Abstract
Human-Computer-Interface (HCI) has become an emerging area of research among the scientific community. The uses of machine learning algorithms are dominating the subject of data mining, to achieve the optimized result in various areas. One such area is related with emotional state classification using bio-electrical signals. The aim of the paper is to investigate the efficacy, efficiency and computational loads of different algorithms scientific comparisons that are used in recognizing emotional state through cardiovascular physiological signals. In this paper, we have used Decision tables, Neural network, C4.5 and Naïve Bayes as a subject under study, the classification is done into two domains: High Arousal and Low Arousal.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sun, Y., Li, Z., Zhang, L., Qiu, S., Chen, Y.: Evaluating data mining tools for authentic emotion classification in Intelligent Computation Technology and Automation (ICICTA). 2010 International Conference, vol. 2, pp. 228–232 (2010)
Jang, E.-H., Park, B.-J., Kim, S.-H., Eum, Y., Sohn, J.-H.: Identification of the optimal emotion recognition algorithm using physiological signals. 2011 International Conference on Engineering and Industries (ICEI), 2011, pp. 1–6
Chang, C.-Y., Tsai, J.-S., Wang, C.-J., Chung, P.-C.: Emotion recognition with consideration of facial expression and physiological signals in computational intelligence in bioinformatics and computational biology. CIBCB ’09 IEEE Symposium, 2009, pp. 278–283
Gouizi, K., Reguig, F.B., Maaoui, C.: Analysis physiological signals for emotion recognition in Systems, Signal processing and their Applications (WOSSPA). 2011 7th International Workshop , 2011, pp. 147–150.
Li, L., Chen, J.-H.: Emotion recognition using physiological signals from multiple subjects in intelligent information hiding and multimedia signal processing. IIH-MSP ’06 International Conference, 2006, pp. 355–358
Siraj, F., Yusoff, N., Kee, L. C.: Emotion classification using neural network. International Conference on Computing and Informatics ICOCI ’06, 2006, pp. 1–7
Li, M., Lu, B. -L.: Emotion classification based on gamma-band EEG in engineering in medicine and biology society. Annual International Conference of the IEEE, 2009, pp. 1223–1226
Murugappan, M.: Electromyogram signal based human emotion classification using KNN and LDA in System Engineering and Technology (ICSET). IEEE International Conference, 2011, pp. 106–110
Ma, C.-W., Liu, G.-Y.: Feature extraction, feature selection and classification from electrocardiography to emotions in computational intelligence and natural computing. CINC ’09 International Conference, vol. 1, pp. 190–193 (2009)
Wagner, J., Kim, J., Andre, E.: Signals, from physiological, to emotions: Implementing and comparing selected methods for feature extraction and classification. IEEE International Conference in multimedia and expo (ICME), 2005, pp. 940–943
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer India
About this paper
Cite this paper
Vaish, A., Kumari, P. (2014). A Comparative Study on Machine Learning Algorithms in Emotion State Recognition Using ECG. In: Babu, B., et al. Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012. Advances in Intelligent Systems and Computing, vol 236. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1602-5_147
Download citation
DOI: https://doi.org/10.1007/978-81-322-1602-5_147
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-1601-8
Online ISBN: 978-81-322-1602-5
eBook Packages: EngineeringEngineering (R0)