University of Oulu

H. Ferdinando, T. Seppänen and E. Alasaarela, "Comparing features from ECG pattern and HRV analysis for emotion recognition system," 2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Chiang Mai, 2016, pp. 1-6. doi: 10.1109/CIBCB.2016.7758108

Comparing features from ECG pattern and HRV analysis for emotion recognition system

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Author: Ferdinando, Hany1,2; Seppänen, Tapio3; Alasaarela, Esko1
Organizations: 1Optoelectronics and Measurement Technique Unit, University of Oulu, Oulu, Finland
2Department of Electrical Engineering, Petra Christian University, Surabaya, Indonesia
3Physiological signal analysis team, University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.2 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe202003208623
Language: English
Published: Institute of Electrical and Electronics Engineers, 2017
Publish Date: 2020-03-20
Description:

Abstract

We propose new features for emotion recognition from short ECG signals. The features represent the statistical distribution of dominant frequencies, calculated using spectrogram analysis of intrinsic mode function after applying the bivariate empirical mode decomposition to ECG. KNN was used to classify emotions in valence and arousal for a 3-class problem (low-medium-high). Using ECG from the Mahnob-HCI database, the average accuracies for valence and arousal were 55.8% and 59.7% respectively with 10-fold cross validation. The accuracies using features from standard Heart Rate Variability analysis were 42.6% and 47.7% for valence and arousal respectively for the 3-class problem. These features were also tested using subject-independent validation, achieving an accuracy of 59.2% for valence and 58.7% for arousal. The proposed features also showed better performance compared to features based on statistical distribution of instantaneous frequency, calculated using Hilbert transform of intrinsic mode function after applying standard empirical mode decomposition and bivariate empirical mode decomposition to ECG. We conclude that the proposed features offer a promising approach to emotion recognition based on short ECG signals. The proposed features could be potentially used also in applications in which it is important to detect quickly any changes in emotional state.

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ISBN: 978-1-4673-9472-7
ISBN Print: 978-1-5090-0012-8
Pages: 1 - 6
DOI: 10.1109/CIBCB.2016.7758108
OADOI: https://oadoi.org/10.1109/CIBCB.2016.7758108
Host publication: 2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). 5-7 Oct.2016
Conference: Annual IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
217 Medical engineering
Subjects:
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