Intracranial pressure (ICP) measurements help monitor patient status following cerebral injury, and currently require implantation of an invasive pressure probe. The potential complications associated with this implantation have restricted the application of ICP measurements in less severe conditions. We propose a non-invasive alternative that derives features from the cardiac waveforms present in near-infrared spectroscopy (NIRS) measurements and inputs these features into a decision tree regressor to estimate ICP. We evaluated this method in nine subjects already fitted with invasive ICP sensors. The non-invasive nature of NIRS instrumentation eases the clinical adoption of this ICP estimation approach.
Current standard-of-care methods for measuring intracranial pressure (ICP) are highly invasive. To overcome this limitation, we recently demonstrated non-invasive quantification of ICP in an animal model using morphological analysis of the pulsatile cerebral blood flow (CBF) measured with Diffuse Correlation Spectroscopy. Here, we present results from a pilot study in pediatric patients admitted to an intensive care unit. We show that the CBF pulsatile waveform changes with ICP. Using a regression forest-based machine learning algorithm on a cohort of patients (n>15) we demonstrate that ICP extraction in humans can be possible, suggesting the potential for successful clinical translation in future.
We have previously developed a non-invasive intracranial pressure sensor based on cerebral blood flow cardiac pulse shape changes. Here, we present steps towards clinical translation in pediatric patients in critical care.
Intracranial pressure (ICP) is an important metric in the management of severe head injury. We show alternatives to today’s standard of highly invasive measurement devices using near-infrared spectroscopy and diffuse correlation spectroscopy to create a real-time ICP monitor. The algorithms were developed and tested in an animal model. First results of a clinical validation will be presented.
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