Key result
Ensemble empirical mode decomposition (EEMD) based algorithms demonstrated statistically significant correlations with blood pressure changes in a pig model.
EEMD-based algorithms demonstrate potential utility for analyzing nonlinear and nonstationary cardiovascular signals to assess arterial stiffness and systemic resistance.
May advance EEMD research in animal cardiovascular models; leaves open human translation.
Cardiovascular system is known to be nonlinear and nonstationary. Traditional linear assessments algorithms of arterial stiffness and systemic resistance of cardiac system accompany the problem of nonstationary or inconvenience in practical applications. In this pilot study, two new assessment methods were developed: the first is ensemble empirical mode decomposition based reflection index (EEMD-RI) while the second is based on the phase shift between ECG and BP on cardiac oscillation. Both methods utilise the EEMD algorithm which is suitable for nonlinear and nonstationary systems. These methods were used to investigate the properties of arterial stiffness and systemic resistance for a pig's cardiovascular system via ECG and blood pressure (BP). This experiment simulated a sequence of continuous changes of blood pressure arising from steady condition to high blood pressure by clamping the artery and an inverse by relaxing the artery. As a hypothesis, the arterial stiffness and systemic resistance should vary with the blood pressure due to clamping and relaxing the artery. The results show statistically significant correlations between BP, EEMD-based RI, and the phase shift between ECG and BP on cardiac oscillation. The two assessments results demonstrate the merits of the EEMD for signal analysis.
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Yeh et al. (2012) studied Arterial stiffness and systemic resistance. Ensemble empirical mode decomposition (EEMD) based algorithms (EEMD-RI and phase shift) was evaluated on Correlation between blood pressure, EEMD-based reflection index, and phase shift between ECG and BP. Ensemble empirical mode decomposition (EEMD) based algorithms demonstrated statistically significant correlations with blood pressure changes in a pig model.
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