Classification of ECG Signal Using Hybrid Deep Neural Network

Asif, Md. Asfi-Ar-Raihan; Ahmed, Nafew; Khan, Md. Mohi Uddin

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dc.contributor.author	Asif, Md. Asfi-Ar-Raihan
dc.contributor.author	Ahmed, Nafew
dc.contributor.author	Khan, Md. Mohi Uddin
dc.date.accessioned	2022-04-30T06:26:26Z
dc.date.available	2022-04-30T06:26:26Z
dc.date.issued	2021-03-30
dc.identifier.citation	1. J. Huang, B. Chen, B. Yao and W. He, "ECG Arrhythmia Classification Using STFTBased Spectrogram and Convolutional Neural Network," in IEEE Access, vol. 7, pp. 92871-92880, 2019, doi: 10.1109/ACCESS.2019.2928017. 2. F. Liu, X. Zhou, J. Cao, Z. Wang, H. Wang and Y. Zhang, "A LSTM and CNN Based Assemble Neural Network Framework for Arrhythmias Classification," ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, United Kingdom, 2019, pp. 1303-1307, doi: 10.1109/ICASSP.2019.8682299 3. Wang, Pu & Hou, Borui & Shao, Siyu & Yan, Ruqiang. (2019). ECG Arrhythmias Detection Using Auxiliary Classifier Generative Adversarial Network and Residual Network. IEEE Access. PP. 1-1. 10.1109/ACCESS.2019.2930882.) 4. Abdullah, A. (2014). ECG in Medical Practice (4th ed.). Jaypee Brothers Medical Pub. 5. ECG & Echo Waves. (2021, February 22). ECG interpretation: Characteristics of the normal ECG (P-wave, QRS complex, ST segment, T-wave) –. ECG & ECHO. https://ecgwaves.com/topic/ecg-normal-p-wave-qrs-complex-st-segment-t-wave-jpoint/ 6. Furst, J. (2017, February 13). Recording a 12 lead ECG/EKG. First Aid for Free. https://www.firstaidforfree.com/recording-a-12-lead-ecgekg/ 7. Electrocardiography. (n.d.). MSD Manual Professional Edition. https://www.msdmanuals.com/professional/cardiovascular-disorders/cardiovasculartests-and-procedures/electrocardiography#v931549 8. X. Zhang et al., "Classification of Arrhythmia Based on Extreme Learning Machine," 2018 10th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), Hangzhou, China, 2018, pp. 123-126, doi: 10.1109/IHMSC.2018.10135. 9. Kachuee, Mohammad & Fazeli, Shayan & Sarrafzadeh, Majid. (2018). ECG Heartbeat Classification: A Deep Transferable Representation. 10. Rana and K. K. Kim, "ECG Heartbeat Classification Using a Single Layer LSTM Model," 2019 International SoC Design Conference (ISOCC), Jeju, Korea (South), 2019, pp. 267-268, doi: 10.1109/ISOCC47750.2019.9027740 References 67 11. Alarsan, Fajr & Younes, Mamoon. (2019). Analysis and classification of heart diseases using heartbeat features and machine learning algorithms. Journal of Big Data. 6. 10.1186/s40537-019-0244-x. 12. S. Chakroborty and M. A. Patil, "Real-time arrhythmia classification for large databases," 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA, 2014, pp. 1448-1451, doi: 10.1109/EMBC.2014.6943873. 13. R. R. Janghel and S. k. Pandey, "Classification and Detection of Arrhythmia in ECG Signal Using Machine Learning Techniques," 2019 16th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Pattaya, Thailand, 2019, pp. 101-104, doi: 10.1109/ECTICON47248.2019.8955208. 14. R. Banerjee, A. Ghose and K. Muthana Mandana, "A Hybrid CNN-LSTM Architecture for Detection of Coronary Artery Disease from ECG," 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 2020, pp. 1-8, doi: 10.1109/IJCNN48605.2020.9207044. 15. “Jupyter Notebook” (https://www.jupyter.org/) 16. “Anaconda Navigator” (https://docs.anaconda.com/anaconda/navigator/) 17. “Confusion Matrix” (https://en.wikipedia.org/wiki/Confusion_matrix/) 18. H. Dalianis, Clinical Text Mining: Secondary Use of Electronic Patient Records Springer Open, Cham Switzerland (2018), p. 47, 10.1007/978-3-319-78503-5 19. Duboue, Pablo. (2020). The Art of Feature Engineering: Essentials for Machine Learning. 10.1017/9781108671682. 20. A. (2020, October 5). 7 Feature Engineering Techniques in Machine Learning You Should Know. Analytics Vidhya. https://www.analyticsvidhya.com/blog/2020/10/7- feature-engineering-techniques-machine-learning/ 21. https://www.physionet.org/content/mitdb/1.0.0/ 22. M. Kachuee, S. Fazeli and M. Sarrafzadeh, "ECG Heartbeat Classification: A Deep Transferable Representation," 2018 IEEE International Conference on Healthcare Informatics (ICHI), New York, NY, 2018, pp. 443-444, doi: 10.1109/ICHI.2018.00092. 23. Butcher, Brandon & Smith, Brian. (2020). Feature Engineering and Selection: A Practical Approach for Predictive Models: by Max Kuhn and Kjell Johnson. Boca Raton, FL: Chapman & Hall/CRC Press, 2019, xv + 297 pp., $79.95(H), ISBN: 978-1- References 68 13-807922-9.. The American Statistician. 