Multiscale Fluctuation-Based Dispersion Entropy and Its Applications to Neurological Diseases
Fluctuation-based dispersion entropy (FDispEn) is a new approach to estimate the dynamical variability of the fluctuations of signals. It is based on Shannon entropy and fluctuation-based dispersion patterns. To quantify the physiological dynamics over multiple time scales, multiscale FDispEn (MFDE)...
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IEEE
2019-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/8721041/ |
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| author | Hamed Azami Steven E. Arnold Saeid Sanei Zhuoqing Chang Guillermo Sapiro Javier Escudero Anoopum S. Gupta |
| author_facet | Hamed Azami Steven E. Arnold Saeid Sanei Zhuoqing Chang Guillermo Sapiro Javier Escudero Anoopum S. Gupta |
| author_sort | Hamed Azami |
| collection | DOAJ |
| description | Fluctuation-based dispersion entropy (FDispEn) is a new approach to estimate the dynamical variability of the fluctuations of signals. It is based on Shannon entropy and fluctuation-based dispersion patterns. To quantify the physiological dynamics over multiple time scales, multiscale FDispEn (MFDE) is developed in this paper. MFDE is robust to the presence of baseline wanders or trends in the data. We evaluate MFDE, compared with popular multiscale sample entropy (MSE), multiscale fuzzy entropy (MFE), and the recently introduced multiscale dispersion entropy (MDE), on selected synthetic data and five neurological diseases' datasets: 1) focal and non-focal electroencephalograms (EEGs); 2) walking stride interval signals for young, elderly, and Parkinson's subjects; 3) stride interval fluctuations for Huntington's disease and amyotrophic lateral sclerosis; 4) EEGs for controls and Alzheimer's disease patients; and 5) eye movement data for Parkinson's disease and ataxia. The MFDE avoids the problem of the undefined MSE values and, compared with the MFE and MSE, leads to more stable entropy values over the scale factors for white and pink noises. Overall, the MFDE is the fastest and most consistent method for the discrimination of different states of neurological data, especially where the mean value of a time series considerably changes along with the signal (e.g., eye movement data). This paper shows that MFDE is a relevant new metric to gain further insights into the dynamics of neurological diseases' recordings. The MATLAB codes for the MFDE and its refined composite form are available in Xplore. |
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| last_indexed | 2024-12-14T10:25:22Z |
| publishDate | 2019-01-01 |
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| spelling | doaj.art-516a01fbcd754f8aa264d8a1283d54a72022-12-21T23:06:20ZengIEEEIEEE Access2169-35362019-01-017687186873310.1109/ACCESS.2019.29185608721041Multiscale Fluctuation-Based Dispersion Entropy and Its Applications to Neurological DiseasesHamed Azami0https://orcid.org/0000-0001-7612-0840Steven E. Arnold1Saeid Sanei2Zhuoqing Chang3Guillermo Sapiro4Javier Escudero5Anoopum S. Gupta6Department of Neurology and Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USADepartment of Neurology and Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USASchool of Science and Technology, Nottingham Trent University, Nottingham, U.K.Department of Electrical and Computer Engineering, Duke University, Durham, NC, USADepartments of Electrical and Computer Engineering, Computer Sciences, Biomedical Engineering, and Math, Duke University, Durham, NC, USASchool of Engineering, InstituteInstitute for Digital Communications, University of Edinburgh, Edinburgh, U.K.Department of Neurology and Massachusetts General Hospital, Harvard Medical School, Boston, MA, USAFluctuation-based dispersion entropy (FDispEn) is a new approach to estimate the dynamical variability of the fluctuations of signals. It is based on Shannon entropy and fluctuation-based dispersion patterns. To quantify the physiological dynamics over multiple time scales, multiscale FDispEn (MFDE) is developed in this paper. MFDE is robust to the presence of baseline wanders or trends in the data. We evaluate MFDE, compared with popular multiscale sample entropy (MSE), multiscale fuzzy entropy (MFE), and the recently introduced multiscale dispersion entropy (MDE), on selected synthetic data and five neurological diseases' datasets: 1) focal and non-focal electroencephalograms (EEGs); 2) walking stride interval signals for young, elderly, and Parkinson's subjects; 3) stride interval fluctuations for Huntington's disease and amyotrophic lateral sclerosis; 4) EEGs for controls and Alzheimer's disease patients; and 5) eye movement data for Parkinson's disease and ataxia. The MFDE avoids the problem of the undefined MSE values and, compared with the MFE and MSE, leads to more stable entropy values over the scale factors for white and pink noises. Overall, the MFDE is the fastest and most consistent method for the discrimination of different states of neurological data, especially where the mean value of a time series considerably changes along with the signal (e.g., eye movement data). This paper shows that MFDE is a relevant new metric to gain further insights into the dynamics of neurological diseases' recordings. The MATLAB codes for the MFDE and its refined composite form are available in Xplore.https://ieeexplore.ieee.org/document/8721041/Complexitymultiscale fluctuation-based dispersion entropybiomedical signalselectroencephalogramstride interval fluctuationseye movements |
| spellingShingle | Hamed Azami Steven E. Arnold Saeid Sanei Zhuoqing Chang Guillermo Sapiro Javier Escudero Anoopum S. Gupta Multiscale Fluctuation-Based Dispersion Entropy and Its Applications to Neurological Diseases IEEE Access Complexity multiscale fluctuation-based dispersion entropy biomedical signals electroencephalogram stride interval fluctuations eye movements |
| title | Multiscale Fluctuation-Based Dispersion Entropy and Its Applications to Neurological Diseases |
| title_full | Multiscale Fluctuation-Based Dispersion Entropy and Its Applications to Neurological Diseases |
| title_fullStr | Multiscale Fluctuation-Based Dispersion Entropy and Its Applications to Neurological Diseases |
| title_full_unstemmed | Multiscale Fluctuation-Based Dispersion Entropy and Its Applications to Neurological Diseases |
| title_short | Multiscale Fluctuation-Based Dispersion Entropy and Its Applications to Neurological Diseases |
| title_sort | multiscale fluctuation based dispersion entropy and its applications to neurological diseases |
| topic | Complexity multiscale fluctuation-based dispersion entropy biomedical signals electroencephalogram stride interval fluctuations eye movements |
| url | https://ieeexplore.ieee.org/document/8721041/ |
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