A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer's Disease Using EEG Signals.
This study will concentrate on recent research on EEG signals for Alzheimer's diagnosis, identifying and comparing key steps of EEG-based Alzheimer's disease (AD) detection, such as EEG signal acquisition, preprocessing function extraction, and classification methods. Furthermore, highlighting gener...
| Publicado en: | BioMed Research International pp. 1 - 16 |
|---|---|
| Autores principales: | , , , |
| Formato: | diagnostic images review tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
10/27/2021
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=153244335&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153244335 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 10/27/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 153244335 153244335 153244335 10.1155/2021/5425569 153244335 ppf: 1 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer's Disease Using EEG Signals. aug: au: Ouchani, Mahshad Gharibzadeh, Shahriar Jamshidi, Mahdieh Amini, Morteza affil: Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran sug: subj: Alzheimer's Disease Diagnosis Diagnosis, Neurologic Methods Electroencephalography Practice Guidelines Biological Markers Disease Progression ab: This study will concentrate on recent research on EEG signals for Alzheimer's diagnosis, identifying and comparing key steps of EEG-based Alzheimer's disease (AD) detection, such as EEG signal acquisition, preprocessing function extraction, and classification methods. Furthermore, highlighting general approaches, variations, and agreement in the use of EEG identified shortcomings and guidelines for multiple experimental stages ranging from demographic characteristics to outcomes monitoring for future research. Two main targets have been defined based on the article's purpose: (1) discriminative (or detection), i.e., look for differences in EEG-based features across groups, such as MCI, moderate Alzheimer's disease, extreme Alzheimer's disease, other forms of dementia, and stable normal elderly controls; and (2) progression determination, i.e., look for correlations between EEG-based features and clinical markers linked to MCI-to-AD conversion and Alzheimer's disease intensity progression. Limitations mentioned in the reviewed papers were also gathered and explored in this study, with the goal of gaining a better understanding of the problems that need to be addressed in order to advance the use of EEG in Alzheimer's disease science. pubtype: Academic Journal doctype: diagnostic images review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|