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

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 o...

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Bibliographic Details
Published inGeroScience Vol. 44; no. 3; pp. 1599 - 1607
Main Authors Pappalettera, Chiara, Miraglia, Francesca, Cotelli, Maria, Rossini, Paolo Maria, Vecchio, Fabrizio
Format Journal Article
LanguageEnglish
Published Cham Springer International Publishing 01.06.2022
Springer Nature B.V
Subjects
Biomedical and Life Sciences
Brain architecture
Cell Biology
EEG
Entropy
Geriatrics/Gerontology
Information processing
Life Sciences
Molecular Medicine
Movement disorders
Neurodegenerative diseases
Original
Original Article
Parkinson's disease
Rehabilitation
Approximate entropy
EEG; Parkinson
Complexity
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Summary: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.
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ISSN:2509-2715
2509-2723
2509-2723
DOI:10.1007/s11357-022-00552-0