Investigation of EEG abnormalities in the early stage of Parkinson’s disease

Chen, Han; Wang, Jiang; Yi, Guo; Qiu, Yan

doi:10.1007/s11571-013-9247-z

Cited by 134 publications

(113 citation statements)

References 49 publications

Supporting

Mentioning

100

Contrasting

Unclassified

Order By: Relevance

“…Decreased β and γ frequency energy and increased θ frequency energy was observed in the PD-D and AD patients, indicating these patients underwent a slowing of brain oscillatory activity, which is consistent with previous reports [8][9][10][11][12][13][14][15][16]. Slow EEG activity implies that neural activity in the information processing of the brain has become less complex and thus simpler dynamics exist in brains cognitively impaired by dementia.…”

Section: Discussionsupporting

confidence: 90%

Wavelet Energy and Wavelet Coherence as EEG Biomarkers for the Diagnosis of Parkinson’s Disease-Related Dementia and Alzheimer’s Disease

Jeong

Kim

Song

et al. 2015

Entropy

View full text Add to dashboard Cite

Abstract:Parkinson's disease (PD) and Alzheimer's disease (AD) can coexist in severely affected; elderly patients. Since they have different pathological causes and lesions and consequently require different treatments; it is critical to distinguish PD-related dementia (PD-D) from AD. Conventional electroencephalograph (EEG) analysis has produced poor results. This study investigated the possibility of using relative wavelet energy (RWE) and wavelet coherence (WC) analysis to distinguish between PD-D patients; AD patients and healthy elderly subjects. In EEG signals; we found that low-frequency wavelet energy increased and high-frequency wavelet energy decreased in PD-D patients and AD patients relative to healthy subjects. This result suggests that cognitive decline in both diseases is potentially related to slow EEG activity; which is consistent with previous studies. More importantly; WC values were lower in AD patients and higher in PD-D patients compared with healthy subjects. In particular; AD patients exhibited decreased WC primarily in the γ band and in links related to frontal regions; while PD-D patients exhibited increased WC primarily in the α and β bands and in temporo-parietal links. Linear discriminant analysis (LDA) of RWE produced a maximum accuracy of 79.18% for diagnosing PD-D and 81.25% for diagnosing AD. The discriminant accuracy was 73.40% with 78.78% sensitivity and 69.47% specificity. In distinguishing between the two diseases; the maximum performance of LDA using WC was 80.19%. We suggest that using a wavelet approach to evaluate EEG results may facilitate discrimination between PD-D and AD. In particular; RWE is useful for differentiating individuals with and without dementia and WC is useful for differentiating between PD-D and AD.

show abstract

Section: Discussionsupporting

confidence: 90%

Wavelet Energy and Wavelet Coherence as EEG Biomarkers for the Diagnosis of Parkinson’s Disease-Related Dementia and Alzheimer’s Disease

Jeong

Kim

Song

et al. 2015

Entropy

View full text Add to dashboard Cite

show abstract

“…However, several spectral studies with EEG/MEG (Olde Dubbelink et al 2013;Stoffers et al 2007;Neufeld et al 1994;Han et al 2013) have observed an increase in theta or delta powers for PD patients compared to controls. Our results of relative power in Fig.…”

Section: Discussionmentioning

confidence: 99%

“…We referred {c 1 , c 2 , c 3 , …, c q , r 2 } as to AR parameters. The power spectral density (PSD) for y(n) was Han et al 2013). …”

Section: Autoregressive Burg Methodsmentioning

confidence: 99%

“…The recorded EEG waves have been shown to reflect the functional state of cortical layers and their subcortical driving structures, especially the cortical dysfunction of some etiologies (Freeman and Quiroga 2013;Akar et al 2015Akar et al , 2016Bowman et al 2016;Keller et al 2014). In clinical and basic research, EEG has been widely used to investigate the potential markers of neuropsychiatric disorders, including PD, schizophrenia, autism, major depressive disorder, Alzheimer's disease, epilepsy, and so on (Miller and O'Callaghan 2015;Han et al 2013;Gandal et al 2012;Klassen et al 2011;Hampel et al 2010;Kheiri et al 2012;Leuchter et al 2009). Nowadays, it has become a common tool of auxiliary diagnosis used in neurology and psychiatry (Kalia and Lang 2015;Miller and O'Callaghan 2015;Freeman and Quiroga 2013), which is critical for the early intervention and timely treatment of relevant disorders.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Wang

