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Sorbonne Universite, CNRS, UMR Biological Adaptation and Aging, AP-HP, Charles Foix Hospital, F-94200 Ivry-sur-Seine, France

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1 Papers

Multi-Scale Probabilistic Score Fusion for Enhancing Alzheimer’s Disease Detection Using EEG

Topics: Medical Signal Acquisition, Analysis and Processing; Pattern Recognition & Machine Learning for Biosignal Data; Neural Systems

Authors: Maxime Bedoin ¹ , Bernadette Dorizzi ¹ , Jérôme Boudy ¹ , Kiyoka Kinugawa ² and Nesma Houmani ¹

Affiliations: ¹ Télécom SudParis, Institut Polytechnique de Paris, 91120 Palaiseau, France , ² Sorbonne Universite, CNRS, UMR Biological Adaptation and Aging, AP-HP, Charles Foix Hospital, F-94200 Ivry-sur-Seine, France

Digital Object Identifier: 10.5220/0013169700003911

Proceedings of BIOSIGNALS 2025, ISBN: 978-989-758-731-3

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In this work, we propose a fusion approach to analyze EEG signals for the discrimination between patients with Subjective Cognitive Impairment (SCI) and patients suffering from Alzheimer’s Disease (AD). In this framework, we analyze EEG signals at different epoch durations, following a multi-scale procedure, and in different frequency bands, using Phase-Lag Index (PLI) and Dynamic Time Warping (DTW) for functional connectivity measurement. Experiments show that our fusion approach leads to an improvement of classification results, reaching an AUC of 0.902 with PLI, and 0.894 with DTW; whereas we obtain an AUC of 0.845 with PLI and 0.801 with DTW when computing connectivity on the entire signal, as usually done in the literature. Furthermore, with the additional fusion of the scores obtained at different frequency bands, we reach the best performance with both PLI (AUC=0.95, Accuracy=91%) and DTW (AUC=0.98, Accuracy=95%). Finally, we investigate the generalization capability of our proposal on a test set. We found that our fusion scheme allows obtaining better classification results comparatively to when we consider the entire signal to compute functional connectivity.