WO2009086455A1

WO2009086455A1 - Airway instability index calculation system and method

Info

Publication number: WO2009086455A1
Application number: PCT/US2008/088309
Authority: WO
Inventors: Hui Wang; Scott Amundson; Li Li; Yakov A. Blyakhmar; Robin S. Boyce; Steve Vargas; Tonia N. Madere; James Ochs
Original assignee: Nellcor Puritan Bennett Llc
Priority date: 2007-12-28
Filing date: 2008-12-24
Publication date: 2009-07-09

Abstract

An embodiment of the present disclosure provides a method of plotting an airway instability index (AII) of a patient. The method comprises plotting a plurality of oxygen saturation SpO2 data points acquired from a patient over time, and defining a window comprising a time frame containing a first subset of the plurality of SpO2 data points. The method further comprises deriving a first airway instability index (AII) data point based on the first subset, wherein the first AII data point is obtained for a single point in time included within the first time frame, and shifting the window to include a second subset of the plurality of SpO2 data points, wherein the second subset is used to obtain a second AII data point included in the time frame.

Description

AIRWAY INSTABILITY INDEX CALCULATION SYSTEM AND METHOD

BACKGROUND

[0001] The present disclosure relates generally to monitoring physiological parameters of a patient, more particularly, to methods and systems for calculating an airway instability index of the patient.

[0002] This section is intended to introduce the reader to various aspects of art that may be related to various aspects of the present disclosure, which are described and/or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements are to be read in this light, and not as admissions of prior art,

[0003] In the field of medicine, doctors often desire to monitor certain physiological characteristics of their patients. Accordingly, a wide variety of devices have been developed for monitoring physiological characteristics of a patient. Such devices provide doctors and other healthcare personnel with the information they need to provide effective healthcare for their patients. As a result, such monitoring devices have become an important part of modern medicine. [0004] There are many instances when patients are monitored for physiological parameters derived from measurements obtainable by various physiological sensors and monitors. For example, a physiological parameter known as airway instability may be derived from oxygen saturation (SpO₂) measurements obtained from a patient using a pulse oximeter. In such instances, the SpO₂ levels of the patient may be continuously monitored for determining whether breathing airways of the patient are destabilizing, possibly indicating the patient may be suffering or experiencing a medical condition known as sleep apnea, for example. Currently, airway instability indices may be derived from historical SpO₂ data obtained only after the patient has been monitored for prolonged periods of time which, in some instances, may include periods extending one or more days. In other words, the data derived over such long periods is aggregated to produce a single metric indicative of the airway instability for the entire duration during which the SpO₂ measurements were acquired. Accordingly, airway instability metrics derived in such a manner may not give a most up to date and reliable indication of the condition of the patient. Further, such methods may underestimate or overestimate the condition of the patient, thereby possibly misleading healthcare professionals about the condition of the patient.

SUMMARY

[0005] Certain aspects commensurate in scope with the disclosure are set forth below. It should be understood that these aspects are presented merely to provide the reader with a brief summary of certain embodiments and that these aspects are not intended to limit the scope of the disclosure. Indeed, the disclosure may encompass a variety of aspects that may not be set forth below. [0006] In an embodiment, there is provided a method of plotting an airway instability index (AH) of a patient. The method includes plotting a plurality of oxygen saturation SpO₂ data points derived from measurements acquired from a patient over time, and defining a window time frame containing a first subset of the plurality of SpO₂ data points. The method further includes deriving a first airway instability index (AH) data point based on the first subset, such that the first AIT data point is obtained for a single point in time included within the first time frame, and shifting the window time frame to include a second subset of the plurality Of SpO₂ data points, such that the second subset is used to obtain a second All data point included in the time frame. Such a method may be repeatedly applied so as to obtain real time AU plots having patterns providing clinicians most up to date assessment of the condition of the patient.

