EEG epoch selection: lack of alpha rhythm improves discrimination of Alzheimer's disease.

In this work we propose a detailed EEG epoch selection method and compare epochs with rare and abundant alpha rhythm (AR) of patients with Alzheimer's disease (AD) and normal controls. Epochs were classified as Dominant Alpha Scenario (DAS) and Rare Alpha Scenario (RAS) according to the AR percentage (energy within the 8-13 Hz bandwidth) in O1, O2 and Oz electrodes. Participants were divided into four groups: 17 DAS controls (N1), 15 DAS mild-AD patients (AD1), 12 RAS controls (N2) and 15 RAS mild-AD patients (AD2).

EEG epochs with less alpha rhythm improve discrimination of mild Alzheimer's.

Background And Objective: Eyes-closed-awake electroencephalogram (EEG) is a useful tool in the diagnosis of Alzheimer's. However, there is eyes-closed-awake EEG with dominant or rare alpha rhythm. In this paper, we show that random selection of EEG epochs disregarding the alpha rhythm will lead to bias concerning EEG-based Alzheimer's Disease diagnosis.

Extreme Spindles and Leukoencephalopathy after Acute Lymphoblastic Leukemia Treatment: An Undescribed Association.

We report a case of a child whose EEG demonstrated extreme spindles (ES) after acute lymphoblastic leukemia treatment. This finding has not been reported previously. In 1962, Gibbs and Gibbs described the ES EEG pattern due to its high amplitude (200 to 400 μV).

The effects of automated artifact removal algorithms on electroencephalography-based Alzheimer's disease diagnosis.

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.

Clinician's road map to wavelet EEG as an Alzheimer's disease biomarker.

Alzheimer's disease (AD) is considered the main cause of dementia in Western countries. Consequently, there is a need for an accurate, universal, specific and cost-effective biomarker for early AD diagnosis, to follow disease progression and therapy response. This article describes a new diagnostic approach to quantitative electroencephalogram (QEEG) diagnosis of mild and moderate AD.

Index of alpha/theta ratio of the electroencephalogram: a new marker for Alzheimer's disease.

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.

Characterizing Alzheimer's disease severity via resting-awake EEG amplitude modulation analysis.

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.

Alzheimer's disease qEEG: spectral analysis versus coherence. Which is the best measurement?

There is evidence in electroencephalography that alpha, theta and delta band oscillations reflect cognitive and memory performances and that quantitative techniques can improve the electroencephalogram (EEG) sensitivity. This paper presents the results of comparative analysis of qEEG variables as reliable markers for Alzheimer's disease (AD). We compared the sensitivity and specificity between spectral analysis (spectA) and coherence (Coh) within the same group of AD patients.

Improving Alzheimer's disease diagnosis with machine learning techniques.

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.

Cognitive rehabilitation following traumatic brain injury.

Annually, some 500,000 people are hospitalized with brain lesions acquired after traumatic brain injury (TBI) in Brazil. Between 75,000 and 100,000 individuals die within hours of the event and 70,000 to 90,000 evolve to irreversible loss of some neurological function. The principal causes of TBI include motor vehicle accidents (50%), falls (21%), assaults and robberies (12%) and accidents during leisure activities (10%).

qEEG spectral peak in Alzheimer's disease: A possible tool for treatment follow-up.

Unlabelled: qEEG spectral analysis has been considered highly sensitive to cortical functional changes and agrees strongly with the clinical diagnosis of AD. The sensitivity of spectral analysis has ranged from 71% to 81% in several studies..