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Eeg-based Diagnosis of Alzheimer Disease : A Review and Novel Approaches for Feature Extraction and

Eeg-based Diagnosis of Alzheimer Disease : A Review and Novel Approaches for Feature Extraction and - image 1 of 1

About this item

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 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
Number of Pages: 110
Genre: Technology
Format: Paperback
Publisher: Elsevier Science Ltd
Author: Nilesh Kulkarni & Vinayak Bairagi
Language: English
Street Date: April 27, 2018
TCIN: 53610495
UPC: 9780128153925
Item Number (DPCI): 248-36-2355
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