Majlesi Journal of Electrical Engineering

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Editorial Board

Indexing and Abstracting

Review Process

Related Links

Journal Metrics

News

ECG Arrhythmia Classification based on Convolutional Autoencoders and Transfer Learning

Document Type : Reseach Article

Authors

1 College of MLT, Ahl Al Bayt University, Kerbala, Iraq

2 Al Farahidi University / College of Law/ Iraq

3 Al-Manara College For Medical Sciences, Maysan, Iraq

4 Department of Computer Technology Engineering, Al-Hadba University College, Iraq

5 College of Media, Department of Journalism, The Islamic University in Najaf, Najaf, Iraq

6 Law Department, Al-Mustaqbal University College, Babylon, Iraq

7 Al-Nisour University College, Iraq

8 Al-Esraa University College, Baghdad, Iraq

9 Department of Dentistry, Al-Zahrawi University College, Karbala, Iraq

10 Medical device engineering, Ashur University College, Baghdad, Iraq

Abstract

An Electrocardiogram (ECG) is a test that is done with the objective of monitoring the heart’s rhythm and electrical activity. It is conducted by attaching a specific type of sensor to the subject’s skin to detect the signals generated by the heartbeats. These signals can reveal significant information about the wellness of the subjects’ heart state, and cardiologists use them to detect abnormalities. Due to the prevalence of heart diseases amongst individuals around the globe, there is an urgent need to design computer-aided approaches to automatically analyze ECG signals. Recently, computer vision-based techniques have demonstrated remarkable performance in medical image analysis in a variety of applications and use cases. This paper proposes an approach based on Convolutional Autoencoders (CAEs) and Transfer Learning (TL). Our approach is an ensemble way of learning, the most useful features from both the signal itself, which is the input of the CAE, and the spectrogram version of the same signal, which is fed to a convolutional feature extractor named MobileNetV1. Based on the experiments conducted on a dataset collected from 3 well-known hospitals in Baghdad, Iraq, the proposed method claims good performance in classifying four types of problems in the ECG signals. Achieving an accuracy of 97.3% proves that our approach can be remarkably fruitful in situations where access to expert human resources is scarce.

