Image Analysis of 12 Lead Electrocardiogram Using Wavelet Transformation
Downloads
The heart is one of the most important human organs. One of instruments to detect cardiac abnormalities is the electrocardiogram (ECG). This research tries to analyze ECG image in normal heart condition from ECG machine. The previous research related to the pre-processing process is the same, only at the feature extraction process look for peaks P, Q, R, S, T, heart rate, and Deviation-ST. While this research is the characteristic extraction process using wavelet transformation. The image of lead ECG 12 is processed using discrete wavelet transforms with decomposition up to ten levels, by searching for mean square error (MSE). The type of mother wavelet and the wavelet order used are Daubechies (db) with 1 (db1 (Haar)). The smallest MSE value decomposition results are obtained at the level 5, which are lead I, II, III, aVR, aVF, V4 and V5, lead V1 & V2 on level 4, for aVL (level 9), V3 (level 7) and V4 (level 6). It is expected that such research can be followed up to the identification model of cardiac abnormalities using wavelets.
WHO, “Cardiovascular diseases.,” 2021, [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds).
Yang, J.G., Kim, J.K., Kang, U.G., and Lee, Y.H., “Coronary heart disease optimization system on adaptive-network- based fuzzy inference system and linear discriminant analysis ( ANFIS – LDA ),” 2013, doi: 10.1007/s00779-013-0737-0.
Wijaya, N.H., Rijali, W.A., Shahu, N., Ahmad, I., and Atmoko, R.A., “The design of electro cardiograph signal generator using IC 14521 and IC 14017,” J. Robot. Control, vol. 2, no. 4, pp. 270–273, 2021, doi: 10.18196/jrc.2490.
Tunggal, T.P., Juliani, S.A., Widodo, H.A., Atmoko, R.A., and Nguyen, P.T., “The design of digital heart rate meter using microcontroller,” J. Robot. Control, vol. 1, no. 5, pp. 141–144, 2020, doi: 10.18196/jrc.1529.
Febrianty, D., and Aradea, R.A., “Analisis Jaringan Syaraf Tiruan Rprop Untuk Mengenali Pola Elektrokardiografi Dalam Mendeteksi Penyakit Jantung Koroner,” Semin. Nas. Apl. Teknol. Inf., vol. 2007, no. Snati, pp. 1907–5022, 2007, [Online]. Available: https://journal.uii.ac.id/Snati/article/download/1748/1527.
Fauziyah, M., et al., “Pengembangan Jaringan Syaraf Tiruan Backpropagation Untuk Klasifikasi Isyarat EKG,” 2009.
Thanapatay, D., Suwansaroj, C., and Thanawattano, C., “ECG beat classification method for ECG printout with Principle Components Analysis and Support Vector Machines,” ICEIE 2010 - 2010 Int. Conf. Electron. Inf. Eng. Proc., vol. 1, p. 75, 2010, doi: 10.1109/ICEIE.2010.5559841.
Darwan, Hartati, S., Wardoyo, R., and Setianto, B.Y., “The Feature Extraction to Determine the Wave ’ s Peaks in the Electrocardiogram Graphic Image,” Int. J. Image,graphics signal Process., vol. 9, no. 6, pp. 1–13, 2017.
Baldin, A.V., et al., “ECG Signal Spectral Analysis Approaches for High-Resolution Electrocardiography,” Adv. Intell. Syst. Comput., vol. 902, no. Aimee 2018, pp. 197–209, 2020, doi: 10.1007/978-3-030-12082-5_18.
Dai, B., Yang, D., and Feng, D., “Denoising ECG by a New Wavelet Threshold Function,” Proc. - 2021 14th Int. Congr. Image Signal Process. Biomed. Eng. Informatics, CISP-BMEI 2021, p. 9624454, 2021, doi: 10.1109/CISP-BMEI53629.2021.9624454.
Jannah, N., Hadjiloucas, S., and Al-Malki, J., “Arrhythmia detection using multi-lead ECG spectra and Complex Support Vector Machine Classifiers,” Procedia Comput. Sci., vol. 194, pp. 69–79, 2021, doi: 10.1016/j.procs.2021.10.060.
