Deteksi Arritmia pada Sinyal EKG dengan Deep Neural Network

Bayu Wijaya Putra; Rahmat Fadly Isnanto; Purwita Sari

Deteksi Arritmia pada Sinyal EKG dengan Deep Neural Network

Bayu Wijaya Putra, Rahmat Fadly Isnanto, Purwita Sari

Sari

Health practitioners need electrocardiogram (ECG) devices in supervising the heart health.. Where currently p enelitian presents abnormal classification of arritmia in ECG signals based on MIT-BIH dataset using deep neural network.. Before the classification process, good data preparation is required in order to get good accuracy results. The research method starts from data preparation, preprocessing of ECG beat signal, extraction feature and classification of ECG beat signal. The final result of the classification can be seen from the data validation. Validation results get excellent results reaching mencapai 99.62% and sensitivity reaches 98. 92%.

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Referensi

G. B. Casanova, D. O. C. Sarmiento, M. J. I. Bustos, A. O. Duque, and H. A. Caicedo, “Techniques of Acquisition and Processing of Electrocardiographic Signals in the Detection of Cardiac Arrhythmias,” Respuestas, vol. 24, no. 2, pp. 91–102, 2019.

J. Pestana, D. Belo, and H. Gamboa, “Detection of abnormalities in electrocardiogram (ECG) using deep learning,” BIOSIGNALS 2020 - 13th Int. Conf. Bio-Inspired Syst. Signal Process. Proceedings; Part 13th Int. Jt. Conf. Biomed. Eng. Syst. Technol. BIOSTEC 2020, pp. 236–243, 2020.

G. Garcia, G. Moreira, D. Menotti, and E. Luz, “Inter-patient ECG heartbeat classification with temporal VCG optimized by PSO,” Sci. Rep., vol. 7, no. 1, pp. 1–11, 2017.

S. Sahoo, B. Kanungo, S. Behera, and S. Sabut, “Multiresolution wavelet transform based feature extraction and ECG classification to detect cardiac abnormalities,” Meas. J. Int. Meas. Confed., vol. 108, pp. 55–66, 2017.

B. W. Putra, M. Fachrurrozi, M. R. Sanjaya, A. Muliawati, A. N. S. Mukti, and S. Nurmaini, “Abnormality Heartbeat Classification of ECG Signal Using Deep Neural Network and Autoencoder,” in 2019 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS), 2019, pp. 213–218.

A. W. Setiawan, R. A. Djohan, and F. I. Tawakal, “Deteksi Aritmia Menggunakan Sinyal EKG dengan Metoda Deteksi Puncak-R,” Seniati, vol. 5, pp. 123–128, 2019.

Q. Qin, J. Li, L. Zhang, Y. Yue, and C. Liu, “Combining Low-dimensional Wavelet Features and Support Vector Machine for Arrhythmia Beat Classification,” Sci. Rep., vol. 7, no. 1, p. 6067, Dec. 2017.

S. Nurmaini, P. R. Umi, R. M. Naufal, and A. Gani, “Cardiac arrhythmias classification using Deep Neural Networks and principle component analysis algorithm,” Int. J. Adv. Soft Comput. its Appl., 2018.

E. J. da S. Luz, W. R. Schwartz, G. Cámara-Chávez, and D. Menotti, “ECG-based heartbeat classification for arrhythmia detection: A survey,” Comput. Methods Programs Biomed., vol. 127, pp. 144–164, Apr. 2016.

M. M. Al Rahhal, Y. Bazi, H. AlHichri, N. Alajlan, F. Melgani, and R. R. Yager, “Deep learning approach for active classification of electrocardiogram signals,” Inf. Sci. (Ny)., vol. 345, pp. 340–354, Jun. 2016.

F. M. Melgarejo-Meseguer et al., “Electrocardiographic fragmented activity (II): A machine learning approach to detection,” Appl. Sci., vol. 9, no. 17, 2019.

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P-ISSN (cetak): 2654-4032
E-ISSN (online): 2964-6820

Kantor Redaksi:

Fakultas Sains dan Teknologi
Universitas Islam Negeri Raden Fatah Palembang
email: semnassaintek_uin@radenfatah.ac.id

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