Multivariate Multi-Scale Permutation Entropy for Complexity Analysis of Alzheimer’s Disease EEG
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- Francesco Carlo Morabito
- MecMat Department, University “Mediterranea” of Reggio Calabria, Reggio Calabria, 89100, Italy
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- Domenico Labate
- MecMat Department, University “Mediterranea” of Reggio Calabria, Reggio Calabria, 89100, Italy
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- Fabio La Foresta
- MecMat Department, University “Mediterranea” of Reggio Calabria, Reggio Calabria, 89100, Italy
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- Alessia Bramanti
- DFMTFA Department, University of Messina, Messina, 98166, Italy
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- Giuseppe Morabito
- Faculty of Engineering, University of Pavia, Pavia, 27100, Italy
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- Isabella Palamara
- MecMat Department, University “Mediterranea” of Reggio Calabria, Reggio Calabria, 89100, Italy
書誌事項
- 公開日
- 2012-07-04
- 権利情報
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- https://creativecommons.org/licenses/by/3.0/
- DOI
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- 10.3390/e14071186
- 公開者
- MDPI AG
説明
<jats:p>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.</jats:p>
収録刊行物
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- Entropy
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Entropy 14 (7), 1186-1202, 2012-07-04
MDPI AG
