Complexity of EEG Dynamics for Early Diagnosis of Alzheimer's Disease Using Permutation Entropy Neuromarker

Mesut Şeker; Yağmur Özbek; Görsev Yener; Mehmet Siraç Özerdem

doi:10.1016/j.cmpb.2021.106116

Complexity of EEG Dynamics for Early Diagnosis of Alzheimer's Disease Using Permutation Entropy Neuromarker

Comput Methods Programs Biomed. 2021 Jul:206:106116. doi: 10.1016/j.cmpb.2021.106116. Epub 2021 Apr 16.

Authors

Mesut Şeker¹, Yağmur Özbek², Görsev Yener³, Mehmet Siraç Özerdem⁴

Affiliations

¹ Department of Electrical and Electronics Engineering, Dicle University, Diyarbakir, Turkey. Electronic address: mesut.seker@dicle.edu.tr.
² Department of Neurosciences, Health Science Institute, Dokuz Eylül University, Izmir.
³ Department of Neurosciences, Health Science Institute, Dokuz Eylül University, Izmir; Izmir Biomedicine and Genome Center, Izmir, Turkey; Department of Neurology, Faculty of Medicine, Izmir Ekonomi University, Izmir, Turkey; Department of Neurology, Faculty of Medicine, Dokuz Eylül University, Izmir, Turkey.
⁴ Department of Electrical and Electronics Engineering, Dicle University, Diyarbakir, Turkey.

PMID: 33957376
DOI: 10.1016/j.cmpb.2021.106116

Abstract

Background and objective: Electroencephalogram (EEG) is one of the most demanded screening tools that investigates the effects of Alzheimer's Disease (AD) on human brain. Identification of AD in early stage gives rise to efficient treatment in dementia. Mild Cognitive Impairment (MCI) is considered as a conversion stage. Reducing EEG complexity can be used as a marker to detect AD. The aim of this study is to develop a 3-way diagnostic classification using EEG complexity in the detection of MCI/AD in clinical practice. This study also investigates the effects of different eyes states, i.e. eyes-open, eyes-closed on classification performance.

Methods: EEG recordings from 85 AD, 85 MCI subjects, and 85 Healthy Controls with eyes-open and eyes- closed are analyzed. Permutation Entropy (PE) values are computed from frontal, central, parietal, temporal, and occipital regions for each EEG epoch. Distribution of PE values are visualized to observe discrimination of MCI/AD with HC. Visual investigations are combined with statistical analysis using ANOVA to determine whether groups are significant or not. Multinomial Logistic Regression model is applied to feature sets in order to classify participants individually.

Results: Distribution of measured PE shows that EEG complexity is lower in AD and higher in HC group. MCI group is observed as an intermediate form due to heterogeneous values. Results from 3-way classification indicate that F1-scores and rates of sensitivity and specificity achieve the highest overall discrimination rates reaching up to 100% for at TP8 for eyes-closed condition; and C3, C4, T8, O2 electrodes for eyes-open condition. Classification of HC from both patient groups is achieved best. Eyes-open state increases discrimination of MCI and AD.

Conclusions: This nonlinear EEG methodology study contributes to literature with high discrimination rates for identification of AD. PE is recommended as a practical diagnostic neuro-marker for AD studies. Resting state EEG at eyes-open condition can be more advantageous over eyes-closed EEG recordings for diagnosis of AD.

Keywords: Alzheimer; EEG; biomarker; dementia; diagnosis; entropy; mild cognitive impairment.

MeSH terms

Alzheimer Disease* / diagnosis
Cognitive Dysfunction* / diagnosis
Early Diagnosis
Electroencephalography
Entropy
Humans