Detecting Cardiac Arrhythmias through Electrocardiography: Current Advancement and Future Direction from the Standpoint of Deep Learning
Article Sidebar
Page Numbers:
982-1006
Digital Object Identifier:
10.58578/ajstea.v3i3.5902
Viewed: 236 times
Downloaded: 201 times
Downloaded: 115 times
LYAS Publisher cordially invites qualified professionals to serve as Editors or Reviewers. Your expertise will make an important contribution to maintaining and strengthening the academic quality of our publications. Interested applicants are kindly requested to complete the application form at the following link: Editors & Reviewers
Please feel free to contact us if you need any further information about the submission process or if you have any additional questions.
Authors:Abdulhafiz Sabo1*, Abdulsalam Y. Gital2, Abass Babayaro3, Jamilu Usman Waziri4, Sabo Sani Muhammad5, D. M. Nazif6
Copyright :Authors retain copyright and grant the journal right of first publication.
Main Article Content
Abstract
Cardiac arrhythmia remains a leading cause of mortality worldwide and is a significant risk factor for the development of various cardiovascular diseases. Electrocardiography (ECG) is a widely utilized diagnostic tool for the early detection of cardiac arrhythmias, and recent advancements in deep learning (DL) have demonstrated notable success in automating and enhancing this process. Despite the growing body of research, there remains a lack of a focused and comprehensive literature review dedicated specifically to the application of deep learning techniques in ECG-based arrhythmia detection. Addressing this gap, the present study systematically reviews recent contributions that apply deep learning algorithms to ECG data for the identification and classification of cardiac arrhythmias. The review categorizes relevant studies based on architectural approaches, datasets used, performance metrics, and clinical relevance. A novel taxonomy is proposed to classify the domains of deep learning applications in ECG, including supervised, unsupervised, and hybrid learning models, as well as real-time and offline diagnostic systems. The review also identifies current limitations in model generalizability, data quality, and interpretability. Based on these insights, future research directions are proposed to guide the development of more robust, transparent, and clinically applicable deep learning systems for cardiac arrhythmia detection. This review serves as a foundational reference for researchers and practitioners seeking to advance the intersection of artificial intelligence and cardiovascular diagnostics.
Citation Metrics:
Downloads
Citation Metrics & Similar Scopus Articles
-
Tungjitwitayakul J. (2027)SPECIES-SPECIFIC QPCR FOR DETECTING TRIBOLIUM CASTANEUM LEVELS IN WHEAT FLOURIndian Journal of Entomology, 89(2)
-
Deepti D. (2027)MIRROR REPEATS IN PHEROMONE DETECTING GUSTATORY RECEPTOR GENE GR37 OF AEDES AEGYPTIIndian Journal of Entomology, 89(1), 142-151
-
Tan H. (2027)New pulse sequence for improving cardiac black-blood and PSIR MRI quality in cases of arrhythmiaMagnetic Resonance Letters, 7(2)
Article Details
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
References
Acharya UR, Fujita H, Lih OS, Adam M, Tan JH, Chua CK (2017) Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network. Know Based Syst 132:62–71
Altan G, Allahverdi N, Kutlu Y, (2018). A multistage deep learning algorithm for detecting arrhythmia. Paper presented at the 2018 1st international conference on computer applications & information security (ICCAIS)
Amrani M, Hammad M, Jiang F, Wang K, Amrani A (2018). Very deep feature extraction and fusion for arrhythmias detection. Neural Comput Appl 30(7):2047–2057
Asif, A., Wani, A., Ahmad, F., Saduf, B., & Khan, I. (2020). Advances in Deep Learning.
Singapore: Springer Nature.
Assodiky H, Syarif I, Badriyah T (2017). Deep learning algorithm for arrhythmia detection. Paper presented at the 2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)
Azzeh M, Nassif AB, Shahin I, Attili I, Shaalan K (2019) Speech recognition using deep neural networks: A systematic review. IEEE Access 7:19143–19165
Brakel, Laurent, C., Pereyra, G., P., Zhang, Y., & Bengio, Y. (2016). Batch Normalized Recurrent Neural Networks. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ( IEEE., 2657–2661.
