Literature DB >> 17395990

Techniques for early detection of Alzheimer's disease using spontaneous EEG recordings.

W L Woon1, A Cichocki, F Vialatte, T Musha.   

Abstract

Alzheimer's disease (AD) is a degenerative disease which causes serious cognitive decline. Studies suggest that effective treatments for AD may be aided by the detection of the disease in its early stages, prior to extensive neuronal degeneration. In this paper, we propose a set of novel techniques which could help to perform this task, and present the results of experiments conducted to evaluate these approaches. The challenge is to discriminate between spontaneous EEG recordings from two groups of subjects: one afflicted with mild cognitive impairment and eventual AD and the other an age-matched control group. The classification results obtained indicate that the proposed methods are promising additions to the existing tools for detection of AD, though further research and experimentation with larger datasets is required to verify their effectiveness.

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Year:  2007        PMID: 17395990     DOI: 10.1088/0967-3334/28/4/001

Source DB:  PubMed          Journal:  Physiol Meas        ISSN: 0967-3334            Impact factor:   2.833


  8 in total

1.  Automated Multiclass Classification of Spontaneous EEG Activity in Alzheimer's Disease and Mild Cognitive Impairment.

Authors:  Saúl J Ruiz-Gómez; Carlos Gómez; Jesús Poza; Gonzalo C Gutiérrez-Tobal; Miguel A Tola-Arribas; Mónica Cano; Roberto Hornero
Journal:  Entropy (Basel)       Date:  2018-01-09       Impact factor: 2.524

2.  Multivariate multi-scale weighted permutation entropy analysis of EEG complexity for Alzheimer's disease.

Authors:  Bin Deng; Lihui Cai; Shunan Li; Ruofan Wang; Haitao Yu; Yingyuan Chen; Jiang Wang
Journal:  Cogn Neurodyn       Date:  2016-11-15       Impact factor: 5.082

Review 3.  How Machine Learning is Powering Neuroimaging to Improve Brain Health.

Authors:  Nalini M Singh; Jordan B Harrod; Sandya Subramanian; Mitchell Robinson; Ken Chang; Suheyla Cetin-Karayumak; Adrian Vasile Dalca; Simon Eickhoff; Michael Fox; Loraine Franke; Polina Golland; Daniel Haehn; Juan Eugenio Iglesias; Lauren J O'Donnell; Yangming Ou; Yogesh Rathi; Shan H Siddiqi; Haoqi Sun; M Brandon Westover; Susan Whitfield-Gabrieli; Randy L Gollub
Journal:  Neuroinformatics       Date:  2022-03-28

4.  Improving the specificity of EEG for diagnosing Alzheimer's disease.

Authors:  François-B Vialatte; Justin Dauwels; Monique Maurice; Toshimitsu Musha; Andrzej Cichocki
Journal:  Int J Alzheimers Dis       Date:  2011-05-30

5.  Automatic Diagnosis of Mild Cognitive Impairment Using Electroencephalogram Spectral Features.

Authors:  Masoud Kashefpoor; Hossein Rabbani; Majid Barekatain
Journal:  J Med Signals Sens       Date:  2016 Jan-Mar

6.  A machine learning approach to identify functional biomarkers in human prefrontal cortex for individuals with traumatic brain injury using functional near-infrared spectroscopy.

Authors:  Nader Karamzadeh; Franck Amyot; Kimbra Kenney; Afrouz Anderson; Fatima Chowdhry; Hadis Dashtestani; Eric M Wassermann; Victor Chernomordik; Claude Boccara; Edward Wegman; Ramon Diaz-Arrastia; Amir H Gandjbakhche
Journal:  Brain Behav       Date:  2016-08-24       Impact factor: 2.708

Review 7.  Complexity Analysis of EEG, MEG, and fMRI in Mild Cognitive Impairment and Alzheimer's Disease: A Review.

Authors:  Jie Sun; Bin Wang; Yan Niu; Yuan Tan; Chanjuan Fan; Nan Zhang; Jiayue Xue; Jing Wei; Jie Xiang
Journal:  Entropy (Basel)       Date:  2020-02-20       Impact factor: 2.524

8.  (D-Ser2) oxyntomodulin recovers hippocampal synaptic structure and theta rhythm in Alzheimer's disease transgenic mice.

Authors:  Guang-Zhao Yang; Qi-Chao Gao; Wei-Ran Li; Hong-Yan Cai; Hui-Min Zhao; Jian-Ji Wang; Xin-Rui Zhao; Jia-Xin Wang; Mei-Na Wu; Jun Zhang; Christian Hölscher; Jin-Shun Qi; Zhao-Jun Wang
Journal:  Neural Regen Res       Date:  2022-09       Impact factor: 5.135

  8 in total

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