ABSTRACT
Objective: Multiscale entropy (MSE) analysis has been widely used to analyze the physiological signals in the frequency domain. Higher complexities of MSE curve present in the physiological system have the better ability to adapt under environmental change. Most people use the subjective experience to distinguish different complexity groups of MSE curves. When the difference between curves is hard to distinguish, the results are often misinterpreted. Methodology: In this study, four features were designed for the purpose to use the support vector machine technique to develop an automatic recognition procedure for the MSE curve. Results: A dataset of the electrocardiogram was used to illustrate the proposed analytical process. The results show that AUC is not the only MSE curve feature that should be employed, and new design features may increase recognition ability of MSE curves for electrocardiogram data. Conclusion: The study results imply that the proposed process can facilitate MSE recognition among nonprofessionals.