Alzheimer Disease is one of the leading neuro-degenerative diseases. It is the most expensive disease in the modern society which is characterized by cognitive, intellectual as well as behavioral disturbance. Due to this, the early diagnosis of the disease is essential. Electroencephalography can be used as standardized tools for diagnosis of Alzheimer Disease. This paper discusses the important aspects of Electroencephalography & spectral & wavelet based features for early diagnosis of Alzheimer’s disease. This paper discusses the use of the different spectral based features such as Relative EEG power in various bands of EEG signal. In this study, it is observed that the EEG of the Alzheimer patients slows down & the EEG of the AD infected patients is less complex as that compared to the Controlled patients. In present research work, classification accuracy of 96% is achieved by use of K nearest Neighbor classifier by combination of Spectral & Wavelet based features. EEG can be therefore used as the tool for the early & automated diagnosis of Alzheimer disease.

Alzheimer Disease, Dementia, EEG, Spectral features, Wavelet features, K nearesst neighbor classifier

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