The Activity Activation Function Of Multilayer Perceptron - Based Cardiac Abnormalities The Activity Activation Function Of Multilayer Perceptron - Based Cardiac Abnormalities
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Published:
May 31, 2020
Keywords:
Activation, Multilayer Perceptron, Electrocardiogram, Backpropagation, Bayesian Regularity
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Computer Science
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Abstract
Cardiac disorders refer to irregular activity at the heart. Cardiac abnormalities sometimes do not exhibit any and unreasonable symptoms that can lead to sudden death due to heart-cracking functions. This article is to develop a program capable to detect cardiac abnormalities activity through the application of Multilayer Perceptron (MLP). A certain number of heart rate signal data from an electrocardiogram (EKG) will be used in this paper to train and to test the network performance of the MLP. MLP is trained by several techniques that Backpropagation (BP), Bayesian regularity (BR), and Levenberg-Marquardt (LM).
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How to Cite
Simanjuntak, M. S., Wanayumini, W., Rosnelly, R. and Gunawan, T. S. (2020) “The Activity Activation Function Of Multilayer Perceptron - Based Cardiac Abnormalities: The Activity Activation Function Of Multilayer Perceptron - Based Cardiac Abnormalities”, Jurnal Mantik, 4(1), pp. 555-561. Available at: https://iocscience.org/ejournal/index.php/mantik/article/view/747 (Accessed: 28May2026).
References
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[14] M. Hamdan, O. O. Khalifah, and T. S. Gunawan, “Measuring the road traffic intensity using neural network with computer vision,” Indones. J. Electr. Eng. Comput. Sci., vol. 10, no. 1, pp. 184–190, 2018, doi: 10.11591/ijeecs.v10.i1.pp184-190.
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[16] F. R. Bin Hashim, J. J. Soraghan, and L. Petropoulakis, “Multi-classify Hybrid Multilayered Perceptron (HMLP) network for pattern recognition applications,” IFIP Adv. Inf. Commun. Technol., vol. 381 AICT, no. PART 1, pp. 19–27, 2012, doi: 10.1007/978-3-642-33409-2_3.
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[18] L. A. M. Pereira et al., “Multilayer perceptron neural networks training through charged system search and its Application for non-technical losses detection,” 2013 IEEE PES Conf. Innov. Smart Grid Technol. ISGT LA 2013, 2013, doi: 10.1109/ISGT-LA.2013.6554383.
[2] M. S. Simanjuntak, R. Wijaya, J. T. Informatika, and P. I. Komputer, “Melalui Gejala Dan Pola Hidup Menggunakan Metode,” vol. 3, no. 3, pp. 122–125, 2019.
[3] B. Rifai, “Algoritma Neural Network Untuk Prediksi,” Techno Nusa Mandiri, vol. IX, no. 1, pp. 1–9, 2013.
[4] Kemenkes RI, Profil Kesehatan Republik Indonesia Tahun 2009. 2009.
[5] M. S. Simanjuntak, R. Wijaya, and P. I. Komputer, “APLIKASI PENCARIAN GAMBAR DENGANALGORITMA CONTENT-,” vol. 3, no. 3, 2019.
[6] H. H. Rahma and R. B. Wirjatmadi, “Hubungan Asupan Zat Gizi Makro Dan Profil Lipid Dengan Kejadian Penyakit Jantung Koroner Pada Pasien Lansia Di Rumah Sakit Islam Jemursari Surabaya,” Media Gizi Indones., vol. 12, no. 2, p. 129, 2018, doi: 10.20473/mgi.v12i2.129-133.
[7] I. Iskandar, A. Hadi, and A. Alfridsyah, “Faktor Risiko Terjadinya Penyakit Jantung Koroner pada Pasien Rumah Sakit Umum Meuraxa Banda Aceh,” AcTion Aceh Nutr. J., vol. 2, no. 1, p. 32, 2017, doi: 10.30867/action.v2i1.34.
[8] F. R. Hashim, J. J. Soraghan, L. Petropoulakis, and N. G. N. Daud, “EMG cancellation from ECG signals using modified NLMS adaptive filters,” IECBES 2014, Conf. Proc. - 2014 IEEE Conf. Biomed. Eng. Sci. “Miri, Where Eng. Med. Biol. Humanit. Meet,” no. December, pp. 735–739, 2014, doi: 10.1109/IECBES.2014.7047605.
[9] F. R. Hashim, L. Petropoulakis, J. Soraghan, and S. I. Safie, “Wavelet based motion artifact removal for ECG signals,” 2012 IEEE-EMBS Conf. Biomed. Eng. Sci. IECBES 2012, no. December, pp. 339–342, 2012, doi: 10.1109/IECBES.2012.6498019.
[10] Wanayumini, O. S. Sitompul, M. Zarlis, and S. Suwilo, “Automatic Detection of Chaos Phenomenon in Tornadoes Prediction Using Edge Detection,” 2019 3rd Int. Conf. Electr. Telecommun. Comput. Eng. ELTICOM 2019 - Proc., pp. 48–52, 2019, doi: 10.1109/ELTICOM47379.2019.8943845.
[11] S. Palaniappan and R. Awang, “Intelligent heart disease prediction system using data mining techniques,” AICCSA 08 - 6th IEEE/ACS Int. Conf. Comput. Syst. Appl., pp. 108–115, 2008, doi: 10.1109/AICCSA.2008.4493524.
[12] R. Rosnelly, L. Wahyuni, and J. Kusanti, “Optimization of Region of Interest (ROI) Image of Malaria Parasites,” J. Appl. Intell. Syst., vol. 3, no. 2, pp. 87–95, 2018, doi: 10.33633/jais.v3i2.2060.
[13] F. R. Hashim, N. G. N. Daud, S. N. Mokhtar, A. F. Rashidi, J. Adnan, and K. A. Ahmad, “Optimization of ECG Peaks (Amplitude and Duration) in Predicting ECG Abnormality using Artificial Neural Network,” Indian J. Sci. Technol., vol. 10, no. 12, pp. 1–5, 2017, doi: 10.17485/ijst/2017/v10i12/112970.
[14] M. Hamdan, O. O. Khalifah, and T. S. Gunawan, “Measuring the road traffic intensity using neural network with computer vision,” Indones. J. Electr. Eng. Comput. Sci., vol. 10, no. 1, pp. 184–190, 2018, doi: 10.11591/ijeecs.v10.i1.pp184-190.
[15] W. Wanayumini, O. S Sitompul, M. Zarlis, S. Suwilo, and A. M H Pardede, “A Research Framework for Supervised Image Classification For Tornado Chaos Phenomena,” Int. J. Eng. Technol., vol. 7, no. 4.15, p. 447, 2018, doi: 10.14419/ijet.v7i4.15.25254.
[16] F. R. Bin Hashim, J. J. Soraghan, and L. Petropoulakis, “Multi-classify Hybrid Multilayered Perceptron (HMLP) network for pattern recognition applications,” IFIP Adv. Inf. Commun. Technol., vol. 381 AICT, no. PART 1, pp. 19–27, 2012, doi: 10.1007/978-3-642-33409-2_3.
[17] J. Adnan et al., “Heart abnormality activity detection using multilayer perceptron (MLP) network,” AIP Conf. Proc., vol. 2016, no. September, 2018, doi: 10.1063/1.5055415.
[18] L. A. M. Pereira et al., “Multilayer perceptron neural networks training through charged system search and its Application for non-technical losses detection,” 2013 IEEE PES Conf. Innov. Smart Grid Technol. ISGT LA 2013, 2013, doi: 10.1109/ISGT-LA.2013.6554383.