2023,doi: 10.1109/ICCoSITE57641.2023.10127690.

H. Saadi, M. Ferroukhi, Y. L. Elghandja, and F. Lahmari, "Low-Cost ECG Monitoring System with Classification Using Deep Learning," 2023 International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS), BLIDA, Algeria, pp. 1-6, 2023, doi: 10.1109/ICAECCS56710.2023.10104707.

F. F. Liu, C. Y. Liu, L. N. Zhao, X. Y. Zhang, X. L. Wu, X. Y. Xu, Y. L. Liu, C. Y. Ma, S. S. Wei, Z. Q. He, J. Q. Li, and N. Y. Kwee, “An open access database for evaluating the algorithms of ECG rhythm and morphology abnormal detection” Journal of Medical Imaging and Health Informatics, 8(7): 1368–1373, 2018.

Published

2024-03-20

How to Cite

Darwan, Kaffah, F. M. ., Kurniawan, H. P. ., Farhatuaini, L. ., & Rusydi, I. . (2024). Utilizing Deep Learning Methods for Heart Defect Identification via Electrocardiogram (ECG): A Literature Review . Journal of Applied Artificial Intelligence, 5(1), 28–40. https://doi.org/10.48185/jaai.v5i1.1007