Alzheimer's Classification from EGG Signals Employing Machine Learning Algorithms

Abstract

The study has shown how classifiers behave when identifying and categorizing Alzheimer's disease stages. The main characteristics of various frequency bands were fed into the classifier as input. The accuracy of recognition is evaluated using machine learning classifiers. The effort aims to create a novel model that combines pre-processing, feature extraction, and classification to identify different stages of disease. The study starts with band filtering, moves on to feature extraction, which derives several bands from the EEG signals, and then employs KNN, SVM, and MLP algorithms to measure classification performance. AD detection and classification using machine learning classifiers such as KNN, SVM, and MLP is the main focus of this research. Five wavelet band characteristics are used by the built-in classifiers to categorize different disease phases. These characteristics are computed using DWT, PCA, and ICA, which aid in obtaining wavelet-related knowledge for learning. The proposed machine learning model achieves a classification accuracy of 95% overall.