Keywords

References
[1] Ebrahimi Z, Loni M, Daneshtalab M, Gharehbaghi A, “A review on deep learning methods for ECG arrhythmia classification,” Expert Systems with Applications: X. 2020 Sep 1;7:100033.
[2] Sahoo S, Dash M, Behera S, Sabut S, “Machine learning approach to detect cardiac arrhythmias in ECG signals: a survey,” IRBM. 2020 Aug 1;41(4):185-94.
[3] Huang J, Chen B, Yao B, He W, “ECG arrhythmia classification using STFT-based spectrogram and convolutional neural network,” IEEE access. 2019 Jul 11;7:92871-80.
[4] Sai YP, “A review on arrhythmia classification using ECG signals,” In2020 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) 2020 Feb 22 (pp. 1-6). IEEE.
[5] Mathunjwa BM, Lin YT, Lin CH, Abbod MF, Shieh JS “ECG arrhythmia classification by using a recurrence plot and convolutional neural network,” Biomedical Signal Processing and Control. 2021 Feb 1;64:102262.
[6] Houssein EH, Ibrahim IE, Neggaz N, Hassaballah M, Wazery YM, “An efficient ECG arrhythmia classification method based on Manta ray foraging optimization,” Expert Systems with Applications. 2021 Nov 1;181:115131.
[7] Sangaiah AK, Arumugam M, Bian GB, “An intelligent learning approach for improving ECG signal classification and arrhythmia analysis,” Artificial intelligence in medicine. 2020 Mar 1;103:101788.
[8] Wu M, Lu Y, Yang W, Wong SY, “A study on arrhythmia via ECG signal classification using the convolutional neural network,” Frontiers in computational neuroscience. 2021 Jan 5;14:564015.
[9] Pisner DA, Schnyer DM, “Support vector machine,” In Machine learning 2020 Jan 1 (pp. 101-121). Academic Press.
[10] Probst P, Wright MN, Boulesteix AL, “Hyperparameters and tuning strategies for the random forest,” Wiley Interdisciplinary Reviews: data mining and knowledge discovery. 2019 May;9(3):e1301.
[11] Dong Y, Ma X, Fu T, “Electrical load forecasting: A deep learning approach based on K-nearest neighbors,” Applied Soft Computing. 2021 Feb 1;99:106900.
[12] Samek W, Montavon G, Lapuschkin S, Anders CJ, Müller KR, “Explaining deep neural networks and beyond: A review of methods and applications,” Proceedings of the IEEE. 2021 Mar 4;109(3):247-78.
[13] Alfaras M, Soriano MC, Ortin S, “A fast machine learning model for ECG-based heartbeat classification and arrhythmia detection,” Frontiers in Physics. 2019 Jul 18;7:103.
[14] Sharghi E, Nourani V, Najafi H, Molajou A, “Emotional ANN (EANN) and wavelet-ANN (WANN) approaches for Markovian and seasonal based modeling of rainfall-runoff process,” Water resources management. 2018 Aug;32(10):3441-56.
[15] Nourani V, Davanlou Tajbakhsh A, Molajou A, Gokcekus H, “Hybrid wavelet-M5 model tree for rainfall-runoff modeling,” Journal of Hydrologic Engineering. 2019 May 1;24(5):04019012.
[16] Nourani V, Molajou A, Tajbakhsh AD, Najafi H, “A wavelet based data mining technique for suspended sediment load modeling,” Water Resources Management. 2019 Mar;33(5):1769-84.
[17] Nourani V, Molajou A, “Application of a hybrid association rules/decision tree model for drought monitoring,” Global and Planetary Change. 2017 Dec 1;159:37-45.
[18] Karimi D, Dou H, Warfield SK, Gholipour A, “Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis,” Medical Image Analysis. 2020 Oct 1;65:101759.
[19] Singh A, Sengupta S, Lakshminarayanan V, “Explainable deep learning models in medical image analysis,” Journal of Imaging. 2020 Jun 20;6(6):52.
[20] Budd S, Robinson EC, Kainz B “A survey on active learning and human-in-the-loop deep learning for medical image analysis,” Medical Image Analysis. 2021 Jul 1;71:102062.
[21] Fourcade A, Khonsari RH, “Deep learning in medical image analysis: A third eye for doctors,” Journal of stomatology, oral and maxillofacial surgery. 2019 Sep 1;120(4):279-88.
[22] Hibat-Allah M, Ganahl M, Hayward LE, Melko RG, Carrasquilla J, “Recurrent neural network wave functions,” Physical Review Research. 2020 Jun 17;2(2):023358.
[23] Li Z, Liu F, Yang W, Peng S, Zhou J, “A survey of convolutional neural networks: analysis, applications, and prospects,” IEEE transactions on neural networks and learning systems. 2021 Jun 10.
[24] Shaker AM, Tantawi M, Shedeed HA, Tolba M, “Generalization of convolutional neural networks for ECG classification using generative adversarial networks,” IEEE Access. 2020 Feb 17;8:35592-605.
[25] Jiang J, Chen M, Fan JA, “Deep neural networks for the evaluation and design of photonic devices,” Nature Reviews Materials. 2021 Aug;6(8):679-700.
[26] Wasimuddin M, Elleithy K, Abuzneid AS, Faezipour M, Abuzaghleh O, “Stages-based ECG signal analysis from traditional signal processing to machine learning approaches: A survey,” IEEE Access. 2020 Sep 28;8:177782-803.
[27] Jun, Tae Joon, Hoang Minh Nguyen, Daeyoun Kang, Dohyeun Kim, Daeyoung Kim, and Young-Hak Kim, "ECG arrhythmia classification using a 2-D convolutional neural network," arXiv preprint arXiv:1804.06812 (2018).
[28] Acharya, U. Rajendra, Hamido Fujita, Muhammad Adam, Oh Shu Lih, Vidya K. Sudarshan, Tan Jen Hong, Joel EW Koh et al, "Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: A comparative study," Information Sciences 377 (2017): 17-29.
[29] Zhou, Lin, Yan Yan, Xingbin Qin, Chan Yuan, Dashun Que, and Lei Wang, "Deep learning-based classification of massive electrocardiography data," In 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), pp. 780-785. IEEE, 2016.
[30] Sannino G., and G. De Pietro, “A Deep Learning Approach for ECG-based Heartbeat Classification for Arrhythmia Detection,” Future Generation Computer Systems (2018) pp. 1-31.
[31] Jo JM, “Effectiveness of normalization pre-processing of big data to the machine learning performance,” The Journal of the Korea institute of electronic communication sciences. 2019;14(3):547-52.
[32] Singh D, Singh B, “Investigating the impact of data normalization on classification performance,” Applied Soft Computing. 2020 Dec 1;97:105524.
[33] Koonce B, “EfficientNet,” InConvolutional neural networks with swift for tensorflow 2021 (pp. 109-123). Apress, Berkeley, CA.
[34] Tan M, Le Q, “Efficientnet: Rethinking model scaling for convolutional neural networks,” InInternational conference on machine learning 2019 May 24 (pp. 6105-6114). PMLR.
[35] Atila Ü, Uçar M, Akyol K, Uçar E, “Plant leaf disease classification using EfficientNet deep learning model,” Ecological Informatics. 2021 Mar 1;61:101182.
[36] Chow JK, Su Z, Wu J, Tan PS, Mao X, Wang YH, “Anomaly detection of defects on concrete structures with the convolutional autoencoder,” Advanced Engineering Informatics. 2020 Aug 1;45:101105.
[37] Ahmed AS, El-Behaidy WH, Youssif AA, “Medical image denoising system based on stacked convolutional autoencoder for enhancing 2-dimensional gel electrophoresis noise reduction,” Biomedical Signal Processing and Control. 2021 Aug 1;69:102842.
[38] Saravanan S, Sujitha J, “Deep medical image reconstruction with autoencoders using deep Boltzmann machine training,” EAI Endorsed Transactions on Pervasive Health and Technology. 2020;6(24):e2.
[39] Öztürk Ş, “Stacked auto-encoder based tagging with deep features for content-based medical image retrieval,” Expert Systems with Applications. 2020 Dec 15;161:113693.
Majlesi Journal of Electrical Engineering
Volume 16, Issue 3 - Serial Number 3
September 2022
Pages 41-46
Files
Share
How to cite
Statistics