A New Approach to Early Diagnosis of Congestive Heart Failure Disease by Using Hilbert-Huang Transform

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 non-linear and non-stationary signals, is used to extract the features from R-R intervals obtained from the raw electrocardiogram ddata. 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.

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Eser Adı
(dc.title)
A New Approach to Early Diagnosis of Congestive Heart Failure Disease by Using Hilbert-Huang Transform
Yayın Türü
(dc.type)
Makale
Yazar/lar
(dc.contributor.author)
ALLAHVERDİ, Novruz
Yazar/lar
(dc.contributor.author)
KUTLU, Yakup
Yazar/lar
(dc.contributor.author)
ALTAN, Gökhan
Atıf Dizini
(dc.source.database)
Wos
Atıf Dizini
(dc.source.database)
Scopus
Konu Başlıkları
(dc.subject)
Congestive Heart Failure
Konu Başlıkları
(dc.subject)
HRV
Konu Başlıkları
(dc.subject)
Multilayer Perceptron
Konu Başlıkları
(dc.subject)
ECG
Konu Başlıkları
(dc.subject)
Hilbert-HuangTransform
Konu Başlıkları
(dc.subject)
Coronary Artery Disease
Yayın Tarihi
(dc.date.issued)
2016
Kayıt Giriş Tarihi
(dc.date.accessioned)
2019-07-09T13:50:37Z
Açık Erişim tarihi
(dc.date.available)
2019-07-09T13:50:37Z
Özet
(dc.description.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 non-linear and non-stationary signals, is used to extract the features from R-R intervals obtained from the raw electrocardiogram ddata. 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.
Yayın Dili
(dc.language.iso)
en
Tek Biçim Adres
(dc.identifier.uri)
https://hdl.handle.net/20.500.12498/927
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