Inter and intra signal variance in feature extraction and classification of affective state
| dc.contributor.author | Dair, Zachary | en |
| dc.contributor.author | Dockray, Samantha | en |
| dc.contributor.author | O’Reilly, Ruairi | en |
| dc.contributor.funder | Science Foundation Ireland | en |
| dc.date.accessioned | 2023-03-31T12:17:30Z | |
| dc.date.available | 2023-03-31T12:17:30Z | |
| dc.date.issued | 2023-02-23 | en |
| dc.description.abstract | Psychophysiology 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.status | Peer reviewed | en |
| dc.description.version | Published Version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Dair, 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.doi | 10.1007/978-3-031-26438-2_1 | en |
| dc.identifier.endpage | 17 | en |
| dc.identifier.isbn | 9783031264375 | en |
| dc.identifier.isbn | 9783031264382 | en |
| dc.identifier.issn | 1865-0929 | en |
| dc.identifier.issn | 1865-0937 | en |
| dc.identifier.journaltitle | Communications in Computer and Information Science | en |
| dc.identifier.startpage | 3 | en |
| dc.identifier.uri | https://hdl.handle.net/10468/14345 | |
| dc.identifier.volume | 1662 | en |
| dc.language.iso | en | en |
| dc.publisher | Springer | en |
| dc.relation.ispartof | Communications in Computer and Information Science | en |
| dc.relation.ispartof | Artificial Intelligence and Cognitive Science | en |
| dc.relation.project | info: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 made | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | Machine learning | en |
| dc.subject | Classification | en |
| dc.subject | Psychophysiology | en |
| dc.subject | Electrocardiogram | en |
| dc.subject | Photoplethysmography | en |
| dc.subject | Affective states | en |
| dc.title | Inter and intra signal variance in feature extraction and classification of affective state | en |
| dc.type | Conference item | en |
| dc.type | Book chapter | en |
