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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London :
Academic Press,
2019.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Malik, Aamir Saeed, |d 1969- |e author. | |
| 245 | 1 | 0 | |a EEG-based experiment design for major depressive disorder : |b machine learning and psychiatric diagnosis / |c Aamir Saeed Malik, Wajid Mumtaz |
| 264 | 1 | |a London : |b Academic Press, |c 2019. | |
| 300 | |a 1 online resource (xvii, 235 pages) : |b illustrations (some color) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Online resource; title from PDF title page (EBSCO, viewed May 23, 2019). | |
| 520 | |a 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 the use of automated EEG methods for mental illness, it details for readers how to design a successful experiment, providing experiment designs for both clinical and behavioral applications. This book details the EEG-based functional connectivity correlates for several conditions, including depression, anxiety, and epilepsy, along with pathophysiology of depression, underlying neural circuits and detailed options for diagnosis. It is a necessary read for those interested in developing EEG methods for addressing challenges for mental illness and researchers exploring automated methods for diagnosis and objective treatment assessment. | ||
| 505 | 0 | |a 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 | |
| 650 | 0 | |a Electroencephalography |x Methodology. | |
| 650 | 0 | |a Depression, Mental. | |
| 650 | 0 | |a Brain |x Research. | |
| 650 | 1 | 2 | |a Depressive Disorder, Major |x diagnosis |0 (DNLM)D003865Q000175 |
| 650 | 1 | 2 | |a Electroencephalography |x methods |0 (DNLM)D004569Q000379 |
| 650 | 2 | 2 | |a Machine Learning |0 (DNLM)D000069550 |
| 650 | 6 | |a �Electroenc�ephalographie |0 (CaQQLa)201-0021971 |x M�ethodologie. |0 (CaQQLa)201-0379663 | |
| 650 | 6 | |a D�epression. |0 (CaQQLa)201-0007222 | |
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| 650 | 7 | |a MEDICAL |x Internal Medicine. |2 bisacsh | |
| 650 | 7 | |a Brain |x Research |2 fast |0 (OCoLC)fst00837661 | |
| 650 | 7 | |a Depression, Mental |2 fast |0 (OCoLC)fst00890931 | |
| 700 | 1 | |a Mumtaz, Wajid, |e author. | |
| 776 | 0 | 8 | |i Print version: |a Malik, Aamir Saeed. |t EEG-based experiment design for major depressive disorder : machine learning and psychiatric diagnosis. |d London, United Kingdom : Academic Press, an imprint of Elsevier, 2019 |h 255 pages |z 9780128174203 |
| 856 | 4 | 0 | |u https://sciencedirect.uam.elogim.com/science/book/9780128174203 |z Texto completo |