Byeon Y-H, Pan S-B, Kwak K-C (2020). Ensemble Deep Learning Models for ECG-based Biometrics. Paper presented at the 2020 Cybernetics & Informatics (K&I)
Cai W, Chen Y, Guo J, Han B, Shi Y, Ji L, Luo J (2020). Accurate detection of atrial fibrillation from 12-lead ECG using deep neural net- work. Comput Biol Med 116:103378
Cambria E, Young T, Hazarika D, Poria S (2018) Recent trends in deep learning based natural language processing. IEEE Comput Intell Mag 13(3):55–75
Chang K-C, Hsieh P-H, Wu M-Y, Wang Y-C, Chen J-Y, Tsai F-J, Huang T-C (2020). Usefulness of Machine-Learning-Based Detection and Classification of Cardiac Arrhythmias with 12-Lead Electro cardiograms. Canadian Journal of Cardiology
Chen Y Yao Z, (2018) Arrhythmia classification from single lead ecg by multi-scale convolutional neural networks. Paper presented at the Conf Proc IEEE Eng Med Biol Soc
Cheng M, Sori WJ, Jiang F, Khan A, Liu S (2017). Recurrent neural network based classification of ECG signal features for obstruction of sleep apnea detection. Paper presented at the 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC).
Cho H-M, Park H, Dong S-Y, Youn I (2019). Ambulatory and Laboratory Stress Detection Based on Raw Electrocardiogram Signals Using a Convolutional Neural Network. Sensors 19(20):4408
Darmawahyuni A, Nurmaini S (2019). Deep Learning with Long Short- Term Memory for Enhancement Myocardial Infarction Classification. Paper presented at the 2019 6th International Conference on Instrumentation, Control, and Automation (ICA)
Deng, L. (2014) A tutorial survey of architectures, algorithms, 3. https://doi.org/10.1017/ATSIP. 2013. 99
Deshmane M, Madhe S (2018) ECG based biometric human identification using convolutional neural network in smart health applications. Paper presented at the 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA)
Dey D, Chaudhuri S, Munshi S (2018). Obstructive sleep apnoea detection using convolutional neural network based deep learning framework. Biomed Eng Lett 8(1):95–100
Dilaveris, P.E.; Gialafos, E.J.; Sideris, S.K.; Theopistou, A.M.; Andrikopoulos, G.K.; Kyriakidis, M.; Gialafos, J.E.; Toutouzas, P.K (1998). Simple electrocardiographic markers for the prediction of paroxysmal idiopathic atrial fibrillation. Am. Heart J, 135, 733–738. [CrossRef]
Diker A, Engin A (2019). Feature extraction of ECG signal by using deep feature. Paper presented at the 2019 7th International Symposium on Digital Forensics and Security (ISDFS)
Dokur Z, Ölmez T (2020). Heartbeat classification by using a convolutional neural network trained with Walsh functions. Neural Computing and Applications, 1–20
E. Essa and X. and Xie, "An Ensemble of Deep Learning-Based Multi-Model for ECG Heartbeats Arrhythmia Classification,” IEEE Access, vol. 9, pp. 103452-103464, 2021
Elgendi, M.; Eskofier, B.; Dokos, S.; Abbott, D. (2014). Revisiting QRS detection methodologies for portable, wearable, battery-operated, and wireless ECG systems. PLoS ONE, 9, e84018. [CrossRef]
Elman, J. L. (1990). Finding Structure in Time. Cognitive Science, 14(2), 179–211.
Falkner, K., Thilakaratne, M., & Atapattu, T. (2019). A systematic review on literature-based discovery workflow [PeerJ]. PeerJ Computer Science, 5, e235.
Farman, Ahmad, J., H., & Jan, Z. (2019). Deep Learning Methods and Applications. https://doi.org/10.1007/978-981-13-3459-7
Gashler M, Pierson HA S (2017) Deep learning in robotics: a review of recent research. Adv Robot 31(16):821–835
Hanbay, K. (2019). Deep neural network based approach for ECG classification using hybrid differential features and active learning. IET Signal Process. 13, 165–175. doi:10.1049/iet-spr.2018.5103
Hammad Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.
