There is not a specific test to diagnose Alzheimer's disease (AD). Its diagnosis should be based upon clinical history, neuropsychological and laboratory tests, neuroimaging and electroencephalography (EEG). Therefore, new approaches are necessary to enable earlier and more accurate diagnosis and to follow treatment results. In this study we used a Machine Learning (ML) technique, named Support Vector Machine (SVM), to search patterns in EEG epochs to differentiate AD patients from controls. As a result, we developed a quantitative EEG (qEEG) processing method for automatic differentiation of patients with AD from normal individuals, as a complement to the diagnosis of probable dementia. We studied EEGs from 19 normal subjects (14 females/5 males, mean age 71.6 years) and 16 probable mild to moderate symptoms AD patients (14 females/2 males, mean age 73.4 years. The results obtained from analysis of EEG epochs were accuracy 79.9% and sensitivity 83.2%. The analysis considering the diagnosis of each individual patient reached 87.0% accuracy and 91.7% sensitivity.

Changes in electroencephalography (EEG) amplitude modulations have recently been linked with early-stage Alzheimer’s disease (AD). Existing tools available to perform such analysis (e.g., detrended fluctuation analysis), however, provide limited gains in discriminability power over traditional spectral based EEG analysis. In this paper, we explore the use of an innovative EEG amplitude modulation analysis technique based on spectro-temporal signal processing. More specifically, full-band EEG signals are first decomposed into the five well-known frequency bands and the envelopes are then extracted via a Hilbert transform. Each of the five envelopes are further decomposed into four so-called modulation bands, which were chosen to coincide with the delta, theta, alpha and beta frequency bands. Experiments on a resting-awake EEG dataset collected from 76 participants (27 healthy controls, 27 diagnosed with mild-AD, and 22 with moderate-AD) showed significant differences in amplitude modulations between the three groups. Most notably, i) delta modulation of the beta frequency band disappeared with an increase in disease severity (from mild to moderate AD), ii) delta modulation of the theta band appeared with an increase in severity, and iii) delta modulation of the beta frequency band showed to be a reliable discriminant feature between healthy controls and mild-AD patients. Taken together, it is hoped that the developed tool can be used to assist clinicians not only with early detection of Alzheimer’s disease, but also to monitor its progression.

Objective: We evaluated quantitative EEG measures to determine a screening index to discriminate Alzheimer’s disease (AD) patients from normal individuals.Methods: Two groups of individuals older than 50 years, comprising a control group of 57 normal volunteers and a study group of 50 patients with probable AD, were compared. EEG recordings were obtained from subjects in a wake state with eyes closed at rest for 30 min. Logistic regression analysis was conducted.Results: Spectral potentials of the alpha and theta bands were computed for all electrodes and the alpha/theta ratio calculated. Logistic regression of alpha/theta of the mean potential of the C3 and O1 electrodes was carried out. A formula was calculated to aid the diagnosis of AD yielding 76.4% sensitivity and 84.6% specificity for AD with an area under the ROC curve of 0.92.Conclusion: Logistic regression of alpha/theta of the spectrum of the mean potential of EEG represents a good marker discriminating AD patients from normal controls.

