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Entropy 2016, 18(1), 8; doi:10.3390/e18010008

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

1
Department of Bio and Brain Engineering, KAIST, Daejeon 34141, Korea
2
Department of Neurology, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Incheon 21431, Korea
3
Department of Radiology, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Incheon 21431, Korea
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Academic Editor: Carlo Cattani
Received: 7 August 2015 / Revised: 9 November 2015 / Accepted: 18 December 2015 / Published: 29 December 2015
(This article belongs to the Special Issue Wavelets, Fractals and Information Theory)
View Full-Text   |   Download PDF [2450 KB, uploaded 29 December 2015]   |  

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. View Full-Text
Keywords: wavelet analysis; relative wavelet energy; wavelet coherence; Parkinson-related dementia; Alzheimer’s disease; EEG wavelet analysis; relative wavelet energy; wavelet coherence; Parkinson-related dementia; Alzheimer’s disease; EEG
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MDPI and ACS Style

Jeong, D.-H.; Kim, Y.-D.; Song, I.-U.; Chung, Y.-A.; Jeong, J. Wavelet Energy and Wavelet Coherence as EEG Biomarkers for the Diagnosis of Parkinson’s Disease-Related Dementia and Alzheimer’s Disease. Entropy 2016, 18, 8.

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