EEG Complexity Modifications and Altered Compressibility in Mild Cognitive Impairment and Alzheimer’s Disease

  • Domenico Labate
  • Fabio La Foresta
  • Isabella Palamara
  • Giuseppe Morabito
  • Alessia Bramanti
  • Zhilin Zhang
  • Francesco C. Morabito
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 26)


The objective of this work is to respond to the question: can quantitative electroencephalography (EEG) distinguish among Alzheimer’s Disease (AD) patients, mild cognitive impaired (MCI) subjects and elderly healthy controls? In other words, are there nonlinear indexes extracted from raw EEG data that are able to manifest the background difference among EEG? The response we give here is that a synthetic index of entropic complexity (Permutation Entropy, PE) as well as a measure of compressibility of the EEG can be used to discriminate among classes of subjects. An experimental database has been analyzed to make these measurements and the results we achieved are encouraging also in terms of disease evolution. Indeed, it is clearly shown that the condition of MCI has intermediate properties with respect to the analyzed markers: thus, these markers could in principle be used to evaluate the probability of transition from MCI to mild AD.


EEG Alzheimer’s Disease Compressive Sensing Permutation Entropy 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Domenico Labate
    • 1
    • 2
  • Fabio La Foresta
    • 1
  • Isabella Palamara
    • 1
  • Giuseppe Morabito
    • 3
  • Alessia Bramanti
    • 4
  • Zhilin Zhang
    • 5
  • Francesco C. Morabito
    • 1
  1. 1.DICEAMMediterranea University of Reggio CalabriaReggio CalabriaItaly
  2. 2.DIMESUniversity of CalabriaCosenzaItaly
  3. 3.University of PaviaPaviaItaly
  4. 4.IRCCS Centro Neurolesi bonino PulejoMessinaItaly
  5. 5.Department of Electrical & Computer EngineeringUniversity of CaliforniaSan DiegoUSA

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