Konjestif kalp yetmezliğinin Hilbert-Huang dönüşüm ile analizi
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Date
2014
Journal Title
Journal ISSN
Volume Title
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Access Rights
info:eu-repo/semantics/openAccess
Abstract
Hilbert-Huang Dönüşümü (HHD) liner olmayan ve sabit olmayan sinyaller üzerinde öznitelik çıkartma, filtreleme gibi işlemlerde sıkça kullanılan bir yöntemdir. Bu çalışmada, HHD yönteminin kalp sinyallerine uygulanması sonucu özniteliklerin belirlenmesi ve belirlenen bu özniteliklerin Kongestif Kalp Yetmezliği (KKY) olan hastaların kontrol grubundan ayırt edilerek sınıflama yapılması üzerine bir çalışma yapılmıştır. Kalp hızı değişkenlerinden elde edilen RR sinyalleri, HHD işleminden geçirilerek içsel mod fonksiyonları (İMF) bileşenleri elde edilmiş, dönüşüm sonrası elde edilen sinyallerin istatistiksel bilgileri öznitelik olarak belirlenmiştir. Elde edilen öznitelikler, Yapay Sinir Ağları (YSA) kullanılarak sınıflandırma başarımı incelenmiştir. Sonuç olarak, RR sinyallerden elde edilen İMF bileşenlerin istatistiksel öznitelikleri kullanılarak sınıflama işleminde iyi sonuçlar alınabileceğini göstermiştir.
Hilbert-Huang Transform(HHT) is a method that is often used for feature extraction, filtering and similar processes on nonlinear and non-stationary signals. In this study, HHT is used for feature extraction that can be obtained from applying to Heart Rate variability signals and studied on classifying by distinguishing Congestive Heart Failure (CHF) patients from the control group of patients by selected features. Instrict Mode Functions (IMF) components are sifted by applying HHT to RR signals obtained from the heart rate variability, statistical informations of the signals obtained after the transformation have been set as features. Classification accuracy of Obtained statistical features are analyzed with Artificial Neural Networks (ANN). In conclusion, using statistical features of IMF components transformed from RR signals shows good classification results can be taken. ANAHTAR KELİMELER/KEYWORDS
Hilbert-Huang Transform(HHT) is a method that is often used for feature extraction, filtering and similar processes on nonlinear and non-stationary signals. In this study, HHT is used for feature extraction that can be obtained from applying to Heart Rate variability signals and studied on classifying by distinguishing Congestive Heart Failure (CHF) patients from the control group of patients by selected features. Instrict Mode Functions (IMF) components are sifted by applying HHT to RR signals obtained from the heart rate variability, statistical informations of the signals obtained after the transformation have been set as features. Classification accuracy of Obtained statistical features are analyzed with Artificial Neural Networks (ANN). In conclusion, using statistical features of IMF components transformed from RR signals shows good classification results can be taken. ANAHTAR KELİMELER/KEYWORDS
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Mühendislik, Ortak Disiplinler
Journal or Series
Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi
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Scopus Q Value
Volume
16
Issue
48
