Role of EEG for Diagnosis of Alzheimer Disease
Sachin M. Elgandelwar1, Vinayak K. Bairagi2

1Prof. Sachin M. Elgandelwar, Research Scholar, Department of E&TC, AISSMS Institute of Information and Technology, Pune, India and Assistant Professor, Department of E&TC, ZCOER, Pune, India.
2Dr. Vinayak K. Bairagi, Professor, Department of E&TC, AISSMS Institute of Information and Technology, Pune, India.

Manuscript received on 12 August 2019 | Revised Manuscript received on 18 August 2019 | Manuscript published on 30 August 2019 | PP: 3675-3679 | Volume-8 Issue-10, August 2019 | Retrieval Number: J96500881019/2019©BEIESP | DOI: 10.35940/ijitee.J9650.0881019
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Abstract: In the recent years, costliest Alzheimer disease (AD) is now primary reason for the cause of death. An early finding is essential as there is no cure for severe AD. Despite recent advances, early finding of Alzheimer disease from electroencephalography (EEG) remains a difficult job. In this paper, we focus a spectral and signal complexity measures through which such early findings can possibly be improved. Power spectral and nonlinear features, which have been utilized for classification of Alzheimer disease subjects (ADS) from the normal healthy subject (NHS). So far, the power in the various EEG bands has been intensely analyzed. The main aim of this research article is to study the power and nonlinear analysis for the finding of AD to consider as a probable biomarker to recognize AD subject and normal healthy subject. Relative power (RP) was independently calculated from various EEG bands which indicate the slowing of EEG signals acknowledge the Alzheimer disease subjects. In this study, EEGs signal had been acquired at the rest condition from 20 normal healthy subject whose age around 60 years along with same number of Alzheimer disease subjects. The result shows that relative power is increased towards lower frequencies while decreased towards higher frequencies in AD. Such analysis of power may additionally explore to differentiate Alzheimer disease’s stages.
Keywords: Alzheimer Diseases (AD), Electroencephalography (EEG), Relative Power (RP), Bump Modeling, Nonlinear Analysis

Scope of the Article: Design and Diagnosis