Analysis of complexity in the EEG activity of Parkinson’s disease patients by means of approximate entropy

Chiara Pappalettera, Francesca Miraglia, Maria Cotelli, Paolo Maria Rossini, Fabrizio Vecchio

Research output: Contribution to journalArticlepeer-review


The objective of the present study is to explore the brain resting state differences between Parkinson’s disease (PD) patients and age- and gender-matched healthy controls (elderly) in terms of complexity of electroencephalographic (EEG) signals. One non-linear approach to determine the complexity of EEG is the entropy. In this pilot study, 28 resting state EEGs were analyzed from 13 PD patients and 15 elderly subjects, applying approximate entropy (ApEn) analysis to EEGs in ten regions of interest (ROIs), five for each brain hemisphere (frontal, central, parietal, occipital, temporal). Results showed that PD patients presented statistically higher ApEn values than elderly confirming the hypothesis that PD is characterized by a remarkable modification of brain complexity and globally modifies the underlying organization of the brain. The higher-than-normal entropy of PD patients may describe a condition of low order and consequently low information flow due to an alteration of cortical functioning and processing of information. Understanding the dynamics of brain applying ApEn could be a useful tool to help in diagnosis, follow the progression of Parkinson’s disease, and set up personalized rehabilitation programs.
Original languageEnglish
Pages (from-to)1599-1607
Number of pages9
Publication statusPublished - 2022
Externally publishedYes


  • Approximate entropy
  • Complexity
  • EEG
  • Parkinson


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