Refined Composite Multiscale Dispersion Entropy and its Application to Biomedical Signals

Hamed Azami, Mostafa Rostaghi, Daniel Abásolo, Javier Escudero

Research output: Contribution to journalArticlepeer-review


Objective: We propose a novel complexity measure to overcome the deficiencies of the widespread and powerful multiscale entropy (MSE), including, MSE values may be undefined for short signals, and MSE is slow for real-time applications.
Methods: We introduce multiscale dispersion entropy (DisEn - MDE) as a very fast and powerful method to quantify the complexity of signals. MDE is based on our recently developed DisEn, which has a computation cost of O(N), compared with O(N^2) for sample entropy used in MSE. We also propose the refined composite MDE (RCMDE) to improve the stability of MDE.
Results: We evaluate MDE, RCMDE, and refined composite MSE (RCMSE) on synthetic signals and three biomedical datasets. The MDE, RCMDE, and RCMSE methods show similar results, although the MDE and RCMDE are faster, lead to more stable results, and discriminate different types of physiological signals better than MSE and RCMSE.
Conclusion: For noisy short and long time series, MDE and RCMDE are noticeably more stable than MSE and RCMSE, respectively. For short signals, MDE and RCMDE, unlike MSE and RCMSE, do not lead to undefined values. The proposed MDE and RCMDE are significantly faster than MSE and RCMSE, especially for long signals, and lead to larger differences between physiological conditions known to alter the complexity of the physiological recordings.
Significance: MDE and RCMDE are expected to be useful for the analysis of physiological signals thanks to their ability to distinguish different types of dynamics. The Matlab codes used in this paper are freely available at
Original languageEnglish
Pages (from-to)2872-2879
Number of pages8
JournalIEEE Transactions on Biomedical Engineering
Issue number12
Early online date8 Mar 2017
Publication statusPublished - 31 Dec 2017


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