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- W3204208106 abstract "Despite the increasing interests in utilizing electroencephalogram (EEG) as a biomarker for Alzheimer’s disease, the relationship between EEG signals and neuropsychiatric symptoms remains unclear. We studied EEG signals of Alzheimer’s patients to explore the association between patients’ neuropsychiatric symptoms and clusters of patients based on their EEG powers. Sixty-nine patients with mild Alzheimer’s disease (Clinical Dementia Rating = 1) were enrolled and their EEG signals from 19 channels/electrodes were recorded in three sessions for each patient. The Fourier transform was performed on the EEG data as a function of voltage over time to yield the Welch’s periodogram of the power spectral density versus frequency. The EEG power was then calculated by integrating the power spectral density with respect to frequency for the four frequency bands (delta/theta/alpha/beta). We performed K-means cluster analysis to classify the 69 patients into two distinct groups by the log-transformed EEG powers (4 frequency bands x 19 channels) for the three EEG segments. In each segment, both clusters were compared with each other to assess the differences in their cognitive and neuropsychiatric symptoms. EEG band powers were different between the two clusters in each of the three segments, especially for the delta waves. The delta band powers differed significantly between the two clusters in most channels across the three segments. Patients’ demographics and cognitive function were not different between both clusters. However, their behavioral/psychological symptoms were different between the two clusters classified based on EEG powers. A higher Neuropsychiatric Inventory (NPI) score was associated with the clustering of higher EEG powers. The results suggest that EEG delta band power correlates to behavioral symptoms amongst patients with mild Alzheimer’s disease. The clustering approach of EEG signals may provide a novel and effective method to differentiate the severity of neuropsychiatric symptoms and/or predict the prognosis for Alzheimer’s patients." @default.
- W3204208106 created "2021-10-11" @default.
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- W3204208106 date "2020-01-01" @default.
- W3204208106 modified "2023-09-26" @default.
- W3204208106 title "Clustering Approach of EEG Powers for Neuropsychiatric Symptoms among Patients with Alzheimer" @default.
- W3204208106 hasPublicationYear "2020" @default.
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