Analysis of magnetoencephalography recordings from Alzheimer's disease patients using embedding entropies

Gómez, Carlos; Poza, Jesús; Monge, Jesús; Fernández, Alberto; Hornero, Roberto

doi:10.1109/embc.2014.6943687

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…Gómez et al [ 108 , 109 ] analyzed MEG complexity based on cross-approximate entropy, which revealed decreases that indicated better synchronization in AD and MCI patients than in NC subjects. Using the ApEn, SampEn, and FuzzyEn methods to analyze MEG signals at 148 locations, it was found that the entropy in AD patients was lower than that in controls, suggesting that this neurological disorder may be accompanied by a regular increase in MEG activity.…”

Section: Resultsmentioning

confidence: 99%

Complexity Analysis of EEG, MEG, and fMRI in Mild Cognitive Impairment and Alzheimer’s Disease: A Review

Sun

Wang

Niu

et al. 2020

Entropy

103

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Alzheimer’s disease (AD) is a degenerative brain disease with a high and irreversible incidence. In recent years, because brain signals have complex nonlinear dynamics, there has been growing interest in studying complex changes in the time series of brain signals in patients with AD. We reviewed studies of complexity analyses of single-channel time series from electroencephalogram (EEG), magnetoencephalogram (MEG), and functional magnetic resonance imaging (fMRI) in AD and determined future research directions. A systematic literature search for 2000–2019 was performed in the Web of Science and PubMed databases, resulting in 126 identified studies. Compared to healthy individuals, the signals from AD patients have less complexity and more predictable oscillations, which are found mainly in the left parietal, occipital, right frontal, and temporal regions. This complexity is considered a potential biomarker for accurately responding to the functional lesion in AD. The current review helps to reveal the patterns of dysfunction in the brains of patients with AD and to investigate whether signal complexity can be used as a biomarker to accurately respond to the functional lesion in AD. We proposed further studies in the signal complexities of AD patients, including investigating the reliability of complexity algorithms and the spatial patterns of signal complexity. In conclusion, the current review helps to better understand the complexity of abnormalities in the AD brain and provide useful information for AD diagnosis.

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Section: Resultsmentioning

confidence: 99%

Complexity Analysis of EEG, MEG, and fMRI in Mild Cognitive Impairment and Alzheimer’s Disease: A Review

Sun

Wang

Niu

et al. 2020

Entropy

103

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“…Compared to EEG, MEG is less distorted by the resistive properties of the skull, skin, and cerebral fluid, which act as a low pass filter. Several studies have been performed on AD and mild cognitive impairment (MCI) using MEG [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ].…”

Section: Biomedical Techniques For Detecting Alzheimer’s Brain Signalsmentioning

confidence: 99%

An Overview of ICA/BSS-Based Application to Alzheimer’s Brain Signal Processing

2021

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Alzheimer’s disease (AD) is by far the most common cause of dementia associated with aging. Early and accurate diagnosis of AD and ability to track progression of the disease is increasingly important as potential disease-modifying therapies move through clinical trials. With the advent of biomedical techniques, such as computerized tomography (CT), electroencephalography (EEG), magnetoencephalography (MEG), positron emission tomography (PET), magnetic resonance imaging (MRI), and functional magnetic resonance imaging (fMRI), large amounts of data from Alzheimer’s patients have been acquired and processed from which AD-related information or “signals” can be assessed for AD diagnosis. It remains unknown how best to mine complex information from these brain signals to aid in early diagnosis of AD. An increasingly popular technique for processing brain signals is independent component analysis or blind source separation (ICA/BSS) that separates blindly observed signals into original signals that are as independent as possible. This overview focuses on ICA/BSS-based applications to AD brain signal processing.

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Stroke-related mild cognitive impairment detection during working memory tasks using EEG signal processing

Al-Qazzaz

Ali

Ahmad

et al. 2017

2017 Fourth International Conference on Advances in Biomedical Engineering (ICABME)

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The aim of the present study was to reveal markers from the electroencephalography (EEG) using approximation entropy (ApEn) and permutation entropy (PerEn). EEGs' of 15 strokerelated patients with mild cognitive impairment (MCI) and 15 control healthy subjects during a working memory (WM) task have EEG artifacts were removed using a wavelet (WT) based method. A t-test (p <; 0.05) was used to test the hypothesis that the irregularity (ApEn and PerEn) in MCIs was reduced in comparison with control subjects. ApEn and PerEn showed reduced irregularity in the EEGs of MCI patients. Therefore, ApEn and PerEn could be used as markers associated with MCI detection and identification and the EEG could be a valuable tool for inspecting the background activity in the identification of patients with MCI.

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Analysis of magnetoencephalography recordings from Alzheimer's disease patients using embedding entropies

Cited by 4 publications

References 18 publications

Complexity Analysis of EEG, MEG, and fMRI in Mild Cognitive Impairment and Alzheimer’s Disease: A Review

Complexity Analysis of EEG, MEG, and fMRI in Mild Cognitive Impairment and Alzheimer’s Disease: A Review

An Overview of ICA/BSS-Based Application to Alzheimer’s Brain Signal Processing

Stroke-related mild cognitive impairment detection during working memory tasks using EEG signal processing

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