Deep learning of resting-state electroencephalogram signals for three-class classification of Alzheimer's disease, mild cognitive impairment and healthy ageing

Huggins, Cameron J and Escudero, Javier and Parra, Mario A and Scally, Brian and Anghinah, Renato and Vitória Lacerda De Araújo, Amanda and Basile, Luis F and Abasolo, Daniel (2021) Deep learning of resting-state electroencephalogram signals for three-class classification of Alzheimer's disease, mild cognitive impairment and healthy ageing. Journal of Neural Engineering, 18 (4). 046087. ISSN 1741-2552 (https://doi.org/10.1088/1741-2552/ac05d8)

[thumbnail of Huggins-etal-JNE-2021-Deep-learning-of-resting-state-electroencephalogram-signals-for-3-class-classification]
Preview
 Text. Filename: Huggins_etal_JNE_2021_Deep_learning_of_resting_state_electroencephalogram_signals_for_3_class_classification.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (4MB)| Preview

Abstract

Objective. This study aimed to produce a novel deep learning (DL) model for the classification of subjects with Alzheimer's disease (AD), mild cognitive impairment (MCI) subjects and healthy ageing (HA) subjects using resting-state scalp electroencephalogram (EEG) signals. Approach. The raw EEG data were pre-processed to remove unwanted artefacts and sources of noise. The data were then processed with the continuous wavelet transform, using the Morse mother wavelet, to create time-frequency graphs with a wavelet coefficient scale range of 0-600. The graphs were combined into tiled topographical maps governed by the 10-20 system orientation for scalp electrodes. The application of this processing pipeline was used on a data set of resting-state EEG samples from age-matched groups of 52 AD subjects (82.3 ± 4.7 years of age), 37 MCI subjects (78.4 ± 5.1 years of age) and 52 HA subjects (79.6 ± 6.0 years of age). This resulted in the formation of a data set of 16197 topographical images. This image data set was then split into training, validation and test images and used as input to an AlexNet DL model. This model was comprised of five hidden convolutional layers and optimised for various parameters such as learning rate, learning rate schedule, optimiser, and batch size. Main results. The performance was assessed by a tenfold cross-validation strategy, which produced an average accuracy result of 98.9 ± 0.4% for the three-class classification of AD vs MCI vs HA. The results showed minimal overfitting and bias between classes, further indicating the strength of the model produced. Significance. These results provide significant improvement for this classification task compared to previous studies in this field and suggest that DL could contribute to the diagnosis of AD from EEG recordings.

ORCID iDs

Huggins, Cameron J, Escudero, Javier, Parra, Mario A ORCID logoORCID: https://orcid.org/0000-0002-2412-648X, Scally, Brian, Anghinah, Renato, Vitória Lacerda De Araújo, Amanda, Basile, Luis F and Abasolo, Daniel;
CORE (COnnecting REpositories)