Toward Recognizing Two Emotion States from ECG Signals

Emotion recognition based on physiological signals which can reflect people’s real emotion correctly is more robust and objective than any other ways, so it has a bright prospect of research and applications. This paper may firstly carry out the work of feature extraction for electrocardiogram (ECG) obtained from 391 subjects containing two emotion states (joy, sad) by the method of discrete wavelet transform (DWT). Then feature selection could be performed using the method on the combination of Particle Swarm Optimization (PSO) and KNN classifier. Eventually, the optimal feature subset could be found and the total recognition rate reached 84.45%. Experiment and simulation results showed that it is feasible and efficiency that using PSO and KNN to recognize emotion states by physiological signals.

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

[2]  Albert A. Groenwold,et al.  A Study of Global Optimization Using Particle Swarms , 2005, J. Glob. Optim..

[3]  K. Ouni,et al.  ECG Signal Maxima Detection Using Wavelet Transform , 2006, 2006 IEEE International Symposium on Industrial Electronics.

[4]  Johannes Wagner,et al.  From Physiological Signals to Emotions: Implementing and Comparing Selected Methods for Feature Extraction and Classification , 2005, 2005 IEEE International Conference on Multimedia and Expo.

[5]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

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

[7]  A. Ahmadian,et al.  ECG Feature Extraction Based on Multiresolution Wavelet Transform , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[8]  W PicardRosalind,et al.  Toward Machine Emotional Intelligence , 2001 .

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

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