EEG-Based Diagnosis of Alzheimer Disease - 1st Edition - ISBN: 9780128153925, 9780128153932

EEG-Based Diagnosis of Alzheimer Disease

1st Edition

A Review and Novel Approaches for Feature Extraction and Classification Techniques

Authors: Nilesh Kulkarni Vinayak Bairagi
eBook ISBN: 9780128153932
Paperback ISBN: 9780128153925
Imprint: Academic Press
Published Date: 18th April 2018
Page Count: 110
Sales tax will be calculated at check-out Price includes VAT/GST
Price includes VAT/GST

Institutional Subscription

Secure Checkout

Personal information is secured with SSL technology.

Free Shipping

Free global shipping
No minimum order.


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.

Key Features

  • 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


Biomedical engineers and researchers and engineers in EEG signal processing and allied domains

Table of Contents

Chapter 1: Introduction

1.1 What is Alzheimer’s Disease?

1.2 Causes and Symptoms of the disease

1.3 Stages and Clinical Diagnosis of the Disease

1.4 Importance of Diagnosis of Alzheimer’s 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 Alzheimer’s 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 Alzheimer’s Disease

Chapter 5: Classification Algorithms in the Diagnosis of Alzheimer’s Disease

Chapter 6: Discussion and Research Challenges


No. of pages:
© Academic Press 2018
Academic Press
eBook ISBN:
Paperback ISBN:

About the Author

Nilesh Kulkarni

Vinayak Bairagi

Vinayak K. Bairagi completed his M.E. (electronics) at Sinhgad COE and his Ph.D. in engineering at University of Pune. He has 10 years of teaching experience and 7 years of research experience. He has filed eight patents and five copyrights in field of biomedical engineering. He has published dozens of research papers in this field and is a reviewer for nine scientific journals. He has received the IEI national level Young Engineer Award (2014) and the ISTE national level Young Researcher Award (2015) for excellence in the field of engineering. He is a member of INENG (UK), IETE (India), ISTE (India), and BMS (India). He is a recognized PhD mentor in electronics engineering of Savitribai Phule Pune University

Affiliations and Expertise

Associate Professor, AISSMS-IOIT

Ratings and Reviews