BRIEF DESCRIPTION

[0007] Advantages of the disclosure may become apparent upon reading the following detailed description and upon reference to the drawings in which:

[0008] Fig. 1 is a perspective view of a patient monitoring system coupled to a multi-parameter patient monitor and a sensor, in accordance with an embodiment;

[0009] Fig. 2; is a chart depicting of pulse oximetry data, and airway instability data in accordance with an embodiment;

[0010] Fig. 3; is another chart depicting graphs of pulse oximetry data, and airway instability data in accordance with an embodiment; and [0011] Fig, 4 is a flow chart of a method for obtaining an airway instability index of a patient in accordance with an embodiment.

DETAILED DESCRIPTION

[0012] One or more embodiments will be described below. In an effort to provide a concise description of these embodiments, not all features of an actual implementation are described in the specification. It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business- related constraints, which may vary from one implementation to another. Moreover, it should be appreciated that such a development effort might be complex and time consuming, but would nevertheless be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of this disclosure.

[0013] Referring now to Fig. 1, a sensor 10 according to the present disclosure may be used in conjunction with a patient monitor 12. In the depicted embodiment, a cable 14 connects the sensor 10 to the patient monitor 12. As will be appreciated by those of ordinary skill in the art, the sensor 10 and/or the cable 14 may include or incorporate one or more integrated circuit devices or electrical devices, such as a memory, processor chip, or resistor, that may facilitate or enhance communication between the sensor 10 and the patient monitor 12. [0014] In one embodiment, the patient monitor 12 may be a suitable pulse oximeter, such as those available from Nellcor Puritan Bennett LLC, Furthermore, the monitor 12 may be a muiti -purpose monitor suitable for performing pulse oximetry and measurement of tissue water fraction, or other combinations of physiological and/or biochemical monitoring processes, using data acquired via the sensor 10. Furthermore, to upgrade conventional monitoring functions provided by the monitor 12 to provide additional functions, the patient monitor 12 may be coupled to a multi-parameter patient computer 16 via a cable 18 connected to a sensor input port and/or via a cable 20 connected to a digital communication port.

[0015] As will be discussed further below, physiological data, such oxygen saturation (SpO₂) levels of the patient may be acquired and/or provided by the pulse oximetry sensor 10 to the monitor 12 and/or the computer 16 for deriving various physiological parameters, some of which are associated the instability of airways of a patient. Hence, in an embodiment,, raw data representative of the patient's oxygen saturation levels may be fed to a processor of the computer 16 for calculating the realtime airway instability indices of the patient. As described below, routines, algorithms and the like executed by the computer 16 are adapted to perform such functionalities by applying a moving or sliding window across a plurality of the acquired SpO₂ data points contained within a specific time frame. Accordingly, the SpO₂ data points contained within such a time frame are processed and analyzed for yielding airway instability data points in particular instances in time corresponding to those defined by the aforementioned sliding window. In this manner, the sliding window may be adapted to continuously move along the plurality of the acquired SpO₂ data points for calculating subsequent airway instability (All) index points based on subsequently acquired SpO₂ measurements. In an embodiment, this enables providing instantaneous real-time continuous airway instability patterns and/or additional related parameters for healthcare professionals and/or the patient. Such information can be used by the healthcare professionals to readily diagnose and, subsequently, treat the medical condition(s) of the patient.

