Advances in Electrodermal Activity Processing with Applications for Mental Health

From Heuristic Methods to Convex Optimization

  • Alberto Greco
  • Gaetano Valenza
  • Enzo Pasquale Scilingo

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Alberto Greco, Gaetano Valenza, Enzo Pasquale Scilingo
    Pages 1-17
  3. Alberto Greco, Gaetano Valenza, Enzo Pasquale Scilingo
    Pages 19-33
  4. Alberto Greco, Gaetano Valenza, Enzo Pasquale Scilingo
    Pages 35-43
  5. Alberto Greco, Gaetano Valenza, Enzo Pasquale Scilingo
    Pages 45-54
  6. Alberto Greco, Gaetano Valenza, Enzo Pasquale Scilingo
    Pages 55-109
  7. Alberto Greco, Gaetano Valenza, Enzo Pasquale Scilingo
    Pages 111-121
  8. Back Matter
    Pages 123-138

About this book

Introduction

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 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 

Keywords

Affective Computing Autonomic Nervous System Continuous Deconvolution Analysis Convex Optimization Analysis Mood and Emotion Recognition Physiological Modeling Psychometrics Psychophysiology Statistical Biosignal Processing

Authors and affiliations

  • Alberto Greco
    • 1
  • Gaetano Valenza
    • 2
  • Enzo Pasquale Scilingo
    • 3
  1. 1.Department of Information Engineering, Bioengineering and Robotics Research Center “E. Piaggio”University of PisaPisaItaly
  2. 2.Department of Information Engineering, Bioengineering and Robotics Research Center “E. Piaggio”University of PisaPisaItaly
  3. 3.Department of Information Engineering, Bioengineering and Robotics Research Center “E. Piaggio”University of PisaPisaItaly

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-46705-4
  • Copyright Information Springer International Publishing AG 2016
  • Publisher Name Springer, Cham
  • eBook Packages Biomedical and Life Sciences
  • Print ISBN 978-3-319-46704-7
  • Online ISBN 978-3-319-46705-4
  • About this book