Detection of Heart Abnormalities Based On ECG Signal Characteristics using Multilayer Perceptron with Firefly Algorithm-Simulated Annealing

Sofiah Ishlakhul Abda, Auli Damayanti, Edi Winarko

= http://dx.doi.org/10.20473/conmatha.v3i1.26941
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Abstract


Heart disease is one of the causes of death worldwide. Therefore, detecting heart disease is very important to reduce the increased mortality rate. One of the methods used to detect the abnormalities or disorders of the heart is to use computer assistance to determine the characteristics of an electrocardiogram. Electrocardiogram (ECG) is a test that detects and records the activity of the heart through small metal electrodes attached to the skin of one's chest, arms and legs. This test shows how fast the heart beats and whether the rhythm is stable or not. The purpose of this thesis is to apply a multi-layer perceptron model with firefly algorithm and simulated annealing in detecting cardiac abnormalities based on the ECG signal characteristics. The initial step of this research is image processing. The stages of ECG image processing are grayscale, thresholding, edge detection, segmentation and normalization processes. The results of this image processing are used as input matrices in the perceptron multilayer network training using firefly algorithm and simulated annealing. In the training process, we will get optimal weights and biases for validation tests on test data. The training data in this thesis uses 20 ECG images and in the validation test process uses 10 ECG images. The validation results in the validation test show that the accuracy in detecting heart abnormalities based on the characteristics of ECG signals using multi- layer perceptron with firefly algorithm and simulated annealing is 100%.

Keywords


electrocardiogram, firefly algorithm, heart disease, multi-layer perceptron, simulated annealing

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References


Hammad, M., A. Maher, K. Wang, F. Jiang, M. Amrani, 2018, Detection of Abnormal Heart Conditions Based on Characteristics of ECG Signals, Measurement.

Scarmoth, L., 1990, An Introduction to Electrography, Blackwell Scientific Publication, Oxford.

Fausett, L., 2003, Fundamentals of Neural Networks: Architectures, Algorithms, and Applications, Printice-Hall Inc., London.

Siang, J.J., 2005, Jaringan Syaraf Tiruan dan Pemrogamannya menggunakan MATLAB, Penerbit ANDI, Yogyakarta.

Yang, X. S., 2010, Engineering Optimization: An Introduction with Metaheuris-tic Applications, Wiley & Sons, Inc, New Jersey.

Kirkpatrick, C. D. Gelatt, M. P. Vecchi, 1983, Optimization by Simulated Annealing, Science, Vol 220, No 4598, Halaman 671-680.

Baharuddin, 2008, Perancangan Alokasi Kanal Dinamik pada GSM, Teknik A, Vol.1, N0.29, April 2008, ISSN: 0854-8471.

Putra, darma, 2008, Sistem Biometrika : Konsep Dasar Teknik Analisis Citra, penerbit Andi, Yogyakarta.

Basuki, A., Palandi, J. F., dan Fatchurrochman, 2005, Pengolahan Citra Digital Menggunakan Visual Basic, Penerbit Graha Ilmu, Yogyakarta.

Ahmad, U., 2005, Pengolahan Citra Digital dan Teknik Pemrogamannya, Graha Ilmu, Yogyakarta..

Riedmiller, M., 1994, Advanced Supervised Learning in Multi-Layer Perceptrons from Backpropagation to Adaptive Learning Algorithms, International Journal on Computer Standards and Interfaces.


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