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ABroAD: A Machine Learning Based Approach to Detect Broadband NIRS Artefacts

Russell-Buckland, J; Bale, G; de Roever, I; Tachtsidis, I; (2018) ABroAD: A Machine Learning Based Approach to Detect Broadband NIRS Artefacts. Advances in Experimental Medicine and Biology , 1072 pp. 319-324. 10.1007/978-3-319-91287-5_51. Green open access

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Abstract

Artefacts are a common and unwanted aspect of any measurement process, especially in a clinical environment, with multiple causes such as environmental changes or motion. In near-infrared spectroscopy (NIRS), there are several existing methods that can be used to identify and remove artefacts to improve the quality of collected data.We have developed a novel Automatic Broadband Artefact Detection (ABroAD) process, using machine learning methods alongside broadband NIRS data to detect common measurement artefacts using the broadband intensity spectrum. Data were collected from eight subjects, using a broadband NIRS monitoring over the frontal lobe with two sensors. Six different artificial artefacts - vertical head movement, horizontal head movement, frowning, pressure, ambient light, torch light - were simulated using movement and light changes on eight subjects in a block test design. It was possible to identify both light artefacts to a good degree, as well as pressure artefacts. This is promising and, by expanding this work to larger datasets, it may be possible to create and train a machine learning pipeline to automate the detection of various artefacts, making the analysis of collected data more reliable.

Type: Article
Title: ABroAD: A Machine Learning Based Approach to Detect Broadband NIRS Artefacts
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-319-91287-5_51
Publisher version: http://dx.doi.org/10.1007/978-3-319-91287-5_51
Language: English
Additional information: © The Author(s) 2018 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. The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science > CoMPLEX: Mat&Phys in Life Sci and Exp Bio
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10055506
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