Dementia classification using two-channel electroencephalography features.

This study aimed to develop a novel classification model using wearable two-channel electroencephalography (EEG) data to differentiate between patients with dementia and normal controls (NCs). We employed an extreme gradient boosting (Xgboost) model combined with recursive feature elimination with cross-validation (RFECV) to classify patients and NCs. The study included 54 NCs and 29 patients with dementia. Resting-state EEG was recorded, and Mini-Mental Status Exam (MMSE) and Clinical Dementia Rating (CDR) assessments were conducted. Significant differences were observed in peak frequency (PF), alpha (A), theta (T), the ratio of alpha to theta (A/T), the ratio of alpha to low-beta (A/BL), and coherence (CH) between patients and NCs. Patients with dementia exhibited decreases in PF, CH_A/T, CH_A/BL, A/T, and A/BL, while an increase in T was noted. The primary finding was that the Xgboost model, a tree ensemble classification, achieved a balanced accuracy of 97.05% with the RFECV-selected feature, which was PF. This study suggests that the novel Xgboost with RFECV classification model using two-channel EEG data could be a valuable tool for diagnosing dementia.