Early detection of Alzheimer’s disease from cortical and hippocampal local field potentials using an ensembled machine learning model

Fabietti, M., Mahmud, M. ORCID: 0000-0002-2037-8348, Lotfi, A. ORCID: 0000-0002-5139-6565, Leparulo, A., Fontana, R., Vassanelli, S. and Fasolato, C., 2023. Early detection of Alzheimer’s disease from cortical and hippocampal local field potentials using an ensembled machine learning model. IEEE Transactions on Neural Systems and Rehabilitation Engineering. ISSN 1534-4320

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Abstract

Early diagnosis of Alzheimer’s disease (AD) is a very challenging problem and has been attempted through data-driven methods in recent years. However, considering the inherent complexity in decoding higher cognitive functions from spontaneous neuronal signals, these data-driven methods benefit from the incorporation of multimodal data. This work proposes an ensembled machine learning model with explainability (EXML) to detect subtle patterns in cortical and hippocampal local field potential signals (LFPs) that can be considered as a potential marker for AD in the early stage of the disease. The LFPs acquired from healthy and two types of AD animal models (n=10 each) using linear multielectrode probes were endorsed by electrocardiogram and respiration signals for their veracity. Feature sets were generated from LFPs in temporal, spatial and spectral domains and were fed into selected machine-learning models for each domain. Using late fusion, the EXML model achieved an overall accuracy of 99.4%. This provided insights into the amyloid plaque deposition process as early as 3 months of the disease onset by identifying the subtle patterns in the network activities. Lastly, the individual and ensemble models were found to be robust when evaluated by randomly masking channels to mimic the presence of artefacts.

Item Type:	Journal article
Publication Title:	IEEE Transactions on Neural Systems and Rehabilitation Engineering
Creators:	Fabietti, M., Mahmud, M., Lotfi, A., Leparulo, A., Fontana, R., Vassanelli, S. and Fasolato, C.
Publisher:	Institute of Electrical and Electronics Engineers
Date:	22 June 2023
ISSN:	1534-4320
Identifiers:	Number Type 10.1109/tnsre.2023.3288835 DOI 37347628 PubMed ID 1775951 Other
Rights:	This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/.
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	29 Jun 2023 08:13
Last Modified:	29 Jun 2023 08:13
URI:	https://irep.ntu.ac.uk/id/eprint/49304

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