Background: Intracranial pressure (ICP) normally ranges from 5 to 15 mmHg. Elevation in ICP is an important clinical indicator of neurological injury, and ICP is therefore monitored routinely in several neurological conditions to guide diagnosis and treatment decisions. Current measurement modalities for ICP monitoring are highly invasive, largely limiting the measurement to critically ill patients. An accurate noninvasive method to estimate ICP would dramatically expand the pool of patients that could benefit from this cranial vital sign. Methods: This article presents a spectral approach to model-based ICP estimation from arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV) measurements. The model captures the relationship between the ABP, CBFV, and ICP waveforms and utilizes a second-order model of the cerebral vasculature to estimate ICP. Results: The estimation approach was validated on two separate clinical datasets, one recorded from thirteen pediatric patients with a total duration of around seven hours, and the other recorded from five adult patients, one hour and 48 minutes in total duration. The algorithm was shown to have an accuracy (mean error) of 0.4 mmHg and −1.5 mmHg, and a precision (standard deviation of the error) of 5.1 mmHg and 4.3 mmHg, in estimating mean ICP (range of 1.3 mmHg to 24.8 mmHg) on the pediatric and adult data, respectively. These results are comparable to previous results and within the clinically relevant range. Additionally, the accuracy and precision in estimating the pulse pressure of ICP on a beat-by-beat basis were found to be 1.3 mmHg and 2.9 mmHg respectively. Conclusion: These contributions take a step towards realizing the goal of implementing a real-time noninvasive ICP estimation modality in a clinical setting, to enable accurate clinical-decision making while overcoming the drawbacks of the invasive ICP modalities.

The waveform acquisition system allows for robust real-time data acquisition, processing, and archiving of waveforms. The temporal drift between waveforms archived from different devices is entirely negligible, even for long-term recording.

Monitoring of intracranial pressure (ICP) is indicated in patients with a variety of conditions affecting the brain and cerebrospinal fluid space. The measurement of ICP, however, is highly invasive as it requires placement of a catheter in the brain tissue or cerebral ventricular spaces. Several noninvasive techniques have been proposed to overcome this issue, and one class of approaches is based on analyzing cerebral blood flow velocity (CBFV) and arterial blood pressure (ABP) waveforms to infer ICP. Here, we analyze a physiologic model linking ICP to CBFV and ABP and present a regression-based approach to estimating ICP. We tested the model on 20 datasets recorded from three patients in intensive care. Our estimates achieve a mean error (bias) of -1.12 mmHg and a standard deviation of the error of 5.56 mmHg, for a root-mean-square error of 5.68 mmHg, when compared against the invasive ICP measurement. Since transcranial Doppler ultrasound based CBFV measurements depend on the Doppler angle φ between the direction of the ultrasound beam and the (main) direction of blood flow velocity, we investigated the robustness of our ICP estimates against variations in φ. Our results show a change in the estimated ICP that is <;1 mmHg if we assume φ ~ N(μ; σ), with μ = 0 and σ = 10°.

Intracranial pressure (ICP) is a cranial vital sign, crucial in the monitoring and treatment of several neurological injuries. The clinically accepted measurement modalities of ICP are highly invasive, carrying risks of infection and limiting the benefits of ICP measurement to a small subset of critically ill patients. This work aims to take a step towards developing an accurate noninvasive means of estimating ICP, by utilizing a model-based frequency-domain approach. The mean ICP and pulse pressures of ICP are estimated from arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV) waveforms, and the estimates are validated on an adult population, comprising of around two hours of data from five patients. The algorithm was shown to have an accuracy (mean error) of −1.5 mmHg and a precision (standard deviation of the error) of 4.3 mmHg in estimating the mean ICP. These results are comparable to the previously reported errors among the currently accepted invasive measurement methods, and well within the clinically relevant range.