|
|
|
| An Entropy Measure of Emotional Arousal via Skin Conductance Response |
| Zhaofang Yang, Guangyuan Liu |
|
|
|
|
Abstract Whether different affective states have specific physiological activation patterns still does not have an exact interpretation and clear validation. Skin Conductance Response (SCR) is under strict control of the autonomic nervous system, providing an effcient way to measure the emotional reactions. Since the emotional SCR signals are always short and noisy, it is of great value to study the methods suitable for short-term SCR analysis. According to the characteristic of SCR signal, we proposed a symbolic method and the symbolic information entropy, further, applied the method to analyse emotional SCR signals. Experiment results show that the symbolic information entropy of SCR is in accordance with the arousal level of emotions, and SCR is more sensitive to the variations of emotional arousal rather than to valence. Symbolic information entropy is less influenced by noise and non-stationary, providing an effective method in analyzing SCR signals or other complex physiological signals.
|
|
|
|
|
|
| Cite this article: |
|
Zhaofang Yang,Guangyuan Liu. An Entropy Measure of Emotional Arousal via Skin Conductance Response[J]. Journal of Fiber Bioengineering and Informatics, 2014, 7(1): 67-80.
|
|
[1] Lang PJ, Bradley MM, Cuthbert BN. Emotion and motivation: Measuring affective perception. Journal of Clinical Neurophysiology: 1998; 15(5); 397.
[2] Boucsein W, Electrodermal activity: Springer Verlag: 2011.
[3] Edelberg R. Electrical activity of the skin: Its measurement and uses in psychophysiology. Hand-book of psychophysiology: 1972; 12; 1011.
[4] Kim K, Bang S, Kim S. Emotion recognition system using short-term monitoring of physiological signals. Medical and biological engineering and computing: 2004; 42(3); 419-427.
[5] Lanatμa A, Valenza G, Scilingo EP. A novel eda glove based on textile-integrated electrodes for affective computing. Medical & biological engineering & computing: 2012; 50(11); 1163-1172.
[6] Babloyantz A, Destexhe A. Is the normal heart a periodic oscillator? Biological cybernetics: 1988; 58(3); 203-211.
[7] Bogaert C, Beckers F, Ramaekers D, Aubert AE. Analysis of heart rate variability with corre-lation dimension method in a normal population and in heart transplant patients. Autonomic Neuroscience: 2001; 90(1-2); 142-147.
[8] Eckmann JP, Kamphorst SO, Ruelle D, Ciliberto S. Liapunov exponents from time series. Physical Review A: 1986; 34(6); 4971-4979.
[9] Gupta V, Suryanarayanan S, Reddy NP. Fractal analysis of surface emg signals from the biceps. International journal of medical informatics: 1997; 45(3); 185-192.
[10] Hassan M, Terrien J, Marque C, Karlsson B. This comparison between approximate entropy, correntropy and time reversibility: Application to uterine electromyogram signals. Medical Engi-neering & Physics: 2011; 33(8); 980-986.
[11] Ocak H. Automatic detection of epileptic seizures in eeg using discrete wavelet transform and approximate entropy. Expert Systems with Applications: 2009; 36(2); 2027-2036.
[12] Kim J, Ande E. Emotion recognition based on physiological changes in music listening. Pattern Analysis and Machine Intelligence, IEEE Transactions on: 2008; 30(12); 2067-2083.
[13] Bashir K, Xiang T, Gong S. Gait recognition without subject cooperation. Pattern Recognition Letters: 2010; 31(13); 2052-2060.
[14] Yentes JM, Hunt N, Schmid KK, Kaipust JP, McGrath D, Stergiou N. The appropriate use of approximate entropy and sample entropy with short data sets. Annals of biomedical engineering: 2013; 41(2); 349-365.
[15] Richman JS, Moorman JR. Physiological time-series analysis using approximate entropy and sam-ple entropy. American Journal of Physiology: Heart and Circulatory Physiology: 2000; 278(6); H2039-49.
[16] Pincus SM. Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences: 1991; 88(6); 2297-2301.
[17] Pincus S. Approximate entropy (apen) as a complexity measure. Chaos: An Interdisciplinary Journal of Nonlinear Science: 1995; 5(1); 110-117.
[18] Eduardo Virgilio Silva L, Otavio Murta L. Evaluation of physiologic complexity in time series using generalized sample entropy and surrogate data analysis. Chaos: An Interdisciplinary Journal of Nonlinear Science: 2012; 22(4); 043105-043112.
[19] Daw CS, Finney CEA, Tracy ER. A review of symbolic analysis of experimental data. Review of Scientific Instruments: 2003; 74; 915.
[20] Guzzetti S, Borroni E, Garbelli PE, Ceriani E, Della Bella P, Montano N, Cogliati C, Somers VK, Mallani A, Porta A. Symbolic dynamics of heart rate variability. Circulation: 2005; 112(4); 465-470.
[21] Poh MZ, Swenson NC, Picard RW. A wearable sensor for unobtrusive, long-term assessment of electrodermal activity. Biomedical Engineering, IEEE Transactions on: 2010; 57(5); 1243-1252.
[22] Picard RW, Vyzas E, Healey J. Toward machine emotional intelligence: Analysis of affective physiological state. IEEE TRANSACTIONS PATTERN ANALYSIS AND MACHINE INTELLI-GENCE: 2001; 23(10); 1175-1191.
[23] Belavkin RV. On relation between emotion and entropy. 2004.
[24] Brock WA, Hsieh DDA, LeBaron BD, Nonlinear dynamics, chaos ans instability: Statistical theory and economic evidence: The MIT Press: 1991.
[25] Kim HS, Eykholt R, Salas J. Nonlinear dynamics, delay times, and embedding windows. Physica D: Nonlinear Phenomena: 1999; 127(1-2); 48-60.
[26] Edelberg R. Electrodermal mechanisms: A critique of the two-effector hypothesis and a proposed replacement. Progress in electrodermal research: 1993; 7-29.
[27] Tabachnick BG, Fidell LS, Osterlind SJ. Using multivariate statistics. 2001.
[28] Bradley MM, Lang PJ. Affective reactions to acoustic stimuli. Psychophysiology: 2000; 37(2); 204-215.
[29] Eckmann JP, Ruelle D. Fundamental limitations for estimating dimensions and lyapunov expo-nents in dynamical systems. Physica D: Nonlinear Phenomena: 1992; 56(2-3); 185-187.
[30] Pincus SM. Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences: 1991; 88(6); 2297.
[31] Kugiumtzis D, Tsimpiris A. Measures of analysis of time series (mats): A matlab toolkit for computation of multiple measures on time series data bases. arXiv preprint arXiv: 1002. 1940:2010.
[32] Anders S, Lotze M, Erb M, Grodd W, Birbaumer N. Brain activity underlying emotional valence and arousal: A response-related fmri study. Human brain mapping: 2004; 23(4); 200-209.
[33] Lewis P, Critchley H, Rotshtein P, Dolan R. Neural correlates of processing valence and arousal in affective words. Cerebral Cortex: 2007; 17(3); 742-748. |
|
|
|
2014, Vol. 7 
