Deep Time-Frequency Feature Extraction for Alzheimer's Dementia Eeg Classification

Abstract

Alzheimer's Dementia (AD), one of the age-related neurological disorders, causes loss of cognitive functions and seriously affects the daily life of patients. Electroencephalogram (EEG) is one of the most frequently used clinical tools to investigate the effects of AD on the brain. In the proposed study, a time-frequency representation and deep feature extraction based model is introduced to distinguish EEG segments of control subjects and AD patients. TF representations of EEG segments are obtained using high-resolution SynchroSqueezing Transform (SST), and conventional short-time Fourier transform (STFT) methods. The magnitudes of SST and STFT are used for deep feature extraction. Various classifiers are used to classify the extracted features to distinguish the EEG segments of control subjects and AD patients. STFT based deep feature extraction approach yielded better classification results than that of the SST method.