Chapter 1: Introduction
1.1 What is Alzheimers Disease?
1.2 Causes and Symptoms of the disease
1.3 Stages and Clinical Diagnosis of the Disease
1.4 Importance of Diagnosis of Alzheimers disease and its impact on Society
1.5 A Brief Review on Different methods used for diagnosis of Alzheimer of Alzheimer disease
1.5.1 Role of Neuroimaging based techniques in diagnosis of Alzheimer disease
1.5.2 Role of Electroencephalogram techniques in diagnosis of Alzheimer disease
1.6 Summary
Chapter 2: Electroencephalogram and Its Use in Clinical Neuroscience
2.1 Introduction
2.2 EEG Recording techniques and Measurement
2.3 EEG Rhythms and their significance
2.4 Early Diagnosis of Alzheimer disease using EEG signals
2.5 Summary
Chapter 3: Role of Different Features in Diagnosis of Alzheimers Disease
3.1 Introduction
3.2 What is Feature extraction?
3.3 Need of Feature Extraction in EEG signals
3.4 Linear Features
3.4.1 Spectral Features
3.4.2 Wavelet Based Features
3.5 Non-Linear Features
3.5.1 Role of Complexity based features
3.5.2 Synchrony based features
Chapter 4: Use of Complexity-Based Features in the Diagnosis of Alzheimers Disease
Chapter 5: Classification Algorithms in the Diagnosis of Alzheimers Disease
Chapter 6: Discussion and Research Challenges
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, Alzheimers disease, and dementia diagnostics. The book examines different features of EEG signals used to properly diagnose Alzheimers Disease early, presenting new and innovative results in the extraction and classification of Alzheimers 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 Alzheimers Disease.
