GeronGnosis: a database for diagnosing dementias using EEG and machine learning

Abstract Proposal:The aging population has led to an increase in chronic degenerative diseases, such as Alzheimer's disease (AD) and frontotemporal dementia (FTD). Future projections indicate that this increase will be more pronounced, with cases tripling by 2025. Therefore, it is necessary to identify patterns of brain activity related to dementia to assist in earlier diagnosis. Objective: In this study, we propose to compare the performance of machine learning (ML) models across different databases in patients with dementia and elderly individuals using EEG data. Methods: We investigated various machine learning models using two distinct EEG databases: OpenNeuro (AD, FTD, and healthy controls) and GeronGnosis (mild cognitive impairment - MCI, AD, FTD, and healthy controls), the latter developed by the researchers and presented here for the first time. After extracting different signal attributes, we applied the Bayes Network, Random Tree, Random Forest, and SVM models and compared their best performances. Results: The Random Forest model with 500 trees demonstrated the best classification performance, achieving an accuracy of 98% on the GeronGnosis database, while the OpenNeuro database reached an accuracy of 95%. Conclusion: The GeronGnosis database was able to correctly classify patients with dementia and healthy controls with excellent performance across the studied metrics. Additionally, as a national and unprecedented database, it allows for better representation of the local population, helping to improve the clinical applicability of classification models.