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Dergiler / Turkish Journal of Electrical Engineering and Computer Science / 2010 / Cilt: 18 - Sayı: 2

Nonlinear dynamic analysis of mitral valve doppler signals: surrogate data analysis

Esma UZUNHİSARCIKLI
Sayfa
327–337
Yayın Tarihi
2010-02-01
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Correlation dimension features can be used to detect the change in blood flow velocity of patients with mitral valve stenosis.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:In our study, the nonlinear dynamics of mitral valve Doppler signals from 32 healthy and 28 patients with mitral valve stenosis was evaluated using by the computation of Lyapunov exponents, correlation dimension values and surrogate data analysis.
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Temel Bulgular & Veri:It was found that the largest Lyapunov exponent and correlation dimension values derived from patient subjects were larger than that of healthy subjects (r < 0.005).
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Sonuç & Katkı:Correlation dimension features can be used to detect the change in blood flow velocity of patients with mitral valve stenosis.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Doppler · mitral valve · largest lyapunov exponent

Özet

In our study, the nonlinear dynamics of mitral valve Doppler signals from 32 healthy and 28 patients with mitral valve stenosis was evaluated using by the computation of Lyapunov exponents, correlation dimension values and surrogate data analysis. Two chaotic features are compared for healthy and patient subjects. It was found that the largest Lyapunov exponent and correlation dimension values derived from patient subjects were larger than that of healthy subjects (r < 0.005). Surrogate data analysis was performed to determine the chaotic dynamics of Doppler signals. It was observed that the original and surrogate data have similar spectral features. Receiver operating characteristic (ROC) curves were used to evaluate the nonlinearity character Area Under the curve (AUC) values were obtained as 0.99 and 0.978 for the largest Lyapunov exponent and correlation dimension values, respectively. According to these results, Doppler signals have a nonlinear dynamic property and the largest Lyapunov exponent. Correlation dimension features can be used to detect the change in blood flow velocity of patients with mitral valve stenosis.

Abstract

In our study, the nonlinear dynamics of mitral valve Doppler signals from 32 healthy and 28 patients with mitral valve stenosis was evaluated using by the computation of Lyapunov exponents, correlation dimension values and surrogate data analysis. Two chaotic features are compared for healthy and patient subjects. It was found that the largest Lyapunov exponent and correlation dimension values derived from patient subjects were larger than that of healthy subjects (r < 0.005). Surrogate data analysis was performed to determine the chaotic dynamics of Doppler signals. It was observed that the original and surrogate data have similar spectral features. Receiver operating characteristic (ROC) curves were used to evaluate the nonlinearity character Area Under the curve (AUC) values were obtained as 0.99 and 0.978 for the largest Lyapunov exponent and correlation dimension values, respectively. According to these results, Doppler signals have a nonlinear dynamic property and the largest Lyapunov exponent. Correlation dimension features can be used to detect the change in blood flow velocity of patients with mitral valve stenosis.

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Bu Makaleye Atıf Yap

UZUNHİSARCIKLI, E. (2010). Nonlinear dynamic analysis of mitral valve doppler signals: surrogate data analysis. Turkish Journal of Electrical Engineering and Computer Science, Cilt: 18 - Sayı: 2, 327–337. https://ajindex.com/articles/189191

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Kaynakça (11)

  1. M.D. McGoon; Mayo Clinic Heart Book: The Ultimate Guide to Heart Health.;ew York: William Morrow and Co., Inc., 1993. M. Akay,;.M. Akay,;. Welkowitz. “Neural networks for the diagnosis of coronary artery disease.” International Joint Conference on Neural Networks (IJCNN), Vol. 2, pp. 419–424, 1992. P. Gutgesell,;. Howard. “Common cardiovascular problems in the young: Part I – Murmurs, chest pain, syncope and irregular rhythms.” American Family Physician, Vol. 56, pp. 1825–1827, 1997. N.C. Nanda. Doppler Echocardiography, 2nd Ed.;ondong: Lea & Febiger, 1993. M. Ramesh,;.A. Gowda,;. Ijaz,;. Balendu,;.T. Vasavada,;. Riti. “History of the evolution of echocardiography.” Intl. J. Cardiol., Vol. 97, pp. 1–6, 2004.
  2. P.I.J. Keeton, F.S. Schlindwein. “Application of wavelets in Doppler Ultrasound.” Sensor Review, (ISSN 0260-2288),
  3. MCB University Press, Vol. 17, pp. 38–45, 1997.
  4. D.H. Evans, W.N. McDicken, R. Skidmore, J.P. Woodcock. Doppler Ultrasound: Physics, Instrumentation and Clinical Applications.;hichester: Wiley, 1989.
  5. I.A. Wright, N.A.J. Gough, F. Rakebrandt, M. Wahab, J.P. Woodcock. “Neural network analysis of Doppler ultrasound blood flow signals: A pilot study.” Ultrasound in Medicine and Biology, Vol. 23, pp. 683–690, 1997.
  6. V. D. Saini, N. C. Nanda, and D. Maulik. Basic principles of ultrasound and Doppler effect. Doppler echocardiog- raphy. Philadelphia, London: Lea & Febiger, 1993.
  7. S. Kara. “ClassiŞcation of Mitral Valve Stenosis from Doppler Signals Using Short Time Fourier Transform and ArtiŞcial Neural Network.” Expert Systems with Applications, Vol. 33, pp. 468-75, 2007.
  8. P.J. Vaitkus, R.S.C. Cobbold,;.W. Johnston. “A comparative study and assessment of Doppler ultrasound spectral estimation techniques, Part II: Methods and results.” Ultrasound Med. Biol., Vol.14, pp. 673–688, 1988.
  9. H. Sava, L.-G. Durand, G. Cloutier. “Performance of short-time spectral parametric methods for reducing the variance of the Doppler Ultrasound mean instantaneous frequency estimation.” Medical & Biological Engineering & Computing, Vol. 37, pp. 291–297, 1999.
  10. J. C. Mohan, A. R. Patel, R. Passey, D. Gupta, M. Kumar, R. Arora, et al. Is the mitral valve area flow-dependent in mitral stenosis? A dobutamine stress echocardiographic study.” Journal of the American College of Cardiology, Vol. 40, pp. 1809–1815, 2002.
  11. S. Papadimitriou, A. Bezerianos. “Nonlinear analysis of the performance and reability of wavelet singularity detec- tion based denoising for doppler ultrasound feta