Hao C, Wibowo S, Majmudar M, Rajput KS (2019). Spectro-Temporal Feature Based Multi-Channel Convolutional Neural Network for ECG Beat Classification. Paper presented at the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Haque IRI, Neubert J (2020) Deep learning approaches to biomedical image segmentation. Inform Med Unlocked 18:100297
Hassan, S.U.; Zahid, M.S.M.; Abdullah, T.A.A.; Husain, K. (2022) Classification of cardiac arrhythmia using a convolutional neural network and bi-directional long short-term memory. Digit. Health, 8, 1–13. [CrossRef]
Hinton GE, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554
Jun S, Lee J-G, Cho Y-W, Lee H, Kim GB, Seo JB, Kim N (2017) Deep learning in medical imaging: general overview. Korean J Radiol 18(4):570–584
Kaouter K, Mohamed T, Sofiene D, Abbas D, Fouad M (2019). Full training convolutional neural network for ECG signals classification. Paper presented at the AIP Conference Proceedings
Khan, M. A., and Kim, Y. (2021). Cardiac arrhythmia disease classification using lstm deep learning approach. Comput. Mater. Continua 67, 427–443. doi:10.32604/cmc.2021.014682
Khan, I., Wani, A., Ahmad, F., Saduf, B., & Asif, A., (2020). Advances in Deep Learning. Singapore: Springer Nature.
Kim M-G, Choi C, Pan SB (2020) Ensemble Networks for User Recognition in Various Situations Based on Electrocardiogram. IEEE Access 8:36527–36535
Koppad, D. (2021). Arrhythmia classification using deep learning: A review. WSEAS Trans. Biol. Biomed. 18, 96–105. doi:10.37394/23208.2021.18.11
Li D, Zhang J, Zhang Q, Wei X (2017). Classification of ECG signals based on 1D convolution neural network. Paper presented at the 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom)
Liang X, Zhang Y, Wang J, Ye Q, Liu Y, Tong J. Diagnosis of COVID 19 pneumonia based on graph convolutional network. Front Med. 2021;7:1071.
Masci, J., Ciresan, D., Meier, U., & Schmidhuber, J. (2012). Multi-Column Deep Neural Network for Traffic Sign Classification. Neural Networks, 32, 333–338.
Mathews SM, Kambhamettu C, Barner KE (2018). A novel application of deep learning for single-lead ECG classification. Comput Biol Med 99:53–62
Midani, W., Ouarda, W., and Ayed, M. B. (2023). DeepArr: an investigative tool for arrhythmia detection using a contextual deep neural network from electrocardiograms (ECG) signals. Biomed. Signal Process. Control 85, 104954. doi:10.1016/j.bspc.2023.104954
Mostafa SS, Mendonça F, Morgado-Dias F, Ravelo-García A (2017). SpO2 based sleep apnea detection using deep learning. Paper presented at the 2017 IEEE 21st international conference on intelligent engineering systems (INES)
Ojha, M. K., Wadhwani, S., Wadhwani, A. K., and Shukla, A. (2022). Automatic detection of arrhythmias from an ECG signal using an auto-encoder and SVM classifier. Phys. Eng. Sci. Med. 45, 665–674. doi:10.1007/s13246-022-01119-1
Pandey SK, Janghel RR (2019). Automatic detection of arrhythmia from imbalanced ECG database using CNN model with SMOTE. Australasian Phys Eng Sci Med 42(4):1129–1139
Pak M, Kim S (2017) A review of deep learning in image recognition. Paper presented at the 2017 4th international conference on computer applications and information processing technology (CAIPT)
Pyakillya B, Kazachenko N, Mikhailovsky N (2017). Deep learning for ECG classification. Paper presented at the Journal of physics: conference series, 16(2), 596–606. https://doi.org/10.1109/TCBB.2017.2784434
Rav, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-perez, J., Lo, B., & Yang, G.-Z. (2017). Deep Learning for Health Informatics. IEEE Journal of Biomedical and Health Informatics, 21(1), 4–21.
Ribeiro AH, Ribeiro MH, Paixão GM, Oliveira DM, Gomes PR, Canaz- art JA, Wagner M Jr (2020). Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun 11(1):1–9
Saadatnejad S, Oveisi M, Hashemi M (2019). LSTM-based ECG classification for continuous monitoring on personal wearable devices. IEEE J biomedical health Inf 24(2):515–523
Saduf, B., Wani, A., Ahmad, F., Asif, A., & Khan, I. (2020). Advances in Deep Learning. Singapore: Springer Nature.
