SYSTEM AND METHOD FOR IDENTIFYING AUTOREGULATION ZONES

• US 20160324425A1
• Filed: 05/04/2016
• Published: 11/10/2016
• Est. Priority Date: 05/08/2015
• Status: Active Grant

• Current Assignee: Covidien LP (Medtronic Plc)
• Original Assignee: Covidien LP (Medtronic Plc)
• Inventors: Montgomery, Dean, Addison, Paul S.
• Granted Patent: US 10,219,705 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 5/02028 Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction
• 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 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/14553 specially adapted for cerebral tissue
• CPC Class Codes: A61B 5/4064 Evaluating the brain A61B5/031, A61B5/369, A61B5/14553 take precedence
• CPC Class Codes: A61B 5/6801 specially adapted to be attached to or worn on the body surface
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

A method for monitoring autoregulation includes using a processor for receiving one or more physiological signals, determining a correlation-based measure indicative of the patient'"'"'s autoregulation based on the one or more physiological signals, calculating a data clustering metric indicative of a distribution of the correlation-based measure within a window of blood pressures, and determining whether the window of blood pressures is within an intact autoregulation zone or an impaired autoregulation zone based at least in part on the data clustering metric.

Claims

1. A method for monitoring autoregulation comprising:

using a processor for;

receiving one or more physiological signals from a patient;

determining a correlation-based measure indicative of the patient'"'"'s autoregulation based at least in part on the one or more physiological signals;

calculating a data clustering metric indicative of a distribution of the correlation-based measure within a window of blood pressures; and

determining whether the window of blood pressures is within an intact autoregulation zone or an impaired autoregulation zone based at least in part on the data clustering metric.

2. The method of claim 1, wherein the data clustering metric comprises an entropy metric.

3. The method of claim 1, wherein the data clustering metric comprises a Mean Absolute Derivation.

4. The method of claim 1, wherein the data clustering metric comprises a sum of squares.

5. The method of claim 1, wherein the data clustering metric comprises at least one of a range, an interquartile range, and a variance.

6. The method of claim 1, comprising comparing the data clustering metric to a predetermined threshold.

7. The method of claim 6, comprising determining that blood pressures within the window of blood pressures are within the intact autoregulation zone if the data clustering metric is greater than the predetermined threshold.

8. The method of claim 1, comprising determining a quality level related to the determination of whether the window of blood pressures is within the intact autoregulation zone or the impaired autoregulation zone based at least in part on a number of data points of the correlation-based measure utilized to calculate the data clustering metric for the window of blood pressures.

9. The method of claim 1, comprising determining which autoregulation zone encompasses the window of blood pressures in less than approximately 15 minutes.

10. The method of claim 1, wherein the correlation-based measure is one of a cerebral oximetry index, a hemoglobin volume index, a mean velocity index, and a pressure reactivity index.

11. The method of claim 1, comprising providing an output indicative of the patient'"'"'s autoregulation status to an output device.

12. A non-transitory computer-readable medium having computer executable code stored thereon, the code comprising instructions to:

receive a blood pressure signal and a regional oxygen saturation signal;

determine a cerebral oximetry index (COx) based on the blood pressure signal and the regional oxygen saturation signal;

calculate a data clustering metric indicative of a distribution of the COx within a window of blood pressures; and

determine an autoregulation status of a patient based at least in part on the data clustering metric.

13. The non-transitory computer-readable medium of claim 12, wherein the data clustering metric comprises at least one of an entropy metric, a range metric, a Mean Absolute Derivation metric, a sum of squares, an interquartile range, and a variance.

14. The non-transitory computer-readable medium of claim 12, wherein the memory stores instructions for comparing the data clustering metric to a predetermined threshold.

15. The non-transitory computer-readable medium of claim 14, wherein the memory stores instructions for determining that the patient'"'"'s autoregulation status is intact if the data clustering metric is greater than the predetermined threshold.

16. The non-transitory computer-readable medium of claim 12, wherein the memory stores instructions for providing an indication of the patient'"'"'s autoregulation status to an output device.

17. A system for monitoring autoregulation comprising:

a regional oxygen saturation sensor configured to obtain a regional oxygen saturation signal from a patient; and

a controller comprising a processor configured to;

receive a blood pressure signal and the regional oxygen saturation signal;

determine a cerebral oximetry index (COx) based on the blood pressure signal and the regional oxygen saturation signal;

calculate a data clustering metric indicative of a distribution of the COx within a window of blood pressures; and

identify blood pressures associated with an intact autoregulation zone based at least in part on the data clustering metric.

18. The system of claim 17, wherein the processor is configured to identify blood pressures associated with an impaired autoregulation zone based at least in part on the data clustering metric.

19. The system of claim 17, wherein the data clustering metric comprises at least one of an entropy metric, a range metric, a Mean Absolute Derivation metric, a sum of squares, an interquartile range, and a variance.

20. The system of claim 17, wherein the processor is configured to provide an indication of the patient'"'"'s autoregulation status on a display.