Inter and intra signal variance in feature extraction and classification of affective state

dc.contributor.authorDair, Zacharyen
dc.contributor.authorDockray, Samanthaen
dc.contributor.authorO’Reilly, Ruairien
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2023-03-31T12:17:30Z
dc.date.available2023-03-31T12:17:30Z
dc.date.issued2023-02-23en
dc.description.abstractPsychophysiology investigates the causal relationship of physiological changes resulting from psychological states. There are significant challenges with machine learning-based momentary assessments of physiology due to varying data collection methods, physiological differences, data availability and the requirement for expertly annotated data. Advances in wearable technology have significantly increased the scale, sensitivity and accuracy of devices for recording physiological signals, enabling large-scale unobtrusive physiological data gathering. This work contributes an empirical evaluation of signal variances acquired from wearables and their associated impact on the classification of affective states by (i) assessing differences occurring in features representative of affective states extracted from electrocardiograms and photoplethysmography, (ii) investigating the disparity in feature importance between signals to determine signal-specific features, and (iii) investigating the disparity in feature importance between affective states to determine affect specific features. Results demonstrate that the degree of feature variance between ECG and PPG in a dataset is reflected in the classification performance of that dataset. Additionally, beats-per-minute, inter-beat interval and breathing rate are identified as common best-performing features across both signals. Finally feature variance per-affective state identifies hard-to-distinguish affective states requiring one-versus-rest or additional features to enable accurate classification.en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationDair, Z., Dockray, S. and O’Reilly, R. (2023) ‘Inter and intra signal variance in feature extraction and classification of affective state’, AICS2022, in L. Longo and R. O’Reilly (eds) Communications in Computer and Information Science, vol 1662: Springer Nature Switzerland, pp. 3–17. https://doi.org/10.1007/978-3-031-26438-2_1.en
dc.identifier.doi10.1007/978-3-031-26438-2_1en
dc.identifier.endpage17en
dc.identifier.isbn9783031264375en
dc.identifier.isbn9783031264382en
dc.identifier.issn1865-0929en
dc.identifier.issn1865-0937en
dc.identifier.journaltitleCommunications in Computer and Information Scienceen
dc.identifier.startpage3en
dc.identifier.urihttps://hdl.handle.net/10468/14345
dc.identifier.volume1662en
dc.language.isoenen
dc.publisherSpringeren
dc.relation.ispartofCommunications in Computer and Information Scienceen
dc.relation.ispartofArtificial Intelligence and Cognitive Scienceen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Centres for Research Training Programme::Data and ICT Skills for the Future/18/CRT/6222/IE/SFI Centre for Research Training in Advanced Networks for Sustainable Societies/en
dc.rights© 2023 The Author(s). Open Access. This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were madeen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectMachine learningen
dc.subjectClassificationen
dc.subjectPsychophysiologyen
dc.subjectElectrocardiogramen
dc.subjectPhotoplethysmographyen
dc.subjectAffective statesen
dc.titleInter and intra signal variance in feature extraction and classification of affective stateen
dc.typeConference itemen
dc.typeBook chapteren
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
978-3-031-26438-2_1.pdf
Size:
1.62 MB
Format:
Adobe Portable Document Format
Description:
Published version
License bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
2.71 KB
Format:
Item-specific license agreed upon to submission
Description: