Diagnostic Protocol for Mild Cognitive Impairment Based on Dimensional Transformation of EEG Signals
LI Xin1,2,LI Zi-peng1,2,LIU Yi1,2,XIE Ping1,2,3,WANG Yu-lin4
1. School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004,China
2. Measurement Technology and Instrumentation Key Lab of Hebei Province Qinhuangdao, Hebei 066004, China
3. Institute of Health and Wellness Industry Technology, Yanshan University, Qinhuangdao, Hebei 066004, China
4. Qinhuangdao First Hospital, Qinhuangdao, Hebei 066099, China
Abstract：A spectral entropy dimension transformation method matching the convolutional neural network (CNN) was proposed. The EEG data samples of 26 amnestic mild cognitive impairment patients and 20 healthy subjects were compared and analyzed from three aspects: short-time Fourier transform, wavelet transform and spectral entropy.A novel diagnosis scheme of mild cognitive impairment was constructed by using spectrum entropy and CNN.Then, through the comparison of feedforward neural network, k-nearest neighbor, support vector machine, decision tree, naive Bayes, logistic regression and other models, it was verified that the scheme can effectively completed the pattern recognition of EEG in patients with preclinical Alzheimers disease from two aspects of accuracy and stability.Experimental results show that the proposed method can achieve an accuracy of (92.662±1.216)%, and still has high recognition accuracy and generalization ability for EEG signals with noise interference.
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2023, Vol. 44 
