Extreme learning machine approach on heart abnormalities identification in ECG images

• Autorzy: Nababan, Anandhini Medianty , Nasution, Umaya Rhamadhani Putri , Pandiangan, Tito Daniel , Nadi, Farhad , Al-Khowarizmi , Budiarto, Rahmat , Rahmat, Romi Fadillah
• Języki publikacji: EN

Abstrakty

EN

Heart abnormalities are atypical heart conditions that can lead to chronic heart disease. Heart abnormalities can be severe if not treated directly due to the crucial function of the heart as the blood circulation center. Heart abnormalities cannot be seen with the naked eye so it requires the recording of a heartbeat wave or electrocardiogram (EKG) for the disease to be detected. Therefore, a strategy that uses image processing and artificial neural networks to detect anomalies in the heart is strongly advocated. The proposed methods for feature extraction and identification are Invariant Moments and Extreme Learning Machine respectively. The testing procedure for this research employed a total of 386 ECG images as training data. and 44 ECG images for test data, and the heart condition was classified into 4 classes, namely Atrial Fibrillation, T-Wave, ST-Segment, and normal heart conditions. The test was carried out using 3 choices of extreme learning machine activation functions, namely sigmoidal, sine and hard-lim. The test also applied the parameter of hidden neurons in which amounting to 10, 30, 50, 100 and 500. The system accuracy in identifying heart abnormalities achieved 95.45% by the application of the sigmoid function with the total number of hidden neurons equal to 500.

Słowa kluczowe

EN

heart abnormality; electrocardiogram; image processing; extreme learning machine; invariant moments

• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2024
• Tom: Vol. 70, No. 2
• Strony: 473--480
• Opis fizyczny: Bibliogr. 24 poz., rys., wykr.

Twórcy

autor

Nababan, Anandhini Medianty

• Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Indonesia,

autor

Nasution, Umaya Rhamadhani Putri

• Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Indonesia,

autor

Pandiangan, Tito Daniel

• Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Indonesia,

autor

Nadi, Farhad

• School of Information Technology, UNITAR International University, Malaysia,

autor

Al-Khowarizmi

• Department of Information Technology, Universitas Muhammadiyah Sumatera Utara, Medan, Indonesia,

autor

Budiarto, Rahmat

• College of Computer Science and Information Technology, Albaha University, Saudi Arabia,

autor

Rahmat, Romi Fadillah

• Department of Information Technology, Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Indonesia,

