Construction of cardiac arrhythmia prediction model using deep learning and gradient boosting
Main Article Content
Abstract
Arrhythmia is a condition in which the rhythm of heartbeat becomes irregular. This condition in extreme cases can lead to fatal heart attack accidents. In order to reduce heart attack risk, appropriate early treatments should be conducted right after getting results of Arrhythmia condition, which is generated by electrocardiography ECG tools. However, reading ECG results should be done by qualified medical staff in order to diagnose the existence of arrhythmia accurately. This paper proposes a deep learning algorithm method to classify and detect the existence of arrhythmia from ECG reading. Our proposed method relies on Convolutional Neural Network (CNN) to extract feature from a single lead ECG signal and also Gradient Boosting algorithm to predict the final outcome of single lead ECG reading. This method achieved the accuracy of 96.18% and minimized the number of parameters used in CNN Layer.
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How to Cite
[1]
D. Pratama, F. Dewanta, and S. Rizal, “Construction of cardiac arrhythmia prediction model using deep learning and gradient boosting”, INFOTEL, vol. 13, no. 3, pp. 114-119, Aug. 2021.
Section
Informatics
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References
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[2] S. L. Oh, E. Y. K. Ng, R. S. Tan, and U. R. Acharya, “Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats,” Comput. Biol. Med., vol. 102, pp. 278–287, 2018, doi: 10.1016/j.compbiomed.2018.06.002.
[3] Ö. Yıldırım, P. Pławiak, R. S. Tan, and U. R. Acharya, “Arrhythmia detection using deep convolutional neural network with long duration ECG signals,” Comput. Biol. Med., vol. 102, pp. 411–420, 2018, doi: 10.1016/j.compbiomed.2018.09.009.
[4] A. Esteva et al., “A guide to deep learning in healthcare,” Nat. Med., vol. 25, no. 1, pp. 24–29, 2019, doi: 10.1038/s41591-018-0316-z.
[5] G. Sannino and G. De Pietro, “A deep learning approach for ECG-based heartbeat classification for arrhythmia detection,” Futur. Gener. Comput. Syst., vol. 86, pp. 446–455, 2018, doi: 10.1016/j.future.2018.03.057.
[6] A. Isin and S. Ozdalili, “Cardiac arrhythmia detection using deep learning,” Procedia Comput. Sci., vol. 120, pp. 268–275, 2017, doi: 10.1016/j.procs.2017.11.238.
[7] Z. Ebrahimi, M. Loni, M. Daneshtalab, and A. Gharehbaghi, “A review on deep learning methods for ECG arrhythmia classification,” Expert Syst. with Appl. X, vol. 7, p. 100033, 2020, doi: 10.1016/j.eswax.2020.100033.
[8] F. Murat, O. Yildirim, M. Talo, U. B. Baloglu, Y. Demir, and U. R. Acharya, “Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review,” Comput. Biol. Med., vol. 120, no. April, p. 103726, 2020, doi: 10.1016/j.compbiomed.2020.103726.
[9] U. R. Acharya et al., “A deep convolutional neural network model to classify heartbeats,” Comput. Biol. Med., vol. 89, pp. 389–396, 2017, doi: 10.1016/j.compbiomed.2017.08.022.
[10] M. Kachuee, S. Fazeli, and M. Sarrafzadeh, “ECG heartbeat classification: A deep transferable representation,” Proc. - 2018 IEEE Int. Conf. Healthc. Informatics, ICHI 2018, pp. 443–444, 2018, doi: 10.1109/ICHI.2018.00092.
[11] A. Batra and V. Jawa, “Classification of Arrhythmia Using Conjunction of Machine Learning Algorithms and ECG Diagnostic Criteria,” Int. J. Biol. Biomed., vol. 1, pp. 1–7, 2016.
[12] S. Hong et al., “Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings,” Physiol. Meas., vol. 40, no. 5, p. 054009, 2019, doi: 10.1016/j.snb.2007.07.003.
[13] B. R. Manju and A. R. Nair, “Classification of Cardiac Arrhythmia of 12 Lead ECG Using Combination of SMOTEENN, XGBoost and Machine Learning Algorithms,” Proc. 2019 Int. Symp. Embed. Comput. Syst. Des. ISED 2019, pp. 48–55, 2019, doi: 10.1109/ISED48680.2019.9096244.
[14] J. Elith, J. R. Leathwick, and T. Hastie, “A working guide to boosted regression trees,” J. Anim. Ecol., vol. 77, no. 4, pp. 802–813, 2008, doi: 10.1111/j.1365-2656.2008.01390.x.
[15] G. B. Moody and R. G. Mark, “The impact of the MIT-BIH arrhythmia database,” IEEE Eng. Med. Biol. Mag., vol. 20, no. 3, pp. 45–50, 2001, doi: 10.1109/51.932724.
[16] M. S. Shelke, P. R. Deshmukh, and P. V. K. Shandilya, “A Review on Imbalanced Data Handling Using Undersampling and Oversampling Technique,” Int. J. Recent Trends Eng. Res., vol. 3, no. 4, pp. 444–449, 2017, doi: 10.23883/ijrter.2017.3168.0uwxm.