{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:12:25Z","timestamp":1778080345007,"version":"3.51.4"},"publisher-location":"Cham","reference-count":8,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783319912851","type":"print"},{"value":"9783319912875","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-319-91287-5_51","type":"book-chapter","created":{"date-parts":[[2018,9,3]],"date-time":"2018-09-03T06:09:05Z","timestamp":1535954945000},"page":"319-324","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["ABroAD: A Machine Learning Based Approach to Detect Broadband NIRS Artefacts"],"prefix":"10.1007","author":[{"given":"Joshua","family":"Russell-Buckland","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gemma","family":"Bale","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Isabel","family":"de Roever","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ilias","family":"Tachtsidis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,8,30]]},"reference":[{"issue":"9","key":"51_CR1","doi-asserted-by":"publisher","first-page":"091307","DOI":"10.1117\/1.JBO.21.9.091307","volume":"21","author":"G Bale","year":"2016","unstructured":"Bale G, Elwell CE, Tachtsidis I (2016) From J\u00f6bsis to the present day: a review of clinical near-infrared spectroscopy measurements of cerebral cytochrome-c-oxidase. J Biomed Opt 21(9):091307","journal-title":"J Biomed Opt"},{"key":"51_CR2","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1007\/978-3-319-55231-6_19","volume":"977","author":"I Roever de","year":"2017","unstructured":"de Roever I, Bale G, Cooper RJ et al (2017) Functional NIRS measurement of cytochrome-C-oxidase demonstrates a more brain-specific marker of frontal lobe activation compared to the Haemoglobins oxygen transport to tissue. Adv Exp Med Biol 977:141\u2013147","journal-title":"Adv Exp Med Biol"},{"key":"51_CR3","doi-asserted-by":"publisher","first-page":"3450","DOI":"10.1364\/BOE.5.003450","volume":"5","author":"G Bale","year":"2014","unstructured":"Bale G, Mitra S, Meek J, Robertson N et al (2014) A new broadband near-infrared spectroscopy system for in-vivo measurements of cerebral cytochrome-c-oxidase changes in neonatal brain injury. Biomed Opt Express 5:3450","journal-title":"Biomed Opt Express"},{"issue":"10","key":"51_CR4","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1145\/2347736.2347755","volume":"55","author":"P Domingos","year":"2012","unstructured":"Domingos P (2012) A few useful things to know about machine learning. Commun ACM 55(10):78\u201387","journal-title":"Commun ACM"},{"issue":"6","key":"51_CR5","first-page":"H2039","volume":"278","author":"JS Richman","year":"2000","unstructured":"Richman JS, Moorman JR (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Phys\u00a0\u2013 Heart Circ Phys 278(6):H2039\u2013H2049","journal-title":"Am J Phys\u00a0\u2013 Heart Circ Phys"},{"key":"51_CR6","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: machine learning in python. Learn Res 12:2825","journal-title":"Learn Res"},{"issue":"1","key":"51_CR7","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332","journal-title":"Mach Learn"},{"key":"51_CR8","unstructured":"Fran\u00e7ois R, Langrognet F. (2006) Double cross validation for model based classification. Book of Abstracts, p. 50"}],"container-title":["Advances in Experimental Medicine and Biology","Oxygen Transport to Tissue XL"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-91287-5_51","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,4]],"date-time":"2019-10-04T15:09:53Z","timestamp":1570201793000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-91287-5_51"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783319912851","9783319912875"],"references-count":8,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-91287-5_51","relation":{},"ISSN":["0065-2598","2214-8019"],"issn-type":[{"value":"0065-2598","type":"print"},{"value":"2214-8019","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"30 August 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}