title: Role of EEG as biomarker in the early detection and classification of dementia.
abstract: The early detection and classification of dementia are important clinical support tasks for medical practitioners in customizing patient treatment programs to better manage the development and progression of these diseases. Efforts are being made to diagnose these neurodegenerative disorders in the early stages. Indeed, early diagnosis helps patients to obtain the maximum treatment benefit before significant mental decline occurs. The use of electroencephalogram as a tool for the detection of changes in brain activities and clinical diagnosis is becoming increasingly popular for its capabilities in quantifying changes in brain degeneration in dementia. This paper reviews the role of electroencephalogram as a biomarker based on signal processing to detect dementia in early stages and classify its severity. The review starts with a discussion of dementia types and cognitive spectrum followed by the presentation of the effective preprocessing denoising to eliminate possible artifacts. It continues with a description of feature extraction by using linear and nonlinear techniques, and it ends with a brief explanation of vast variety of separation techniques to classify EEG signals. This paper also provides an idea from the most popular studies that may help in diagnosing dementia in early stages and classifying through electroencephalogram signal processing and analysis.
authors.affiliation: Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), 43600 Bangi, Selangor, Malaysia ; Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, Baghdad University, Baghdad, Iraq.
authors.forename: Noor Kamal
authors.initials: NK
authors.lastname: Al-Qazzaz
authors.affiliation: Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), 43600 Bangi, Selangor, Malaysia.
authors.forename: Sawal Hamid Bin Md
authors.initials: SH
authors.lastname: Ali
authors.affiliation: Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), 43400 Serdang, Selangor, Malaysia.
authors.forename: Siti Anom
authors.initials: SA
authors.lastname: Ahmad
authors.affiliation: Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), 43600 Bangi, Selangor, Malaysia.
authors.forename: Kalaivani
authors.initials: K
authors.lastname: Chellappan
authors.affiliation: Institute of Microengineering and Nanoelectronics (IMEN), Universiti Kebangsaan, Malaysia (UKM), 43600 Bangi, Selangor, Malaysia.
authors.forename: Md Shabiul
authors.initials: MS
authors.lastname: Islam
authors.affiliation: Institute for Digital Communications, School of Engineering, The University of Edinburgh, Edinburgh EH9 3JL, UK.
authors.forename: Javier
authors.initials: J
authors.lastname: Escudero
mesh_terms: D003704:Dementia / Q000175:diagnosis*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D012815:Signal Processing, Computer-Assisted
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