eprintid: 10179358
rev_number: 6
eprint_status: archive
userid: 699
dir: disk0/10/17/93/58
datestamp: 2023-10-23 08:29:06
lastmod: 2023-10-23 08:29:06
status_changed: 2023-10-23 08:29:06
type: proceedings_section
metadata_visibility: show
sword_depositor: 699
creators_name: Bili, Danai
creators_name: Lange, Frédéric
creators_name: Jones, Kelly H
creators_name: Parfentyeva, Veronika
creators_name: Durduran, Turgut
creators_name: Robertson, Nikki
creators_name: Mitra, Subhabrata
creators_name: Tachtsidis, Ilias
title: Classification of brain injury severity using a hybrid broadband NIRS and DCS instrument with a machine learning approach
ispublished: pub
divisions: UCL
divisions: B02
divisions: B04
divisions: C05
divisions: D11
divisions: F42
divisions: G14
note: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: Optical biomarkers of neonatal hypoxic ischemic (HI) brain injury can offer the advantage of continuous, cot-side assessment of the degree of injury; research thus far has focused on examining different optical measured brain physiological signals and feature combinations to achieve this. To maximize the breadth of physiological characteristics being taken into consideration, a multimodal optical platform has been developed, allowing unique physiological insights into brain injury. In this paper we present an assessment of severity of injury using a state-of-the-art hybrid broadband Near Infrared Spectrometer (bNIRS) and Diffusion Correlation Spectrometer (DCS) instrument called FLORENCE with a machine learning pipeline. We demonstrate in the preclinical neonatal model (the newborn piglet) that our approach can identify different HI insult severity (controls, mild, severe). We show that a machine learning pipeline based on k-means clustering can be used to differentiate between the controls and the HI piglets with an accuracy of 78%, the mild severity insult piglets from the severe insult piglets with an accuracy of 90% and can also differentiate the 3 piglet groups with an accuracy of 80%. So, this analytics pipeline demonstrates how optical data from multiple instruments can be processed towards markers of brain health.
date: 2023-08-09
date_type: published
publisher: SPIE
official_url: https://doi.org/10.1117/12.2670657
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2095433
doi: 10.1117/12.2670657
isbn_13: 9781510664654
lyricists_name: Tachtsidis, Ilias
lyricists_name: Robertson, Nicola
lyricists_name: Mitra, Subhabrata
lyricists_name: Lange, Frederic
lyricists_id: ITACH19
lyricists_id: NROBE79
lyricists_id: MITRA07
lyricists_id: FLANG91
actors_name: Flynn, Bernadette
actors_id: BFFLY94
actors_role: owner
full_text_status: public
pres_type: paper
series: European Conferences on Biomedical Optics
publication: Proceedings of SPIE - The International Society for Optical Engineering
volume: 12628
place_of_pub: Munich, Germany
pagerange: 126280D
event_title: European Conferences on Biomedical Optics 2023
issn: 0277-786X
book_title: Proceedings Volume 12628, Diffuse Optical Spectroscopy and Imaging IX
editors_name: Contini, Davide
editors_name: Hoshi, Yoko
editors_name: O'Sullivan, Thomas D
citation:        Bili, Danai;    Lange, Frédéric;    Jones, Kelly H;    Parfentyeva, Veronika;    Durduran, Turgut;    Robertson, Nikki;    Mitra, Subhabrata;           Bili, Danai;  Lange, Frédéric;  Jones, Kelly H;  Parfentyeva, Veronika;  Durduran, Turgut;  Robertson, Nikki;  Mitra, Subhabrata;  Tachtsidis, Ilias;   - view fewer <#>    (2023)    Classification of brain injury severity using a hybrid broadband NIRS and DCS instrument with a machine learning approach.                     In: Contini, Davide and Hoshi, Yoko and O'Sullivan, Thomas D, (eds.) Proceedings Volume 12628, Diffuse Optical Spectroscopy and Imaging IX.  (pp. 126280D).  SPIE: Munich, Germany.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10179358/1/126280D.pdf