Advances in Electrodermal Activity Processing with Applications for Mental Health: From Heuristic Methods to Convex Optimization
Autor Alberto Greco, Gaetano Valenza, Enzo Pasquale Scilingoen Limba Engleză Hardback – 24 noi 2016
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 dynamics
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Specificații
ISBN-13: 9783319467047
ISBN-10: 3319467042
Pagini: 154
Ilustrații: XVIII, 138 p. 51 illus., 22 illus. in color.
Dimensiuni: 155 x 235 x 11 mm
Greutate: 0.4 kg
Ediția:1st ed. 2016
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
ISBN-10: 3319467042
Pagini: 154
Ilustrații: XVIII, 138 p. 51 illus., 22 illus. in color.
Dimensiuni: 155 x 235 x 11 mm
Greutate: 0.4 kg
Ediția:1st ed. 2016
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
Cuprins
1. Electrodermal Phenomena and Recording Techniques.- 2. Modeling for the Analysis of the EDA.- 3. Evaluation of CDA and CvxEDA models.- 4. Emotions and Mood States: Modeling, Elicitation, and Recognition.- 5. Experimental Applications on Multi-Sensory Affective Stimulation.- 6. Conclusions.
Recenzii
“The book presents an appraisal of the state of the art of EDA methodologies and applications, typical analysis tools, and recording means. … The book has well-balanced and highly interdisciplinary content and might be of interest to a large audience, including psychologists, dermatologists, and physicians, or signal processing researchers, mathematicians, engineers, and scientists involved in affective computing. … By its structure and content, the book may serve as a guide to newcomers in psychological science and affective computing.” (Computing Reviews, October, 2017)
Notă biografică
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.
Enzo Pasquale Scilingo, Ph.D.is an Associate Professor in Electronic and Information Bioengineering at the University of Pisa. He received the Laurea Degree in Electronic Engineering from the University of Pisa, Italy and the Ph.D.degree in Bioengineering from the University of Milan, in 1995 and 1998 respectively. For two years he was postdoctoral fellow with the Italian National Research Council and for two years post-doctoral fellow at Information Engineering Department of the University of Pisa. Currently, he is pursuing his research work mainly at the Research Center “E. Piaggio”. He has several teaching activities, he is supervisor of several PhD students and he is leading the laboratory Biolab at the Information Engineering Department . He coordinated a European project EC-FP7-ICT-247777 “PSYCHE-Personalised monitoring SYstems for Care in mental Health”, and he is currently coordinating the European project H2020-PHC-2015-689691 NEVERMIND - NEurobehavioural predictiVE and peRsonalised Modelling of depressIve symptoms duriNg primary somatic Diseases with ICT-enabled self-management procedures. Currently he is also involved as WP leader in the European project EC-FP7-ICT-601165 “WEARHAP – WEARable HAPtics for humans and robots”. His main research interests are in wearable monitoring systems, human-computer interfaces, biomedical and biomechanical signal processing, modelling, control and instrumentation. He is author of more than 150 papers on peer-review journals, contributions to international conferences and chapters in international books. He is co-author of the book “Autonomic Nervous System Dynamics for Mood and Emotional-State Recognition – Significant Advances in Data Acquisition, Signal Processing and Classification” edited by Springer in 2014. He is currently serving as reviewer to many international journals and as member of Program and Scientific Committees of yearly international conferences. He is guest associate editor of Frontiers in Neuroengineering journal, guest editor of the Journal of Biomedical and Health Informatics, special issue on Sensor Informatics for Managing Mental Health, 2015, and associate editor of ETRI Journal, Electronics, and Complexity journalAlberto 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.
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.
Enzo Pasquale Scilingo, Ph.D.is an Associate Professor in Electronic and Information Bioengineering at the University of Pisa. He received the Laurea Degree in Electronic Engineering from the University of Pisa, Italy and the Ph.D.degree in Bioengineering from the University of Milan, in 1995 and 1998 respectively. For two years he was postdoctoral fellow with the Italian National Research Council and for two years post-doctoral fellow at Information Engineering Department of the University of Pisa. Currently, he is pursuing his research work mainly at the Research Center “E. Piaggio”. He has several teaching activities, he is supervisor of several PhD students and he is leading the laboratory Biolab at the Information Engineering Department . He coordinated a European project EC-FP7-ICT-247777 “PSYCHE-Personalised monitoring SYstems for Care in mental Health”, and he is currently coordinating the European project H2020-PHC-2015-689691 NEVERMIND - NEurobehavioural predictiVE and peRsonalised Modelling of depressIve symptoms duriNg primary somatic Diseases with ICT-enabled self-management procedures. Currently he is also involved as WP leader in the European project EC-FP7-ICT-601165 “WEARHAP – WEARable HAPtics for humans and robots”. His main research interests are in wearable monitoring systems, human-computer interfaces, biomedical and biomechanical signal processing, modelling, control and instrumentation. He is author of more than 150 papers on peer-review journals, contributions to international conferences and chapters in international books. He is co-author of the book “Autonomic Nervous System Dynamics for Mood and Emotional-State Recognition – Significant Advances in Data Acquisition, Signal Processing and Classification” edited by Springer in 2014. He is currently serving as reviewer to many international journals and as member of Program and Scientific Committees of yearly international conferences. He is guest associate editor of Frontiers in Neuroengineering journal, guest editor of the Journal of Biomedical and Health Informatics, special issue on Sensor Informatics for Managing Mental Health, 2015, and associate editor of ETRI Journal, Electronics, and Complexity journalAlberto 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.
Textul de pe ultima copertă
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:
Deftly 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
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 dynamics
Caracteristici
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 dynamics
Deftly 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
Includes supplementary material: sn.pub/extras
Authoritatively presents groundbreaking research achieved using EDA as an exemplary biomarker of ANS dynamics
Deftly 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
Includes supplementary material: sn.pub/extras