Emotion recognition using neighborhood components analysis and ECG/HRV-based features |
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Author: | Ferdinando, Hany1,2; Seppänen, Tapio3; Alasaarela, Esko1 |
Organizations: |
1Health and Wellness Measurement Research Unit, Opto-electronic and Measurement Technique (OPEM) unit, University of Oulu, Finland 2Department of Electrical Engineering, Petra Christian University, Indonesia 3Physiological Signal Team Analysis, University of Oulu, Finland |
Format: | article |
Version: | accepted version |
Access: | open |
Online Access: | PDF Full Text (PDF, 0.4 MB) |
Persistent link: | http://urn.fi/urn:nbn:fi-fe202003097634 |
Language: | English |
Published: |
Springer Nature,
2018
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Publish Date: | 2020-03-09 |
Description: |
AbstractPrevious research showed that supervised dimensionality reduction using Neighborhood Components Analysis (NCA) enhanced the performance of 3-class problem emotion recognition using ECG only where features were the statistical distribution of dominant frequencies and the first differences after applying bivariate empirical mode decomposition (BEMD). This paper explores how much NCA enhances emotion recognition using ECG-derived features, esp. standard HRV features with two difference normalization methods and statistical distribution of instantaneous frequencies and the first differences calculated using Hilbert-Huang Transform (HHT) after empirical mode decomposition (EMD) and BEMD. Results with the MAHNOB-HCI database were validated using subject-dependent and subject-independent scenarios with kNN as classifier for 3-class problem in valence and arousal. A t-test was used to assess the results with significance level 0.05. Results show that NCA enhances the performance up to 74% from the implementation without NCA with p-values close to zero in most cases. Different feature extraction methods offered different performance levels in the baseline but the NCA enhanced them such that the performances were close to each other. In most experiments use of combined standardized and normalized HRV-based features improved performance. Using NCA on this database improved the standard deviation significantly for HRV-based features under subject-independent scenario. see all
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Series: |
Lecture notes in computer science |
ISSN: | 0302-9743 |
ISSN-E: | 1611-3349 |
ISSN-L: | 0302-9743 |
ISBN: | 978-3-319-93647-5 |
ISBN Print: | 978-3-319-93646-8 |
Pages: | 99 - 113 |
DOI: | 10.1007/978-3-319-93647-5_6 |
OADOI: | https://oadoi.org/10.1007/978-3-319-93647-5_6 |
Host publication: |
Pattern Recognition Applications and Methods. ICPRAM 2017 |
Host publication editor: |
Fred, Ana De Marsico, Maria di Baja, Gabriella Sanniti |
Conference: |
International Conference on Pattern Recognition Applications and Methods |
Type of Publication: |
A1 Journal article – refereed |
Field of Science: |
113 Computer and information sciences 217 Medical engineering |
Subjects: | |
Copyright information: |
© Springer International Publishing AG, part of Springer Nature 2018. This is a post-peer-review, pre-copyedit version of an article published in Pattern Recognition Applications and Methods. ICPRAM 2017. The final authenticated version is available online at: https://doi.org/10.1007/978-3-319-93647-5_6. |