System and method for automatic detection and indication of airway instability

• US 6,748,252 B2
• Filed: 04/24/2002
• Issued: 06/08/2004
• Est. Priority Date: 08/19/1992
• Status: Expired due to Term

• Current Assignee: Lawrence A. Lynn, Rodger Dzwonczyk
• Original Assignee: Lawrence A. Lynn, Rodger Dzwonczyk
• Inventors: Lynn, Lawrence A., Dzwonczyk, Rodger
• Primary Examiner(s): Winakur, Eric F.
• Assistant Examiner(s): KREMER, MATTHEW J
• Application Number:
  US10/132,535
• Publication Number:
  US 20020173707A1
• Time in Patent Office: 776 Days
• Field of Search: 600/323, 600/324, 600/309, 600/310, 600/322, 600/484, 600/483, 600/529, 600/534, 600/538
• US Class Current: 600/323
• CPC Class Codes: A61B 17/155 Cutting femur
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/4818 Sleep apnoea
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• CPC Class Codes: A61B 7/003 Detecting lung or respiration noise

Abstract

A system for automatically detecting and indicating the presence of clinically significant airway instability comprises a patient monitor such as a pulse oximeter for generating a time series of oxygen saturation values and a processor programmed to convert the time series to at least one set of frequency components to detect the occurrence of clustered variations indicative of instability of the airway by identifying an indicative pattern of said frequency components. The system is further programmed to provide an automatic output in response to the detection of said occurrence.

Claims

1. A patient monitoring system for automatic detection of ventilation instability comprising:

a. a pulse oximeter for repeatedly determining arterial oxygen saturation values to generate a time series output of said oxygen saturation values,

b. a processor programmed to;

convert said time series to its frequency components, said frequency components defining a power spectrum, said spectrum having a peak power and a bandwidth,

detect the occurrence of ventilation instability by identifying a relationship between said peak power and said bandwidth indicative of the presence of a cluster of variations in said values,

and to provide an output in response to the detection of said occurrence.

2. The system of claim 1 wherein said cluster comprises a grouping of closely spaced desaturations.

3. The system of claim 1 wherein said output is at least a visual output.

4. The system of claim 3 wherein said output is at least a textual output.

5. The system of claim 1 wherein said cluster comprises a grouping of closely spaced desaturations coupled with intervening resaturations.

6. The system of claim 1 wherein the processor is further programmed to calculate the a mean square value of said time series and to use said value to automatically identify ventilation instability.

7. A patient monitoring system for automatically detecting airway instability comprising:

a. a pulse oximeter for repeatedly determining arterial oxygen saturation values to generate a time series output of said oxygen saturation values,

b. a processor programmed to;

convert said time series to its frequency components to derive a power spectrum, said power spectrum having a peak power component and a bandwidth,

detect the occurrence of airway instability by identifying a relationship between said peak power and said bandwidth indicative of the presence of a cluster pattern of variations of said oxygen saturation values along said time series, and

output in response to said detection of said occurrence.

8. The system of claim 7 wherein said pattern comprises at least one grouping of closely spaced declines in said values.

9. The system of claim 7 wherein said output is comprised of at least a visual output.

10. The system of claim 7 wherein said output is comprised of at least a textual output.

11. The system of claim 7 wherein said processor is further programmed to calculate a quotient of said peak power and the bandwidth.

12. The system of claim 7 wherein said processor is further programmed to calculate a mathematical relationship between the peak power and the root mean square value and the result of said mathematical relationship is divided by the signal bandwidth.

13. The system of claim 12 wherein said mathematical relationship between the peak power and the root mean square value is the product of said peak power and said root mean square value.

14. A processing method for detecting at least one occurrence of airway instability of a patient using a pulse oximeter and a processor and to provide an automatic indication of said airway instability, said pulse oximeter connected with said patient, the method including processing steps of:

a. repetitively determine arterial oxygen saturation values of said patient to generate a time series output of said oxygen saturation values,

b. convert said time series to its frequency components and transforming at least a portion of said time series into a corresponding power spectrum,

c. determining the peak power of said power spectrum,

d. determining the bandwidth of said power spectrum,

e. detecting the occurrence of airway instability by identifying a relationship of said peak power and said bandwidth indicative of the presence of a cluster of variations in said values, and

e. providing an automatic output in response to said detecting.

15. The method of claim 14 wherein said output is at least a visual output.

16. The method of claim 14 wherein said output is at least a textual output.

17. The method of claim 14 wherein said cluster comprises a grouping of closely spaced desaturations coupled with intervening resaturations.

18. The method of claim 14 wherein said cluster defines a plurality of declines in oxygen saturation, said declines having and at least one interval between said declines, the method including the step of determining said at least one interval.