Hammad, M., et al., “Automated detection of shockable ECG signals: A review,” Inf. Sci. (Ny)., vol. 571, no. xxxx, pp. 580–604, 2021, doi: 10.1016/j.ins.2021.05.035.
Fonseca, A., Vieira, G.S., Felix, J., Freire Sobrinho, P., Silva, A.V.P., and Soares, F., “Automatic Orientation Identification of Pediatric Chest X-Rays,” Proc. - 2020 IEEE 44th Annu. Comput. Software, Appl. Conf. COMPSAC 2020, pp. 1449–1454, 2020, doi: 10.1109/COMPSAC48688.2020.00-51.
Rogal, S.R., Neto, A.B., Figueredo, M.V.M., Paraiso, E.C., and Kaestner, C.A.A., “Automatic detection of arrhythmias using wavelets and self-organized artificial neural networks,” ISDA 2009 - 9th Int. Conf. Intell. Syst. Des. Appl. IEEE, pp. 648–653, 2009, doi: 10.1109/ISDA.2009.22.
Adams, E.R., and Choi, A., “Using neural networks to predict cardiac arrhythmias,” Conf. Proc. - IEEE Int. Conf. Syst. Man Cybern., pp. 402–407, 2012, doi: 10.1109/ICSMC.2012.6377734.
Sarma, P., Nirmala, S.R., and Sarma, K.K., “ECG classification using wavelet subband energy based features,” 2014 Int. Conf. Signal Process. Integr. Networks, SPIN 2014, pp. 785–790, 2014, doi: 10.1109/spin.2014.6777061.
Dandotiya, S., and Ramaiya, P.M., “A Review of ECG Signal De-noising and Peaks Detection Techniques,” Int. J. Adv. Eng. Res. Sci., vol. 3, no. 3, pp. 91–93, 2016.
Aqil, A.M.., Jbari, A., Bourouhou, “ECG Signal Denoising by Discrete Wavelet Transform,” iJOE, vol. 13, no. 9, pp. 51–68, 2017.
Ahmad, A.A., Nyitamen, D.S., Lawan, S. and Wamdeo, C.L., “Fetal Heart Rate Estimation: Adaptive Filtering Approach vs Time-Frequency Analysis,” 2019 2nd Int. Conf. IEEE Niger. Comput. Chapter, Niger. 2019, 2019, doi: 10.1109/NigeriaComputConf45974.2019.8949643.
Chowdhury, M., Poudel, K., and Hu, Y., “Compression, Denoising and Classification of ECG Signals using the Discrete Wavelet Transform and Deep Convolutional Neural Networks,” 2020 IEEE Signal Process. Med. Biol. Symp. SPMB 2020 - Proc., p. 9353618, 2020, doi: 10.1109/SPMB50085.2020.9353618.
Li, Z., Lu, W., Gao, L., and Zhang, J., “Research on ECG Denoising method Based on Empirical Mode Decomposition and Wavelet Transform,” 2020 IEEE 5th Int. Conf. Signal Image Process. ICSIP 2020, pp. 675–679, 2020, doi: 10.1109/ICSIP49896.2020.9339369.
Datta, A., Kolwadkar, B., Rauta, A., Handal, S., and Ingale, V.V., “ECG heartbeat classification using Wavelet transform and different Neural network Architectures,” 2021 6th Int. Conf. Converg. Technol. I2CT 2021, p. 9418101, 2021, doi: 10.1109/I2CT51068.2021.9418101.
Qaisar, S.M., and Alkorbi, F., “Automated arrhythmia diagnosis based on ECG signal filtering wavelet transformation and machine learning,” Proc. - 2021 IEEE 4th Natl. Comput. Coll. Conf. NCCC 2021, pp. 4–5, 2021, doi: 10.1109/NCCC49330.2021.9428834.
Xu, B., Liu, R., Shu, M., Shang, X., and Wang, Y., “An ECG Denoising Method Based on the Generative Adversarial Residual Network,” Comput. Math. Methods Med., vol. 2021, 2021, doi: 10.1155/2021/5527904.
Umer, M., Bhatti, B.A., Tariq, M.H., Zia-ul-Hassan, M., Khan, M.Y., and Zaidi, T., “Electrocardiogram Feature Extraction and Pattern Recognition Using a Novel Windowing Algorithm,” Adv. Biosci. Biotechnol., vol. 05, no. 11, pp. 886–894, 2014, doi: 10.4236/abb.2014.511103.
