Phase and morphology analysis of cerebral blood flow signals for intracranial pressure characterization

Abstract

Intracranial pressure (ICP) is an important health indicator for managing patients in neurocritical care. However, since current ICP monitoring methods are invasive, it is only monitored in critical cases. Consequently, there is a need to develop noninvasive monitoring techniques that can broaden the scope of ICP management. This study aims to analyze Cerebral Blood Flow (CBF) signals, which have been shown to undergo morphological changes in response to ICP variations, and identify novel features that hold significance for ICP estimation. We analyzed the morphology and phase evolution characteristics of CBF signals from 20 patients. From these analyses, a total of 6 features were extracted. Linear correlations between these characteristics and ICP were evaluated, and strong correlations of up to 0.9 were found for both analyses. To assess the clinical potential of these features, we conducted a preliminary evaluation of their ability to diagnose high ICP using machine learning. A random forest classifier was trained, and it achieved moderate results. This bachelor thesis highlights the utility of using phase and morphology characteristics of CBF signals as a non-invasive method for ICP estimation, providing a foundation for further studies and potential clinical applications.