Advances in Electrodermal Activity Processing with Applications for Mental Health : From Heuristic Methods to Convex Optimization
Language: English
Published by Springer, 2016
- Hardcover
- Used
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£ 45.04
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Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | This book explores Autonomic Nervous System (ANS) dynamics as investigated through Electrodermal Activity (EDA) processing. It presents groundbreaking research in the technical field of biomedical engineering, especially biomedical signal processing, as well as clinical fields of psychometrics, affective computing, and psychological assessment. This volume describes some of the most complete, effective, and personalized methodologies for extracting data from a non-stationary, nonlinear EDA signal in order to characterize the affective and emotional state of a human subject. These methodologies are underscored by discussion of real-world applications in mood assessment. The text also examines the physiological bases of emotion recognition through noninvasive monitoring of the autonomic nervous system. This is an ideal book for biomedical engineers, physiologists, neuroscientists, engineers, applied mathmeticians, psychiatric and psychological clinicians, and graduate students in these fields.This book also: Expertly introduces a novel approach for EDA analysis based on convex optimization and sparsity, a topic of rapidly increasing interest Authoritatively presents groundbreaking research achieved using EDA as an exemplary biomarker of ANS dynamicsDeftly explores EDA's potential as a source of reliable and effective markers for the assessment of emotional responses in healthy subjects, as well as for the recognition of pathological mood states in bipolar patients.…
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- Title
- Advances in Electrodermal Activity Processing with Applications for Mental Health : From Heuristic Methods to Convex Optimization
- Author
- Scilingo, Enzo Pasquale
- Publisher
- Springer
- Publication year
- 2016
- Condition
- Sehr gut
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 3319467042
- ISBN 13
- 9783319467047
- Seller catalogs
- Bücher
This book explores Autonomic Nervous System (ANS) dynamics as investigated through Electrodermal Activity (EDA) processing. It presents groundbreaking research in the technical field of biomedical engineering, especially biomedical signal processing, as well as clinical fields of psychometrics, affective computing, and psychological assessment. This volume describes some of the most complete, effective, and personalized methodologies for extracting data from a non-stationary, nonlinear EDA signal in order to characterize the affective and emotional state of a human subject. These methodologies are underscored by discussion of real-world applications in mood assessment. The text also examines the physiological bases of emotion recognition through noninvasive monitoring of the autonomic nervous system. This is an ideal book for biomedical engineers, physiologists, neuroscientists, engineers, applied mathmeticians, psychiatric and psychological clinicians, and graduate students in these fields.
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About the Author
Alberto Greco, M.Eng., Ph.D., is currently a Research Fellow of Bioengineering at the University of Pisa, Italy.
He received his master degree in Biomedical Engineering in 2010 and the Ph.D. degree in Automation, Robotics, and Bioengineering in 2015 from the University of Pisa (Italy) with a thesis about the processing and the modelling of the electrodermal activity.
In 2014, He has been a Visiting Fellow at the School of Computer Science and Electronic Engineering at the University of Essex, U.K. where He deeply studied convex optimization methods applied to physiological signal modelling.
His research interests include statistical biomedical signal processing, machine learning, physiological modeling, and wearable systems for physiological monitoring. Applications include the assessment of the autonomic nervous system and central nervous system, affective computing and the assessment of mood and consciousness disorders. He is author of several international scientific contributions in these fields published in peer-reviewed international journals, conference proceedings, and book. He has been involved in several European research projects.
Gaetano Valenza, M.Eng., Ph.D., is currently an Assistant Professor of Bioengineering at the University of Pisa, Pisa, Italy.
In 2009, He started working at the Bioengineering and Robotics Research Centre “E. Piaggio” in Pisa and, in 2011, He joined the Neuro-Cardiovascular Signal Processing unit within the Neuroscience Statistics Research Laboratory at Massachusetts Institute of Technology, Cambridge, USA. In 2013, He received the Ph.D. degree in Automation, Robotics, and Bioengineering from the University of Pisa and, in the same year, was appointed as a Research Fellow at Harvard Medical School/ Massachusetts General Hospital, Boston, USA.
His research interests include statistical and nonlinear biomedical signal and image processing, cardiovascular and neural modeling, and wearable systems for physiological monitoring. Applications of his research include the assessment of autonomic nervous system activity on cardiovascular control, brain-heart interactions, affective computing, assessment of mood and mental/neurological disorders. He is author of more than 100 international scientific contributions in these fields published in peer-reviewed international journals, conference proceedings, books and book chapters, and is official reviewer of more than sixty international scientific journals, and research funding agencies. He has been involved in several international research projects, and currently is the scientific co-coordinator of the European collaborative project H2020-PHC-2015-689691-NEVERMIND. Dr. Valenza has been guest editor and member of the editorial board of several international scientific journals.
He received his master degree in Biomedical Engineering in 2010 and the Ph.D. degree in Automation, Robotics, and Bioengineering in 2015 from the University of Pisa (Italy) with a thesis about the processing and the modelling of the electrodermal activity.
In 2014, He has been a Visiting Fellow at the School of Computer Science and Electronic Engineering at the University of Essex, U.K. where He deeply studied convex optimization methods applied to physiological signal modelling.
His research interests include statistical biomedical signal processing, machine learning, physiological modeling, and wearable systems for physiological monitoring. Applications include the assessment of the autonomic nervous system and central nervous system, affective computing and the assessment of mood and consciousness disorders. He is author of several international scientific contributions in these fields published in peer-reviewed international journals, conference proceedings, and book. He has been involved in several European research projects.
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