84 36
Full Length Article
Volume 3 , Issue 1, PP: 43-53 , 2021


Electrocardiogram Classification Based on Deep Convolutional Neural Networks: A Review

Authors Names :   Rozin Majeed Abdullah   1     Adnan Mohsin Abdulazeez   2  

1  Affiliation :  Master Student at ICT Department, Duhok Polytechnic University, Duhok-Kurdistan Region, Iraq

    Email :  rozin.abdullah@dpu.edu.krd

2  Affiliation :  Duhok Polytechnic University, Duhok-Kurdistan Region, Iraq,

    Email :  adnan.mohsin@dpu.edu.krd

Doi   :  10.5281/zenodo.4642772

Received August 10, 2020 Revised October 22, 2020 Accepted March 03, 2021

Abstract :

Due to many new medical uses, the value of ECG classification is very demanding. There are some Machine Learning (ML) algorithms currently available that can be used for ECG data processing and classification. The key limitations of these ML studies, however, are the use of heuristic hand-crafted or engineered characteristics of shallow learning architectures. The difficulty lies in the probability of not having the most suitable functionality that will provide this ECG problem with good classification accuracy. One choice suggested is to use deep learning algorithms in which the first layer of CNN acts as a feature. This paper summarizes some of the key approaches of ECG classification in machine learning, assessing them in terms of the characteristics they use, the precision of classification important physiological keys ECG biomarkers derived from machine learning techniques, and statistical modeling and supported simulation.

Keywords :

ECG classification , deep learning , machine learning , convolutional neural networks.

References :

[1].      Mortazavi BJ, Downing NS, Bucholz EM, Dharmarajan K, Manhapra A, Li S-X, et al. Analysis of machine learning techniques for heart failure readmissions. Circ Cardiovasc Qual Outcomes. 2016;9(6):629–40.

[2].      Attia, Z. I., Kapa, S., Lopez-Jimenez, F., McKie, P. M., Ladewig, D. J., Satam, G., et al. (2019). Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram

[3].      Benjamin, E. J., Blaha, M. J., Chiuve, S. E., Cushman, M., Das, S. R., Deo, R., et al. (2017). Heart disease and stroke statistics-2017 update: a report from the American heart association. Circulation 135, e146–e603.

[4].      Celin, S., and Vasanth, K. (2018). ECG signal classification using various machine learning techniques. J. Med. Syst. 42:241.

[5].      Diker, A., Avci, D., Avci, E., and Gedikpinar, M. (2019). A new technique for ECG signal classification genetic algorithm Wavelet Kernel extreme learning machine. Optik 180, 46–55.

[6].      Heden  B,  Ohlsson,  M  et  al  1996  Detection  of  frequently  overlooked  electrocardiographic  lead  reversals using artificial neural networks The American journal of cardiology78 (5) p 600–604.

[7].      Rajpurkar  P,  Hannun  Awni  Y,  Haghpanahi  M,  Bourn  С  and  Ng  Y  A  2017  Cardiologist-Level  Arrhythmia Detection with Convolutional Neural Networks (Preprint arXiv:1707.01836v1).

[8].      El-Khafif  S  H  and  El-Brawany  M  A  2013  Artificial  Neural  Network-Based  Automated  ECG  Signal Classifier ISRN Biomedical Engineering2013(261917) 6 p .

[9].      Weems  A,  Harding  M  and  Choi  A  2016  Classification  of  the  ECG  Signal  Using  Artificial  Neural   Network   Proceedings   of   the   3rd   International   Conference   on   Intelligent   Technologies and Engineering Systems (ICITES2014) p 545–555.

[10].   Gupta A, Thomas B, Kumar P, Kumar Snn and Kumar Y 2014 Neural Network based indicative ECG   classification   5th   International   Conference   –   Confluence   The   Next   Generation   Information Technology Summit (Noida, 2014) p 277–279.

[11].   Jianbo Y, Nguyen M. N, San P. P, Li X and Krishnaswamy S 2015 Deep Convolutional Neural Networks on Multichannel Time Series for Human Activity Recognition Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015) AAAI Press 3995-4001.

