Diagnose Alzheimer’s disease and mild cognitive impairment using deep CascadeNet and handcrafted features from EEG signals

Abstract

Alzheimer’s disease (AD) is the most prevalent clinically diagnosed neurodegenerative disorder. Early detection of mild cognitive impairment (MCI) is crucial for implementing effective interventions and potentially preventing further cognitive decline. Due to its efficiency, the electroencephalogram (EEG) is a promising tool for AD diagnosis. This paper proposes a computer-aided diagnostic model for identifying AD using EEG data analysis. The proposed approach comprises two key steps: signal processing and classification. Initially, the EEG signal is decomposed into sub-bands using the discrete wavelet transform (DWT), followed by windowing for data augmentation. Subsequently, an improved CascadeNet model is employed for feature extraction and classification from the windowed EEG data. CascadeNet’s architecture was specifically chosen for its ability to achieve high accuracy in AD and MCI detection, even with relatively small EEG datasets. Moreover, the paper explores the potential of deep Cascade learning for AD prediction. The effectiveness of the proposed strategy is evaluated using metrics such as F-measures, specificity, sensitivity, and overall detection accuracy. The developed model attains impressive accuracies of 98.84 % and 97.78 % on the Figshare and Brazilian datasets, respectively, significantly outperforming existing methods. To validate the model’s efficacy, it was applied to the Figshare and Brazilian datasets encompassing control (CO), MCI, and AD classes. The results confirm the proposed method as a valuable tool for identifying potential biomarkers aiding in the clinical diagnosis of AD. Notably, unlike previous methods limited to two-class identification, this technique effectively distinguishes between MCI and AD.