AUTOMATIC ARRHYTHMIAS DETECTION USING VARIOUS TYPES OF ARTIFICIAL NEURAL NETWORK BASED LEARNING VECTOR QUANTIZATION (LVQ)

Authors

  • Diane Fitria
  • Muhammad Anwar Ma'sum
  • Elly Matul Imah
  • Alexander Agung Gunawan

DOI:

https://doi.org/10.21609/jiki.v7i2.262

Keywords:

Automatic Arrythmias detection, ECG, Classification, LVQ1, LVQ2, LVQ2.1, FNLVQ, FNLVQ MSA, FNLVQ-PSO, GLVQ, FNGLVQ

Abstract

Abstract An automatic Arrythmias detection system is urgently required due to small number of cardiologits in Indonesia. This paper discusses only about the study and implementation of the system. We use several kinds of signal processing methods to recognize arrythmias from ecg signal. The core of the system is classification. Our LVQ based artificial neural network classifiers based on LVQ, which includes LVQ1, LVQ2, LVQ2.1, FNLVQ, FNLVQ MSA, FNLVQ-PSO, GLVQ and FNGLVQ. Experiment result show that for non round robin dataset, the system could reach accuracy of 94.07%, 92.54%, 88.09% , 86.55% , 83.66%, 82.29 %, 82.25%, and 74.62% respectively for FNGLVQ, FNLVQ-PSO, GLVQ, LVQ2.1, FNLVQ-MSA, LVQ2, FNLVQ and LVQ1. Whereas for round robin dataset, system reached accuracy of 98.12%, 98.04%, 94.31%, 90.43%, 86.75%, 86.12 %, 84.50%, and 74.78% respectively for GLVQ, LVQ2.1, FNGLVQ, FNLVQ-PSO, LVQ2, FNLVQ-MSA, FNLVQ and LVQ1.

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Published

2014-08-21

How to Cite

Fitria, D., Ma’sum, M. A., Imah, E. M., & Gunawan, A. A. (2014). AUTOMATIC ARRHYTHMIAS DETECTION USING VARIOUS TYPES OF ARTIFICIAL NEURAL NETWORK BASED LEARNING VECTOR QUANTIZATION (LVQ). Jurnal Ilmu Komputer Dan Informasi, 7(2), 90–100. https://doi.org/10.21609/jiki.v7i2.262

Issue

Vol. 7 No. 2 (2014): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)

Section

Articles

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