Sannino G, De Pietro G (2018). A deep learning approach for ECG- based heartbeat classification for arrhythmia detection. Future Generation Computer Systems 86:446–455
Sharma A, Garg N, Patidar S, San Tan R, Acharya UR (2020). Automated pre-screening of arrhythmia using hybrid combination of Fourier-Bessel expansion and LSTM. Computers in biology and medicine, 103753
Shaker AM, Tantawi M, Shedeed HA, Tolba MF (2020). Generalization of Convolutional Neural Networks for ECG Classification Using Generative Adversarial Networks. IEEE Access 8:35592–35605
Shen, J.-Y., Sun, H., Qu, Q., Sun, W., and Kong, X.-Q. (2021). The application of Deep Learning in electrocardiogram: where we came from and where we should go? Int. J. Cardiol. 337, 71–78. doi:10.1016/j.ijcard.2021.05.017
Shepley AJ (2019) Deep Learning For Face Recognition: A CriticalAnalysis. arXiv preprint arXiv:1907.12739
Singh S, Pandey SK, Pawar U, Janghel RR (2018). Classification of ECG arrhythmia using recurrent neural networks. Procedia Comput Sci 132:1290–1297
Taha TM, Alom MZ, Yakopcic C, Westberg S, Sidike P, Nasrin MS, Asari VK (2019) A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3):292
Taji, B., Chan, A. D., and Shirmohammadi, S. (2017). False alarm reduction in atrial fibrillation detection using deep belief networks. IEEE Trans. Instrum. Meas. 67, 1124–1131. doi:10.1109/tim.2017.2769198
Tao, Ma, X., Z., Wang, Y., Yu, H., & Wang, Y. (2015). Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transportation Research Part C: Emerging Technologies, 54, 187–197. https://doi.org/10.1016/j.trc.2015.03.014
Vullings R (2019). Fetal Electrocardiography and Deep Learning for Prenatal Detection of Congenital Heart Disease. Paper presented at the 2019 Computing in Cardiology (CinC)
Wani, A., Ahmad, F., Saduf, B., Asif, A., & Khan, I. (2020). Advances in Deep Learning. Singapore: Springer Nature.
Wang K, Zhang S, Hammad M (2019). A novel two-dimensional ECG feature extraction and classification algorithm based on convolution neural network for human authentication. Future Generation Computer Systems 101:180–196
Wang X (2016) Deep learning in object recognition, detection, and segmentation. Found Trends Signal Process 8(4):217–382
Weidt, F., & Silva, R. (2016) Systematic Literature Review in Computer Science-A Practical Guide. Relatórios Técnicos Do DCC/UFJF, 1.).
Wu Z, Pan S, Chen F, Long G, Zhang C, Philip SY. A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst. 2020;32(1):4–24.
Xia Y, Wulan N, Wang K, Zhang H (2018). Detecting atrial fibrillation by deep convolutional neural networks. Comput Biol Med 93:84–92
Xiong Z, Stiles MK, Zhao J (2017). Robust ECG signal classification for detection of atrial fibrillation using a novel neural network. Paper presented at the 2017 computing in Cardiology (CinC)
Xu SS, Mak M-W, Cheung C-C (2018). Towards end-to-end ECG clas sification with raw signal extraction and deep neural networks. IEEE J biomedical health Inf 23(4):1574–1584
Yildirim Ö (2018). A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification. Com put Biol Med 96:189–202
Yuanlu Li, Renfei Qian, Kun Li, (2022) "Inter-patient arrhythmia classification with improved deep residual convolutional neural network," Computer Methods and Programs in Biomedicine, vol. 214, pp. 106582.
Zhang, C., Wang, G., Zhao, J., Gao, P., Lin, J., and Yang, H. (2017). “Patient-specific ECG classification based on recurrent neural networks and clustering technique,” 13th IASTED International Conference on Biomedical Engineering, Austria, 20-21 February 2017.
Zhou, Y., Hong, S., Shang, J., Xiao, C., and Sun, J. (2020). Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review. Comput. Biol. Med. 122, 103801. doi:10.1016/j.compbiomed.2020.103801