eprintid: 10055506 rev_number: 26 eprint_status: archive userid: 608 dir: disk0/10/05/55/06 datestamp: 2018-09-06 10:31:40 lastmod: 2021-10-09 22:32:07 status_changed: 2018-09-06 10:31:40 type: article metadata_visibility: show creators_name: Russell-Buckland, J creators_name: Bale, G creators_name: de Roever, I creators_name: Tachtsidis, I title: ABroAD: A Machine Learning Based Approach to Detect Broadband NIRS Artefacts ispublished: pub divisions: UCL divisions: B04 divisions: C05 divisions: F48 divisions: F55 divisions: F42 note: © 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. 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. date: 2018 date_type: published official_url: http://dx.doi.org/10.1007/978-3-319-91287-5_51 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green article_type_text: Journal Article verified: verified_manual elements_id: 1581826 doi: 10.1007/978-3-319-91287-5_51 lyricists_name: Bale, Gemma lyricists_name: Russell-Buckland, Joshua lyricists_name: Tachtsidis, Ilias lyricists_id: GMBAL14 lyricists_id: JJRUS94 lyricists_id: ITACH19 actors_name: Russell-Buckland, Joshua actors_id: JJRUS94 actors_role: owner full_text_status: public publication: Advances in Experimental Medicine and Biology volume: 1072 pagerange: 319-324 event_location: United States issn: 0065-2598 citation: 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 <https://doi.org/10.1007/978-3-319-91287-5_51>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10055506/1/Russell-Buckland2018_Chapter_ABroADAMachineLearningBasedApp.pdf