%P 126280D
%T Classification of brain injury severity using a hybrid broadband NIRS and DCS instrument with a machine learning approach
%B Proceedings Volume 12628, Diffuse Optical Spectroscopy and Imaging IX
%C Munich, Germany
%D 2023
%S European Conferences on Biomedical Optics
%V 12628
%L discovery10179358
%I SPIE
%E Davide Contini
%E Yoko Hoshi
%E Thomas D O'Sullivan
%O This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
%J Proceedings of SPIE - The International Society for Optical Engineering
%A Danai Bili
%A Frédéric Lange
%A Kelly H Jones
%A Veronika Parfentyeva
%A Turgut Durduran
%A Nikki Robertson
%A Subhabrata Mitra
%A Ilias Tachtsidis
%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.