TY  - GEN
AV  - public
SN  - 0277-786X
A1  - Bili, Danai
A1  - Lange, Frédéric
A1  - Jones, Kelly H
A1  - Parfentyeva, Veronika
A1  - Durduran, Turgut
A1  - Robertson, Nikki
A1  - Mitra, Subhabrata
A1  - Tachtsidis, Ilias
N2  - 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.
T3  - European Conferences on Biomedical Optics
PB  - SPIE
ID  - discovery10179358
UR  - https://doi.org/10.1117/12.2670657
Y1  - 2023/08/09/
TI  - Classification of brain injury severity using a hybrid broadband NIRS and DCS instrument with a machine learning approach
CY  - Munich, Germany
N1  - This version is the version of record. For information on re-use, please refer to the publisher?s terms and conditions.
ER  -