Bibliografia

• [1] “Detection of Obstructive Sleep Apnea from ECG Signal Using SVM Based Grid Search,” International Journal of Electronics and Telecommunications, Jul. 2023, doi: https://doi.org/10.1680/jsmic.22.00028.
• [2] V. Upadhyaya and M. Salim, “Modified Block Sparse Bayesian Learning-Based Compressive Sensing Scheme for EEG Signals,” International Journal of Electronics and Telecommunications, Jul. 2023, doi: https://doi.org/10.24425/ijet.2021.135985.
• [3] V. Rama, C. B. R. Rao, and N. Duggirala, “Analysis of Signal Processing Techniques to Identify Cardiac Disorders,” International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, vol. 3, no. 6, 2015, doi: http://dx.doi.org/10.13140/RG.2.1.2445.9922.
• [4] Li, N., He F., Ma W., Wang R., Jiang L., Zhang X, “The Identification of ECG Signals Using Wavelet Transform and WOA-PNN,” in Sensors, 2022, 22(12), 4343, https://doi.org/10.3390/s22124343.
• [5] Saparudin, E. Ramadhan “Heart abnormalities classification using Gauss Method,” Jurnal Generic, vol 5, no 1, 2010. https://repository.unsri.ac.id/23377/1/5-Saparudin.pdf.
• [6] M. K. Shahsavari, H. Rashidi, and H. R. Bakhsh, “Efficient classification of Parkinson’s disease using extreme learning machine and hybrid particle swarm optimization,” in 2016 4th International Conference on Control, Instrumentation, and Automation (ICCIA), IEEE, Jan. 2016, pp. 148-154. doi: https://doi.org/10.1109/ICCIAutom.2016.7483152.
• [7] N. Joshy and D. Pamela, “Heart Diseases Identification System Using Fuzzy Cluster Algorithm,” International Journal of Engineering Research & Technology (IJERT), vol. 03, no. 03, pp. 1217-1220, Mar. 2014. doi: htpps://doi.org/10.17577/IJERTV3IS031095.
• [8] C. Slovis, “ABC of clinical electrocardiography: Conditions not primarily affecting the heart,” BMJ, vol. 324, no. 7349, pp. 1320-1323, Jun. 2002, doi: https://doi.org/10.1136/bmj.324.7349.1320.
• [9] P. A. Chousou, R. Chattopadhyay, V. Tsampasian, V. S. Vassiliou, and P. J. Pugh, “Electrocardiographic Predictors of Atrial Fibrillation,” Medical Sciences, vol. 11, no. 2, p. 30, Apr. 2023, doi: https://doi.org/10.3390/medsci11020030.
• [10] “Atrial Fibrillation - Symptoms Causes.” Accessed: Jun. 17, 2022. [Online]. Available: https://mayoclinic.org
• [11] S. B. Eysmann, F. E. Marchlinski, A. E. Buxton, and M. E. Josephson, “Electrocardiographic changes after cardioversion of ventricular arrhythmias.,” Circulation, vol. 73, no. 1, pp. 73-81, Jan. 1986, doi: https://doi.org/10.1161/01.CIR.73.1.73.
• [12] I. Campero Jurado, A. Fedjajevs, J. Vanschoren, and A. Brombacher, “Interpretable Assessment of ST-Segment Deviation in ECG Time Series,” Sensors, vol. 22, no. 13, p. 4919, Jun. 2022, doi: https://doi.org/10.3390/s22134919.
• [13] “ST Segment.” Accessed: Jun. 20, 2022. [Online]. Available: https://inaecg.com
• [14] L. A. Walder and D. H. Spodick, “Global T wave inversion,” J Am Coll Cardiol, vol. 17, no. 7, pp. 1479-1485, Jun. 1991, doi: https://doi.org/10.1016/0735-1097(91)90635-M.
• [15] D. Azab, M. Zahran, and A. Elmahmoudy, “Initial T wave morphology in the chest leads in patients presenting with anterior ST-segment elevation myocardial infarction and its correlation with spontaneous reperfusion of the left anterior descending coronary artery,” International Journal of the Cardiovascular Academy, vol. 5, no. 2, p. 52, 2019, doi: https://doi.org/10.4103/IJCA.IJCA_1_19.
• [16] A. Yong JK, N. JXT, and R. Linch, “Camel Hump T waves and the Tee Pee sign electrocardiographic evidence of severe electrolyte abnormalities,” Emerg Med Investig, vol. 3, no. 3, Apr. 2017, doi: https://doi.org/10.29011/2475-5605.000040.
• [17] “Physiobank.” Accessed: Oct. 24, 2023. [Online]. Available: https://beecardia.com
• [18] S. Decherchi, P. Gastaldo, A. Leoncini, and R. Zunino, “Efficient Digital Implementation of Extreme Learning Machines for Classification,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 59, no. 8, pp. 496-500, Aug. 2012, doi: https://doi.org/10.1109/TCSII.2012.2204112.
• [19] Z.-L. Sun, T.-M. Choi, K.-F. Au, and Y. Yu, “Sales forecasting using extreme learning machine with applications in fashion retailing,” Decis Support Syst, vol. 46, no. 1, pp. 411-419, Dec. 2008, doi: https://doi.org/10.1016/j.dss.2008.07.009.
• [20] G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: Theory and applications,” Neurocomputing, vol. 70, no. 1-3, pp. 489-501, Dec. 2006, doi: https://doi.org/10.1016/j.neucom.2005.12.126.
• [21] M. Tiwari, J. Adamowski, and K. Adamowski, “Water demand forecasting using extreme learning machines,” Journal of Water and Land Development, vol. 28, no. 1, pp. 37-52, Mar. 2016, doi: https://.doi.org/10.1515/jwld-2016-0004.
• [22] B. Yadav, S. Ch, S. Mathur, and J. Adamowski, “Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction,” Journal of Water and Land Development, vol. 32, no. 1, pp. 103-112, Mar. 2017, doi: https://doi.org/10.1515/jwld-2017-0012.
• [23] R. F. Rahmat, A. B. Pangaribuan, E. Suwarno, and T. Z. Lini, “Lake Toba Water Quality Prediction using Extreme Learning Machine,” ICIC Express Letters, Part B: Applications, vol. 13, no. 1, pp. 89-97, 2022. doi: https://doi.org/10.24507/icicelb.13.01.89.
• [24] F. Mercaldo, L. Brunese, F. Martinelli, A. Santone, and M. Cesarelli, “Experimenting with Extreme Learning Machine for Biomedical Image Classification,” Applied Sciences, vol. 13, no. 14, p. 8558, Jul. 2023, doi: https://doi.org/10.3390/app13148558.

Uwagi

This work was supported by Universitas Sumatera Utara TALENTA Research Grant no 139/UN5.2.3.1/PPM/KP-TALENTA/R/2023.

Identyfikatory

DOI

10.24425/ijet.2024.149568