EEG-based experiment design for major depressive disorder : machine learning and psychiatric diagnosis /

EEG-Based Experiment Design for Major Depressive Disorder: Machine Learning and Psychiatric Diagnosis introduces EEG-based machine learning solutions for diagnosis and assessment of treatment efficacy for a variety of conditions. With a unique combination of background and practical perspectives for...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Malik, Aamir Saeed, 1969- (Autor), Mumtaz, Wajid (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : Academic Press, 2019.
Temas:
Electroencephalography > Methodology.
Depression, Mental.
Brain > Research.
Depressive Disorder, Major > diagnosis
Electroencephalography > methods
Machine Learning
�Electroenc�ephalographie > M�ethodologie.
D�epression.
Cerveau > Recherche.
HEALTH & FITNESS > Diseases > General.
MEDICAL > Clinical Medicine.
MEDICAL > Diseases.
MEDICAL > Evidence-Based Medicine.
MEDICAL > Internal Medicine.
Brain > Research
Depression, Mental
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 1. Introduction: Depression and Challenges; 2. EEG Fundamentals; 3. EEG-Based Brain Functional Connectivity and Clinical Implications; 4. Pathophysiology of Depression; 5. Using EEG for Diagnosing and Treating Depression; 6. Neural Circuits and EEG Based Neurobiology for Depression; 7. Design of EEG Experiment for Assessing MDD; 8. EEG-based Diagnosis of Depression; 9. EEG-based Treatment Efficacy Assessment Involving Depression

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