[0016] In an embodiment, the SpO₂ and/or All data acquired by the sensor 10 and processed by the computer 16 may be displayed on a display monitor 17, adapted for illustrating the acquired oxygen saturation data and metrics associated with airway instability of the patient (e.g., All). The display 17 may further be adapted to display additional physiological parameters, trends and/or patterns such as those derived from the above SpO₂ data, as well as other parameters which may or may not be directly related to airway instability, but pertain to the general health (e.g., heart rate, blood pressure, etc.) of the patient. The aforementioned data may be depicted on the display 17 using graphs, charts, tables, and so forth to enable clinicians to readily evaluate the condition of the patient and provide treatment thereto accordingly. Further, in embodiments provided, the computer 16 may be adapted to generate plots depicting combined graphs of pulse oximetry and airway instability patterns for providing summarized illustration of essential and relevant physiological data. In one embodiment, such graphs may have various color legends corresponding to, for example, labeling and/or coloring of various regions of the above graphs. Such coloring may be used for indicating relative severity the airway instability so that healthcare providers can easily ascertain adverse indications warranting prompt notification and/or corresponding treatment. [0017] As mentioned above, the pulse oximetry measurements acquired from the patient may be facilitated by the sensor 10, which may employ optical and/or electro- optical components, such as an electro-optical emitter and/or an electro- optical detector of any suitable type. For example, the emitter of the sensor 10 may be one or more light emitting diodes adapted to transmit one or more wavelengths of light, such as in the red to infrared range, and the detector may be a photodetector, such as a silicon photodiode package, selected to receive light in the range emitted from the emitter. Further, in the depicted embodiment, the sensor 10 is coupled to a cable 14 that is responsible for transmitting electrical and/or optical signals to and from the emitter and/or the detector employed by the sensor 10. The cable 14 may be permanently coupled to the sensor 10. Alternatively, the cable 14 may be removably coupled to the sensor 10. Such a configuration is more useful and cost efficient in situations where the sensor 10 is disposable.

[0018] Those skilled in the art will appreciate that for pulse oximetry applications, either transmission or reflectance type sensors may be used for determining the oxygen saturation of the patient's arterial blood may be determined using two or more wavelengths of light, most commonly red and near infrared wavelengths. Similarly, in other applications a tissue water fraction (or other body fluid related metric) or a concentration of one or more biochemical components in an aqueous environment may be measured using two or more wavelengths of light, most commonly near infrared wavelengths between about 1,000 nm to about 2,500 nm. It should be understood that, as used herein, the term "light" may refer to one or more of infrared, visible, ultraviolet, or even X-ray electromagnetic radiation, and may also include any wavelength within the infrared, visible, ultraviolet, or X-ray spectra. [0019] Pulse oximetry and other spectrophotometric sensors, whether transmission-type or reflectance-type, are typically placed on a patient in a location conducive to measurement of the desired physiological parameters. For example, pulse oximetry sensors may typically placed on a patient in a location that is normally perfused with arterial blood to facilitate measurement of the desired blood characteristics, such as the oxygen saturation measurement (SpO₂), from which airway instability data can be derived,

[002Θ] Fig. 2 is a chart depicting graphs of pulse oximetry data and airway instability data in accordance with an embodiment. Particularly, Fig. 2 illustrates a chart 40 including real-time plots of oxygen saturation data from which the airway instability index (All) data is derived and simultaneously plotted on the chart 40. in an embodiment, the chart 40 includes a time (in seconds) axis 42 indicating elapsed monitoring time, a left-hand axis 44 denoting SpO₂ coordinates, and a right-hand axis 46 denoting All coordinates. Accordingly, the chart 40 further includes a graph 48 corresponding to acquired SpO₂ data, and a graph 50 corresponding to the All data derived from the plot 48. Thus, the graph 48 is a plot of the left-hand axis 44 versus the time axis 42, and the graph 50 is a plot of the right-hand axis 46 versus the time axis 42. As mentioned, the chart 40 illustrated by the Fig. 2 may be adapted to be displayed on the display 17 coupled to the computer 16 shown in Fig. 1.

[0021] As further illustrated, the graph 48 representing the SpO₂ data contains data points 52 representing levels of oxygen saturation measurements of the patient as measured by the pulse oximeter sensor 10 (Fig. 1) at various instances in time. The SpO₂ data points 52 may be acquired periodically such as, for example, every 1, 2, 3...etc., seconds. In an embodiment, the data points may be acquired aperiodically or they may be acquired in accordance with a preset pattern to which the sensor 10 is configured or programmed. As further illustrated, the graph 48 may include a sliding window 54 which defines a moving time frame or time segment encompassing a subset of a plurality of the SpO₂ data points from which single ATI data points forming the graph 50 are obtained. Hence, the time frame of the sliding window 54 has a time starting point 56 and a time ending point 58. The distance between the points 56 and 58 covers a time period labeled Tl, as illustrated by the Fig. 1.

