CLASSIFICATION OF ARRHYTHMIA DISEASES BY THE CONVOLUTIONAL NEURAL NETWORK METHOD BASED ON ECG IMAGES
Abstract
Arrhythmia is a heart disorder that refers to an abnormal heartbeat rhythm. Arrhythmia detection uses an electrocardiogram (ECG) to describe the heart's electrical activity. This research aimed to know the performance of the Convolutional Neural Network method in classifying arrhythmia diseases based on ECG signal images. Several stages were used to classify arrhythmias: the pre-processing data stage, CNN model formation stage, model compiling, training, model testing, and evaluation. The CNN model architecture that is formed involves 7 Convolution Layers, 7 Pooling Layers, 2 Dropout Layers, 2 Dense Layers, and 1 Flatten Layer, as well as ReLu and Softmax activation functions. The input variable in the classification process with CNN is an ECG image. The output variable is the classification of ECG signals into 17 classes, including normal sinus and pacemaker rhythms. The processed data are 1000 images; the division scenario is 750 training data and 250 testing data. The result of arrhythmia's classification based on ECG image testing data using the CNN model shows the levels of Accuracy, Precision, Recall, and F1-score levels are 81%, 80%, 71%, and 73%, respectively, respectively. With the F1-score value as a measurement reference, the CNN model performs well in classifying ECG images
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References
S. M. Anwar, M. Gul, M. Majid, and M. Alnowami, "Arrhythmia classification of ECG signals using hybrid features," Comput. Math. Methods Med., vol. 2018, 2018, doi: 10.1155/2018/1380348.
S. H. Rampengan, Kardioversi Pada Fibrilasi Atrium [Cardioversion in Atrial Fibrillation]. Jakarta: Faculty of Medicine, University of Indonesia, 2015.
R. Rohmantri and N. Surantha, "Arrhythmia classification using 2D convolutional neural network," Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 4, pp. 201-208, 2020, doi: 10.14569/IJACSA.2020.0110427.
M. Sampson and A. McGrath, "Understanding the ECG. Part 1: Anatomy and physiology," Br. J. Card. Nurs., vol. 10, no. 11, pp. 548-554, 2015, doi: 10.12968/bjca.2015.10.11.548.
J. Hampton, The ECG Made Easy, 9th ed. China: Elsevier, 2019.
G. J. Klein, Strategies for ECG Arrhythmia Diagnosis: Breaking Down Complexity. Minneapolis: Cardiotext Publishing LLC, 2016.
A. W. Sugiyarto, A. M. Abadi, and Sumarna, "Classification of heart disease based on PCG signal using CNN," Telkomnika (Telecommunication Comput. Electron. Control., vol. 19, no. 5, pp. 1697-1706, 2021, doi: 10.12928/TELKOMNIKA.v19i5.20486.
A. M. Abadi and Sumarna, "Construction of fuzzy system for classification of heart disease based on phonocardiogram signal," Proc. - 2019 1st Int. Conf. Artif. Intell. Data Sci. AiDAS 2019, pp. 64-69, 2019, doi: 10.1109/AiDAS47888.2019.8970975.
S. H. Jambukia, V. K. Dabhi, and H. B. Prajapati, "Classification of ECG signals using machine learning techniques: A survey," in Conference Proceeding - 2015 International Conference on Advances in Computer Engineering and Applications, ICACEA 2015, 2015, pp. 714-721, doi: 10.1109/ICACEA.2015.7164783.
A. Luthra, ECG Made Easy, 6th ed. New Delhi: Jaypee Brothers Medical Publishers (P) Ltd, 2020.
G. T. Ramadhan, Adiwijaya, and D. Utama, “Klasifikasi penyakit aritmia melalui sinyal elektrokardiogram (EKG) menggunakan metode local features dan support vector machine,” [Classification of arrhythmia diseases through electrocardiogram (ECG) signals using local features and support vector machine methods], e-Proceeding Eng., vol. 5, no. 1, pp. 1787-1792, 2018, [Online]. Available: https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/6113/6091.
A. Turnip, M. Ilham Rizqywan, D. E. Kusumandari, M. Turnip, and P. Sihombing, "Classification of ECG signal with support vector machine method for arrhythmia detection," J. Phys. Conf. Ser., vol. 970, no. 1, 2018, doi: 10.1088/1742-6596/970/1/012012.
F. Lutfi and A. Arifin, "Classification of electrocardiographic signals using wavelet transform and neural network," 13th Semin. Intell. Technol. Its Appl., vol. 62, no. 62 31, pp. 136-140, 2012, [Online]. Available: https://www.researchgate.net/publication/231537137_Klasifikasi_Sinyal_Elektrokardiografi_Menggunakan_Wavelet_Transform_dan_Neural_Network.
R. Avanzato and F. Beritelli, "Automatic ECG diagnosis using convolutional neural network," Electron., vol. 9, no. 6, pp. 1-14, 2020, doi: 10.3390/electronics9060951.
A. P. Wibawa, M. G. A. Purnama, M. F. Akbar, and F. A. Dwiyanto, “Metode-metode klasifikasi" [Classification methods], in Proceedings of Computer Science and Information Technology Seminar, 2018, vol. 3, no. 1, pp. 134-138, [Online]. Available: http://e-journals.unmul.ac.id/index.php/SAKTI/article/view/2101.
A. Bajaj and S. Kumar, "A robust approach to denoise ECG signals based on fractional Stockwell transform," Biomed. Signal Process. Control, vol. 62, p. 102090, 2020, doi: 10.1016/j.bspc.2020.102090.
D. Li, J. Zhang, Q. Zhang, and X. Wei, "Classification of ECG signals based on 1D convolution neural network," 2017 IEEE 19th Int. Conf. e-Health Networking, Appl. Serv. Heal. 2017, vol. 2017-Decem, pp. 1-6, 2017, doi: 10.1109/HealthCom.2017.8210784.
A. Fansyuri, “Klasifikasi kelas penyakit jantung berdasarkan sinyal elektrokardiogram menggunakan metode convolutional neural network 1-dimensi,” [Classification of heart disease classes based on electrocardiogram signals using 1-dimensional convolutional neural network method] Final Project Chapter 1, Sriwijaya University, 2021.
J. Gowrishankar, T. Narmadha, M. Ramkumar, and N. Yuvaraj, "Convolutional neural network classification on 2D craniofacial images," Int. J. Grid Distrib. Comput., vol. 13, no. 1, pp. 1026-1032, 2020, [Online]. Available: https://www.researchgate.net/publication/341868842_Convolutional_Neural_Network_Classification_On_2d_Craniofacial_Images.
E. Izci, M. A. Ozdemir, M. Degirmenci, and A. Akan, "Cardiac arrhythmia detection from 2d ecg images by using deep learning technique," TIPTEKNO 2019 - Tip Teknol. Congress, pp. 1-4, 2019, doi: 10.1109/TIPTEKNO.2019.8895011.
P. Pławiak, "ECG signals (1000 fragments)," Mendeley Data, V3, 2017. https://data.mendeley.com/datasets/7dybx7wyfn/3 (accessed Oct. 21, 2021).
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