Discrete Wavelet Packet Transform (Dwpt) Based Feature Selection And Classification Using Optimization Based Deep Convolutional Neural Network (Dcnn) And Long Short-Term Memory (Lstm) For Alzheimer’s Disease Detection In Eeg
Keywords:
Alzheimer’s disease, average energy, discrete wavelet transform, electroencephalogram, Discrete Wavelet Packet Transform (DWPT), Band-Pass Elliptic Digital FilterAbstract
Alzheimer’s disease (AD) stands out as one of the main causes of dementia worldwide and it represents around 65% of all dementia cases, affecting mainly elderly people. AD is composed of three evolutionary stages: Mild Cognitive Impairment (MCI), Mild and Moderate AD (ADM) and Advanced AD (ADA). It is crucial to create a tool for assisting AD diagnosis in its early stages with the aim of halting the disease progression.The main purpose of this study is to develop a system with the ability of differentiate each disease stage by means of Electroencephalographic Signals (EEG). Thereby, an EEG nonlinear multi-band analysis by Wavelet Packet was performed enabling to extract several features from each study group which are not processed in existing works. Classic Machine Learning (ML) and Deep Learning (DL) methods have been used for data classification per EEG channel. In this research work, initially,
disturbances and interference from the EEG dataset are eliminated using a Band-Pass Elliptic Digital Filter (BEF).
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