Abstract
Electrocardiogram (ECG) based affective computing is a new research field that aims to find correlates between human emotions and the registered ECG signals. Typically, emotion recognition systems are personalized, i.e. the discrimination models are subject-dependent. Building subject-independent models is a harder problem due to the high ECG variability between individuals. In this paper, we study the potential of two machine learning methods (Logistic Regression and Artificial Neural Network) to discriminate human emotional states across multiple subjects. The users were exposed to movies with different emotional content (neutral, fear, disgust) and their ECG activity was registered. Based on extracted features from the ECG recordings, the three emotional states were partially discriminated.
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References
Konar, A., Chakraborty, A.: Emotion Recognition: A Pattern Analysis Approach. Wiley, Hoboken (2014)
Xu, Y.: A method of emotion recognition based on ECG signal. In: 2009 International Conference on Computational Intelligence and Natural Computing, pp. 202–205 (2009)
Min, H.: Analysis of affective ECG signals toward emotion I. Introduction ECG data collection III. ECG feature extraction. J. Electron. (China) 9–14 (2010)
Cai, J.: The research on emotion recognition from ECG signal. In: Proceedings of 2009 International Conference on Information Technology and Computer Science, ITCS 2009, pp. 497–500 (2009)
Tivatansakul, S.: Emotion recognition using ECG signals with local pattern description methods. Int. J. Affect. Eng. 15, 51–61 (2016)
Murugappan, M.: Frequency band analysis of electrocardiogram (ECG) signals for human emotional state classification using discrete wavelet transform (DWT). J. Phys. Ther. Sci. 25, 753–759 (2013)
Selvaraj, J.: Classification of emotional states from electrocardiogram signals: a non-linear approach based on hurst. BioMed. Eng. 12, 44 (2013)
Keren, G.: End-to-end learning for dimensional emotion recognition from physiological signals. In: Proceedings of IEEE International Conference on Multimedia and Expo, pp. 985–990 (2017)
Acknowledgements
This work was supported by European Regional Development Fund and the Operational Program “Science and Education for Smart Growth” under contract UNITe BG05M2OP001-1.001-0004-01 (2018–2023).
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Nikolova, D., Mihaylova, P., Manolova, A., Georgieva, P. (2019). ECG-Based Human Emotion Recognition Across Multiple Subjects. In: Poulkov, V. (eds) Future Access Enablers for Ubiquitous and Intelligent Infrastructures. FABULOUS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 283. Springer, Cham. https://doi.org/10.1007/978-3-030-23976-3_3
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DOI: https://doi.org/10.1007/978-3-030-23976-3_3
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