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Review
. 2014:2014:906038.
doi: 10.1155/2014/906038. Epub 2014 Jun 30.

Role of EEG as biomarker in the early detection and classification of dementia

Noor Kamal Al-Qazzaz  1 , Sawal Hamid Bin Md Ali  2 , Siti Anom Ahmad  3 , Kalaivani Chellappan  2 , Md Shabiul Islam  4 , Javier Escudero  5
Affiliations
Review

Role of EEG as biomarker in the early detection and classification of dementia

Noor Kamal Al-Qazzaz et al. ScientificWorldJournal. 2014.

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.

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Figures

Figure 1
Figure 1
Block diagram of dementia spectrum.
Figure 2
Figure 2
EEG frequency waveform. (a) One second of EEG signal. (b) Delta wave. (c) Theta wave. (d) Alpha wave. (e) Beta wave. (f) Gamma wave.
Figure 3
Figure 3
EEG signal processing main stages.
Figure 4
Figure 4
EEG machine schematic diagram.
Figure 5
Figure 5
EEG cap electrodes.
Figure 6
Figure 6
EEG referential montage.
Figure 7
Figure 7
The 10–20 EEG electrodes placement system. (a) and (b) Three-dimensional side view and top view, respectively [85].
Figure 8
Figure 8
Examples of mother wavelet of Daubechies, coiflets, and dyme.
Figure 9
Figure 9
Wavelet multiresolution analysis.
Figure 10
Figure 10
Block diagram of ICA-Wavelet for EEG denoising.
Figure 11
Figure 11
Block diagram of EEG features dimension reduction.
Figure 12
Figure 12
Support vector machine classifier [130].
See this image and copyright information in PMC

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