Classification of brain injury severity using a hybrid broadband NIRS and DCS instrument with a machine learning approach

Danai Bili; Frédéric Lange; Kelly Harvey Jones; Veronika Parfentyeva; Turgut Durduran; Nikki Robertson; Subhabrata Mitra; Ilias Tachtsidis

doi:10.1117/12.2670657

9 August 2023 Classification of brain injury severity using a hybrid broadband NIRS and DCS instrument with a machine learning approach

Danai Bili, Frédéric Lange, Kelly Harvey Jones, Veronika Parfentyeva, Turgut Durduran, Nikki Robertson, Subhabrata Mitra, Ilias Tachtsidis

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Proceedings Volume 12628, Diffuse Optical Spectroscopy and Imaging IX; 126280D (2023) https://doi.org/10.1117/12.2670657
Event: European Conferences on Biomedical Optics, 2023, Munich, Germany

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.

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Danai Bili, Frédéric Lange, Kelly Harvey Jones, Veronika Parfentyeva, Turgut Durduran, Nikki Robertson, Subhabrata Mitra, and Ilias Tachtsidis "Classification of brain injury severity using a hybrid broadband NIRS and DCS instrument with a machine learning approach", Proc. SPIE 12628, Diffuse Optical Spectroscopy and Imaging IX, 126280D (9 August 2023); https://doi.org/10.1117/12.2670657

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KEYWORDS

Machine learning

Feature extraction

Near infrared spectroscopy

Traumatic brain injury

Equipment

Injuries

Brain

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