rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: EEG-based diagnosis of Alzheimer disease
    sub title: a review and novel approaches for feature extraction and classification techniques
  name:
    name part: Kulkarni, Nilesh
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Bairagi, Vinayak
    role:
      role term: author.
  name:
    name part: ScienceDirect (Online service)
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: enk
    date issued: 2018
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource : illustrations
  abstract: 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.
  table of contents: Intro; Title page; Table of Contents; Copyright; Dedication; Acknowledgments; Chapter One: Introduction; Abstract; 1.1. Alzheimer disease; 1.2. Causes and symptoms of the disease; 1.3. Stages and clinical diagnosis of Alzheimer's 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 disease; Summary; Chapter Two: Electroencephalogram and Its Use in Clinical Neuroscience; Abstract; 2.1. EEG recording and measurement; 2.2. EEG rhythms.
  table of contents: 2.3. Early diagnosis of Alzheimer's disease by means of EEG signalsSummary; Chapter Three: Role of Different Features in Diagnosis of Alzheimer Disease; Abstract; 3.1. Introduction; 3.2. What is feature extraction?; 3.3. Need of feature extraction; 3.4. Linear features; 3.5. Conclusions; Chapter Four: Use of Complexity Features for Diagnosis of Alzheimer Disease; Abstract; 4.1. Introduction; 4.2. Use of new complexity features in Alzheimer's disease diagnosis; 4.3. Discussion and conclusion; Summary; Chapter Five: Classification Algorithms in Diagnosis of Alzheimer's Disease; Abstract.
  table of contents: 5.1. IntroductionSummary; Chapter Six: Results, Discussions, and Research Challenges; Abstract; 6.1. Results; 6.2. Conclusions; 6.3. Contribution; 6.4. Limitations of the study; 6.5. Future scope; Index.
  note: Nilesh Kulkarni and Vinayak Bairagi.
  note: Includes bibliographical references and index.
  subject:
    topic: Alzheimer's disease
    topic: Diagnosis
  subject:
    topic: Electroencephalography
  subject:
    topic: Alzheimer Disease
    topic: diagnosis
  subject:
    topic: Electroencephalography
    topic: methods
  subject:
    topic: Electroencephalography
  subject:
    topic: Électroencéphalographie
  subject:
    topic: HEALTH & FITNESS
    topic: Diseases
    topic: General
  subject:
    topic: MEDICAL
    topic: Clinical Medicine
  subject:
    topic: MEDICAL
    topic: Diseases
  subject:
    topic: MEDICAL
    topic: Evidence-Based Medicine
  subject:
    topic: MEDICAL
    topic: Internal Medicine
  subject:
    topic: Alzheimer's disease
    topic: Diagnosis
  subject:
    topic: Electroencephalography
  classification: RC523
  classification: 616.831075
  classification: 2018 G-221
  classification: WT 156
  identifier: 9780128153932
  identifier: 0128153938
  identifier: 9780128153925
  identifier: 012815392X
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: N$T
    record creation date: 180416
    record change date: 20260327153225.7
    record identifier: in00005763156
    language of cataloging:
      language term: eng