%0 Generic
%A Bili, Danai
%A Lange, Frédéric
%A Jones, Kelly H
%A Parfentyeva, Veronika
%A Durduran, Turgut
%A Robertson, Nikki
%A Mitra, Subhabrata
%A Tachtsidis, Ilias
%C Munich, Germany
%D 2023
%E Contini, Davide
%E Hoshi, Yoko
%E O'Sullivan, Thomas D
%F discovery:10179358
%I SPIE
%P 126280D
%T Classification of brain injury severity using a hybrid broadband NIRS and DCS instrument with a machine learning approach
%U https://discovery.ucl.ac.uk/id/eprint/10179358/
%V 12628
%X 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.
%Z This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.