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http://dx.doi.org/10.6109/jkiice.2016.20.11.2137

A Classification of lschemic Heart Disease using Neural Network in Magnetocardiogram  

Eum, Sang-hee (Department of Shipbuilding and Marine, Dongju College)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.20, no.11, 2016 , pp. 2137-2142 More about this Journal
Abstract
The electrical current generated by heart creates not only electric potential but also a magnetic field. In this study, the signals obtained magnetocardiogram(MCG) using 61 channel superconducting quantum interference device(SQUID) system, and the clinical significance of various feature parameters has been developed MCG. Neural network algorithm was used to perform the classification of ischemic heart disease. The MCG signal was obtained to facilitate the extraction of parameters through a process of pre-processing. The data used to research the normal group 10 and ischemic heart disease group 10 with visible signs of stable angina patients. The available clinical indicators were extracted by characteristic point, characteristic interval parameter, and amplitude ratio parameter. The extracted parameters are determined to analysis the significance and clinical parameters were defined. It is possible to classify ischemic heart disease using the MCG feature parameters as a neural network input.
Keywords
MCG; Ischemic; Heart Disease; Neural Network; Classification;
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