Abstract
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.
References
- Bursikova S. (2007): “Application of Hilbert-Huang Transform to the Data of Blood Glucose Profile”, Student's Conference STC 2007, Faculty of Mechanical Engineering, Czech Teknik Universitesi.
- Duda R. O., Hart P. E., Stork D. G. (2000): “Pattern Classification”, 2. Basım, Wiley, New York, s.545,556.
- Duman F., Özdemir N., Yıldırım E. (2012): “Patient Specific Seizure Prediction Algorithm Using Hilbert-HuangTransform”, IEEE-EMBS Intern. BHI 2012, s.705–708 .
- Goldberger A. L., Amaral L. A. N. (2000). “PhysioBank, PhysioToolkit, and PhysioNet”, Cilt 101, No. 23, s.215–e220.
- Hou Y., Tian H. (2010): "An Automatic Modulation Recognition Algorithm Based on HHT and SVD”, Image and Signal Processing(CTSP), Cilt 8, s.3577-3581.
- Huang A. B. N. E., Shen Z., Long S. R., Wu M. L., Shih H. H., Zheng Q. (1998): “The Empirical Mode Decomposition and Hilbert Spectrum for Nonlinear and Nonstationary Time Series Analysis”, Londra A, Cilt 454, s.903–995.
- İşler Y., Kuntalp M. (2007): “Combining Classical HRV Indices with Wavelet Entropy Measures Improves to Performance in Diagnosing Congestive Heart Failure”, Comput. Biol. Med., Cilt. 37, No. 10, s.1502–10.
- İşler Y., Kuntalp M. (2010): “Heart Rate Normalization in the Analysis of Heart Rate Variability in Congestive Heart Failure”, Proceedings of the IMechE Part H: Journal of Engineering in Medicine, Cilt 224, No. 3, s.453-463.
- Johansson M. (1999): “The Hilbert Transform”, Växjo University, Yüksek Lisans Tezi, Schweden, s.33.
- Maji U., Mitra M., Pal S. (2013): “Automatic Detection of Atrial Fibrillation Using Empirical Mode Decomposition and Statistical Approach”, Cilt 10, s.45-52.
- Orhan U. (2013): “Real-time CHF Detection from ECG Signals Using a Novel Discretization Method.,” Comput. Biol. Med., Cilt 43, No. 10, s.1556–1562.
- Tanç Y., Akan A. (2013): “Hilbert-Huang Dönüşümü İle Sayısal Modülasyon Türü Sınıflandırması”, 21. Sinyal İşleme ve İletişim Uygulamaları Kurultayı (SİU2013), s.1-4, DOI: 10.1109/SIU.2013.6531183.
- Thuraisingham R. A. (2009): “A Classification System to Detect Congestive Heart Failure Using Second-Order Difference Plot of RR Intervals”, Cardiol. Res. Pract.
- Ubeyli E. D. (2009): “Adaptive Neuro-Fuzzy Inference System for Classification of ECG Signals Using Lyapunov Exponents”, Comput. Methods Prog Biomed., s:313-321.
- Verner M. (2007): ”Hilbert-Huang Transform, its Features and Application to the Audio Signal”, Student's Conference STC 2007, Mekanik Mühendisliği Fakültesi, Czech Teknik Universitesi.
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
References
- Bursikova S. (2007): “Application of Hilbert-Huang Transform to the Data of Blood Glucose Profile”, Student's Conference STC 2007, Faculty of Mechanical Engineering, Czech Teknik Universitesi.
- Duda R. O., Hart P. E., Stork D. G. (2000): “Pattern Classification”, 2. Basım, Wiley, New York, s.545,556.
- Duman F., Özdemir N., Yıldırım E. (2012): “Patient Specific Seizure Prediction Algorithm Using Hilbert-HuangTransform”, IEEE-EMBS Intern. BHI 2012, s.705–708 .
- Goldberger A. L., Amaral L. A. N. (2000). “PhysioBank, PhysioToolkit, and PhysioNet”, Cilt 101, No. 23, s.215–e220.
- Hou Y., Tian H. (2010): "An Automatic Modulation Recognition Algorithm Based on HHT and SVD”, Image and Signal Processing(CTSP), Cilt 8, s.3577-3581.
- Huang A. B. N. E., Shen Z., Long S. R., Wu M. L., Shih H. H., Zheng Q. (1998): “The Empirical Mode Decomposition and Hilbert Spectrum for Nonlinear and Nonstationary Time Series Analysis”, Londra A, Cilt 454, s.903–995.
- İşler Y., Kuntalp M. (2007): “Combining Classical HRV Indices with Wavelet Entropy Measures Improves to Performance in Diagnosing Congestive Heart Failure”, Comput. Biol. Med., Cilt. 37, No. 10, s.1502–10.
- İşler Y., Kuntalp M. (2010): “Heart Rate Normalization in the Analysis of Heart Rate Variability in Congestive Heart Failure”, Proceedings of the IMechE Part H: Journal of Engineering in Medicine, Cilt 224, No. 3, s.453-463.
- Johansson M. (1999): “The Hilbert Transform”, Växjo University, Yüksek Lisans Tezi, Schweden, s.33.
- Maji U., Mitra M., Pal S. (2013): “Automatic Detection of Atrial Fibrillation Using Empirical Mode Decomposition and Statistical Approach”, Cilt 10, s.45-52.
- Orhan U. (2013): “Real-time CHF Detection from ECG Signals Using a Novel Discretization Method.,” Comput. Biol. Med., Cilt 43, No. 10, s.1556–1562.
- Tanç Y., Akan A. (2013): “Hilbert-Huang Dönüşümü İle Sayısal Modülasyon Türü Sınıflandırması”, 21. Sinyal İşleme ve İletişim Uygulamaları Kurultayı (SİU2013), s.1-4, DOI: 10.1109/SIU.2013.6531183.
- Thuraisingham R. A. (2009): “A Classification System to Detect Congestive Heart Failure Using Second-Order Difference Plot of RR Intervals”, Cardiol. Res. Pract.
- Ubeyli E. D. (2009): “Adaptive Neuro-Fuzzy Inference System for Classification of ECG Signals Using Lyapunov Exponents”, Comput. Methods Prog Biomed., s:313-321.
- Verner M. (2007): ”Hilbert-Huang Transform, its Features and Application to the Audio Signal”, Student's Conference STC 2007, Mekanik Mühendisliği Fakültesi, Czech Teknik Universitesi.
Details
| Primary Language | Turkish |
|---|---|
| Other ID | JA54BR78FU |
| Journal Section | Research Article |
| Authors | |
| Publication Date | September 1, 2014 |
| Published in Issue | Year 2014 Volume: 16 Issue: 48 |
Cite
Dokuz Eylül Üniversitesi, Mühendislik Fakültesi Dekanlığı Tınaztepe Yerleşkesi, Adatepe Mah. Doğuş Cad. No: 207-I / 35390 Buca-İZMİR.