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Early Detection of Alzheimer’s Using EEG

Journal: International Journal of Computer Science and Mobile Computing - IJCSMC (Vol.12, No. 3)

Publication Date:

Authors : ; ;

Page : 34-39

Keywords : Alzheimer’s disease (AD) Electroencephalogram (EEG); Discrete wavelet transform (DWT) Empirical mode decomposition (EMD); (KNN) Support vector machine (SVM); Regularized linear discriminant analysis (RLDA);

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Alzheimer's disease (AD) is a brain disorder which is irreversible as well as progressive. AD slowly destroys memory and thinking skills. The most common cause of Dementia is AD. Dementia that consists of (loss of Cognitive functioning – thinking, remembering and reasoning – and behavioural ability) is caused due to death of neurons due to AD, it has serious interference with Daily life. The chronicity of the disease is increasing rapidly in the world. The advancements in medical have initiated many research studies to automatically detect the disease by capturing the brain signals. The main aim of this paper is to help the doctors detect the Alzheimer's disease at earliest and more number of patients can be prevented before irreversible changes occur in brain. In this paper we present some common features that will improve the detection of AD accurately from EEG signals in early stage of the disease. Apart from this, different signal decomposition methods including filtering into brain frequency bands, discrete wavelet transform (DWT) and empirical mode decomposition (EMD), and various classification algorithms including support vector machine (SVM), K-nearest neighbours (KNN) and regularized linear discriminant analysis (RLDA) are evaluated.

Last modified: 2023-04-07 19:04:00