RESUMO -O EEG digital (DEEG) e o quantitativo (QEEG) representam métodos recém desenvolvidos na prática clínica que, além da utilidade didática e em pesquisa, também mostram importância clínica. As aplicações clínicas são enumeradas a seguir: 1. O DEEG representa um substituto estabelecido para o EEG convencional, pois acrescenta claros avanços técnicos. 2. Algumas técnicas do QEEG são consideradas estabelecidas para uso clínico como adjunto ao DEEG: 2a) detecção automática de possíveis descargas epileptiformes ou crises epilépticas em registros prolongados, facilitando o trabalho subsequente do especialista; 2b) monitoração contínua do EEG na sala cirúrgica ou na UTI, visando reduzir complicações. 3. Certas técnicas de QEEG são consideradas possíveis opções práticas como uma adição ao DEEG: 3a) análise topográfica e temporal de voltagens e dipolos de espículas na avaliação pré-cirúrgica de alguns tipos de epilepsia; 3b) análise de frequências em certos casos com doença cérebro-vascular, em quadros demenciais e em encefalopatias, principalmente quando outros testes, como os exames de imagem e o EEG convencional se mostrarem inconclusivos. 4. O QEEG permanece apenas como instrumento de pesquisa em doenças como síndrome pós-concussional, distúrbios do aprendizado, déficit de atenção, esquizofrenia, depressão, alcoolismo e dependência a drogas. O QEEG deve ser usado sempre em conjunto com o DEEG. Devido aos sérios riscos de erros de interpretação, é inaceitável o uso clínico do QEEG e de técnicas correlatas por médicos sem a adequada especialização em interpretação do EEG convencional e também nessas novas técnicas. PALAVRAS-CHAVE: EEG digital; EEG quantitativo; mapeamento cerebral do EEG; recomendações; epilepsia; doença cérebro-vascular; demências; encefalopatias. Guidelines for recording/analyzing quantitative EEG and evoked potentials: Part II. Clinical aspectsABSTRACT -Digital EEG (DEEG) and quantitative EEG (QEEG) are recently developed tools present in many clinical situations. Besides showing didactic and research utility, they may also have a clinical role. Although a considerable amount of scientific literature has been published related to QEEG, many controversies still subsist regarding its clinical utilization. Clinical applications are: 1. DEEG is already an established substitute for conventional EEG, representing a clear technical advance. 2. Certain QEEG techniques are an established addition to DEEG for: 2a) screening for epileptic spikes or seizures in long-term recordings; 2b) Operation room and intensive care unit EEG monitoring. 3. Certain QEEG techniques are considered possible useful additions to DEEG: 3a) topographic voltage and dipole analysis in epilepsy evaluations; 3b) frequency analysis in cerebrovascular disease and dementia, mostly when other tests have been inconclusive. 4. QEEG remains investigational for clinical use in postconcussion syndrome, learning disability, attention disorders, schizophrenia, depression, alcoholism and drug abuse. EEG brain mapping and other QEEG technique...

Over the last decade, electroencephalography (EEG) has emerged as a reliable tool for the diagnosis of cortical disorders such as Alzheimer's disease (AD). EEG signals, however, are susceptible to several artifacts, such as ocular, muscular, movement, and environmental. To overcome this limitation, existing diagnostic systems commonly depend on experienced clinicians to manually select artifact-free epochs from the collected multi-channel EEG data. Manual selection, however, is a tedious and time-consuming process, rendering the diagnostic system “semi-automated.” Notwithstanding, a number of EEG artifact removal algorithms have been proposed in the literature. The (dis)advantages of using such algorithms in automated AD diagnostic systems, however, have not been documented; this paper aims to fill this gap. Here, we investigate the effects of three state-of-the-art automated artifact removal (AAR) algorithms (both alone and in combination with each other) on AD diagnostic systems based on four different classes of EEG features, namely, spectral, amplitude modulation rate of change, coherence, and phase. The three AAR algorithms tested are statistical artifact rejection (SAR), blind source separation based on second order blind identification and canonical correlation analysis (BSS-SOBI-CCA), and wavelet enhanced independent component analysis (wICA). Experimental results based on 20-channel resting-awake EEG data collected from 59 participants (20 patients with mild AD, 15 with moderate-to-severe AD, and 24 age-matched healthy controls) showed the wICA algorithm alone outperforming other enhancement algorithm combinations across three tasks: diagnosis (control vs. mild vs. moderate), early detection (control vs. mild), and disease progression (mild vs. moderate), thus opening the doors for fully-automated systems that can assist clinicians with early detection of AD, as well as disease severity progression assessment.