Minimising redundancy, maximising relevance: HRV feature selection for stress classification

Ihianle, IK ORCID: https://orcid.org/0000-0001-7445-8573, Machado, P ORCID: https://orcid.org/0000-0003-1760-3871, Owa, K ORCID: https://orcid.org/0000-0002-1393-705X, Adama, DA ORCID: https://orcid.org/0000-0002-2650-857X, Otuka, R ORCID: https://orcid.org/0009-0006-0198-8999 and Lotfi, A ORCID: https://orcid.org/0000-0002-5139-6565, 2024. Minimising redundancy, maximising relevance: HRV feature selection for stress classification. Expert Systems with Applications, 239: 122490. ISSN 0957-4174

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Abstract

Heart rate variability serves as a valuable indicator and biomarker for stress detection and monitoring. Feature selection, which aims to identify relevant features from a large set of variables, is a crucial preprocessing step towards this. However, this task becomes challenging due to high dimensionality and the presence of irrelevant and redundant attributes. The Minimum Redundancy and Maximum Relevance (mRMR) feature selection method addresses this challenge by selecting relevant features while controlling redundancy. This paper presents extensions and evaluated versions of the mRMR feature selection methods for stress detection using Heart Rate Variability (HRV) measures. The proposed feature selection methods extend the traditional mRMR by replacing the Pearson correlation redundancy with non-linear feature redundancy measures capable of capturing more complex relationships between variables. An extensive empirical evaluation is conducted on the proposed mRMR extensions, comparing them with four other baseline feature selection methods using three publicly available datasets. The experimental results demonstrate the effectiveness of incorporating the nonlinear feature redundancy measure into the feature selection process.

Item Type:	Journal article
Publication Title:	Expert Systems with Applications
Creators:	Ihianle, I.K., Machado, P., Owa, K., Adama, D.A., Otuka, R. and Lotfi, A.
Publisher:	Elsevier
Date:	1 April 2024
Volume:	239
ISSN:	0957-4174
Identifiers:	Number Type 10.1016/j.eswa.2023.122490 DOI S0957417423029925 Publisher Item Identifier 1777524 Other
Rights:	This is an open access article under the CC BY license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	15 Nov 2023 12:03
Last Modified:	15 Nov 2023 12:03
URI:	https://irep.ntu.ac.uk/id/eprint/50380

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