Ferdinando, H.; Ferdinando, H.; Seppänen, T. and Alasaarela, E. (2017). Enhancing Emotion Recognition from ECG Signals using Supervised Dimensionality Reduction. In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 112-118. DOI: 10.5220/0006147801120118
Enhancing emotion recognition from ECG signals using supervised dimensionality reduction
|Author:||Ferdinando, Hany1,2; Seppänen, Tapio3; Alasaarela, Esko1|
1Optoelectronics and Measurement Technique Research Unit, University of Oulu, Oulu, Finland
2Department of Electrical Engineering, Petra Christian University, Surabaya, Indonesia
3Physiological Signal Analysis Team, University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019081924650
SCITEPRESS Science And Technology Publications,
|Publish Date:|| 2019-08-19
Dimensionality reduction (DR) is an important issue in classification and pattern recognition process. Using features with lower dimensionality helps the machine learning algorithms work more efficient. Besides, it also can improve the performance of the system. This paper explores supervised dimensionality reduction, LDA (Linear Discriminant Analysis), NCA (Neighbourhood Components Analysis), and MCML (Maximally Collapsing Metric Learning), in emotion recognition based on ECG signals from the Mahnob-HCI database. It is a 3-class problem of valence and arousal. Features for kNN (k-nearest neighbour) are based on statistical distribution of dominant frequencies after applying a bivariate empirical mode decomposition. The results were validated using 10-fold cross and LOSO (leave-one-subject-out) validations. Among LDA, NCA, and MCML, the NCA outperformed the other methods. The experiments showed that the accuracy for valence was improved from 55.8% to 64.1%, and for arousal from 59.7% to 66.1% using 10-fold cross validation after transforming the features with projection matrices from NCA. For LOSO validation, there is no significant improvement for valence while the improvement for arousal is significant, i.e. from 58.7% to 69.6%.
|Pages:||112 - 118|
6th International Conference on Pattern Recognition Applications and Methods (ICPRAM)
|Host publication editor:||
De Marsico, Maria
Sanniti di Baja, Gabriella
International Conference on Pattern Recognition Applications and Methods
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
217 Medical engineering
This research was supported by by the Directorate General of Higher Education, Ministry of Higher Education and Research, Republic of Indonesia, No. 2142/E4.4/K/2013, the Finnish Cultural Foundation North Ostrobothnia Regional Fund and the Optoelectronics and Measurement Techniques unit, University of Oulu, Finland.
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