Alzheimer’s Disease (AD) is a neurodegenaritive disorder characterized by a progressive dementia, for which actually no cure is known. An early detection of patients affected by AD can be obtained by analyzing their electroencephalography (EEG) signals, which show a reduction of the complexity, a perturbation of the synchrony, and a slowing down of the rhythms. In this work, we apply a procedure that exploits feature extraction and classification techniques to EEG signals, whose aim is to distinguish patient affected by AD from the ones affected by Mild Cognitive Impairment (MCI) and healthy control (HC) samples. Specifically, we perform a time-frequency analysis by applying both the Fourier and Wavelet Transforms on 109 samples belonging to AD, MCI, and HC classes. The classification procedure is designed with the following steps: (i) preprocessing of EEG signals; (ii) feature extraction by means of the Discrete Fourier and Wavelet Transforms; and (iii) classification with tree-based supervised methods. By applying our procedure, we are able to extract reliable human-interpretable classification models that allow to automatically assign the patients into their belonging class. In particular, by exploiting a Wavelet feature extraction we achieve 83%, 92%, and 79% of accuracy when dealing with HC vs AD, HC vs MCI, and MCI vs AD classification problems, respectively. Finally, by comparing the classification performances with both feature extraction methods, we find out that Wavelets analysis outperforms Fourier. Hence, we suggest it in combination with supervised methods for automatic patients classification based on their EEG signals for aiding the medical diagnosis of dementia.

In spite of the worldwide financial and research efforts made, the pathophysiological mechanism at the basis of Alzheimer's disease (AD) is still poorly understood. Previous studies using electroencephalography (EEG) have focused on the slowing of oscillatory brain rhythms, coupled with complexity reduction of the corresponding time-series and their enhanced compressibility. These analyses have been typically carried out on single channels. However, limited investigations have focused on the possibility yielded by computational intelligence methodologies and novel machine learning approaches applied to multichannel schemes. The study at screening level on EEG recordings of subjects at risk could be useful to highlight the emergence of underlying AD progression (or at least support any further clinical investigation). In this work, the representational power of Deep Learning on Convolutional Neural Networks (CNN) is exploited to generate suitable sets of features that are then able to classify EEG patterns of AD from a prodromal version of dementia (Mild Cognitive Impairment, MCI) and from age-matched Healthy Controls (HC). The processing system here used enforces a series of convolutional-subsampling layers in order to derive a multivariate assembly of latent, novel patterns, finally used to categorize sets of EEG from different classes of subjects. The final processor here proposed is able to reach an averaged 80% of correct classification with good performance on both sensitivity and specificity by using a Multilayered Feedforward Perceptron (MLP) of the standard type as a final block of the procedure.

Electroencephalography (EEG) signal analysis is a fast, inexpensive, and accessible technique to detect the early stages of dementia, such as Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD). In the last years, EEG signal analysis has become an important topic of research to extract suitable biomarkers to determine the subject’s cognitive impairment. In this work, we propose a novel simple and efficient method able to extract features with a finite response filter (FIR) in the double time domain in order to discriminate among patients affected by AD, MCI, and healthy controls (HC). Notably, we compute the power intensity for each high- and low-frequency band, using their absolute differences to distinguish among the three classes of subjects by means of different supervised machine learning methods. We use EEG recordings from a cohort of 105 subjects (48 AD, 37 MCI, and 20 HC) referred for dementia to the IRCCS Centro Neurolesi “Bonino-Pulejo” of Messina, Italy. The findings show that this method reaches 97%, 95%, and 83% accuracy when considering binary classifications (HC vs. AD, HC vs. MCI, and MCI vs. AD) and an accuracy of 75% when dealing with the three classes (HC vs. AD vs. MCI). These results improve upon those obtained in previous studies and demonstrate the validity of our approach. Finally, the efficiency of the proposed method might allow its future development on embedded devices for low-cost real-time diagnosis.

Cortical sources of resting state electroencephalographic (EEG) rhythms are abnormal in subjects with Alzheimer's disease (AD). Here we tested the hypothesis that these sources are also sensitive to the progression of early stage AD over the course of one year. The resting state eyes-closed EEG data were recorded in 88 mild AD patients at baseline (Mini Mental State Evaluation, MMSE I = 21.7 ± 0.2 standard error, SE) and at approximately one-year follow up (13.3 months ± 0.5 SE; MMSE II = 20 ± 0.4 SE). All patients received standard therapy with acetylcholinesterase inhibitors. EEG recordings were also performed in 35 normal elderly (Nold) subjects as controls. EEG rhythms of interest were delta (2–4 Hz), theta (4–8 Hz), alpha 1 (8–10.5 Hz), alpha 2 (10.5–13 Hz), beta 1 (13–20 Hz), beta 2 (20–30 Hz), and gamma (30–40 Hz). Cortical EEG sources were estimated by low-resolution brain electromagnetic tomography (LORETA). Compared to the Nold subjects, the mild AD patients were characterized by a power increase of widespread delta sources and by a power decrease of posterior alpha sources. In the mild AD patients, the follow-up EEG recordings showed increased power of widespread delta sources as well as decreased power of widespread alpha and posterior beta 1 sources. These results suggest that the resting state EEG sources were sensitive, at least at group level, to the cognitive decline occurring in the mild AD group over a one-year period, and might represent cost-effective and non-invasive markers with which to enrich cohorts of AD patients that decline faster for clinical studies.

Alzheimer's Disease (AD) is a neurodegenerative disorder that causes a loss of connections between neurons. The goal of the present work was to construct a complex network model of the brain-electrical activity using high-density EEG (HD-EEG) recordings and to compare the network organization in AD, Mild Cognitive Impaired (MCI) and healthy control (CNT) subjects. The HD-EEG of 16 AD, 16 MCI and 12 CNT were recorded during an eye-closed resting state. The Permutation Disalignment Index (PDI) was used to describe the dissimilarity between EEG signals and to construct the connection matrices of the network model. The three groups were found to have significantly different ( $p\lt0.001$ ) Characteristic Path Length (Lambda), average Clustering Coefficient (CC) and Global Efficiency (GE). This is the first time that HD-EEG signals of AD, MCI and CNT have been compared and that PDI has been used to discriminate between the three groups. Considered the large amount of data originating from the HD-EEG acquisition, compared to standard EEG, the aim of the present work was also to assess that compression did not alter the results of the complex network analysis. Compressive sensing was adopted to compress and reconstruct the HD-EEG signals with minimal information loss, achieving an average Structural SIMilarity index (SSIM) of 0.954 (AD), 0.957 (MCI) and 0.959 (CNT). When applied to the reconstructed HD-EEG, the complex network analysis provided a substantially unaltered performance, compared to the analysis of the original signals. Indeed, Lambda, CC, GE and of the three groups were still significantly different ( $p\lt0.001$ ).