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Journals / 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
Pages
327–337
Publication Date
2010-02-01
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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).

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
📊
Key Findings & Data: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).
💡
Conclusion & Impact:Correlation dimension features can be used to detect the change in blood flow velocity of patients with mitral valve stenosis.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Doppler · mitral valve · largest lyapunov exponent

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.

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.

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Cite This Article

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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