Akhmed-Zaki, D.Z., Mukhambetzhanov, T.S., Nurmakhanova, Z.M., and Abdiakhmetova, Z.M., “Using Wavelet Transform and Machine Learning to Predict Heart Fibrillation Disease on ECG,” SIST 2021 - 2021 IEEE Int. Conf. Smart Inf. Syst. Technol., p. 9465990, 2021, doi: 10.1109/SIST50301.2021.9465990.
Cornely, A.K., Carrillo, A., and Mirsky, G.M., “Reduced-Lead Electrocardiogram Classification Using Wavelet Analysis and Deep Learning,” Comput. Cardiol. (2010)., vol. September, p. 9662813, 2021, doi: 10.23919/CinC53138.2021.9662813.
Iftode, I.A., and Fosalau, C., “Wavelet-based Techniques Applied to Digital Processing of ECG Signals,” EPE 2020 - Proc. 2020 11th Int. Conf. Expo. Electr. Power Eng., pp. 419–424, 2020, doi: 10.1109/EPE50722.2020.9305683.
Hammad, M., Pławiak, P., Wang, K., and Acharya, U.R., “ResNet-Attention model for human authentication using ECG signals,” Expert Syst., vol. 38, no. 6, pp. 1–17, 2021, doi: 10.1111/exsy.12547.
Houamed, I., Saidi, L., and Srairi, F., “ECG signal denoising by fractional wavelet transform thresholding,” Res. Biomed. Eng., vol. 36, no. 3, pp. 349–360, 2020, doi: 10.1007/s42600-020-00075-7.
Banerjee, S., and Singh, G.K., “Quality guaranteed ECG signal compression using tunable-Q wavelet transform and Möbius transform-based AFD,” IEEE Trans. Instrum. Meas., vol. 70, 2021, doi: 10.1109/TIM.2021.3122119.
Tuncer, T., Dogan, S., Plawiak, P., and Subasi, A., “A novel Discrete Wavelet-Concatenated Mesh Tree and ternary chess pattern based ECG signal recognition method,” Biomed. Signal Process. Control, vol. 72, no. PA, p. 103331, 2022, doi: 10.1016/j.bspc.2021.103331.
Alharbey, R.A., Alsubhi, S., Daqrouq, K., and Alkhateeb, A., “The continuous wavelet transform using for natural ECG signal arrhythmias detection by statistical parameters,” Alexandria Eng. J., vol. 61, no. 12, pp. 9243–9248, 2022, doi: 10.1016/j.aej.2022.03.016.
Thilagavathy, R., and Venkataramani, B., “A novel ECG signal compression using wavelet and discrete anamorphic stretch transforms,” Biomed. Signal Process. Control, vol. 71, p. 102773, 2022.
Haddadi, R., Abdelmounim, E., El Hanine, M., and Belaguid, A., “A Wavelet-Based ECG Delineation and Automated Diagnosis of Myocardial Infarction in PTB Database,” 2019, doi: 10.4108/eai.24-4-2019.2284216.
Chen, C.C., and Tsui, F.R., “Comparing different wavelet transforms on removing electrocardiogram baseline wanders and special trends,” BMC Med. Inform. Decis. Mak., vol. 20, 2020, doi: 10.1186/s12911-020-01349-x.
Li, W., “Wavelets for electrocardiogram: Overview and taxonomy,” IEEE Access, vol. 7, pp. 25627–25649, 2019, doi: 10.1109/ACCESS.2018.2877793.
Alharbey, R.A., Alsubhi, S., Daqrouq, K., and Alkhateeb, A., “The continuous wavelet transform using for natural ECG signal arrhythmias detection by statistical parameters,” Alexandria Eng. J., vol. 61, no. 12, pp. 9243–9248, 2022, doi: 10.1016/j.aej.2022.03.016.
Bouaziz, F., Boutana, D., and Oulhadj, H., “Diagnostic of ECG Arrhythmia using Wavelet Analysis and K-Nearest Neighbor Algorithm,” Proc. 2018 Int. Conf. Appl. Smart Syst. ICASS 2018, p. 8652020, 2019, doi: 10.1109/ICASS.2018.8652020.