[0022] Accordingly, in the illustrated embodiment, the sliding window 54 encompasses four (4) of the SpO₂ data points 52 which form a subset of four distinct time measurements of oxygen saturation levels acquired using the sensor 10 (Fig. 1). While in the present embodiment only four SpO₂ are illustrated as being encompassed by the window 54, it should be appreciated that the length of the time frame, i.e., Tl can be lengthened or shortened so that the sliding window 54 may include more or less SpO₂ data points, as desired or as is minimally necessary for deriving AU data points of the graph 50 as described below.

[0023] By further example, in one embodiment, the sliding window 54 may have a time frame extending for fifteen (15) minutes, thereby encompassing nine hundred (900) data points 52 representative of 900 SpO₂ measurements, In accordance with the illustrated embodiment, the SpO₂ data points 52 included between the starting and end points 56 and 58 of the sliding window 54 are combined and processed using algorithms by a processor, such as one provided by the computer 16 (Fig, 1), for yielding a single All data point 60 forming the graph 50. Further, as illustrated in the present embodiment, the All data point 60 derived from the SpO₂ data 52 is obtained to coincide with the time end point 58 of the sliding window 54. In other words, the present embodiment utilizes computational methods for processing of all of the SpO₂ data points 52 included within the sliding window 54 to represent a single AU data point at an instance in time corresponding to the end point 58 of the time frame encompassed by the window 54.

[0024] Those skilled in the art will appreciate that the above All data, i.e., data point 60, is derived using, for example, time series analyses of the raw SpO₂ data points 52. In an embodiment, such time series analysis may include representing the SpO₂ data points 52 as a series of dipole objects with their associated polarities and slopes, thereby removing spatial attributes of the points and highlighting relative changes in the SpO₂ levels. In another embodiment, various boundary types can be used to separate the above mentioned dipoles of the SpO₂ data into composite sequential objects, boundary types, pattern limits, inflection points, and polarity changes. In so doing, critical boundary points can be ascertained from which the wave pattern of the SpO₂ data can be segmented to derive further parameters and properties of the SpO₂ trend illustrated by the graph 48.

[0025] The aforementioned analysis may be combined with additional techniques including iterative relational processing of time series fragments or their derivatives along and between corresponding time series for yielding the All data points provided by the graph 50. Examples of additional techniques are describe in U.S. Patent Application No. 11/351,961 filed on September 29, 2006, entitled "System and Method for Automatic Detection of a Plurality of SpO₂ Time Series Pattern Types," the contents of which are incorporated by reference for all purposes as if fully set forth herein. These methods are adapted for analyzing and classifying variations in SpO₂ levels, such as those represented by the data points 52, which may be correlated to airway instability patterns given by the graph 50.

[0026] Thus, by employing the above computational methods together with the sliding window 54, the present technique may enable obtaining a real time ATI data of a patient whose oxygen saturation levels are continuously monitored. This may further be implemented by continuously or intermittently sliding window 54 such that the end point 58 always coincides with the last acquired SpO₂ measurement 52 (illustrated in the graph 48 as the right-most data point 52). In an embodiment, as illustrated by Fig. 2, after the All data point 60 is calculated, the sliding window 54 may shift to the right by an amount T2, during which additional SpO₂ data points 52 are acquired, one of which is the most recently acquired data point. Tn this manner, all the newly acquired SpO₂ data points 52 contained within the time frame Tl can be used to calculate a corresponding new All data point 62 of the graph 50, It should be appreciated that the time period T2 may be varied so as to vary the rate at which the sliding window moves across the graph 48. For example, in one embodiment, the time period T2 may be chosen so that the sliding window 54 slides at a rate equivalent to or less than a rate at which the SpO₂ data points 52 are acquired and/or plotted. Still in other embodiments, the time T2 may be chosen so that the sliding window moves every 3, 4, or 5, or n seconds etc., during which new SpO₂ data points are plotted to form the graph 48. Those skilled in the art will appreciate that the wider the sliding window 54 becomes, i.e., large Tl, the more SpO₂ data points are encompassed by the window 54 for calculating the All data points. Such an implementation may be statistically robust and more desirable to the extent it does not sacrifice the real timeliness of the airway instability index.

