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

被引:238
作者
Cassani, Raymundo [1 ]
Estarellas, Mar [1 ,2 ]
San-Martin, Rodrigo [3 ]
Fraga, Francisco J. [4 ]
Falk, Tiago H. [1 ]
机构
[1] Univ Quebec, EMT, INRS, Montreal, PQ, Canada
[2] Imperial Coll London, Dept Bioengn, London, England
[3] Univ Fed ABC, Ctr Math Computat & Cognit, Sao Bernardo Do Campo, Brazil
[4] Univ Fed ABC, Engn Modeling & Appl Social Sci Ctr, Sao Bernardo Do Campo, Brazil
基金
巴西圣保罗研究基金会;
关键词
MILD COGNITIVE IMPAIRMENT; FREQUENCY POWER RATIO; MINI-MENTAL-STATE; NATIONAL INSTITUTE; ASSOCIATION WORKGROUPS; SPECTRAL-ANALYSIS; QUANTITATIVE EEG; DIFFERENTIAL-DIAGNOSIS; NEURAL SYNCHRONIZATION; CORTICAL CONNECTIVITY;
D O I
10.1155/2018/5174815
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
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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页数:26
相关论文
共 189 条
[21]   Elman neural network for the early identification of cognitive impairment in Alzheimer's disease [J].
Berte, Francesco ;
Lamponi, Giuseppe ;
Calabro, Rocco Salvatore ;
Bramanti, Placido .
FUNCTIONAL NEUROLOGY, 2014, 29 (01) :57-65
[22]   Discrimination of Alzheimer's disease and normal aging by EEG data [J].
Besthorn, C ;
Zerfass, R ;
GeigerKabisch, C ;
Sattel, H ;
Daniel, S ;
SchreiterGasser, U ;
Forstl, H .
ELECTROENCEPHALOGRAPHY AND CLINICAL NEUROPHYSIOLOGY, 1997, 103 (02) :241-248
[23]  
Bishop C.M., 2006, PATTERN RECOGN, V4, P738, DOI DOI 10.1117/1.2819119
[24]   Functional and effective brain connectivity for discrimination between Alzheimer's patients and healthy individuals: A study on resting state EEG rhythms [J].
Blinowska, Katarzyna J. ;
Rakowski, Franciszek ;
Kaminski, Maciej ;
Fallani, Fabrizio De Vico ;
Del Percio, Claudio ;
Lizio, Roberta ;
Babiloni, Claudio .
CLINICAL NEUROPHYSIOLOGY, 2017, 128 (04) :667-680
[25]   DIAGNOSTIC EFFICACY OF COMPUTERIZED SPECTRAL VERSUS VISUAL EEG ANALYSIS IN ELDERLY NORMAL, DEMENTED AND DEPRESSED SUBJECTS [J].
BRENNER, RP ;
REYNOLDS, CF ;
ULRICH, RF .
ELECTROENCEPHALOGRAPHY AND CLINICAL NEUROPHYSIOLOGY, 1988, 69 (02) :110-117
[26]   BNCI Horizon 2020: towards a roadmap for the BCI community [J].
Brunner, Clemens ;
Birbaumer, Niels ;
Blankertz, Benjamin ;
Guger, Christoph ;
Kuebler, Andrea ;
Mattia, Donatella ;
Millan, Jose del R. ;
Miralles, Felip ;
Nijholt, Anton ;
Opisso, Eloy ;
Ramsey, Nick ;
Salomon, Patric ;
Mueller-Putz, Gernot R. .
BRAIN-COMPUTER INTERFACES, 2015, 2 (01) :1-10
[27]   The IFAST Model Allows the Prediction of Conversion to Alzheimer Disease in Patients with Mild Cognitive Impairment with High Degree of Accuracy [J].
Buscema, M. ;
Grossi, E. ;
Capriotti, M. ;
Babiloni, C. ;
Rossini, P. .
CURRENT ALZHEIMER RESEARCH, 2010, 7 (02) :173-187
[28]   An improved I-FAST system for the diagnosis of Alzheimer's disease from unprocessed electroencephalograms by using robust invariant features [J].
Buscema, Massimo ;
Vernieri, Fabrizio ;
Massini, Giulia ;
Scrascia, Federica ;
Breda, Marco ;
Rossini, Paolo Maria ;
Grossi, Enzo .
ARTIFICIAL INTELLIGENCE IN MEDICINE, 2015, 64 (01) :59-74
[29]   Towards automated electroencephalography-based Alzheimer's disease diagnosis using portable low-density devices [J].
Cassani, Raymundo ;
Falk, Tiago H. ;
Fraga, Francisco J. ;
Cecchi, Marco ;
Moore, Dennis K. ;
Anghinah, Renato .
BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2017, 33 :261-271
[30]   The effects of automated artifact removal algorithms on electroencephalography-based Alzheimer's disease diagnosis [J].
Cassani, Raymundo ;
Falk, Tiago H. ;
Fraga, Francisco J. ;
Kanda, Paulo A. M. ;
Anghinah, Renato .
FRONTIERS IN AGING NEUROSCIENCE, 2014, 6