Trend-extracted MSE based on adaptive aligned EEMD with early termination scheme: Analysis of the acute stroke patients' physiological signals

Multiscale entropy (MSE) analysis method has been widely used to evaluate the physiologic control mechanisms. However, MSE is vulnerable to trends. Ensemble empirical mode decomposition (EEMD) is a powerful tool to remove the trend from non-stationary physiological signals before MSE analysis. In this paper, trend-extracted MSE (T-MSE) based on adaptive aligned EEMD (AA-EEMD) with early termination scheme is proposed. AA-EEMD not only reduces the computing time, but also considers the frequency meaning of different physiological signals and different subjects. We have applied T-MSE based on AA-EEMD to analyze the acute stroke patients' physiological signals in intensive care unit (ICU). We find that the complexity of electrocardiogram (EKG) is higher in the acute stroke patients with good functional outcome than those with bad functional outcome. For EKG parameter, the p-value is approximately 10^-8, which shows significant statistical difference. Moreover, the average number of IMFs in a single member of ensemble is reduced to 74% of the original. The average computing time in a single member of ensemble is reduced to 76%. Also, the average computing time of combining EEMD and MSE is reduced to 72%.