Intracranial pressure (ICP) is affected in many neurological conditions. Clinical measurement of pressure on the brain currently requires placing a probe in the cerebrospinal fluid compartment, the brain tissue, or other intracranial space. This invasiveness limits the measurement to critically ill patients. As ICP is also clinically important in conditions ranging from brain tumors and hydrocephalus to concussions, noninvasive determination of ICP would be desirable. Our model-based approach to continuous estimation and tracking of ICP uses routinely obtainable time-synchronized, noninvasive (or minimally invasive) measurements of peripheral arterial blood pressure and blood flow velocity in the middle cerebral artery (MCA), both at intra-heartbeat resolution. A physiological model of cerebrovascular dynamics provides mathematical constraints that relate the measured waveforms to ICP. Our algorithm produced patient-specific ICP estimates with no calibration or training. Using 35 hours of data from 37 patients with traumatic brain injury, we generated ICP estimates on 2,665 non-overlapping 60-beat data windows. Referenced against concurrently recorded invasive parenchymal ICP that varied over 100 mmHg across all records, our estimates achieved a mean error (bias) of 1.6 mmHg and standard deviation of error (SDE) of 7.6 mmHg. For the 1,673 data windows over 22 hours in which blood flow velocity recordings were available from both the left and right MCA, averaging the resulting bilateral ICP estimates reduced the bias to 1.5 mmHg and SDE to 5.9 mmHg. This accuracy is already comparable to that of some invasive ICP measurement methods in current clinical use.

We present a review of the diversity of concepts and motivations for improving the concentration and resolution of timefrequency distributions (TFDs) along the individual components of the multi-component signals. The central idea has been to obtain a distribution that represents the signal's energy concentration simultaneously in time and frequency without blur and crosscomponents so that closely spaced components can be easily distinguished. The objective is the precise description of spectral content of a signal with respect to time, so that first, necessary mathematical and physical principles may be developed, and second, accurate understanding of a time-varying spectrum may become possible. The fundamentals in this area of research have been found developing steadily, with significant advances in the recent past.

BACKGROUND:Traditional patient monitoring may not detect cerebral tissue hypoxia, and typical interventions may not improve tissue oxygenation. Therefore, monitoring cerebral tissue oxygen status with regional oximetry is being increasingly used by anesthesiologists and perfusionists during surgery. In this study, we evaluated absolute and trend accuracy of a new regional oximetry technology in healthy volunteers.METHODS:A near-infrared spectroscopy sensor connected to a regional oximetry system (O3TM, Masimo, Irvine, CA) was placed on the subject’s forehead, to provide continuous measurement of regional oxygen saturation (rSo2). Reference blood samples were taken from the radial artery and internal jugular bulb vein, at baseline and after a series of increasingly hypoxic states induced by altering the inspired oxygen concentration while maintaining normocapnic arterial carbon dioxide pressure (Paco2). Absolute and trend accuracy of the regional oximetry system was determined by comparing rSo2 against reference cerebral oxygen saturation (Savo2), that is calculated by combining arterial and venous saturations of oxygen in the blood samples.RESULTS:Twenty-seven subjects were enrolled. Bias (test method mean error), standard deviation of error, standard error of the mean, and root mean square accuracy (ARMS) of rSo2 compared to Savo2 were 0.4%, 4.0%, 0.3%, and 4.0%, respectively. The limits of agreement were 8.4% (95% confidence interval, 7.6%–9.3%) to −7.6% (95% confidence interval, −8.4% to −6.7%). Trend accuracy analysis yielded a relative mean error of 0%, with a standard deviation of 2.1%, a standard error of 0.1%, and an ARMS of 2.1%. Multiple regression analysis showed that age and skin color did not affect the bias (all P > 0.1).CONCLUSIONS:Masimo O3 regional oximetry provided absolute root-mean-squared error of 4% and relative root-mean-squared error of 2.1% in healthy volunteers undergoing controlled hypoxia.

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