Multifractal organization of EEG signals in multiple sclerosis
Multifractal organization of EEG signals in multiple sclerosis
StatusVoR
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Authors
Wątorek, Marcin
Tomczyk, Wojciech
Gawłowska, Maga
Golonka-Afek, Natalia
Żyrkowska, Aleksandra
Marona, Monika
Wnuk, Marcin
Słowik, Agnieszka
Ochab, Jeremi K.
Fafrowicz, Magdalena
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Date
2024-01-13
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Biomedical Signal Processing and Control
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91
Pages
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1-13
ISSN
1746-8094
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Access date
2024-01-13
Abstract PL
Abstract EN
Quantifying the complex/multifractal organization of the brain signals is crucial to fully understanding the brain processes and structure. In this contribution, we performed the multifractal analysis of the electroen-cephalographic (EEG) data obtained from a controlled multiple sclerosis (MS) study, focusing on the correlation between the degree of multifractality, disease duration, and disability level. Our results reveal a significant correspondence between the complexity of the time series and multiple sclerosis development, quantified respectively by scaling exponents and the Expanded Disability Status Scale (EDSS). Namely, for some brain regions, a well-developed multifractality and little persistence of the time series were identified in patients with a high level of disability, whereas the control group and patients with low EDSS were characterized by persistence and monofractality of the signals. The analysis of the cross-correlations between EEG signals supported these results, with the most significant differences identified for patients with EDSS > 1 and the combined group of patients with EDSS ≤ 1 and controls. No association between the multifractality and disease duration was observed, indicating that the multifractal organization of the data is a hallmark of developing the disease. The observed complexity/multifractality of EEG signals is hypothetically a result of neuronal compensation – i.e., of optimizing neural processes in the presence of structural brain degeneration. The presented study is highly relevant due to the multifractal formalism used to quantify complexity and due to scarce resting-state EEG evidence for cortical reorganization associated with compensation.
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Keywords PL
Keywords EN
Multifractal
Time series
EEG
Nonlinearity
Complexity
Time series
EEG
Nonlinearity
Complexity
Keywords other
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