Identification of Arousal and Relaxation by using SVM-Based Fusion of PPG Features
In this paper, we propose a new method to distinguish
between arousal and relaxation states by using multiple features
acquired from a photoplethysmogram (PPG) and support vector
machine (SVM). To induce arousal and relaxation states in subjects, 2
kinds of sound stimuli are used, and their corresponding biosignals are
obtained using the PPG sensor. Two features–pulse to pulse interval
(PPI) and pulse amplitude (PA)–are extracted from acquired PPG
data, and a nonlinear classification between arousal and relaxation is
performed using SVM.
This methodology has several advantages when compared with
previous similar studies. Firstly, we extracted 2 separate features from
PPG, i.e., PPI and PA. Secondly, in order to improve the classification
accuracy, SVM-based nonlinear classification was performed.
Thirdly, to solve classification problems caused by generalized
features of whole subjects, we defined each threshold according to
individual features.
Experimental results showed that the average classification
accuracy was 74.67%. Also, the proposed method showed the better
identification performance than the single feature based methods.
From this result, we confirmed that arousal and relaxation can be
classified using SVM and PPG features.
[1] J. A. Russell, "A circumplex model of affect," Journal of Personality and
Social Psychology, vol. 39, no. 6, pp. 1161-1178, Dec. 1980.
[2] L. I. Aftanas, A. A. Varlamov, S. V. Pavlov, V. P. Makhnev, and N. V. Reva,
"Time-dependent cortical asymmetries induced by emotional arousal: EEG
analysis of event-related synchronization and desynchronization in
individually defined frequency bands," International Journal of
Psychophysiology, vol. 44, issue 1, pp. 67-82, Apr. 2002.
[3] C. Amrhein, A. M├╝hlberger, P. Pauli, and G. Wiedemann, "Modulation of
event-related brain potentials during affective picture processing: a
complement to startle reflex and skin conductance response?," International
Journal of Psychophysiology, vol. 54, issue. 3, pp. 231- 240, Nov. 2004.
[4] C. Collet, C. Petit, A. Priez, and A. Dittmar, "Stroop color-word test, arousal,
electrodermal activity and performance in a critical driving situation,"
Biological Psychology, vol. 69, issue. 2, pp. 195-203, May 2005.
[5] S. C. Chung et al., "Development of the Real-Time Subjective
Emotionality Assessment (RTSEA) system," Behavior Research
Methods, vol. 39, no. 1, pp. 144-150, Feb. 2007.
[6] A. Haag, S. Goronzy, P. Schaich, and J. Williams, "Emotion Recognition
Using Bio-sensors: First Steps towards an Automatic System," in
Affective Dialogue Systems : Lecture Notes in Computer Science , vol.
3068, E. André, L. Dybkj├ªr, W. Minker, and P. Heisterkamp, Ed.
Heidelberg : Springer Berlin, 2004, pp. 36-48
[7] B. E. Boser, I. M. Guyon, and V. N. Vapnik, "A training algorithm for
optimal margin classifiers," in Proc. 5th annual workshop on
Computational learning theory (COLT '92), New York, 1992,
pp.144-152.
[8] K. H. Kim, S. W. Bang, and S. R. Kim, "Emotion recognition system
using short-term monitoring of physiological signals," Medical and
Biological Engineering and Computing, vol. 42, no. 3, pp. 419-427, May
2004.
[9] R. Nocua, N. Noury, C. Gehin, and A. Dittmar, "Evaluation of the
autonomic nervous system for fall detection," in Proc. 31th Annu. Int.
Conf. of the IEEE Engineering in Medicine and Biology Society 2009,
Minneapolis, 2009, pp. 3225-3228.
[10] K. Shelley and S. Shelley, "Pulse Oximeter Waveform: Photoelectric
Plethysmography," in Clinical monitoring practical applications for
anesthesia and critical care, C. L. Lake, R. Hines, and C. Blitt, Ed. W.B.:
Saunders Company, 2001, pp. 420-428.
[11] "winSVM," M. Sewell, Retrieved 29 June 2011,
<http://www.cs.ucl.ac.uk/staff/M.Sewell/winsvm/>
[1] J. A. Russell, "A circumplex model of affect," Journal of Personality and
Social Psychology, vol. 39, no. 6, pp. 1161-1178, Dec. 1980.
