Background: Monitoring of intracranial pressure (ICP) is highly important for detecting abnormal brain conditions such as intracranial hemorrhage, cerebral edema, or brain tumor. Until now, the monitoring of ICP requires an invasive method which has many disadvantages including the risk of infections, hemorrhage, or brain herniation. Therefore, many non-invasive methods have been proposed for estimating ICP. However, these methods are still insufficient to estimate sudden increases in ICP.
Methods: We proposed a simplified intracranial hemo- and hydro-dynamics model that consisted of two simple resistance circuits. From this proposed model, we designed an ICP estimation algorithm to trace ICP changes. First, we performed a simulation based on the original Ursino model with the real arterial blood pressure to investigate our proposed approach. We subsequently applied it to experimental data that were measured during the Valsalva maneuver (VM) and resting state, respectively.
Results: Simulation result revealed a small root mean square error (RMSE) between the estimated ICP by our approach and the reference ICP derived from the original Ursino model. Compared to the pulsatility index (PI) based approach and Kashif's model, our proposed method showed more statistically significant difference between VM and resting state.
Conclusion: Our proposed method successfully tracked sudden ICP increases. Therefore, our method may serve as a suitable tool for non-invasive ICP monitoring.