[12].   Wong  S  C,  Gatt  A,  Stamatescu  V,  McDonnell  M  D  2016  Understanding  data  augmentation  for classification: when to warp? (Preprint arXiv:1609.08764v2).

[13].   Le  Guennec  A,  Malinowski  S  and  Tavenard  R  2016  Data  Augmentation  for  Time  Series  Classification  using  Convolutional  Neural  Networks  ECML/PKDD  Workshop  on  Advanced  Analytics and Learning on Temporal Data (Sep 2016, Riva Del Garda, Italy) 8 p.

[14].   Salamon  J  and  Bello  J  P  2017  Deep  Convolutional  Neural  Networks  and  Data  Augmentation  for Environmental Sound Classification IEEE Signal Processing Letters24 (3) p 279–283.

[15].   AF Classification from a short single lead ECG recording: the PhysioNet/Computing in Cardiology Challenge 2017.

[16].   D. Q. Zeebaree, A. M. Abdulazeez, D. A. Zebari, H. Haron and H. Nuzly, "Multi-level fusion in ultrasound for cancer detection based on uniform lbp features," Computers, Materials & Continua, vol. 66, no.3, pp. 3363–3382, 2021.

[17].   Zeebaree, Diyar Qader, Adnan Mohsin Abdulazeez, Omer Mohammed Salih Hassan, Dilovan Asaad Zebari, and Jwan Najeeb Saeed. "Hiding Image by Using Contourlet Transform." (2020).

[18].   Y. Wang et al. A short-time multifractal approach for arrhythmia detection based on fuzzy neural network IEEE Trans. Biomed. Eng. (2001).

[19].   Zebari, Nichirvan Asaad, Dilovan Asaad Zebari, Diyar Qader Zeebaree, and Jwan Najeeb Saeed. "Significant features for steganography techniques using deoxyribonucleic acid: a review."

[20].   Application of deep learning techniques for heartbeats detection using ECG signals- Analysis and Review - Scientific Figure on ResearchGate.

[21].   K. Najarian et al. Biomedical Signal and Image Processing (2012) National Institute on Aging – turning discovery into health. Global health and aging. Assess. Costs Aging Health.

[22].   Zebari, Dilovan Asaad, Diyar Qader Zeebaree, Adnan Mohsin Abdulazeez, Habibollah Haron, and Haza Nuzly Abdull Hamed. "Improved Threshold Based and Trainable Fully Automated Segmentation for Breast Cancer Boundary and Pectoral Muscle in Mammogram Images." IEEE Access 8 (2020): 203097-203116.

[23].   Abdulqader, Dildar Masood, Adnan Mohsin Abdulazeez, and Diyar Qader Zeebaree. "Machine Learning Supervised Algorithms of Gene Selection: A Review." Machine Learning 62, no. 03 (2020).

[24].   Zeebaree, Diyar Qader, Habibollah Haron, Adnan Mohsin Abdulazeez, and Dilovan Asaad Zebari. "Trainable Model Based on New Uniform LBP Feature to Identify the Risk of the Breast Cancer." In 2019 International Conference on Advanced Science and Engineering (ICOASE), pp. 106-111. IEEE, 2019.

[25].   Zebari, D. A., Haron, H., Zeebaree, D. Q., & Zain, A. M. (2019, August). A Simultaneous Approach for Compression and Encryption Techniques Using Deoxyribonucleic Acid. In 2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA) (pp. 1-6). IEEE.

[26].   Cuocolo R, Perillo T, De Rosa E, Ugga L, Petretta M. Current applications of big data and machine learning in 

[27].   cardiology. J Geriatr Cardiol. 2019;16(8):601–7.

[28].   Abdulazeez, Adnan Mohsin, Diyar Qader Zeebaree, and Dildar Masood Abdulqader. "Wavelet Applications in Medical Images: A Review." transformation (DWT) 21 (2020): 22.

[29].   Mincholé A, Camps J, Lyon A, Rodríguez B. Machine learning in the electrocardiogram. J Electrocardiol. 2019;57:S61–S4.

