Using shallow neural networks with functional connectivity from EEG signals for early diagnosis of Alzheimer's and frontotemporal dementia

Zaineb Ajra; Binbin Xu; Gérard Dray; Jacky Montmain; Stéphane Perrey

doi:10.3389/fneur.2023.1270405

Using shallow neural networks with functional connectivity from EEG signals for early diagnosis of Alzheimer's and frontotemporal dementia

Front Neurol. 2023 Oct 12:14:1270405. doi: 10.3389/fneur.2023.1270405. eCollection 2023.

Authors

Zaineb Ajra¹, Binbin Xu², Gérard Dray², Jacky Montmain², Stéphane Perrey¹

Affiliations

¹ EuroMov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, Montpellier, France.
² EuroMov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, Ales, France.

Abstract

Introduction: Dementia is a neurological disorder associated with aging that can cause a loss of cognitive functions, impacting daily life. Alzheimer's disease (AD) is the most common cause of dementia, accounting for 50-70% of cases, while frontotemporal dementia (FTD) affects social skills and personality. Electroencephalography (EEG) provides an effective tool to study the effects of AD on the brain.

Methods: In this study, we propose to use shallow neural networks applied to two sets of features: spectral-temporal and functional connectivity using four methods. We compare three supervised machine learning techniques to the CNN models to classify EEG signals of AD / FTD and control cases. We also evaluate different measures of functional connectivity from common EEG frequency bands considering multiple thresholds.

Results and discussion: Results showed that the shallow CNN-based models achieved the highest accuracy of 94.54% with AEC in test dataset when considering all connections, outperforming conventional methods and providing potentially an additional early dementia diagnosis tool.

Keywords: Alzheimer; EEG; dementia; functional connectivity; neural network.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The authors acknowledge support from the open-access publishing fund provided by both AXIAUM Université Montpellier-ISDM (ANR-20-THIA-0005-01) and the EuroMov Digital Health in Motion Lab.