Accurate deep-learning model to differentiate dementia severity and diagnosis using a portable electroencephalography device

Mild cognitive impairment (MCI) and dementia pose significant health challenges in aging societies, emphasizing the need for accessible, cost-effective, and noninvasive diagnostic tools. Electroencephalography (EEG) is a promising biomarker, but traditional systems are limited by size, cost, and the need for skilled technicians. This study proposes a deep-learning-based approach using data from a portable EEG device to distinguish healthy volunteers (HVs) from patients with dementia-related conditions. We analyzed EEG data from 233 participants, including 119 HVs and 114 patients, and transformed the signals into frequency-domain features using a short-time Fourier transform. A customized transformer-based model was trained and evaluated using 10-fold cross-validation and a holdout dataset. In the cross-validation, the model achieved an area under the curve (AUC) of 0.872 and a balanced accuracy (bACC) of 80.8% in distinguishing HVs from patients. Subgroup analyses were conducted for HVs versus patients stratified by dementia severity and by clinical diagnosis, yielding AUCs ranging from 0.812 to 0.898 and bACCs from 74.9 to 86.4%. Comparable results were obtained in the holdout dataset. These findings suggest that portable EEG data combined with deep learning may serve as a practical tool for the early detection and classification of dementia-related conditions.