A new approach to early diagnosis of congestive heart failure disease by using Hilbert-Huang transform
Altan, Gokhan and Kutlu, Yakup and Allahverdi, Novruz
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Abstract
Congestive heart failure (CHF) is a degree of cardiac disease occurring
as a result of the heart's inability to pump enough blood for the human
body. In recent studies, coronary artery disease (CAD) is accepted as
the most important cause of CHF. This study focuses on the diagnosis of
both the CHF and the CAD. The Hilbert-Huang transform (HHT), which is
effective on nonlinear and non-stationary signals, is used to extract
the features from R-R intervals obtained from the raw electrocardiogram
daata. The statistical features are extracted from instinct mode
functions that are obtained applying the HHT to R-R intervals.
Classification performance is examined with extracted statistical
features using a multilayer perceptron neural network. The designed
model classified the CHF, the CAD patients and a normal control group
with rates of 97.83\%, 93.79\% and 100\%, accuracy, specificity and
sensitivity, respectively. Also, early diagnosis of the CHF was
performed by interpretation of the CAD with a classification accuracy
rate of 97.53\%, specificity of 98.18\% and sensitivity of 97.13\%. As a
result, a single system having the ability of both diagnosis and early
diagnosis of CHF is performed by integrating the CAD diagnosis method to
the CHF diagnosis method. (C) 2016 Elsevier Ireland Ltd. All rights
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