A Spectral Approach to Model-Based Noninvasive Intracranial Pressure Estimation

Abstract

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.

Fig. 1:

Schematic representation of the second-order circuit model [26].

Fig. 2:

Representative example of the relationship between mean-subtracted ABP and mean-subtracted ICP over the duration of a 60-beat estimation window. There are two clear phases: a systolic upstroke (blue line) and a diastolic decay (black cubic polynomial). The error bars represent the standard deviation of error at each mean-subtracted ABP sample.

Fig. 3:

Example of power spectra of mean-subtracted ABP and CBFV waveforms as computed by the average periodogram method. The first four harmonic peaks are identified in each spectrum.

Fig. 4:

Bland-Altman plot comparing estimated and mean measured ICP on a window-by-window basis in the pediatric and adult datasets.

Fig. 5:

Cumulative distribution functions for the nICP RMSE on both datasets together. The analysis is carried out on a window-by-window, study-by-study, and patient-by-patient basis.

Fig. 6:

RMSE between nICP estimates and mean measured ICP as a function of timing offset between ABP and CBFV waveforms. The zero offset relates to the beat-onset alignment described by Fanelli and co-workers [28].

Fig. 7:

Comparison between the algorithmic reconstruction of the ICP waveform from ABP and the clinically obtained gold-standard ICP measurements.

Fig. 8:

Distribution of ICP pulse pressure estimation errors.