EEG-Based Diagnosis of Alzheimer Disease: A Review and Novel Approaches for Feature Extraction and Classification Techniques

EEG-Based Diagnosis of Alzheimer Disease: A Review and Novel Approaches for Feature Extraction and Classification Techniques provides a practical and easy-to-use guide for researchers in EEG signal processing techniques, Alzheimer’s disease, and dementia diagnostics. The book examines different features of EEG signals used to properly diagnose Alzheimer’s Disease early, presenting new and innovative results in the extraction and classification of Alzheimer’s Disease using EEG signals. This book brings together the use of different EEG features, such as linear and nonlinear features, which play a significant role in diagnosing Alzheimer’s Disease.

Includes the mathematical models and rigorous analysis of various classifiers and machine learning algorithms from a perspective of clinical deploymentCovers the history of EEG signals and their measurement and recording, along with their uses in clinical diagnosticsAnalyzes spectral, wavelet, complexity and other features of early and efficient Alzheimer’s Disease diagnosticsExplores support vector machine-based classification to increase accuracy

1. Introduction 2. Electroencephalogram and Its Use in Clinical Neuroscience 3. Role of Different Features in Diagnosis of Alzheimer’s Disease 4. Use of Complexity-Based Features in the Diagnosis of Alzheimer’s Disease 5. Classification Algorithms in the Diagnosis of Alzheimer’s Disease 6. Discussion and Research Challenges