Elamin, A.A., and Esmail, M.Y., “Wavelet-Based ECG Signal Analysis for Human Recognition,” Proc. 2020 Int. Conf. Comput. Control. Electr. Electron. Eng. ICCCEEE 2020, 2021, doi: 10.1109/ICCCEEE49695.2021.9429655.
Hamza, R.K., Rijab, K.S., and Hussien, M.A., “The ECG data Compression by Discrete Wavelet Transform and Huffman Encoding,” 7th Int. Conf. Contemp. Inf. Technol. Math. ICCITM 2021, pp. 75–81, 2021, doi: 10.1109/ICCITM53167.2021.9677704.
Hsieh, J.H., Hung, K.C., Liu, J.H., and Wu, T.C>, “Wavelet-Based Quality-Constrained ECG Data Compression System without Decoding Process,” IEEE Multimed., vol. 27, no. 2, pp. 33–45, 2020, doi: 10.1109/MMUL.2020.2983690.
Krak, I., Stelia, O., Pashko, A., Efremov, M., and Khorozov, O., “Electrocardiogram Classification Using Wavelet Transformations,” Proc. - 15th Int. Conf. Adv. Trends Radioelectron. Telecommun. Comput. Eng. TCSET 2020, pp. 930–933, 2020, doi: 10.1109/TCSET49122.2020.235573.
Kumar, A., Tomar, H., Mehla, V.K., Komaragiri, R., and Kumar, M., “Stationary wavelet transform based ECG signal denoising method,” ISA Trans., vol. 114, pp. 251–262, 2021, doi: 10.1016/j.isatra.2020.12.029.
Matamoros, A.H., Fujita, H., Hernandez, E.E., Meana, H.P., and Miyatake, M.N., “ScienceDirect Recognition of ECG signals using wavelet based on atomic functions,” J. Biocybern. Biomed. Eng., vol. 40, no. 2, pp. 803–814, 2020.
Patil, D.D., and Singh, R.P., “ECG classification using wavelet transform and wavelet network classifier,” Adv. Intell. Syst. Comput., vol. 668, no. 2018, pp. 289–303, 2018, doi: 10.1007/978-981-10-7868-2_29.
Singh, R., Mehta, R., and Rajpal, N., “Efficient wavelet families for ECG classification using neural classifiers,” Procedia Comput. Sci., vol. 132, no. Iccids, pp. 11–21, 2018, doi: 10.1016/j.procs.2018.05.054.
Yusuf, S.A.A., and Hidayat, R., “Feature Extraction of ECG Signals using Discrete Wavelet Transform and MFCC,” Proceeding - 2019 5th Int. Conf. Sci. Inf. Technol. Embrac. Ind. 4.0 Towar. Innov. Cyber Phys. Syst. ICSITech 2019, pp. 167–170, 2019, doi: 10.1109/ICSITech46713.2019.8987544.
Kumar, A., Komaragiri, R., and Kumar, M., “Design of wavelet transform based electrocardiogram monitoring system,” ISA Trans., vol. 80, no. Ic, pp. 381–398, 2018, doi: 10.1016/j.isatra.2018.08.003.
Melek, M., and Khattab, A., “ECG compression using wavelet-based compressed sensing with prior support information,” Biomed. Signal Process. Control, vol. 68, p. 102786, 2021, doi: 10.1016/j.bspc.2021.102786.
El Boujnouni, I., Zili, H., Tali, A., Tali, T., and Laaziz, Y., “A wavelet-based capsule neural network for ECG biometric identification,” Biomed. Signal Process. Control, vol. 76, 2022, doi: 10.1016/j.bspc.2022.103692.
Mallat, S., A Wavelet Tour of Signal Processing (The Sparse Way), Third. Elsevier, 2009.
Walker, J.S., A Primer on Wavelets and Their Scientific Applications. Chapman & Hall (CRC), 1999.
Dumic, E., Grgic, S., and Grgic, M., “New image quality measure based on wavelets,” J. Electron. Imaging, vol. 9, p. 72480G, 2010, doi: 10.1117/12.810244.
