AUTOMATIC ARRHYTHMIAS DETECTION USING VARIOUS TYPES OF ARTIFICIAL NEURAL NETWORK BASED LEARNING VECTOR QUANTIZATION (LVQ)
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.
Keywords: Automatic Arrythmias
detection, ECG, Classification, LVQ1, LVQ2, LVQ2.1, FNLVQ, FNLVQ MSA,
FNLVQ-PSO, GLVQ, FNGLVQ
Author: Diane Fitria, Muhammad
Anwar Ma'sum, Elly Matul Imah, Alexander Agung Gunawan
Journal Code: jptkomputergg140015
