RT Journal Article
T1 A self-driven approach for multi-class discrimination in Alzheimer's disease based on wearable EEG.
A1 Perez-Valero, Eduardo
A1 Lopez-Gordo, Miguel Ángel
A1 Gutiérrez, Christian Morillas
A1 Carrera-Muñoz, Ismael
A1 Vílchez-Carrillo, Rosa M
K1 Alzheimer's disease
K1 EEG
K1 automated detection
K1 machine learning
AB Early detection is critical to control Alzheimer's disease (AD) progression and postpone cognitive decline. Traditional medical procedures such as magnetic resonance imaging are costly, involve long waiting lists, and require complex analysis. Alternatively, for the past years, researchers have successfully evaluated AD detection approaches based on machine learning and electroencephalography (EEG). Nonetheless, these approaches frequently rely upon manual processing or involve non-portable EEG hardware. These aspects are suboptimal regarding automated diagnosis, since they require additional personnel and hinder portability. In this work, we report the preliminary evaluation of a self-driven AD multi-class discrimination approach based on a commercial EEG acquisition system using sixteen channels. For this purpose, we recorded the EEG of three groups of participants: mild AD, mild cognitive impairment (MCI) non-AD, and controls, and we implemented a self-driven analysis pipeline to discriminate the three groups. First, we applied automated artifact rejection algorithms to the EEG recordings. Then, we extracted power, entropy, and complexity features from the preprocessed epochs. Finally, we evaluated a multi-class classification problem using a multi-layer perceptron through leave-one-subject-out cross-validation. The preliminary results that we obtained are comparable to the best in literature (0.88 F1-score), what suggests that AD can potentially be detected through a self-driven approach based on commercial EEG and machine learning. We believe this work and further research could contribute to opening the door for the detection of AD in a single consultation session, therefore reducing the costs associated to AD screening and potentially advancing medical treatment.
YR 2022
FD 2022-04-27
LK http://hdl.handle.net/10668/22138
UL http://hdl.handle.net/10668/22138
LA en
DS RISalud
RD Apr 7, 2025