Electrocardiogram Abnormal Classification Based on Abnormality Signal Feature
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Abstract
Heart rate abnormalities can lead to many cardiovascular diseases such as heart arrythmia, heart failure, heart valve disease and many more. Some cardiovascular disease can cause death. Abnormalities signal feature can be seen using electrocardiogram. Electrocardiogram is an electric signal record from heart activity. Normal heart and abnormal heart have a different electrocardiogram signal pattern. This research is aim to detect abnormality from heart rate using electrocardiogram abnormality signal feature. Abnormality signal pattern can be used to classify normal and abnormal heart rate. Abnormality feature consists of P signal condition, R signal condition, P – R interval rate, and double R interval. Electrocardiogram data that used in this study is obtain from MIT-BIH Arrythmia database. 20 electrocardiogram data have been used to see detection and classification performance while classifying normal and abnormal heart rate. Research result shows that feature based has 90.00% in accuracy, 90.00%in precision, and 90.00% in sensitivity while classify normal and abnormal heart rate. Research result can conclude that abnormality feature can be used to classify normal and abnormal heart rate. This method can be used for embedded system device that has limitation in memory and size.
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G. B. Moody dan R. G. Mark, “The Impact of the MIT-BIH Arrhythmia Database,” IEEE Engineering in Medicine and Biology, vol. 20, no. 3, pp. 45-50, May-June 2001.
M. Robert O. Bonow, “Specifc Arrhythmias: Diagnosis and Treatment,” dalam Braunwald's Heart Disease a Textbook of Cardiovascular Medicine 9th, Philadelphia, Elsevier Saunders, 2012, p. 771.
World Health Organization, “Hearts: technical package for cardiovascular disease management in primary,” WHO Library Cataloguing-in-Publication Data, Geneva, 2016.
KBBI, “KBBI Daring,” 12 2 2020. [Online]. Available: https://kbbi.kemdikbud.go.id/entri/elektrokardiogram.
Universitas Indonesia, Buku Praktis Kardiologi, Jakarta: Fakultas Kedokteran Universitas Indonesia, 2014.
D. Zavantis, E. Mastora dan G. Manis, “Robust Automatic Detection of P Wave and T Wave in Electrocardiogram,” Computing in Cardiology, vol. 44, pp. 1-4, 2017.
L. I. Rilantono, F. Baraas, S. K. Karo dan P. S. Roebiono, Buku Ajar Kardiologi, Jakarta: Fakultas Kedokteran Universitas Indonesia, 1998.
A. Nazmah, Cara Praktis & Sistematis Belajar Membaca EKG, Jakarta: Elex Media Komputindo, 2011.
S. D. Kreibig, “Autonomic nervous system activity in emotion: A review,” Biological Psychology, p. 394–421, 2010.
O. M. Wani, “Signal Processing of Stress Test ECG using MATLAB,” International Journal of Engineering Research & Technology (IJERT), vol. 6, no. 8, pp. 175-183, August 2017.
G. Ranganathan, R. Rangarajan dan V. Bindhu, “Evaluation of ECG Signals for Mental Stress Assessment using Fuzzy Technique,” International Journal of Soft Computing and Engineering (IJSCE), pp. 195-201, 2011.
J. Parak dan J. Havlik, “ECG Signal Processing and Heart Rate Frequency Detection Methods,” dalam Technical Computing 2011, Prague, 2011.
A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng dan H. E. Stanley, “PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals,” Circulation Journal by American Heart Association, pp. e215-e220, 2000.
L. T.S, “Biometric human identification based on electrocardiogram,” [Master's thesis] Faculty of Computing Technologies and Informatics, Electrotechnical University "LETI", Saint-Petersburg, Russian Federation, 2005.
B. K. Rehman, A. Kumar dan P. Sharma, “A Novel Approach for R-Peak Detection in The Electrocardiogram (ECG) Signal,” ARPN Journal of Engineering and Applied Sciences, vol. 11, pp. 13500-13503, December 2016.
M. Ponnusamy dan S. M, “Detecting and classifying ECG abnormalities using a multi model methods,” Biomedical Research India 2017 special issue, no. Special Issue: S81-S89, pp. s81-s89, 2017.
A. Kaur, A. Agarwal, R. Agarwal dan S. Kumar, “A Novel Approach to ECG R-Peak Detection,” Arabian Journal for Science and Engineering , no. 44, p. 6679–6691, 2018.
Q. Qin, J. Li, Y. Yue dan a. C. Liu, “An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm,” Journal of Healthcare Engineering, vol. 2017, pp. 1-14, 2017.
M. B. Hossain, S. K. Bashar, A. J. Walkey, D. D. McManus dan K. H. Chon, “An Accurate QRS Complex and P Wave Detection in ECG Signals Using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Approach,” IEEE Access, vol. 7, pp. 128869-128880, 2019.
B. Khelil, A. Kachouri, M. B. M. Ghariani dan Hamadi, “P Wave Analysis in ECG Signals using Correlation for Arrhythmias Detection,” Fourth International Multi-Conference on Systems, Signals & Devices, 2007.
M. Umer, B. A. Bhatti, M. H. Tariq, M. Zia-ul-Hassan, M. Y. Khan dan T. Zaidi, “Electrocardiogram Feature Extraction and Pattern Recognition Using a Novel Windowing Algorithm,” Advances in Bioscience and Biotechnology,, vol. 2014, no. 5, pp. 886-894, 2014.
F. M. Vaneghi, M. Oladazimi, F. Shiman, A. Kordi, M. Safari, F. Ibrahim dan M. IEEE, “A Comparative Approach to ECG Feature Extraction Methods,” Third International Conference on Intelligent Systems Modelling and Simulation, pp. 252-256, 2012.
I. R. Haryosuprobo, Y. Sugiarto dan F. Suryadi, “Ekstraksi Ciri Sinyal EKG Aritmia Menggunakan Gelombang Singkat Diskrit,” Techné Jurnal Ilmiah Elektroteknika, vol. 15, no. 2, pp. 149-164, 2016.
S. I. Purnama, H. Kusuma dan T. A. Sardjono, “Electrocardiogram Feature Recognition Algorithm with Windowing and Adaptive Thresholding,” Journal of Physics: Conference Series, vol. 1201, p. 012048, may 2019.
G. B. Moody dan R. G. Mark, “The Impact of the MIT-BIH Arrhythmia Database,” IEEE Engineering in Medicine and Biology, vol. 20, no. 3, pp. 45-50, May-June 2001.
M. Robert O. Bonow, “Specifc Arrhythmias: Diagnosis and Treatment,” dalam Braunwald's Heart Disease a Textbook of Cardiovascular Medicine 9th, Philadelphia, Elsevier Saunders, 2012, p. 771.
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Published
Nov 30, 2021
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Purnama, S. I., & Afandi, M. A. (2021). Electrocardiogram Abnormal Classification Based on Abnormality Signal Feature. Jurnal Nasional Teknik Elektro, 10(3). https://doi.org/10.25077/jnte.v10n3.829.2021
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