| Description |
1 online resource (xix, 162 pages) : illustrations (some color) |
| Series |
Series in BioEngineering, 2196-8861 |
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Series in bioengineering, 2196-8861
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| Contents |
880-01 Part I. Introductory Remarks and State of the Art -- Introduction to Advances in Autonomic Nervous System Dynamics for Mood and Emotional-State Recognition -- Emotions and Mood States: Modeling, Elicitation, and Classification -- Part II. Methodology -- Data Acquisition: Experimental Procedures and Wearable Monitoring Systems -- Advanced Signal Processing and Modeling for ANS Data -- Part III. Results -- Experimental Evidences on Healthy Subjects and Bipolar Patients -- Part IV. Conclusions and Future Works -- Conclusions and Discussion on Mood and Emotional-State Recognition Using the Autonomic Nervous System Dynamics -- Book Summary and Perspectives for Future Research |
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880-01/(S Machine generated contents note: pt. I Introductory Remarks and State of the Art -- 1. Introduction to Advances in Autonomic Nervous System Dynamics for Mood and Emotional-State Recognition -- 2. Emotions and Mood States: Modeling, Elicitation, and Classification -- 2.1. Modeling Emotions -- 2.2. Autonomic Nervous System Correlates of Emotions -- 2.2.1. Heart Rate Variability -- 2.2.2. Electrodermal Response -- 2.2.3. Information Coming from the Eyes: Pupil Size Variation and Eye Tracking -- 2.2.4. Cardio-Respiratory Coupling -- 2.3. Emotion Elicitation -- 2.4. Affective Computing: From Theory to Emotion Recognition -- 2.5. Emotions and Mood Disorders: The Bipolar Disorders -- 2.6. Autonomic Nervous System as a Nonlinear Physiological System -- pt. II Methodology -- 3. Data Acquisition: Experimental Procedures and Wearable Monitoring Systems -- 3.1. Procedures on Healthy Subjects -- 3.1.1. Recruitment of Eligible Subjects -- 3.1.2. Stimulus Elicitation -- 3.2. Procedures on Bipolar Patients -- 3.2.1. Recruitment of Eligible Subjects and Experimental Protocols -- 3.2.2. Mood Model -- 3.3. Portable and Novel Wearable Systems for Autonomic Nervous System Monitoring -- 3.3.1. Glove System -- 3.3.2. PSYCHE System -- 3.3.3. HATCAM---Wearable Eye Gaze Tracking System -- 3.3.4. BIOPAC: Set of Physiological Signals and Instrumentation -- 4. Advanced Signal Processing and Modeling for ANS Data -- 4.1. Overall Methodology -- 4.2. Preprocessing -- 4.2.1. Movement Artifact Removal -- 4.2.2. Electrocardiogram and Heart Rate Variability -- 4.2.3. Respiration -- 4.2.4. Electrodermal Response -- 4.3. Feature Sets -- 4.3.1. Standard Feature Set -- 4.3.2. Features from Higher Order Spectra -- 4.3.3. Pupillometry and Gaze Point -- 4.3.4. Nonlinear Methods for Feature Extraction -- 4.3.5. Cardio-Respiratory Synchronization Analysis -- 4.4. Feature Reduction Strategy -- 4.4.1. Principal Component Analysis -- 4.5. Classification -- 4.5.1. Quadratic Discriminant Classifier -- 4.5.2. κ-Nearest Neighborhood -- 4.5.3. Multi-layer Perceptron -- 4.5.4. Support Vector Machine -- 4.6. Point-Process Theory and the Instantaneous Nonlinear Dynamics -- 4.6.1. Point-Process Nonlinear Model of the Heartbeat -- 4.6.2. Estimation of the Input--Output Volterra Kernels -- 4.6.3. Quantitative Tools: High Order Spectral Analysis -- pt. III Results -- 5. Experimental Evidences on Healthy Subjects and Bipolar Patients -- 5.1. Results from the Healthy Subjects Study -- 5.1.1. Effective Arousal and Valence Levels Recognition Through Autonomic Nervous System Dynamics -- 5.1.2. Approximate Entropy and Dominant Lyapunov Exponent Analysis on Heart Rate Variability -- 5.1.3. Cardio-Respiratory Synchronization Analysis -- 5.1.4. Using Cardio-Respiratory Synchronization Information for Emotion Recognition -- 5.1.5. Instantaneous Bispectral Characterization of the Autonomic Nervous System Through Point-Process Nonlinear Models -- 5.1.6. Instantaneous Emotional Assessment Through Nonlinear Point-Process Models -- 5.1.7. Electrodermal Response Analysis and Sensorized Glove Assessment -- 5.1.8. Eye Tracking and Pupil Area Variation -- 5.2. Modeling the Cardio-Respiratory Coupling During Arousing Elicitation -- 5.3. Results from the Study on Bipolar Patients -- 5.3.1. Long-Term Analysis -- 5.3.2. Long-Term Analysis: The Role of History-Dependence -- 5.3.3. Long-Term Analysis: The Role of Nonlinear Heartbeat Dynamics Through Multiscale Entropy Analysis -- 5.3.4. Short-Term Analysis -- pt. IV Conclusions and Future Works -- 6. Conclusions and Discussion on Mood and Emotional-State Recognition Using the Autonomic Nervous System Dynamics -- 7. Book Summary and Perspectives for Future Research |
| Summary |
This monograph reports on advances in the measurement and study of autonomic nervous system (ANS) dynamics as a source of reliable and effective markers for mood state recognition and assessment of emotional responses. Its primary impact will be in affective computing and the application of emotion-recognition systems. Applicative studies of biosignals such as: electrocardiograms; electrodermal responses; respiration activity; gaze points; and pupil-size variation are covered in detail, and experimental results explain how to characterize the elicited affective levels and mood states pragmatically and accurately using the information thus extracted from the ANS. Nonlinear signal processing techniques play a crucial role in understanding the ANS physiology underlying superficially noticeable changes and provide important quantifiers of cardiovascular control dynamics. These have prognostic value in both healthy subjects and patients with mood disorders. Moreover, Autonomic Nervous System Dynamics for Mood and Emotional-State Recognition proposes a novel probabilistic approach based on the point-process theory in order to model and characterize the instantaneous ANS nonlinear dynamics providing a foundation from which machine understanding of emotional response can be enhanced. Using mathematics and signal processing, this work also contributes to pragmatic issues such as emotional and mood-state modeling, elicitation, and non-invasive ANS monitoring. Throughout the text a critical review on the current state-of-the-art is reported, leading to the description of dedicated experimental protocols, novel and reliable mood models, and novel wearable systems able to perform ANS monitoring in a naturalistic environment. Biomedical engineers will find this book of interest, especially those concerned with nonlinear analysis, as will researchers and industrial technicians developing wearable systems and sensors for ANS monitoring |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed November 4, 2013) |
| In |
Springer eBooks |
| Subject |
Autonomic nervous system.
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Affective disorders.
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Emotions.
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MEDICAL -- Physiology.
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SCIENCE -- Life Sciences -- Human Anatomy & Physiology.
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Ingénierie.
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Affective disorders
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Autonomic nervous system
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Emotions
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| Form |
Electronic book
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| Author |
Scilingo, E. Pasquale, author
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| ISBN |
9783319026398 |
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3319026399 |
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