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

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 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 (CWT), using the Morse mother wavelet, to create time-frequency graphs with a wavelet coefficient scale range of 0 to 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 16,197 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 5 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 10-fold 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.

Abnormal Functional Hierarchies of EEG Networks in Familial and Sporadic Prodromal Alzheimer's Disease During Visual Short-Term Memory Binding

“…impaired function of the fronto-parieto-occipital sites accounted for binding deficits in both familial and sporadic cases of MCI due to AD. These recent studies expanded the evidence provided by previous fMRI studies (Huggins et al, 2021) which had reported a posterior parietal hub responsible for feature binding in VSTM.…”

“…This is particularly relevant if we consider that it has been by means of the EEG, and not by fMRI, that an extended network subserving this function has been identified. This work complements recent classification based studies of EEG signals for AD and MCI with a greater focus on the classification of AD and MCI using machine learning methods (Yu et al, 2019;Núñez et al, 2020;Huggins et al, 2021;Miltiadous et al, 2021;Tzimourta et al, 2021).…”

Identifying Amnestic Mild Cognitive Impairment With Convolutional Neural Network Adapted to the Spectral Entropy Heat Map of the Electroencephalogram

“…Then the structure of the model is optimized, and the generalization accuracy of the opt-model is 94.586 ± 0.4224% when the total number of learnable parameters is reduced by about 35.31%. Compared with the research ( Morabito et al, 2016 ; Ieracitano et al, 2019 , 2020 ; Wen et al, 2020 ; Huggins et al, 2021 ) related to CNN, the model performance of this paper is second only to the study by Huggins et al (2021) . But the diagnosis accuracy is on the same order of magnitude, it benefits from the irregularity and complexity representation information in the spectral entropy image.…”

Deep Convolutional Neural Network-Based Visual Stimuli Classification Using Electroencephalography Signals of Healthy and Alzheimer’s Disease Subjects

“…In recent years, deep neural networks (DNNs) have received increasing attention from researchers for a variety of classification tasks by using EEG data: alcoholism detection [ 26 ], predicting early stages of schizophrenia [ 27 ], classifying motor imagery to assist brain–computer interfaces [ 6 , 28 ], determining the stage of AD [ 29 ], and even the stages of visual processing [ 30 ]. The growing interest in visual perception may open up more opportunities to adapt BCI systems to visually impaired people [ 30 ].…”