[30].   Adeen, Idrees Mohammed Najim, Adnan Mohsin Abdulazeez, and Diyar Qader Zeebaree. "Systematic Review of Unsupervised Genomic Clustering Algorithms Techniques for High Dimensional Datasets."

[31].   Jahwar, Alan Fuad, and Adnan Mohsin Abdulazeez. "META-HEURISTIC ALGORITHMS FOR K-MEANS CLUSTERING: A REVIEW." PalArch's Journal of Archaeology of Egypt/Egyptology 17, no. 7 (2020): 12002-12020.

[32].   Sengupta PP, Adjeroh DA. Will artificial intelligence replace the human echocardiographer? Circulation.

[33].   A. Krizhevsky et al. Imagenet classification with deep convolutional neural networks Adv. Neural Inf. Process. Syst. (2012).

[34].   WHO | Cardiovascular diseases (CVDs). WHO n.d. . (accessed February 3, 2018).

[35].   Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK biobank resource with deep phenotyping and genomic data. Nature. 2018; 562:203.

[36].   Hassan, Omer Mohammed Salih, Adnan Mohsin Abdulazeez, and Volkan Müjdat TİRYAKİ. "Gait-based human gender classification using lifting 5/3 wavelet and principal component analysis." In 2018 International Conference on Advanced Science and Engineering (ICOASE), pp. 173-178. IEEE, 2018.

[37].   Abdulazeez, A., Salim, B., Zeebaree, D., & Doghramachi, D. (2020). Comparison of VPN Protocols at Network Layer Focusing on Wire Guard Protocol.

[38].   Taha, M. S., Rahim, M. S. M., Hashim, M. M., & Khalid, H. N., Zeebaree, D, Q. (2020). Information Hiding: A Tools for Securing Biometric Information. Technology Reports of Kansai University, 62(04), 1383-1394.

[39].   Jahwar, A., & Ahmed, N. (2021). Swarm Intelligence Algorithms in Gene Selection Profile Based on Classification of Microarray Data: A Review. Journal of Applied Science and Technology Trends, 2(01), 01-09.

[40].   LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015; 521:436–44.

[41].   Goodfellow I, Bengio Y, Courville A. Deep Learning. MIT Press; 2016.

[42].   Bai W, Sinclair M, Tarroni G, Oktay O, Rajchl M, Vaillant G, et al. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks. J Cardiovasc Magn Reson. 2018; 20:65.

[43].   Hannun AY, Rajpurkar P, Haghpanahi M, Tison GH, Bourn C, Turakhia MP, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med. 2019; 25:65. 

[44].   Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satam G, et al. Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram. Nat Med. 2019; 25:70.

[45].   Zeebaree, Diyar Qader, Habibollah Haron, and Adnan Mohsin Abdulazeez. "Gene selection and classification of microarray data using convolutional neural network." In 2018 International Conference on Advanced Science and Engineering (ICOASE), pp. 145-150. IEEE, 2018.

[46].   Zeebaree, Imad Majed, Diyar Qader Zeebaree, and Zozan Azeez Ayoub. "FACE RECOGNITION USING STATISTICAL FEATURE EXTRACTION AND NEURAL.". Technology Reports of Kansai University, 62(03), 1131-1141.

[47].   Bishop C. Pattern recognition and machine learning. New York: Springer-Verlag; 2006.

[48].   Bargarai, Faiq, Adnan Abdulazeez, Volkan Tiryaki, and Diyar Zeebaree. "Management of Wireless Communication Systems Using Artificial Intelligence-Based Software Defined Radio." (2020): 107-133.

[49].   Lyon A, Mincholé A, Martínez JP, Laguna P, Rodriguez B. Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances. J R Soc Interface. 2018;15.

[50].   Kiranyaz S, Ince T, Gabbouj M. Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Trans Biomed Eng. 2016; 63:664–75. 

[51].   Moody GB, Mark RG. The impact of the MIT-BIH arrhythmia database. IEEE Eng Med Biol Mag. 2001; 20:45–50.

