Multiclass ECG Signal Analysis Using Global Average-Based 2-D Convolutional Neural Network Modeling
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Authors
Wasimuddin, Muhammad
Elleithy, Khaled M.
Abuzneid, Abdelshakour
Faezipour, Miad
Abuzaghleh, Omar
Issue Date
2021-04-09
Type
Other
Language
en_US
Keywords
Cardiovascular disease , Convolutional neural network , Electrocardiogram
Alternative Title
Abstract
Cardiovascular diseases accounted for approximately 836,546 deaths in the United States in 2018. Nearly 2,300 Americans die of cardiovascular disease each day, an average of one death every 38 seconds. To this end, research has been reported in the literature on Electrocardiogram (ECG) signal analysis to determine arrhythmia and other cardiac conditions. This work introduces a classifier that will detect abnormalities of the ECG signal with its analysis as a 2-D image fed to a Convolutional Neural Network (CNN) classifier.The proposed method classifies the ECG signal as normal or ST-change, V-change by transforming the single-lead ECG signal into images and then applying CNN classification. Images are taken from the European ST-T dataset on PhysioNet databank. Our method yields an accuracy of 99.26%.