[2] L. I. Aftanas, A. A. Varlamov, S. V. Pavlov, V. P. Makhnev, and N. V. Reva,
"Time-dependent cortical asymmetries induced by emotional arousal: EEG
analysis of event-related synchronization and desynchronization in
individually defined frequency bands," International Journal of
Psychophysiology, vol. 44, issue 1, pp. 67-82, Apr. 2002.
[3] C. Amrhein, A. M├╝hlberger, P. Pauli, and G. Wiedemann, "Modulation of
event-related brain potentials during affective picture processing: a
complement to startle reflex and skin conductance response?," International
Journal of Psychophysiology, vol. 54, issue. 3, pp. 231- 240, Nov. 2004.
[4] C. Collet, C. Petit, A. Priez, and A. Dittmar, "Stroop color-word test, arousal,
electrodermal activity and performance in a critical driving situation,"
Biological Psychology, vol. 69, issue. 2, pp. 195-203, May 2005.
[5] S. C. Chung et al., "Development of the Real-Time Subjective
Emotionality Assessment (RTSEA) system," Behavior Research
Methods, vol. 39, no. 1, pp. 144-150, Feb. 2007.
[6] A. Haag, S. Goronzy, P. Schaich, and J. Williams, "Emotion Recognition
Using Bio-sensors: First Steps towards an Automatic System," in
Affective Dialogue Systems : Lecture Notes in Computer Science , vol.
3068, E. André, L. Dybkj├ªr, W. Minker, and P. Heisterkamp, Ed.
Heidelberg : Springer Berlin, 2004, pp. 36-48
[7] B. E. Boser, I. M. Guyon, and V. N. Vapnik, "A training algorithm for
optimal margin classifiers," in Proc. 5th annual workshop on
Computational learning theory (COLT '92), New York, 1992,
pp.144-152.
[8] K. H. Kim, S. W. Bang, and S. R. Kim, "Emotion recognition system
using short-term monitoring of physiological signals," Medical and
Biological Engineering and Computing, vol. 42, no. 3, pp. 419-427, May
2004.
[9] R. Nocua, N. Noury, C. Gehin, and A. Dittmar, "Evaluation of the
autonomic nervous system for fall detection," in Proc. 31th Annu. Int.
Conf. of the IEEE Engineering in Medicine and Biology Society 2009,
Minneapolis, 2009, pp. 3225-3228.
[10] K. Shelley and S. Shelley, "Pulse Oximeter Waveform: Photoelectric
Plethysmography," in Clinical monitoring practical applications for
anesthesia and critical care, C. L. Lake, R. Hines, and C. Blitt, Ed. W.B.:
Saunders Company, 2001, pp. 420-428.
[11] "winSVM," M. Sewell, Retrieved 29 June 2011,
<http://www.cs.ucl.ac.uk/staff/M.Sewell/winsvm/>
@article{"International Journal of Medical, Medicine and Health Sciences:59890", author = "Chi Jung Kim and Mincheol Whang and Eui Chul Lee", title = "Identification of Arousal and Relaxation by using SVM-Based Fusion of PPG Features", abstract = "In this paper, we propose a new method to distinguish
between arousal and relaxation states by using multiple features
acquired from a photoplethysmogram (PPG) and support vector
machine (SVM). To induce arousal and relaxation states in subjects, 2
kinds of sound stimuli are used, and their corresponding biosignals are
obtained using the PPG sensor. Two features–pulse to pulse interval
(PPI) and pulse amplitude (PA)–are extracted from acquired PPG
data, and a nonlinear classification between arousal and relaxation is
performed using SVM.
This methodology has several advantages when compared with
previous similar studies. Firstly, we extracted 2 separate features from
PPG, i.e., PPI and PA. Secondly, in order to improve the classification
accuracy, SVM-based nonlinear classification was performed.
Thirdly, to solve classification problems caused by generalized
features of whole subjects, we defined each threshold according to
individual features.
Experimental results showed that the average classification
accuracy was 74.67%. Also, the proposed method showed the better
identification performance than the single feature based methods.
From this result, we confirmed that arousal and relaxation can be
classified using SVM and PPG features.", keywords = "Support Vector Machine, PPG, Emotion Recognition, Arousal, Relaxation", volume = "5", number = "11", pages = "606-5", }