Multivariate Multi-Scale Permutation Entropy for Complexity Analysis of Alzheimer’s Disease EEG
An original multivariate multi-scale methodology for assessing the complexity of physiological signals is proposed. The technique is able to incorporate the simultaneous analysis of multi-channel data as a unique block within a multi-scale framework. The basic complexity measure is done by using Per...
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| Published in | Entropy (Basel, Switzerland) Vol. 14; no. 7; pp. 1186 - 1202 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Basel
MDPI AG
01.07.2012
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1099-4300 1099-4300 |
| DOI | 10.3390/e14071186 |
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| Abstract | An original multivariate multi-scale methodology for assessing the complexity of physiological signals is proposed. The technique is able to incorporate the simultaneous analysis of multi-channel data as a unique block within a multi-scale framework. The basic complexity measure is done by using Permutation Entropy, a methodology for time series processing based on ordinal analysis. Permutation Entropy is conceptually simple, structurally robust to noise and artifacts, computationally very fast, which is relevant for designing portable diagnostics. Since time series derived from biological systems show structures on multiple spatial-temporal scales, the proposed technique can be useful for other types of biomedical signal analysis. In this work, the possibility of distinguish among the brain states related to Alzheimer’s disease patients and Mild Cognitive Impaired subjects from normal healthy elderly is checked on a real, although quite limited, experimental database. |
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| AbstractList | An original multivariate multi-scale methodology for assessing the complexity of physiological signals is proposed. The technique is able to incorporate the simultaneous analysis of multi-channel data as a unique block within a multi-scale framework. The basic complexity measure is done by using Permutation Entropy, a methodology for time series processing based on ordinal analysis. Permutation Entropy is conceptually simple, structurally robust to noise and artifacts, computationally very fast, which is relevant for designing portable diagnostics. Since time series derived from biological systems show structures on multiple spatial-temporal scales, the proposed technique can be useful for other types of biomedical signal analysis. In this work, the possibility of distinguish among the brain states related to Alzheimer's disease patients and Mild Cognitive Impaired subjects from normal healthy elderly is checked on a real, although quite limited, experimental database. |
| Author | Morabito, Giuseppe Labate, Domenico La Foresta, Fabio Morabito, Francesco Carlo Bramanti, Alessia Palamara, Isabella |
| Author_xml | – sequence: 1 givenname: Francesco Carlo surname: Morabito fullname: Morabito, Francesco Carlo – sequence: 2 givenname: Domenico surname: Labate fullname: Labate, Domenico – sequence: 3 givenname: Fabio surname: La Foresta fullname: La Foresta, Fabio – sequence: 4 givenname: Alessia surname: Bramanti fullname: Bramanti, Alessia – sequence: 5 givenname: Giuseppe surname: Morabito fullname: Morabito, Giuseppe – sequence: 6 givenname: Isabella surname: Palamara fullname: Palamara, Isabella |
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