Patent Publication Number: US-2010113908-A1

Title: System And Method For Facilitating Observation Of Monitored Physiologic Data

Description:
RELATED APPLICATION 
     This application claims the benefit of U.S. Provisional Application No. 61/110,299 filed Oct. 31, 2008, which application is hereby incorporated herein by reference. 
    
    
     BACKGROUND OF THE INVENTION 
     1. Technical Field 
     The present disclosure relates generally to user-interface applications for patient monitoring devices. In particular, present embodiments relate to display features that facilitate observation of monitored physiological data with patient monitoring instruments. 
     2. Description of the Related Art 
     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. 
     Patient monitors include medical devices that facilitate measurement and observation of patient physiological data. For example, pulse oximeters are a type of patient monitor. A typical patient monitor cooperates with a sensor to detect and display a patient&#39;s vital signs (e.g., temperature, pulse rate, or respiratory rate) and/or other physiological measurements (e.g., water content of tissue, or blood oxygen level) for observation by a user (e.g., clinician). For example, pulse oximeters are generally utilized with related sensors to detect and monitor a patient&#39;s functional oxygen saturation of arterial hemoglobin (i.e., SpO 2 ) and pulse rate. Other types of patient monitors may be utilized to detect and monitor other physiological parameters. The use of patient monitors may improve patient care by facilitating supervision of a patient without continuous attendance by a human observer (e.g., a nurse or physician). 
     A patient monitor may include a screen that displays information relating to operation and use of the patient monitor. A typical patient monitor screen may display operational data that is instructive and that facilitates operation of the monitor by a user. For example, the operational data may include status indicators and instructional data relating to the monitor itself and/or monitor applications (e.g., a power indicator, an alarm silenced icon, and a battery low indicator). The screen may also display measurement data from a patient being monitored. For example, the measurement data may include information relating to a physiological feature of the patient being monitored. Specifically, the screen may display a graph or trend (e.g., a pulse rate trend and/or a plethysmographic waveform) of data relating to particular measured physiological parameters. Such trends include historical data that may span short or long periods of time in which particular parameters (e.g., SpO 2  and/or pulse rate) being trended were observed. This historical data can be beneficial for handling and detecting patient issues. However, analysis of this historical information can be inconvenient due to the quantity of the information. Further, such analysis can be difficult because certain aspects of the information are difficult for a user to detect. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Advantages of present embodiments may become apparent upon reading the following detailed description and upon reference to the drawings in which: 
         FIG. 1  is a perspective view of a patient monitor in accordance with an exemplary embodiment of the present disclosure; 
         FIG. 2  is a perspective view of the patient monitor in a system with separate devices in accordance with an exemplary embodiment of the present disclosure; 
         FIG. 3  is a representation of a display including a trend of physiological data with labeled components in accordance with an exemplary embodiment of the present disclosure; 
         FIG. 4  is a representation of a display including a trend of physiological data that exhibits a detected pattern in accordance with an exemplary embodiment of the present disclosure; 
         FIG. 5  is a block diagram of an electronic device in accordance with an exemplary embodiment of the present disclosure; 
         FIG. 6  is a graph of SpO 2  trend data with an upper band and lower band based on mean and standard deviation values in accordance with an exemplary embodiment of the present disclosure; 
         FIG. 7  is an exemplary graph including an SpO 2  trend that contains a ventilatory instability SpO 2  pattern and a trend of the resulting saturation pattern detection index in accordance with an exemplary embodiment of the present disclosure; 
         FIG. 8  is a representation of a display wherein portions of a trend are distinguished by different graphic features to designate a position in time in accordance with an exemplary embodiment of the present disclosure; 
         FIG. 9  is a representation of a display wherein detected patterns in a trend are highlighted in accordance with an exemplary embodiment of the present disclosure; 
         FIG. 10  is a display screen including various textual and graphical indicators to facilitate user review of areas of interest in historical trend data in accordance with an exemplary embodiment of the present disclosure; 
         FIG. 11  is a front view of a control panel in accordance with an exemplary embodiment of the present disclosure; 
         FIG. 12  is a front view of a control panel in accordance with an exemplary embodiment of the present disclosure; and 
         FIG. 13  is a front view of a control panel in accordance with an exemplary embodiment of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS 
     One or more specific embodiments of the present disclosure 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&#39; 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. 
     Embodiments of the present disclosure are directed to a user-interface feature for a patient monitoring device. Specifically, present embodiments include a display control feature that facilitates observation and analysis of historical trend data. The display control feature automatically finds and displays particular designated events in the historical data so that the events may be analyzed by a user. These events may include alarms, detected patterns (e.g., ventilatory instability or desaturation patterns), maximum values, minimum values, markers inserted automatically or by users, and so forth. For example, the display control feature may enable a user to automatically scroll, jump, or snap to a particular event by pressing a scroll button, turning a knob, or selecting an icon on a navigable menu. Thus, a user may utilize present embodiments to avoid the inefficiency of methodically scrolling through large amounts (e.g., hours) of trend data (e.g., a continuous chart of SpO 2  values) in search of patterns (e.g., a desaturation pattern) or other events (e.g., alarms). Indeed, in accordance with present embodiments, the user may simply utilize an activation mechanism (e.g., a control knob, button, or selectable menu) that coordinates with the display control feature to display events. For example, a control knob may be turned or a button may be pressed to display the last detected desaturation pattern in a trend of SpO 2  data. Further, additional turns of the knob or presses of the button may allow the user to cycle through all or a portion of the detected desaturation patterns and/or other events. 
