Contribution of Scalp Regions to Machine Learning-Based Classification of Dementia Utilizing Resting-State qEEG Signals

Chanda Simfukwe,1 Seong Soo A An,1 Young Chul Youn2 1Department of Bionano Technology, Gachon University, Seongnam-si, South Korea; 2Department of Neurology, College of Medicine, Chung-Ang University, Seoul, South KoreaCorrespondence: Young Chul Youn, Department of Neurology, College of Medicine, Chung-Ang University, Seoul, South Korea, Email [email protected]: This study aims to investigate using eyes-open (EO) and eyes-closed (EC) resting-state EEG data to diagnose cognitive impairment using machine learning methods, enhancing timely intervention and cost-effectiveness in dementia research.Participants and Methods: A total of 890 participants aged 40– 90 were included in the study, comprising 269 healthy controls (HC), 356 individuals with mild cognitive impairment (MCI), and 265 with Alzheimer’s disease (AD) from a cohort study. Resting-state EEG (rEEG) signals were recorded and transformed into relative power spectral density (PSD) data for analysis. The processed PSD data, representing 19 scalp regions, were then input into a Random Forest (RF) machine learning classifier to identify distinctive EEG patterns across the groups. Statistical comparisons between the groups were conducted using one-way ANOVA, applied to the relative PSD features extracted from the EEG data, to assess significant differences in EEG activity across the diagnostic categories.Results: The study found that rEEG-based categorization effectively differentiates between cognitively impaired individuals and healthy individuals. The EO rEEG achieved the highest performance metrics across various models. For HC vs MCI (combined hemisphere), the accuracy, sensitivity, specificity, and AUC were 92%, 99%, 83%, and 96%, respectively. For HC vs AD (parietal, temporal, occipital), these metrics were 95%, 96%, 94%, and 99%. The HC vs CASE (MCI + AD) (combined hemisphere) results were 90%, 99%, 73%, and 92%. The metrics for HC vs MCI vs AD (frontal, parietal, temporal) were 89%, 88%, 94%, and 96%.Conclusion: The study demonstrates that EO rEEG can effectively distinguish between cognitive impairment and healthy states, leading to early diagnosis, cost-effective treatment, and better clinical outcomes for dementia patients. EO and EC rEEG models trained with relative PSD, particularly from parietal, temporal, occipital, and central scalp regions, can significantly assist clinicians in practice.Keywords: electroencephalography, eyes-open, eyes-close, quantitative, machine learning, Alzheimer’s disease, mild cognitive impairment, power spectrum density