[0027] Fig. 3 is another chart depicting of pulse oximetry and airway instability graphs in accordance with an embodiment. Accordingly, Fig. 3 illustrates another method of applying a sliding window to a graph formed of pulse oximetry (e.g., SpO₂) data used for obtaining the airway instability index of a patient in real time. Similar to Fig 2, the chart 80 includes a graph 82 including SpO₂ data points representative of oxygen saturation measurements plotted overtime, and a graph 84 representative of All data points derived fi'om the graph 82 and plotted versus time as well. Thus, the graph 82 corresponds to SpO₂ data plotted on the axis 44 versus the time axis 42, and the graph 84 corresponds to All data plotted on the axis 46 versus the time axis 42. Similar to the graph 40 of Fig. 1, the chart 80 depicting the graphs 82 and 84 can be displayed by the display 17 (Fig. 1).

[0028] Further, in the illustrated embodiment, a sliding window 86 is applied to the graph 82, particularly to the data points 52 representing the SpO₂ measurements 52 acquired by the sensor 10 (Fig. 1). The sliding window 86 spans time duration T3 encompassing in time a certain amount of the SpO₂ data points 52 plotted on the graph 82. Again, the sliding window 86 may be adapted to enclose those SpO₂ data points 52 plotted across the time frame T3 for calculating a single All data point used for forming the graph 84. In the illustrated embodiment, the calculation of All data points is commenced after a full range of SpO₂ measurements are acquired. That is, the application of the window 86 to the data points 52 is performed only after all of the data 52 is acquired for a certain time period. In the illustrated embodiment, such a time period may be substantially longer than the time span T3 of the sliding window 86 and may extend through all or a portion of the monitoring period in which SpO₂ measurements are acquired. For example, the time span of such a monitoring period may be bounded by the axis points referenced by reference numerals 88 and 90 on the time axis 42. Such a time frame may correspond to segments of the monitoring period lasting a few seconds, minutes, hours, etc.

[0029] Accordingly, once all the data points 52 are acquired within the time period bounded by the points 88 and 90, the sliding window 86 begins to move across the graph 82 for computing sequential ATT data points and generating the All graph 84. In an embodiment, the window 86 may be applied such that All data points are calculated for points in time that coincide with those centered at the sliding window 86. The techniques and methods used for processing and calculating the ATI data points comprising the graph 84 follow those techniques described herein with reference to Fig. 2.

[0030] For example, in the present embodiment, ATI data point 92 is obtained via the movement of the sliding window 86 across those SpO₂ data points 52 encompassed by the sliding window. The full span of the All graph 84 may be generated using the SpO₂ data by the sliding window 86 rightward by a time increment T4, thereby encompassing additional and/or subsequent SpO₂ data points from which an All data point 94 is obtained using the above methods. While the time increment T4 may be chosen to be less than or equal to the time span T3 of the sliding window 86, some overlap between SpO₂ data points 52 used for calculating the All data points 92 and 94 can occur. Thus, unlike the sliding window 54 (Fig. 2) adapted to move together with the acquisition of the SpO₂ data 52, the sliding window 86 moves only after a full range OfSpO₂ data is acquired before the All of the patient is obtained. Such overlap is desirable as it may generally reduce statistical variations and data outputs. This enhances statistical analysis of the SpO₂ data 52, thereby providing An data that is may be more reliable.

