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

Noor Kamal Al-Qazzaz,Siti Anom Ahmad,Kalaivani Chellappan,Javier Escudero,Md Shabiul Islam,Sawal Hamid Bin Md Ali

doi:10.1155/2014/906038

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.

Highlights

Dementia refers to a group of disorders caused by the gradual dysfunction and death of brain cells
This review is focused on using EEG as a physiological biomarker to detect dementia in the early stages and classifying its severity based on EEG signal analysis and processing
This review has focused on the use of EEG as a physiological biomarker to provide the impetus to detect dementia in the early stages

Summary

Introduction

Dementia refers to a group of disorders caused by the gradual dysfunction and death of brain cells. This disorder can be described clinically as a syndrome that causes a decline in cognitive domain (i.e., attention, memory, executive function, visual-spatial ability, and language) [1]. Predicting dementia in the early stages would be essential for improving treatment management before brain damage occurs. Significant advances have been made to reveal the early stages of dementia through biomarkers. These improvements include biochemical, genetic, neuroimaging, and neurophysiological biomarkers [2, 3]. Developing and integrating these biomarkers to identify dementia in early stages are important to derive an optimal diagnostic index

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