74. 308-309. 10.1080/00031305.2020.1790217. 24. Q.McCallum. Bad Data Hand Book: Cleaning Up The Data So You Can Get Back To Work. O'Reilly Media, 2013 25. Two Channel Histogram. (n.d.). Scientific Volume Imaging. https://svi.nl/TwoChannelHistogram/ 26. https://towardsdatascience.com/deep-learning-unbalanced-training-data-solve-it-likethis-6c528e9efea6/ 27. https://www.saedsayad.com/k_nearest_neighbors.htm/ 28. https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_wi th_python_knn_algorithm_finding_nearest_neighbors.htm/ 29. https://www.javatpoint.com/machine-learning-random-forest-algorithm/ 30. https://en.wikipedia.org/wiki/Support-vector_machine/ 31. https://www.javatpoint.com/machine-learning-support-vector-machine-algorithm/ 32. https://www.geeksforgeeks.org/ml-stochastic-gradient-descent-sgd/ 33. https://en.wikipedia.org/wiki/AdaBoost/ 34. https://www.mygreatlearning.com/blog/xgboost-algorithm/ 35. https://www.programmersought.com/article/16143908973/ 36. https://www.hackerearth.com/practice/machine-learning/machine-learningalgorithms/beginners-tutorial-on-xgboost-parameter-tuning-r/tutorial/ 37. https://colah.github.io/posts/2015-08-Understanding-LSTMs/ 38. https://en.wikipedia.org/wiki/Recurrent_neural_network/ 39. https://en.wikipedia.org/wiki/Convolutional_neural_network/ 40. https://blog.bayeslabs.co/2019/06/04/All-you-need-to-know-about-Vae.html/ 41. https://ermongroup.github.io/cs228-notes/extras/vae/ 42. https://en.wikipedia.org/wiki/Deep_belief_network/ 43. https://missinglink.ai/guides/neural-network-concepts/deep-belief-networks-workapplications/ 44. Asif, Md. Asfi-Ar-Raihan, Mirza Muntasir Nishat, Fahim Faisal, Rezuanur Rahman Dip, Mahmudul Hasan Udoy, Md. Fahim Shikder , and Ragib Ahsan. “Performance Evaluation and Comparative Analysis of Different Machine Learning Algorithms in Predicting Cardiovascular Disease.” IAENG Engineering Letters, 2021-In Press. 45. Géron, Aurélien. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O'Reilly References 69 Media, 2019. 46. Maxwell, A., Li, R., Yang, B. et al. Deep learning architectures for multi-label classification of intelligent health risk prediction. BMC Bioinformatics 18, 523 (2017). https://doi.org/10.1186/s12859-017-1898-z/ 47. Floydhub: Practical Guide to Hyperparameters Optimization for Deep Learning Models (https://blog.floydhub.com/guide-to-hyperparameters-search-for-deep-learningmodels/)	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/1456
dc.description	Supervised by Dr. Golam Sarowar, Supervisor and Professor, Department of Electrical and Electronic Engineering (EEE), Islamic University of Technology (IUT), Boardbazar, Gazipur-1704, Bangladesh.	en_US
dc.description.abstract	This dissertation studies a comprehensive approach to evaluating the performance of different machine learning and deep learning algorithms to classify five ECG signal categories. A novel algorithm is also proposed to achieve the same objective efficiently. Cardiovascular disease is responsible for a prominent amount of mortality among humankind is detected by analyzing ECG signals. ECG signal classification is an arduous task since sometimes the abnormal heartbeats are too similar to categorize. Most of the patients with heart diseases come to the doctor when the person is severely attacked. Therefore, doctors or medical persons cannot take much time to start the treatment. The heart is the most sensitive organ of the body, a misapprehension in classification can cause death to the patient. Machine learning and deep learning can be handy tools for the classification of the ECG signal quickly and efficiently. A Famous MIT-BIH ECG signal dataset was utilized to train and test the models. Six machine learning algorithms and five deep learning algorithms were studied with efficient hyperparameter optimization technique, and their performance was evaluated. Finally, a novel Hybrid Deep Neural Network (HDNN) was proposed which provided the best accuracy of 99.23% among all the algorithms studied for the classification of ECG signal. A detailed comparative analysis of performance with all other algorithms was carried out in terms of accuracy, precision, recall, and F-1 score	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT) The Organization of Islamic Cooperation (OIC) Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.title	Classification of ECG Signal Using Hybrid Deep Neural Network	en_US
dc.type	Thesis	en_US