Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (15): 274-278.

### Application of Gene and Permutation Entropy in Early Diagnosis of Alzheimer’s Disease

HU Ting, SUN Jie, NIU Yan, GAO Yuan, XIANG Jie

1. College of Information and Computer, Taiyuan University of Technology, Taiyuan 030600, China
• Online:2020-08-01 Published:2020-07-30

### 基因和排列熵在阿尔茨海默病早诊中的研究应用

1. 太原理工大学 信息与计算机学院，太原 030600

Abstract:

Because of the high incidence and irreversibility of Alzheimer’s Disease（AD）, the early diagnosis of AD patients is particularly important. It has been found that AD patients are related to Apolipoprotein E（APOE）, and the complexity also changes. Complexity can be used in AD diagnosis, but its classification performance needs to be further improved. Using Permutation Entropy（PE） as index, the complexity change pattern of AD with different gene types are discussed. This paper examines the possible effects of genotype on the brain complexity in the Normal Control（NC）, Early Mild Cognitive Impairment（EMCI）, Later Mild Cognitive Impairment（LMCI） and AD by Permutation Entropy（PE）. Extracting PE values from brain regions with significant differences as training features, and training different classifiers according to gene type. The results show that the accuracy of EMCI and NC is 96.67% by adding gene information, and the classification accuracy of EMCI and LMCI is increased from 40.35% to 88.24%, which significantly improves the accuracy of early diagnosis of AD.