[52].   Castells F, Laguna P, Sörnmo L, Bollmann A, Roig JM. Principal component analysis in ECG signal processing. EURASIP J Adv Signal Process. 2007; 2007:074580.

[53].   Lyon A, Ariga R, Mincholé A, Mahmod M, Ormondroyd E, Laguna P, et al. Distinct ECG phenotypes identified in hypertrophic cardiomyopathy using machine learning associate with arrhythmic risk markers. Front Physiol. 2018; 9:213.

[54].   Mincholé A, Jager F, Laguna P. Discrimination between ischemic and artifactual ST segment events in Holter recordings. Biomed Signal Process Control. 2010;5: 21–31.

[55].   Younis, Z. A., Abdulazeez, A. M., Zeebaree, S. R., Zebari, R. R., & Zeebaree, D. Q. (2021). Mobile Ad Hoc Network in Disaster Area Network Scenario: A Review on Routing Protocols. International Journal of Online & Biomedical Engineering, 17(3).

[56].   Abdulazeez, A. M., Hajy, D. M., Zeebaree, D. Q., & Zebari, D. A. (2021). Robust watermarking scheme based LWT and SVD using artificial bee colony optimization. Indonesian Journal of Electrical Engineering and Computer Science, 21(2), 1218-1229.

[57].   Zebari, Dilovan Asaad, Diyar Qader Zeebaree, Jwan Najeeb Saeed, Nechirvan Asaad Zebari, and A. Z. Adel. "Image steganography based on swarm intelligence algorithms: A survey." people 7, no. 8 (2020): 9.

[58].   Adnan Mohsin Abdulazeez. Sulaiman, Maryam Ameen. Diyar Qader Zeebaree. "Evaluating Data Mining Classification Methods Performance in Internet of Things Applications." Journal of Soft Computing and Data Mining 1, no. 2 (2020): 11-25.

[59].   Zihlmann M, Perekrestenko D, Tschannen M. Convolutional recurrent neural networks for electrocardiogram classification. Computing in Cardiology (CinC). 2017; 2017:1–4.

[60].   Martínez JP, Almeida R, Olmos S, Rocha AP, Laguna P. A wavelet-based ECG delineator: evaluation on standard databases. IEEE Trans Biomed Eng. 2004; 51:570–81.

[61].   Laguna P, Mark RG, Goldberg A, Moody GB. A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG. Comput Cardiol. 1997; 1997:673–6.

[62].   Camps J, Rodriguez B, Minchole A. Deep learning based QRS multilead delineator in electrocardiogram signals; 2018.

[63].   Zacur E, Minchole A, Villard B, Carapella V, Ariga R, Rodriguez B, et al. MRI-based heart and torso personalization for computer modeling and simulation of cardiac electrophysiology. Imaging for patient-customized simulations and Systems for Point-of-Care Ultrasound. Cham: Springer; 2017. p. 61–70. 

[64].   Lyon A, Bueno-Orovio A, Zacur E, Ariga R, Grau V, Neubauer S, et al. Electrocardiogram phenotypes in hypertrophic cardiomyopathy caused by distinct mechanisms: apico-basal repolarization gradients vs. Purkinje-myocardial coupling abnormalities. Europace. 2018;20:iii102–12.

[65].   M. Amrani, M. Hammad, F. Jiang, K. Wang, A. Amrani, Very deep feature extraction and fusion for arrhythmias detection, Neural Comput. Appl. 30 (2018) 2047–2057.

[66].   G. Sannino, G. De Pietro, A deep learning approach for ECG-based heartbeat classification for arrhythmia detection, Future Gener. Comput. Syst. 86 (2018) 446–455.

[67].   M. Kachuee, S. Fazeli, M. Sarrafzadeh, ECG heartbeat classification: A deep transferable representation I.E.E.E. international conference Healthcare Informativa ICHI, pp. 443–444, Jun. 2018.

