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Sample entropy and surrogate data analysis for Alzheimer’s disease

1 College of Science, Zhejiang University of Technology, Hangzhou, China
2 Department of Radiology, The Fifth Peoples Hospital of Shanghai, Fudan University, Shanghai, China

‡ These two authors contributed equally.

Special Issues: Machine Learning and Big Data in Medical Image Analysis

Alzheimer’s disease (AD) is a neurological degenerative disease, which is mainly char-acterized by the memory loss. As electroencephalogram (EEG) device is relatively cheap, portable and non-invasive, it has been widely used in AD-related studies. We proposed a method to detect the differences between healthy subjects and AD patients, which combines classical sample entropy (Sam-pEn) and surrogate data method. EEGs from 14 AD patients and 20 healthy subjects were analyzed. The results based on the original data showed that the SampEn of AD patients was significantly de-creased (p < 0.01) at electrodes c3, f3, o2 and p4, which confirmed that AD could cause complexity loss. However, using original data could be subject to human judgement, so we generated a series of surrogate data. We found that, there were significant difference of SampEn between the original time series and their surrogate data at c3 and o2 electrodes and the differences between healthy subjects and AD patients can be verified. Our method is capable of distinguishing AD patients from healthy subjects, which is consistent with the concept of physiologic complexity, and providing insights for understanding of AD.
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Keywords Alzheimer’s disease; electroencephalogram; sample entropy; surrogate data analysis; nonlinear time series

Citation: Xuewei Wang, Xiaohu Zhao, Fei Li, Qiang Lin, Zhenghui Hu. Sample entropy and surrogate data analysis for Alzheimer’s disease. Mathematical Biosciences and Engineering, 2019, 16(6): 6892-6906. doi: 10.3934/mbe.2019345

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