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

Kanda, Paulo Afonso Medeiros; Oliveira, Eliezyer F.; Fraga, Francisco J.

doi:10.1016/j.cmpb.2016.09.023

Cited by 24 publications

(15 citation statements)

References 44 publications

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“…A limitation reported in a very recent study [ 80 ] is related to the resting-state EC condition: the dominance of alpha band in the spectral power, which is more marked in parietal and occipital electrodes. Indeed, a recent study has shown that the use of EEG epochs with lower alpha activity improves the discriminative power between AD patients and healthy controls [ 95 ].…”

Section: Resultsmentioning

confidence: 99%

Systematic Review on Resting-State EEG for Alzheimer’s Disease Diagnosis and Progression Assessment

et al. 2018

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Alzheimer's disease (AD) is a neurodegenerative disorder that accounts for nearly 70% of the more than 46 million dementia cases estimated worldwide. Although there is no cure for AD, early diagnosis and an accurate characterization of the disease progression can improve the quality of life of AD patients and their caregivers. Currently, AD diagnosis is carried out using standardized mental status examinations, which are commonly assisted by expensive neuroimaging scans and invasive laboratory tests, thus rendering the diagnosis time consuming and costly. Notwithstanding, over the last decade, electroencephalography (EEG) has emerged as a noninvasive alternative technique for the study of AD, competing with more expensive neuroimaging tools, such as MRI and PET. This paper reports on the results of a systematic review on the utilization of resting-state EEG signals for AD diagnosis and progression assessment. Recent journal articles obtained from four major bibliographic databases were analyzed. A total of 112 journal articles published from January 2010 to February 2018 were meticulously reviewed, and relevant aspects of these papers were compared across articles to provide a general overview of the research on this noninvasive AD diagnosis technique. Finally, recommendations for future studies with resting-state EEG were presented to improve and facilitate the knowledge transfer among research groups.

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Section: Resultsmentioning

confidence: 99%

Systematic Review on Resting-State EEG for Alzheimer’s Disease Diagnosis and Progression Assessment

et al. 2018

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“…Alternatively, eyes-closed resting state EEG during periods of attenuated IAPF power may be more informative of cortical activity (Kanda, Oliveira, & Fraga, 2017).…”

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

Resting-state EEG power and connectivity are associated with alpha peak frequency slowing in healthy aging

Scally

Burke

Bunce

et al. 2018

Neurobiology of Aging

176

129

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The individual alpha peak frequency (IAPF) of the human electroencephalography (EEG) typically experiences slowing with increasing age. Despite this hallmark change, studies that investigate modulations of conventional EEG alpha power and connectivity by aging and age-related neuropathology neglect to account for intergroup differences in IAPF. To investigate the relationship of age-related IAPF slowing with EEG power and connectivity, we recorded eyes-closed resting-state EEG in 37 young adults and 32 older adults. We replicated the finding of a slowed IAPF in older adults. IAPF values were significantly correlated with the frequency of maximum global connectivity and the means of their distributions did not differ, suggesting that connectivity was highest at the IAPF. Older adults expressed reduced global EEG power and connectivity at the conventional upper alpha band (10-12 Hz) compared with young adults. By contrast, groups had equivalent power and connectivity at the IAPF. The results suggest that conventional spectral boundaries may be biased against older adults or any group with a slowed IAPF. We conclude that investigations of alpha activity in aging and age-related neuropathology should be adapted to the IAPF of the individual and that previous findings should be interpreted with caution. EEG in the dominant alpha range may be unsuitable for examining cortico-cortical connectivity due to its subcortical origins.

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“…The inputs are the dataset [X|y] formed after feature extraction, the feature space F and a series of scalars, m, U , V , W . After initialization, we conduct individual channel assessment via evaluating the performance of SVM classifier on the feature subset F (u, :, :) (u ∈ [1, U ]) (step 6-10), then seek out OptF eaSub, M axAcc, M axSens, M axSpec (step [11][12][13][14], where OptF eaSub denotes the selected optimal feature subset, and M axAcc, M axSens, M axSpec denote the corresponding accuracy, Algorithm 1 Individual and incremental evaluation on channel dimention…”

Section: Spectral-temporal Feature Extractionmentioning

confidence: 99%

“…Usually, band-stop filters are a good choice for removing power grid interference (50 or 60 Hz, depending on the region). Band-pass filters can be used to enhance only EEG-related spectral components [11,12] . Feature extraction is generally performed after data pre-processing.…”

Section: Introductionmentioning

confidence: 99%

An Integrated MCI Detection Framework Based on Spectral-temporal Analysis

Yin

Cao

Siuly

et al. 2019

Int. J. Autom. Comput.

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Aiming to differentiate between MCI patients and elderly control subjects, this study proposes an integrated framework based on spectral-temporal analysis for the automatic analysis of resting-state EEG recordings. This framework firstly eliminates noise employing stationary wavelet transformation (SWT). Then, a set of features is extracted through spectral-temporal analysis. Next, a new wrapper algorithm, named three-dimensional (3-D) evaluation algorithm, is proposed to derive an optimal feature subset. Finally, the Support Vector Machine (SVM) algorithm is adopted to identify MCI patients on the optimal feature subset. Decision tree and K-Nearest Neighbors (KNN) algorithms are also used to test the effectiveness of the selected feature subset. Twenty-two subjects are involved in experiments, of which eleven persons were in an MCI condition and the rest were elderly control subjects. Extensive experiments show that our method is able to classify MCI patients and elderly control subjects automatically and effectively, with the accuracy of 96.94% achieved by the SVM classifier. Decision Tree and KNN algorithms also achieved superior results based on the optimal feature subset extracted by the proposed framework. This study is conducive to timely diagnosis and intervention for MCI patients, and therefore to delay cognitive decline and dementia onset.

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

Cited by 24 publications

References 44 publications

Systematic Review on Resting-State EEG for Alzheimer’s Disease Diagnosis and Progression Assessment

Systematic Review on Resting-State EEG for Alzheimer’s Disease Diagnosis and Progression Assessment

Resting-state EEG power and connectivity are associated with alpha peak frequency slowing in healthy aging

An Integrated MCI Detection Framework Based on Spectral-temporal Analysis

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