%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.