Physiological Detection of Affective States in Children with Autism Spectrum Disorder

Autism spectrum disorder (ASD) is associated with emotion processing difficulties, including limitations in understanding the emotional states of others and processing one's own internal experiences. The nature of these difficulties remains largely unknown. This is due, in part, to challenges in acquiring reliable self-reports of emotional experiences from this population. Automatically characterizing emotional states with the use of physiological signals is a potential means of overcoming this problem, as physiological signals can provide an objective and nonverbal method for assessing affective states. However, this approach has not been well considered with ASD to date. To this end, we investigated detection of autonomic responses to positive and negative stimuli in children with ASD using four physiological measurements. Electrocardiograms, respiration, skin conductance and temperature were measured while 15 children with ASD viewed standard images known to evoke varying levels of valence (positive and negative) and arousal (low and high intensity). Using an ensemble of classifiers, affective states induced by stimuli of positive and negative valence or high and low arousal was differentiated at average accuracies approaching or exceeding 80 percent. These results suggest the feasibility of discerning affective states in individuals with ASD objectively using physiological signals.

[1]  C. Spielberger,et al.  Measuring Alexithymia: Reliability, Validity, and Prevalence , 2014 .

[2]  Christine L. Lisetti,et al.  Emotion recognition from physiological signals using wireless sensors for presence technologies , 2004, Cognition, Technology & Work.

[3]  J. Lagopoulos.  Electrodermal activity , 2007, Acta Neuropsychiatrica.

[4]  Dimitrios Hatzinakos,et al.  ECG Pattern Analysis for Emotion Detection , 2012, IEEE Transactions on Affective Computing.

[5]  C. Maaoui,et al.  Emotion Recognition through Physiological Signals for Human-Machine Communication , 2010 .

[6]  Matthew S. Goodwin,et al.  Assessing Cardiovascular Responses to Stressors in Individuals With Autism Spectrum Disorders , 2005 .

[7]  Jason Williams,et al.  Emotion Recognition Using Bio-sensors: First Steps towards an Automatic System , 2004, ADS.

[8]  Livia Colle,et al.  Impaired recognition of negative basic emotions in autism: A test of the amygdala theory , 2006, Social neuroscience.

[9]  S. Mostofsky,et al.  Normal Physiological Emotions but Differences in Expression of Conscious Feelings in Children with High-Functioning Autism , 2006, Journal of autism and developmental disorders.

[10]  K. Honkalampi,et al.  Depression is strongly associated with alexithymia in the general population. , 2000, Journal of psychosomatic research.

[11]  Changchun Liu,et al.  Physiology-based affect recognition for computer-assisted intervention of children with Autism Spectrum Disorder , 2008, Int. J. Hum. Comput. Stud..

[12]  Josef Kittler,et al.  Floating search methods in feature selection , 1994, Pattern Recognit. Lett..

[13]  Azadeh Kushki,et al.  A Kalman Filtering Framework for Physiological Detection of Anxiety-Related Arousal in Children With Autism Spectrum Disorder , 2015, IEEE Transactions on Biomedical Engineering.

[14]  K. H. Kim,et al.  Emotion recognition system using short-term monitoring of physiological signals , 2004, Medical and Biological Engineering and Computing.

[15]  Tom Chau,et al.  Decoding subjective preference from single-trial near-infrared spectroscopy signals , 2009, Journal of neural engineering.

[16]  Daniel P. Kennedy,et al.  Failing to deactivate: resting functional abnormalities in autism. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[17]  Geoffrey Bird,et al.  Levels of emotional awareness and autism: An fMRI study , 2008, Social neuroscience.

[18]  Sven Bölte,et al.  Brief Report: Emotional Processing in High-Functioning Autism—Physiological Reactivity and Affective Report , 2008, Journal of autism and developmental disorders.

[19]  P E Sifneos,et al.  The prevalence of 'alexithymic' characteristics in psychosomatic patients. , 1973, Psychotherapy and psychosomatics.

[20]  Jennifer Healey,et al.  Toward Machine Emotional Intelligence: Analysis of Affective Physiological State , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  A. Marcel,et al.  Consciousness and the varieties of emotion experience: a theoretical framework. , 2002, Psychological review.

[22]  Elisabeth André,et al.  Emotion recognition based on physiological changes in music listening , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  P. Ekman,et al.  DIFFERENCES Universals and Cultural Differences in the Judgments of Facial Expressions of Emotion , 2004 .

[24]  A. Barreto,et al.  Stress Detection in Computer Users Based on Digital Signal Processing of Noninvasive Physiological Variables , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[25]  Thierry Pun,et al.  DEAP: A Database for Emotion Analysis ;Using Physiological Signals , 2012, IEEE Transactions on Affective Computing.

[26]  Elmar Nöth,et al.  Classification of user states with physiological signals: On-line generic features vs. specialized feature sets , 2009, 2009 17th European Signal Processing Conference.

[27]  Guillaume Chanel,et al.  Emotion Assessment From Physiological Signals for Adaptation of Game Difficulty , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[28]  Dana Kulic,et al.  Affective State Estimation for Human–Robot Interaction , 2007, IEEE Transactions on Robotics.

[29]  Jonathan Klein,et al.  Frustrating the user on purpose: a step toward building an affective computer , 2002, Interact. Comput..

[30]  Clemens Brunner,et al.  Better than random? A closer look on BCI results , 2008 .

[31]  Christos D. Katsis,et al.  A User Independent, Biosignal Based, Emotion Recognition Method , 2007, User Modeling.

[32]  B. C. Lacey,et al.  Verification and extension of the principle of autonomic response-stereotypy. , 1958, The American journal of psychology.

[33]  U. Frith,et al.  Brief Report: Cognitive Processing of Own Emotions in Individuals with Autistic Spectrum Disorder and in Their Relatives , 2004, Journal of autism and developmental disorders.

[34]  Christine L. Lisetti,et al.  Emotion Recognition from Physiological Signals for User Modeling of Affect , 2003 .

[35]  Pasin Israsena,et al.  Real-Time EEG-Based Happiness Detection System , 2013, TheScientificWorldJournal.

[36]  S. Baron-Cohen,et al.  Understanding other minds : perspectives from autism , 1994 .

[37]  Bo Cheng,et al.  Emotion Recognition from Surface EMG Signal Using Wavelet Transform and Neural Network , 2008, 2008 2nd International Conference on Bioinformatics and Biomedical Engineering.

[38]  K. Scherer,et al.  The Geneva affective picture database (GAPED): a new 730-picture database focusing on valence and normative significance , 2011, Behavior research methods.

[39]  Su-Lim Tan,et al.  A biometric signature based system for improved emotion recognition using physiological responses from multiple subjects , 2010, 2010 8th IEEE International Conference on Industrial Informatics.

[40]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[41]  John L. Andreassi,et al.  Psychophysiology: Human Behavior & Physiological Response , 2000 .

[42]  Mohamed Chetouani,et al.  Hilbert-Huang transform based physiological signals analysis for emotion recognition , 2009, 2009 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[43]  C. Rieffe,et al.  Awareness of Single and Multiple Emotions in High-functioning Children with Autism , 2007, Journal of autism and developmental disorders.

[44]  Minjuan Wang,et al.  Affective e-Learning: Using "Emotional" Data to Improve Learning in Pervasive Learning Environment , 2009, J. Educ. Technol. Soc..

[45]  U. Frith.  Emanuel Miller lecture: confusions and controversies about Asperger syndrome. , 2004, Journal of child psychology and psychiatry, and allied disciplines.