Dementia severity index: A threshold-based approach to classifying dementia levels using resting state EEG.

BACKGROUND: Alzheimer's Disease (AD) and FrontoTemporal Dementia (FTD) are dementia conditions that often overlap clinically, leading to misdiagnoses. Traditional questionnaires are subjective and time-intensive, while neuroimaging is costly and less accessible. EEG-based methods offer a cost-effective alternative but primarily focus on spectral and source analyses, with a limited exploration into quantitative range identification for differentiating dementia states. METHODS: This study presents a threshold-based approach to dementia-level classification using resting-state EEG. In particular, an algorithm is presented for threshold computation followed by Dementia Severity Index (DSI) formulation. Two potential biomarkers for cognitive decline that capture band-specific alterations are explored. These biomarkers form the basis of the DSI, categorizing individuals into AD, FTD, or Healthy Control (HC). The classification performance of the proposed DSI is evaluated comprehensively using multiple machine learning classifiers and subject validation strategies. RESULTS: The proposed DSI-based approach achieves classification accuracies of 81.62% using kNN. The approach reliability is validated across three diverse EEG datasets and through threshold variation analysis. Furthermore, the relationship between EEG features and cognitive performance is analyzed using Spearman's correlation. A significant correlation of 0.79 and 0.62 is obtained between predicted and actual MMSE. CONCLUSION: The proposed DSI effectively differentiates AD, FTD, and HC, providing a robust threshold-based framework for dementia assessment. It enhances interpretability by assigning quantitative values to dementia states and reduces subjective reliance. This study offers a potential EEG-based biomarker suitable for clinical settings, offering minimal stress to patients during assessments.