A Data mining framework of noninvasive intracranial pressure assessment

Abstract

The only established technique for intracranial pressure (ICP) measurement is an invasive direct procedure that has been accepted into routine usage in neurosurgical services. However, there are many other scenarios where a noninvasive assessment of ICP is highly desirable. To make a full use of the vast amount of signals collected in a typical neurosurgical service environment to realize such a noninvasive procedure of ICP assessment, a general data mining framework is proposed in the present work. As a particular implementation of the proposed framework where continuous arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV) are available, we propose to simulate the unobserved ICP as the output of a model built from a database composed of arterial blood pressure, cerebral blood flow velocity and ICP. This model was discovered by exploring the database with the hemodynamic features extracted from measured ABP and CBFV. This approach was evaluated using a database composed of 30-min long measurements from nine traumatic brain injury patients. The approach achieved significant improvements of ICP simulation accuracy over several existing noninvasive ICP assessment methods. Particularly, its median normalized prediction error for ICP is 39% compared to 51% as obtained by an existing method and its median correlation coefficient between estimated and measured normalized ICP is 0.80 compared to 0.35 achieved by the existing method. The proposed framework is flexible in incorporating other relevant signals besides ABP, CBFV and ICP into the database. It allows for designing new hemodynamic feature vectors and for adopting new models for ICP estimation. Hence it is worthwhile to evaluate the method using a larger database and further develop the framework.