Feature Extraction And Analysis Of Alzheimer's Disease Based On Permutation Entropy

Alzheimer's disease(AD)is a neurodegenerative disorder whose symptoms in early state are undetectable.AD usually develops slowly but surely and diagnosis of AD is very hard.The study of the early diagnosis of AD from EEG is attracting widespread interest.In this paper,weighted permutation entropy(WPE)and the multivariate multi-scale weighted permutation entropy(MMSWPE)are applied to the complexity extraction of AD.Finally,an analysis and evaluation system of EEG in AD is realized for the clinical diagnosis.WPE is applied to investigating the complexity abnormalities of AD by analyzing 16-channel EEG signals from 14 severe AD patients and 14 age-matched normal subjects.The advantage of WPE over PE is verified by both the model simulated and the experimental EEG signals.The simulation results indicate that WPE is more sensitive to the noise.Although the results show that both the average PE and WPE of AD patients are decreased in contrast with the normal group in these four sub-bands,especially in the theta band,WPE can exhibit a better performance in distinguishing the AD patients from the normal controls by the more significant differences in the four sub-bands.MMSWPE is proposed to extract the complexity of multichannel EEG more accurately.The effectiveness of MMSWPE is verified by the simulated signals generated by the Lorenz system and 1/f noise.The experimental results indicate that compared with the control group,the significant decreased complexity of AD is found in the temporal and occipitoparietal regions for the theta and the alpha bands,and in the right frontal to the left occipitoparietal regions for all the frequency bands except for the delta band.It may be attributed to the brain dysfunction.The analysis and evaluation system of EEG in AD is completed by MATLAB GUI,and it has the ability to import EEG data,display image,analyze EEG data and save files.Specifically,the data analysis module includes data processing(eg.filtering),complexity analysis(eg.PE)and complex network analysis.The accomplishment of the system provides an intuitive and convenient auxiliary diagnostic tool for clinical workers.