Literature DB >> 11137659

Investigation of non-linear properties of multichannel EEG in the early stages of Parkinson's disease.

L Pezard1, R Jech, E Růzicka.   

Abstract

OBJECTIVES: Modifications of brain activity in the early stages of Parkinson's disease (PD) are difficult to detect using electroencephalography (EEG) signals and are often biased by L-DOPA treatment. We compare here the performances of both linear and non-linear methods in differentiating EEG of L-DOPA naive PD patients from that of control subjects.
METHODS: Resting multichannel EEG (20 electrodes, 30 s epochs) of 9 patients with PD in Hoehn and Yahr stages 1-2 (4 women, 5 men, mean age 54.3 years, range 48-63 years) were compared with those of 9 control subjects (7 women, two men, mean age 51.3 years, range 43-61 years). The following measurements were computed: theta-, alpha- and beta-band relative powers constituted the linear indices; localized entropy, slope asymmetry and number of non-linear EEG segments constituted the non-linear indices.
RESULTS: In the case of linear quantification, only a decrease in the beta-band was observed for patients. Significant non-linear structures were observed in our EEG data. Non-linear quantifiers demonstrate an increase in entropy and in the number of non-linear EEG segments for the patients.
CONCLUSIONS: Changes in EEG dynamics observed here in L-DOPA naive PD patients may represent early signs of cortical dysfunction produced by subcortical dopamine depletion.

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Year:  2001        PMID: 11137659     DOI: 10.1016/s1388-2457(00)00512-5

Source DB:  PubMed          Journal:  Clin Neurophysiol        ISSN: 1388-2457            Impact factor:   3.708


  14 in total

1.  Complexity of resting-state EEG activity in the patients with early-stage Parkinson's disease.

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2.  NoLiTiA: An Open-Source Toolbox for Non-linear Time Series Analysis.

Authors:  Immo Weber; Carina R Oehrn
Journal:  Front Neuroinform       Date:  2022-06-24       Impact factor: 3.739

3.  Analysis of complexity in the EEG activity of Parkinson's disease patients by means of approximate entropy.

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Journal:  Geroscience       Date:  2022-03-28       Impact factor: 7.581

4.  Investigation of EEG abnormalities in the early stage of Parkinson's disease.

Authors:  Chun-Xiao Han; Jiang Wang; Guo-Sheng Yi; Yan-Qiu Che
Journal:  Cogn Neurodyn       Date:  2013-02-10       Impact factor: 5.082

Review 5.  Non-linear dynamics in parkinsonism.

Authors:  Olivier Darbin; Elizabeth Adams; Anthony Martino; Leslie Naritoku; Daniel Dees; Dean Naritoku
Journal:  Front Neurol       Date:  2013-12-25       Impact factor: 4.003

6.  Random forest to differentiate dementia with Lewy bodies from Alzheimer's disease.

Authors:  Meenakshi Dauwan; Jessica J van der Zande; Edwin van Dellen; Iris E C Sommer; Philip Scheltens; Afina W Lemstra; Cornelis J Stam
Journal:  Alzheimers Dement (Amst)       Date:  2016-08-19

7.  What brain signals are suitable for feedback control of deep brain stimulation in Parkinson's disease?

Authors:  Simon Little; Peter Brown
Journal:  Ann N Y Acad Sci       Date:  2012-07-25       Impact factor: 5.691

Review 8.  Non-Linear EMG Parameters for Differential and Early Diagnostics of Parkinson's Disease.

Authors:  Alexander Y Meigal; Saara M Rissanen; Mika P Tarvainen; Olavi Airaksinen; Markku Kankaanpää; Pasi A Karjalainen
Journal:  Front Neurol       Date:  2013-09-17       Impact factor: 4.003

9.  Non-linear dynamical classification of short time series of the rössler system in high noise regimes.

Authors:  Claudia Lainscsek; Jonathan Weyhenmeyer; Manuel E Hernandez; Howard Poizner; Terrence J Sejnowski
Journal:  Front Neurol       Date:  2013-11-12       Impact factor: 4.003

10.  Non-linear dynamical analysis of EEG time series distinguishes patients with Parkinson's disease from healthy individuals.

Authors:  Claudia Lainscsek; Manuel E Hernandez; Jonathan Weyhenmeyer; Terrence J Sejnowski; Howard Poizner
Journal:  Front Neurol       Date:  2013-12-11       Impact factor: 4.003

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