Analisis Klasifikasi Sinyal EKG Berbasis Wavelet dan Jaringan Syaraf Tiruan

  • Arif Surtono Universitas Lampung
  • Thomas Sri Widodo Universitas Gadjah Mada
  • Maesadji Tjokronagoro Bagian Radiologi FK UGM/RSUP Sardjito
Keywords: elektrokardiografi, klasifikasi, wavelet, jaringan syaraf, energi

Abstract

ECG signals analysis at first associated to pattern recognition of the ECG signals marphology. Nonetheless the signals marphology varying not only in different patients but also in the same patient. The varying of the ECG marphology has efected difficulties in ECG analysis, particularly for a trainingless medicines. On the other hand the ECG signals contain much noises. Therefore it was require the suitable methods for ECG signals analysis. This research aim are analyzing and classifying of the ECG signals from heart condition of normal, arrhytmia, ventricular tachyarrhytmia, intracardiac atrial fibrillation dan myocard infarction based on wavelet transformation and artificial neural network backpropagation.
The research stages are data preparing, pre-processing, feature extraction, processing and post-processing. The 60/50 Hz noises in ECG signals from power line interference reduced using IIR notch filter with pole-zero placement method. The baseline wander noises reduced using discrete wavelet transform of 11 level decomposition to find frequency component below 0,5 Hz as a noise source.
Based on this work results obtained that average accuracy percentage of the neural network recognized all of the ECG types reached 87,424 %. Highest accuracy percentage of 95,455 % for ventricular tachyarrhytmia and lowest accuracy percentage of 70 % for arrhytmia classification.

References

Nazmah, A., Cara Praktis dan Sistematis Belajar Membaca EKG, PT. Elex Media Komputindo, Jakarta, 2011.

Anuradha, B. and Redy, V.C.V, Cardiac Arrhytmia Classificatin using Fuzzy Classifiers, Journal of Theoretical and Applied Information Technology,30thApril 2008, Vol.4 No. 4, p. 353-359, 2008.

Elias, M.F.M and Arof, H., Classification of Electrocardiogram Signal using Multiresolution WaveletTransform and Neural Network, IFMBE Proceedings, "3rd Kuala Lumpur International Conference on Biomedical Engineering 2006"Vol. 15, p.360-364, 2006.

MIT-BIH Database, Available : www.physionet.com.

Belhachat, F. and Izeboudjen,N., Conception of Intelligent classifiers for Cardiac Arrhytmias Detection, Proceeding of International Symposiumon Modelling and Implementation of Complex System (MISC), May 30-31, 2010, Constantine, Algeria, 2010.

He, L., Hou, W., Zhen, X. and Peng, C., Recognition of ECG Patterns Using Artificial Neural Network, Proceedings of The Sixth International Conference on Intelligent System Design and Applications (ISDA’06) Vol.1, IEEE Computer Society, 2006.

Kara, S. and Oskandon, M., Atrial Fibrillation Classification with Artificial Neural Network, Journal of Pattern Recognition, vol. 40, p. 2967-2973,2007.

Sahab, A.R and Gilmalek, Y.M., ECG Arrhytmias Classification Using WaveletTransform and Neural Network, Mathematical Model for Engineering Science.,p.256-258, 2010.

Yu, S. N. and Chen, Y. H., Electrocardiogram beat classification based on wavelettransformation and probabilistic neural network, Pattern Recognition Letters vol. 28, p. 1142–1150, 2007.

Guo, L., Rivero,D. Seoane, J. A. and Pazos, A., Classification of EEG Signals Using Relative WaveletEnergy and Artificial Neural Networks, Proceeding of the First Summit on Genetic and Evolutionery Computation, Sanghai, China, June 12-14, 2009.

Omerhodzic,I., Avdakovic,S., Nuhanovic,A., Dizdarevic,K., .Energy Distribution of EEG Signals : EEG Signal Wavelet-Neural Network Classifier, International Journal of Biological and Life Sciences vol.6, No.4, 2010.

Arumugam, S.S., Gurusamy, G., and Gopalasamy, S., Waveletbased detection of ventricular arrhythmias with Neural Network Classifier, Journal of Biomedical Science and Engineering, Vol.2, No.6, 439-444, 2009.

Addison, P. S., WaveletTransform and the ECG : A Review, Journal of Physiological Measurement, vol. 26, p. 155-199, Institute Of Physics Publishing, 2005.

Widodo, T.S, Sistem Neuro Fuzzy, Graha Ilmu, Yogyakarta, 2005.

Fausett L., Fundamentals of Neural Networks (Architecture, Algorithm and Application),Prentice Hall, New Jersey, 1994.

Siang, J.J., Jaringan Syaraf Tiruan dan Pemrogramannya Menggunakan Matlab, Penerbit Andi, Yogyakarta, 2005.

Orfanidis, S.J., (2010), Introduction to Digital Signal Processing. Available : http://www.ece.rutgers.edu/~orfanidi/intro2sp.

Bunluechokchai, C. and Leeudomwong, T., Discrete WaveletTransform-based BaselineWandering Removal for High Resolution Elctrocardiogram, The International Journal on Applied Biomedical Engineering (IJABME), Vol. 3, No. 1 January-June, 2010.

How to Cite
Arif Surtono, Thomas Sri Widodo, & Maesadji Tjokronagoro. (1). Analisis Klasifikasi Sinyal EKG Berbasis Wavelet dan Jaringan Syaraf Tiruan. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi, 1(3), 60-66. Retrieved from https://dev.journal.ugm.ac.id/v3/JNTETI/article/view/3192
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