Deng

et al. 2016

Cogn Neurodyn

Self Cite

View full text Add to dashboard Cite

To investigate the abnormal brain activities in the early stage of Parkinson's disease (PD), the electroencephalogram (EEG) signals were recorded with 20 channels from non-dementia PD patients (18 patients, 8 females) and age matched healthy controls (18 subjects, 8 females) during the resting state. Two methods based on the ordinal patterns of the recorded series, i.e., permutation entropy (PE) and order index (OI), were introduced to characterize the complexity of the cortical activities for two groups. It was observed that the resting-state EEG of PD patients showed lower PE and higher OI than healthy controls, which indicated that the early-stage PD caused the reduced complexity of EEG. We further applied two methods to determine the complexity of EEG rhythms in five sub-bands. The results showed that the gamma, beta and alpha rhythms of PD patients were characterized by lower PE and higher OI, i.e., reduced complexity, than healthy subjects. No significant differences were observed in theta or delta rhythms between two groups. The findings suggested that PE and OI were promising methods to detect the abnormal changes in the dynamics of EEG signals associated with early-stage PD. Further, such changes in EEG complexity may be the early markers of the cortical or subcortical dysfunction caused by PD.

show abstract

“…Compared with classical spectrum estimation methods (e.g. FFT), AR Burg method can reduce the spectral losses and give better frequency resolution (Akin and Kiymik 2000;Han et al 2013). Nowadays, quantitative studies have shown that AD causes EEG signals to slow down.…”

Section: Introductionmentioning

confidence: 99%

Power spectral density and coherence analysis of Alzheimer’s EEG

et al. 2014

Self Cite

View full text Add to dashboard Cite

In this paper, we investigate the abnormalities of electroencephalograph (EEG) signals in the Alzheimer's disease (AD) by analyzing 16-scalp electrodes EEG signals and make a comparison with the normal controls. The power spectral density (PSD) which represents the power distribution of EEG series in the frequency domain is used to evaluate the abnormalities of AD brain. Spectrum analysis based on autoregressive Burg method shows that the relative PSD of AD group is increased in the theta frequency band while significantly reduced in the alpha2 frequency bands, particularly in parietal, temporal, and occipital areas. Furthermore, the coherence of two EEG series among different electrodes is analyzed in the alpha2 frequency band. It is demonstrated that the pair-wise coherence between different brain areas in AD group are remarkably decreased. Interestingly, this decrease of pairwise electrodes is much more significant in inter-hemispheric areas than that in intra-hemispheric areas. Moreover, the linear cortico-cortical functional connectivity can be extracted based on coherence matrix, from which it is shown that the functional connections are obviously decreased, the same variation trend as relative PSD. In addition, we combine both features of the relative PSD and the normalized degree of functional network to discriminate AD patients from the normal controls by applying a support vector machine model in the alpha2 frequency band. It is indicated that the two groups can be clearly classified by the combined feature. Importantly, the accuracy of the classification is higher than that of any one feature. The obtained results show that analysis of PSD and coherence-based functional network can be taken as a potential comprehensive measure to distinguish AD patients from the normal, which may benefit our understanding of the disease.

show abstract

Investigation of EEG abnormalities in the early stage of Parkinson’s disease

Cited by 134 publications

References 49 publications

Wavelet Energy and Wavelet Coherence as EEG Biomarkers for the Diagnosis of Parkinson’s Disease-Related Dementia and Alzheimer’s Disease

Wavelet Energy and Wavelet Coherence as EEG Biomarkers for the Diagnosis of Parkinson’s Disease-Related Dementia and Alzheimer’s Disease

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

Power spectral density and coherence analysis of Alzheimer’s EEG

Contact Info

Product

Resources

About