A novel application of multiscale entropy in electroencephalography to predict the efficacy of acetylcholinesterase inhibitor in Alzheimer's disease

Ping Huang Tsai, Shih Chieh Chang, Fang Chun Liu, Jenho Tsao, Yung Hung Wang, Men Tzung Lo

Research output: Contribution to journalArticlepeer-review

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Abstract

Alzheimer's disease (AD) is the most common form of dementia. According to one hypothesis, AD is caused by the reduced synthesis of the neurotransmitter acetylcholine. Therefore, acetylcholinesterase (AChE) inhibitors are considered to be an effective therapy. For clinicians, however, AChE inhibitors are not a predictable treatment for individual patients. We aimed to disclose the difference by biosignal processing. In this study, we used multiscale entropy (MSE) analysis, which can disclose the embedded information in different time scales, in electroencephalography (EEG), in an attempt to predict the efficacy of AChE inhibitors. Seventeen newly diagnosed AD patients were enrolled, with an initial minimental state examination (MMSE) score of 18.8±4.5. After 12 months of AChE inhibitor therapy, 7 patients were responsive and 10 patients were nonresponsive. The major difference between these two groups is Slope 2 (MSE6 to 20). The area below the receiver operating characteristic (ROC) curve of Slope 2 is 0.871 (95% CI = 0.69-1). The sensitivity is 85.7% and the specificity is 60%, whereas the cut-off value of Slope 2 is -0.024. Therefore, MSE analysis of EEG signals, especially Slope 2, provides a potential tool for predicting the efficacy of AChE inhibitors prior to therapy.

Original languageEnglish
Article number953868
JournalComputational and Mathematical Methods in Medicine
Volume2015
DOIs
StatePublished - 2015

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