Statistical, Noninvasive Measurement of Intracranial Pressure

• US 20110201962A1
• Filed: 02/15/2011
• Published: 08/18/2011
• Est. Priority Date: 10/29/2008
• Status: Active Grant

• Current Assignee: The Board of Regents of the University of Colorado (University of Colorado System)
• Original Assignee: The Board of Regents of the University of Colorado (University of Colorado System)
• Inventors: Moulton, Steven Lee, Grudic, Gregory Zlatko
• Granted Patent: US 8,512,260 B2
• Time in Patent Office: Days
• US Class Current: 600/561
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/02108 from analysis of pulse wave characteristics
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/031 Intracranial pressure
• CPC Class Codes: A61B 5/0836 Measuring rate of CO2 production
• CPC Class Codes: A61B 5/14542 for measuring blood gases A61B5/14551 takes precedence
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/4261 Evaluating exocrine secretion production A61B3/101 takes precedence
• CPC Class Codes: A61B 5/4839 combined with drug delivery
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 8/488 involving Doppler signals
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 20/40 relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• CPC Class Codes: G16H 40/60 for the operation of medical equipment or devices
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

Tools and techniques for the rapid, continuous, invasive and/or noninvasive measurement, estimation, and/or prediction of a patient'"'"'s intracranial pressure. In an aspect, some tools and techniques can predict the onset of conditions such as herniation and/or can recommend (and, in some cases, administer) a therapeutic treatment for the patient'"'"'s condition. In another aspect, some techniques employ high speed software technology that enables active, long term learning from extremely large, continually changing datasets. In some cases, this technology utilizes feature extraction, state-of-the-art machine learning and/or statistical methods to autonomously build and apply relevant models in real-time.

Claims

1. A computational method of predicting intracranial pressure, the method comprising:

receiving, at a computer system, a set of input data from one or more physiological sensors, the input data pertaining to one or more physiological parameters of a patient;

analyzing, with the computer system, the input data against a pre-existing model to generate diagnostic data concerning the patient'"'"'s intracranial pressure; and

displaying, with a display device, at least a portion of the diagnostic data concerning the patient'"'"'s intracranial pressure.

2. The method of claim 1, wherein the diagnostic data comprises an estimate of a current intracranial pressure of the patient.

3. The method of claim 1, wherein the diagnostic data comprises an estimated intracranial pressure at which the patient will suffer uncal herniation

4. The method of claim 1, wherein the diagnostic data comprises one or more predictions of a future intracranial pressure of the patient.

5. The method of claim 1, wherein the method is noninvasive to the patient, and wherein the one or more physiological sensors are noninvasive sensors.

6. The method of claim 1, further comprising:

controlling, with the computer system, a therapeutic treatment of the patient'"'"'s intracranial pressure, based at least in part on the generated diagnostic data.

7. The method of claim 6, wherein controlling a therapeutic treatment comprises selecting one or more recommended treatment options, out of a plurality of treatment options.

8. The method of claim 7, wherein controlling a therapeutic treatment further comprises administering, with the computer system, the one or more recommended treatment options.

9. The method of claim 7, wherein the plurality of treatment options comprises a first treatment option comprising administration of a hyperosmolar solution and a second treatment option comprising administration of a diuretic agent.

10. The method of claim 1, further comprising:

repeating the operation of receiving a set of input data over a plurality of iterations, to produce a plurality of sets of input data, wherein analyzing the input data comprises analyzing the plurality of sets of input data to generate a plurality of sets of diagnostic data;

wherein displaying at least a portion of the diagnostic data comprises updating a display on the display device to display at least some of the plurality of sets of diagnostic data over the period of time.

11. The method of claim 10, wherein the plurality of sets of input data comprise periodic samples of a set of continuous, physiological waveform data.

12. The method of claim 11, wherein the set of continuous, physiological waveform data comprises blood pressure waveform data.

13. The method of claim 1, wherein the one or more physiological sensors comprise an electrocardiograph sensor.

14. The method of claim 1, wherein the one or more physiological sensors comprise an impedance cardiograph sensor.

15. The method of claim 1, wherein the one or more physiological sensors comprise a pulse oximeter.

16. The method of claim 1, wherein the one or more physiological sensors comprise a near infrared sensor.

17. The method of claim 1, wherein the one or more physiological sensors comprise a transcranial Doppler sensor.

18. The method of claim 1, further comprising:

generating the pre-existing model.

19. The method of claim 18, wherein generating the pre-existing model comprises:

receiving data pertaining to a plurality physiological parameters of a test subject to obtain a plurality of physiological data sets;

directly measuring the test subject'"'"'s intracranial pressure with a reference sensor to obtain a plurality of intracranial pressure measurements; and

correlating the received data with the measured intracranial pressure of the test subject.

20. The method of claim 19, wherein correlating the received data with the measured intracranial pressure of the test subject comprises:

identifying a most-predictive set of signals S_k out of a set of signals s₁, s₂, . . . , s_D for each of one or more outcomes o_k, wherein the most-predictive set of signals S_k corresponds to a first data set representing a first physiological parameter, and wherein each of the one or more outcomes o_k represents one of the plurality of intracranial pressure measurements;

autonomously learning a set of probabilistic predictive models ô

_k=M_k(S_k), where ô

_k is a prediction of outcome o_k derived from a model M_k that uses as inputs values obtained from the set of signals S_k;

repeating the operation of autonomously learning incrementally from data that contains examples of values of signals s₁, s₂, . . . , s_D and corresponding outcomes o₁, o₂, . . . , o_K.

21. The method of claim 20, wherein autonomously learning the set of probabilistic predictive models comprises using a linear model framework to identify predictive variables for each increment of data.

22. The method of claim 21, wherein the linear model framework is constructed with the form

23. The method of claim 1, further comprising:

updating the pre-existing model using the set of input data.

24. An apparatus, comprising:

a non-transitory computer readable medium having encoded thereon a set of instructions executable by one or more computers to perform one or more operations, the set of instructions comprising;

instructions for receiving a set of input data from one or more physiological sensors, the input pertaining to one or more physiological parameters of a patient;

instructions for analyzing the input data against a pre-existing model to generate diagnostic data concerning the patient'"'"'s intracranial pressure; and

instructions for displaying, with a display device, at least a portion of the diagnostic data concerning the patient'"'"'s intracranial pressure.

25. A system, comprising:

a computer system, comprising;

one or more processors; and

a computer readable medium in communication with the one or more processors, the computer readable medium having encoded thereon a set of instructions executable by the computer system to perform one or more operations, the set of instructions comprising;

instructions for receiving a set of input data from one or more physiological sensors, the input pertaining to one or more physiological parameters of a patient;

instructions for analyzing the input data against a pre-existing model to generate diagnostic data concerning the patient'"'"'s intracranial pressure; and

instructions for displaying, with a display device, at least a portion of the diagnostic data concerning the patient'"'"'s intracranial pressure.

26. The system of claim 25, further comprising the one or more physiological sensors.