Advances in Electrodermal Activity Processing with Applications for Mental Health From Heuristic Methods to Convex Optimization /

• Κύριοι συγγραφείς: Greco, Alberto (Συγγραφέας), Valenza, Gaetano (Συγγραφέας), Scilingo, Enzo Pasquale (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2016.
• Θέματα: Medicine.; Neurosciences.; Biomedical engineering.; Bioinformatics.; Biomedicine.; Biomedical Engineering/Biotechnology.; Signal, Image and Speech Processing.; Biomedical Engineering.; Computational Biology/Bioinformatics.

 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 . 

 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.