[68].   E. Ramirez, P. Melin, G. Prado-Arechiga, Hybrid model based on neural networks, type-1 and type-2 fuzzy systems for 2-lead cardiac arrhythmia classification, Expert Syst. Appl. 126 (2019) 295–307.

[69].   O. Yildirim, U.B. Baloglu, R.S. Tan, E.J. Ciaccio, U.R. Acharya, A new approach for arrhythmia classification using deep coded features and LSTM networks, Comput. Methods Programs Biomed. 176 (2019) 121–133.

[70].   L.B. Marinho, NdM.M. Nascimento, J.W.M. Souza, M.V. Gurgel, P.P. Rebouças Filho, V.H.C. de Albuquerque, A novel electrocardiogram feature extraction approach for cardiac arrhythmia classification, Future Gener. Comput. Syst. 97 (2019) 564–577.

[71].   S. Mousavi, F. Afghah, U.R. Acharya, Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: A sequence to sequence deep learning approach, I.C.A.S.S.P. 2019 - 2019 I.E.E.E. international conference Acoustic Speech Signal Process. ICASSP, pp. 1308–1312.

[72].   Z. Li, D. Zhou, L. Wan, J. Li, W. Mou, Heartbeat classification using deep residual convolutional neural network from 2-lead electrocardiogram, J. Electrocardiol. 58 (2020) 105–112.

[73].   S.K. Pandey, R.R. Janghel, Automatic arrhythmia recognition from electrocardiogram signals using different feature methods with long short-term memory network model, SIViP (2020).

[74].   J.B. Bidias Mougoufan, J.S.A. Eyebe Fouda, M. Tchuente, W. Koepf, Adaptive ECG beat recognition using ordinal entropies based on patterns, Common. Nonlinear Sci. Numer. Simul 84 (2020).

[75].   A. Chen, F. Wang, W. Liu, S. Chang, H. Wang, J. He, Q. Huang, Automated identification of multi-information fusion neural networks for arrhythmia, Compute. Methods Programs Biomed. 193 (2020).

[76].   Teijeiro, T., García, C. A., Castro, D., & Félix, P. (2018). Abductive reasoning as a basis to reproduce expert criteria in ECG atrial fibrillation identification. Physiological Measurement, 39(8), 084006.

[77].   Sodmann, P., Vollmer, M., Nath, N., & Kaderali, L. (2018). A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms. Physiological Measurement.

[78].   Mousavi, S., & Afghah, F. (2019). Inter- and Intra- Patient Ecg Heartbeat Classification for Arrhythmia Detection: A Sequence-to-Sequence Deep Learning Approach. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). doi:10.1109/icassp.2019.

[79].   Wolk, training K., & Wolk, A. (2019). Early and remote detection of possible heartbeat problems with convolutional neural networks and multipart interactive training.

[80].   Li, F., Wu, J., Jia, M., Chen, Z., & Pu., Y. (2019). Automated Heartbeat Classification Exploiting Convolutional Neural Network with Channel-wise Attention.

[81].   Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., & Ng, A. Y. (2019). Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature Medicine, 25(1), 65–69. 

[82].   Li, Y., Pang, Y., Wang, K., & Li, X. (2020). Toward Improving ECG Biometric Identification Using Cascaded Convolutional Neural Networks. Neurocomputing.

[83].   Fotiadou, E., Konopczyński, T., Hesser, J. W., & Vullings, R. (2020). End-to-end trained CNN encoder-decoder network for fetal ECG signal denoising. Physiological Measurement.

[84].   Shaker, A. M., Tantawi, M., Shedeed, H. A., & Tolba, M. F. (2020). Generalization of Convolutional Neural Networks for ECG Classification Using Generative Adversarial Networks. IEEE Access, 8, 35592–35605.

[85].   186-Mihaela porumb, Saverio Stranges, Antonio pescapè & Leandro pecchia (2020) 10:170 https://doi.org/10.1038/s41598-019-56927-5.

[86].   Z. Li, D. Zhou, L. Wan, et al., Heartbeat classification using deep residual convolutional neural network from 2-lead electrocardiogram, Journal of Electrocardiology (2020).