EEG-Based Diagnosis of Alzheimer Disease A Review and Novel Approaches for Feature Extraction and Classification Techniques
Auteurs : Kulkarni Nilesh, Bairagi Vinayak
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.
1. Introduction2. Electroencephalogram and Its Use in Clinical Neuroscience3. Role of Different Features in Diagnosis of Alzheimer’s Disease4. Use of Complexity-Based Features in the Diagnosis of Alzheimer’s Disease5. Classification Algorithms in the Diagnosis of Alzheimer’s Disease6. Discussion and Research Challenges
Biomedical engineers and researchers and engineers in EEG signal processing and allied domains
- Includes the mathematical models and rigorous analysis of various classifiers and machine learning algorithms from a perspective of clinical deployment
- Covers the history of EEG signals and their measurement and recording, along with their uses in clinical diagnostics
- Analyzes spectral, wavelet, complexity and other features of early and efficient Alzheimer’s Disease diagnostics
- Explores support vector machine-based classification to increase accuracy
Date de parution : 04-2018
Ouvrage de 110 p.
19x23.3 cm
