CLASSIFICATION OF ATRIAL FIBRILLATION IN ECG SIGNAL USING DEEP LEARNING

Raihan Mufid Setiadi, Muhammad Fachrurrozi, Muhammad Naufal Rachmatullah

Abstract

Atrial fibrillation is a type of heart rhythm disorder that most often occurs in the world and can cause death. Atrial fibrillation can be diagnosed by reading an Electrocardiograph (ECG) recording, however, an ECG reading takes a long time and requires specialists to analyze the type of signal pattern. The use of deep learning to classify Atrial Fibrillation in ECG signals was chosen because deep learning has 10% higher performance compared to machine learning methods. In this research, an application for classification of Atrial Fibrillation was developed using the 1-Dimentional Convolutional Neural Network (CNN 1D) method. There are 6 configurations of the 1D CNN model that were developed by varying the configuration on the learning rate and batch size. The best model obtained 100% accuracy, 100% precision, 100% recall, and 100% F1 Score.

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References

European Heart Network, “Atrial Fibrillation and Cardiovascular Diseases,” European Heart Network, 2015.

S. Ghiasi, M. Abdollahpur, N. Madani, K. Kiani, and A. Ghaffari, “Atrial fibrillation detection using feature based algorithm and deep convolutional neural network,” in 2017 Computing in Cardiology (CinC), 2017, pp. 1–4. doi: 10.22489/CinC.2017.159-327.

B. S. Chandra, C. S. Sastry, S. Jana, and S. Patidar, “Atrial fibrillation detection using convolutional neural networks,” in 2017 Computing in Cardiology (CinC), 2017, pp. 1–4. doi: 10.22489/CinC.2017.163-226.

S. Liaqat, K. Dashtipour, A. Zahid, K. Assaleh, K. Arshad, and N. Ramzan, “Detection of Atrial Fibrillation Using a Machine Learning Approach,” Information, vol. 11, no. 12, 2020, doi: 10.3390/info11120549.

A. Dey, “Machine Learning Algorithms : A Review,” 2016.

Z. Xiong, M. K. Stiles, and J. Zhao, “Robust ECG signal classification for detection of atrial fibrillation using a novel neural network,” in 2017 Computing in Cardiology (CinC), 2017, pp. 1–4. doi: 10.22489/CinC.2017.066-138.

F. Murat et al., “Review of Deep Learning-Based Atrial Fibrillation Detection Studies,” Int J Environ Res Public Health, vol. 18, no. 21, 2021, doi: 10.3390/ijerph182111302.

S. Nurmaini et al., “Robust detection of atrial fibrillation from short-term electrocardiogram using convolutional neural networks,” Future Generation Computer Systems, vol. 113, 2020, doi: 10.1016/j.future.2020.07.021.

X. Zhou, X. Zhu, K. Nakamura, and N. Mahito, “Atrial Fibrillation Detection Using Convolutional Neural Networks,” 2018 9th International Conference on Awareness Science and Technology (iCAST), pp. 84–89, 2018.

Y. Xia, N. Wulan, K. Wang, and H. Zhang, “Detecting atrial fibrillation by deep convolutional neural networks.,” Comput Biol Med, vol. 93, pp. 84–92, Feb. 2018, doi: 10.1016/j.compbiomed.2017.12.007.

G. B. Moody and R. G. Mark, “The impact of the MIT-BIH Arrhythmia Database,” IEEE Engineering in Medicine and Biology Magazine, vol. 20, no. 3, pp. 45–50, 2001, doi: 10.1109/51.932724.

Goldberger et al., “MIT-BIH Normal Sinus Rhythm Database,” PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]., vol. 101, no. 23, pp. e215–e220, 2000.

V. Khambhati and M. Patel, “Extraction of a Respiration Rate from ECG Signal using Discrete Wavelet Transform during Exercise,” 2017.

M. Aqil, A. Jbari, and A. Bourouhou, “ECG Signal Denoising by Discrete Wavelet Transform,” International Journal of Online Engineering (iJOE), vol. 13, p. 51, Aug. 2017, doi: 10.3991/ijoe.v13i09.7159.

X. WAN, Z. JIN, H. WU, J. LIU, B. ZHU, and H. XIE, “HEARTBEAT CLASSIFICATION ALGORITHM BASED ON ONE-DIMENSIONAL CONVOLUTION NEURAL NETWORK,” J Mech Med Biol, vol. 20, no. 07, p. 2050046, 2020, doi: 10.1142/S0219519420500463.

S. Kiranyaz, O. Avci, O. Abdeljaber, T. Ince, M. Gabbouj, and D. J. Inman, “1D convolutional neural networks and applications: A survey,” Mech Syst Signal Process, vol. 151, p. 107398, 2021, doi: https://doi.org/10.1016/j.ymssp.2020.107398.

C.-H. Hsieh, Y.-S. Li, B.-J. Hwang, and C.-H. Hsiao, “Detection of Atrial Fibrillation Using 1D Convolutional Neural Network.,” Sensors (Basel), vol. 20, no. 7, Apr. 2020, doi: 10.3390/s20072136.

N. Fayyazifar, “An Accurate CNN Architecture For Atrial Fibrillation Detection Using Neural Architecture Search,” in 2020 28th European Signal Processing Conference (EUSIPCO), 2021, pp. 1135–1139. doi: 10.23919/Eusipco47968.2020.9287496.

DOI: https://doi.org/10.36706/sjia.v4i1.53

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