[0031] As further illustrated by Figs, 2 and 3, the airway instability graphs 50 and 84 may each be segmented and/or partitioned into regions classifying levels of severity of the instability associated with the airways of the patient. Such segments are labeled in Figs. 2 and 3 by reference numerals 100, 102 and 104. In an embodiment, the graphs 50 and 84 may be displayed on the monitor 17 (Fig. 1), so that each of the regions 100-104 of the All graphs is colored by a different color, thus, indicating the level of severity of the airway instability, as deduced from the All. For example, the region 100 indicating a low level of severity of the airway instability may be colored green, while the region 102 indicating a medium level of severity may be colored yellow. Similarly, the region 104 indicating the highest level of severity in the airway instability may be colored red. In another embodiment, the graphs 50 and 84 may be displayed such that those regions indicating a sharp rise in the airway instability may flash on and off while, for example, sounding an audible alarm. Accordingly, when the graphs 50 and/or 84 may indicate that the airway instability of the patient has reached a severe or otherwise critical level, the red portion 104 of the graph 84 may flash while the other portions 100 and 102 may remain stationary. In so doing, a clinician may be alerted to the condition of the patient who may require medical attention. [0032] Fig. 4 is a flow chart of a method for obtaining an airway instability index of a patient in accordance with an embodiment. Accordingly, a method 130 is adapted to generate an All graph or a pattern of such data in real time using oxygen saturation data measurements acquired from a patient and plotted over time. Hence, the method 130 begins at block 132 from which it proceeds to block 134, At the block 134, a plurality of SpO₂ data points are plotted versus time, such as illustrated by the graphs 48 and 82 discussed above with reference to Figs. 2 and 3, respectively. As appreciated, such data points are representative of oxygen saturation levels of a patient acquired using a pulse oximetry sensor, such the sensor 10 discussed above in Fig. 1. Thereafter, the method 130 proceed to block 136 in which a window time frame is defined to contain a subset of the plurality OfSpO₂ data points obtained in a certain time period, such as those obtained and plotted at the block 134. Hence, the window time frame defined at the block 136 corresponds to the sliding windows 54 and/or 86 (Figs. 2 and 3) encompassing the subset of SpO₂ measurements in a particular time span, such as the one defined by the aforementioned siding windows.

[0033] Accordingly, from the block 136, the method 130 proceeds to the block

138 where the subset of the plurality OfSpO₂ data points contained within the window time frame are used for calculating a single airway instability data point at a particular instant in time, specifically one encompassed by the window time frame defined at the block 136. Thus, the subset of SpO₂ data points may be compiled and processed via algorithms implementing statistical and time series analysis, as discussed herein with reference to the Figs. 2 and 3, to produce data that corresponds to the airway instability, i.e., Au of the patient, Once the All data point is obtained, as performed at the block 138, the method 130 proceed to block 140 where the window time frame defined at the block 136 is shifted forward in time to incorporate a new subset of SpO₂ data points contained within the plurality of SpO₂ data points plotted at the block 134. By shifting the window time frame to incorporate a new subset OfSpO₂ data points plotted in a different time frame, a new All data point can be obtained for a new, i.e., subsequent point in time, one which is included in the new time flame to which the sliding window is shifted. Hence, by continuously repeating the steps of the method 130, as indicated by an arrow leading from the block 140 to the block 134, a complete plot of an All graph can be obtained for a time span in which the SpO₂ measurements are acquired fiOni a monitored patient. Furthermore, the method 130 enables obtaining that ATI data in real time, thereby enabling clinicians to readily and quickly assess the airway instability of the patient. Finally, the method 130 terminated at block 142.

[0034] While the disclosure may be suitable to various modifications and alternative forms, embodiments have been shown byway of example in the drawings and have been described in detail herein. However, it should be understood that the disclosure is not intended to be limited to the particular forms disclosed. Rather, the disclosure is intended to encompass all modifications, equivalents, and alternatives falling within the spirit and scope of this disclosure as defined by the following appended claims.

