Real-Time Noninvasive Intracranial State Estimation Using Unscented Kalman Filter

Area of Science:

• Biomedical Engineering
• Neuroscience
• Medical Monitoring

Background:

• Intracranial pressure (ICP) monitoring is crucial for managing acute brain injury.
• Current invasive methods carry risks of infection and hemorrhage.
• Non-invasive ICP monitoring is highly desirable for patient safety and clinical decision-making.

Purpose of the Study:

• To develop and validate a novel algorithm for real-time, non-invasive intracranial pressure estimation.
• To utilize transcranial Doppler ultrasound-derived cerebral blood flow velocity and arterial blood pressure for ICP monitoring.
• To assess the performance of the proposed algorithm against invasive ICP measurements.

Main Methods:

• Development of the direct-current (DC)-ICP algorithm for real-time ICP estimation.
• Integration of cerebral blood flow velocity ([Formula: see text]) and arterial blood pressure (ABP) data.
• Validation study involving 10 acute-brain-injury patients.
• Comparison of DC-ICP estimates with invasive ICP measurements.

Main Results:

• The DC-ICP algorithm demonstrated a significant inter-subject correlation coefficient of 0.75 between estimated and true ICP.
• Receiver Operating Characteristic (ROC) analysis showed Area Under the Curve (AUC) values of 0.83 for predicting increased ICP.
• The study highlights the potential of [Formula: see text] monitoring for reliable ICP tracking.

Conclusions:

• The novel DC-ICP algorithm offers a promising approach for non-invasive, real-time ICP monitoring.
• This technique can provide valuable clinical information, potentially improving patient management.
• Cerebral blood flow velocity and ABP measurements are feasible inputs for accurate ICP estimation.