Claims

CLAIMSWhat is claimed is:

1. A method of plotting an airway instability index (All) of a patient, comprising: plotting a plurality of oxygen saturation SpO₂ data points acquired from the patient over time; defining a window comprising a time frame containing a first subset of the plurality OfSpO₂ data points; deriving a first airway instability index data point based on the first subset, wherein the first All data point is obtained for a single point in time included within the first time frame; and shitting the window to include a second subset of the plurality of SpO₂ data points, wherein the second subset is used to obtain a second AIE data point included in the time frame.

2. The method of claim 1, comprising acquiring SpO₂ measurements using a pulse oximeter for generating the plotted SpO₂ data points.

3. The method of claim 1, wherein the time frame of the window is less than a time during which the plurality of SpO₂ data points are acquired.

4. The method of claim 1 , wherein the first subset and the second subset contain SpO₂ data points which overlap.

5. The method of claim 1, wherein the SpO₂ data and the derived AU data are plotted versus time on a single chart.

6. The method of claim I₅ wherein the All data is derived in real time during the plotting of the plurality OfSpO₂ data points.

7. The method of claim 1, further comprising shifting the window by a rate generally equivalent to rate at which the plurality of SpO₂ data is plotted.

8. The method of claim 1, comprising shifting the window by an amount of time that is generally less than the time frame.

9. The method of claim I₅ comprising shifting the window by an amount of time that is generally equal to the time frame.

10. The method of claim 1, comprising generating a graph comprising a plurality All data in time.

11. The method of claim 10, wherein the All plot is segmented into regions corresponding to levels of severity of the All.

12. The method of claim 11, wherein each of the regions of severity are displayed as having different color from one another.

13. A system for obtaining an airway instability index (All) of a patient, comprising: a pulse oximetry sensor adapted to acquire a plurality of oxygen saturation measurements (SpO₂) from the patient; and a processor coupled to the pulse oximetry sensor, wherein the processor is adapted to plot the plurality of SpO₂ measurements as data points over time, define a window comprising a time frame containing a first subset of the plurality OfSpO₂ data points, derive a first airway instability index data point based on the first subset, wherein the first AlT data point is obtained for a single point in time included within the first time frame; and shift the window to include a second subset of the plurality of SpO₂ data points, wherein the second subset is used to obtain a second AU data point included in the time frame.

14. The system of claim 13, comprising a display adapted to display graphs of the SpO₂ data points and of the derived ATI data points on a single chart.

15. The system of claim 13, wherein the AU graph is segmented into regions corresponding to levels of severity of the All.

16. The method of claim 15, wherein each of the regions of severity are displayed as having a different color from one another.

17. The system of claim 15, wherein the time frame of the window is generally less than a time during which the plurality of SpO₂ data points are plotted.

18. The system of claim 1 , wherein the first subset and the second subset contain SpO₂ data points which overlap.

19. The system of claim 1 , wherein the AU data is derived in real time during the plotting of the plurality of SpO₂ data points.

20. The system of claim 1, comprising shifting the window by a rate generally equivalent to rate at which the plurality of SpO₂ data is plotted.

21. The system of claim 1, comprising shifting the window by an amount of time that is generally less than the time frame.

22. A tangible machine readable medium capable of storing instructions thereon, which if executed, may cause plotting an airway instability index (AlT) of a patient, comprising: plotting a plurality of oxygen saturation SpO₂ data points acquired from a patient over time; defining a window comprising a time frame containing a fust subset of the plurality of SpO₂ data points; deriving a first airway instability index data point based on the first subset, wherein the first AU data point is obtained for a single point in time included within the first time frame; and shifting the window to include a second subset of the plurality OfSpO₂ data points, wherein the second subset is used to obtain a second All data point included in the time firame.