     Additionally, present embodiments may facilitate observation of certain events (e.g., SpO 2  patterns) displayed on a monitor&#39;s user-interface by graphically drawing attention to areas of interest in trend data and by providing graphic indicators that relate to the status of certain features. For example, specific portions of a graphical representation of physiologic data may be highlighted or flashed to draw attention to a particular series of data points because the data points have been identified as corresponding to a particular pattern. As a specific example, a monitor in accordance with present embodiments may display a graphical trend of data values received from a sensor, wherein the data values correspond to physiologic data measurements from a patient. If a series of the data values is identified as corresponding to ventilatory instability, present embodiments may flash or highlight the portion of the graphical trend that has been identified as having a pattern associated with the ventilatory instability. Present embodiments may also facilitate identification of the time of occurrence of events in the monitoring history by placing a time scale along the trend graph of the data. For example, the time scale may include onset and offset times for the section of data that is being viewed and/or the portion of data that has been identified as corresponding to a particular physiologic pattern. 
     Further, present embodiments may include one or more graphic features that are actively representative of a status of pattern detection or a level (e.g., a percentage of an alarm level) of a detected occurrence. Such graphic features may provide an active representation of a gradual build up of indicators that correspond to identification of a particular pattern or that are indicative of a severity level of an identified condition. Indeed, present embodiments may utilize an accumulation of data indicators to identify a physiologic pattern or a severity level of a particular event, and the graphic feature may gradually change as observed indications accumulate. For example, in accordance with present embodiments, ventilatory instability may be detected when a fixed number of certain data features have been detected within a time period. Thus, a percentage value associated with ventilatory instability detection may be identified by dividing the number of detected data features by the fixed number utilized for identification of a ventilatory instability pattern, and the percentage may be represented in a dynamic graphic (e.g., a status bar). As a specific example, a graphic displayed as a triangle outline may gradually fill in the triangle outline from the bottom with coloring as certain indicators of a particular pattern accumulate. Thus, the triangle graphic may be completely filled in with color when the pattern is actually confirmed. Likewise, the triangle may empty of color when certain aspects are reduced. Similarly, a graphic may gradually fill or empty as certain severity thresholds or indexes of a particular event are reached. 
       FIG. 1  is a perspective view of a patient monitor  10  in accordance with an exemplary embodiment of the present disclosure. Specifically, the patient monitor  10  illustrated by  FIG. 1  is a pulse oximeter that is configured to detect and monitor blood oxygen saturation levels, pulse rate, and so forth. It should be noted that while the illustrated embodiment includes a pulse oximeter, other embodiments may include different types of patient monitors  10 . For example, the patient monitor  10  may be representative of a vital signs monitor, a critical care monitor, an obstetrical care monitor, or the like. 
     The illustrated patient monitor  10  includes a front panel  12  coupled to a body  14  of the monitor  10 . The front panel  12  includes a display screen  16  and various indicators  18  (e.g., indicator lights and display screen graphics) that facilitate operation of the monitor  10  and observation of a patient&#39;s physiological metrics (e.g., pulse rate). Some of the indicators  18  are specifically provided to facilitate monitoring of a patient&#39;s physiological parameters. For example, the indicators  18  may include representations of the most recently measured values for SpO 2 , pulse rate, index values, and pulse amplitude. Other indicators  18  may be specifically provided to facilitate operation of the monitor  10 . For example, the indicators  18  may include an A/C power indicator, a low battery indicator, an alarm silence indicator, a mode indicator, and so forth. The front panel  12  may also include a speaker  20  for emitting audible indications (e.g., alarms), a sensor port  22  for coupling with a sensor  24  (e.g., a temperature sensor, a pulse oximeter sensor) and other monitor features. 
     Additionally, the front panel  12  may include various activation mechanisms  26  (e.g., buttons and switches) to facilitate management and operation of the monitor  10 . For example, the front panel  12  may include function keys (e.g., keys with varying functions), a power switch, adjustment buttons, an alarm silence button, and so forth. It should be noted that in other embodiments, the indicators  18  and activation mechanisms  26  may be arranged on different parts of the monitor  10 . In other words, the indicators  18  and activation mechanisms  26  need not be located on the front panel  12 . Indeed, in some embodiments, activation mechanisms  26  are virtual representations in a display or actual components disposed on separate devices. 
     In some embodiments, as illustrated in  FIG. 2 , the monitor  10  may cooperate with separate devices, such as a separate screen  28 , a wireless remote  30 , and/or a keyboard  32 . These separate devices may include some of the indicators  18  and activation mechanisms  26  described above. For example, buttons  34  on the remote  30  and/or keyboard  32  may operate as activation mechanisms  26 . Specifically, for example, the buttons  34  may cause the monitor  10  to perform specific operations (e.g., power up, adjust a setting, silence an alarm) when actuated on the separate device. Similarly, the indicators  18  and/or activation mechanisms  26  may not be directly disposed on the monitor  10 . For example, the indicators  18  may include icons, indicator lights, or graphics on the separate screen  28  (e.g., a computer screen). Further, the activation mechanisms  26  may include programs or graphic features that can be selected and operated via a display. It should be noted that the separate screen  28  and/or the keyboard  32  may communicate directly or wirelessly with the monitor  10 . 
     As briefly set forth above, embodiments of the present disclosure include a display control feature that facilitates observation and analysis of historical data. This display control feature may include software or hardware, as well as an activation mechanism to operate the display control feature. For example,  FIGS. 1 and 2  include a knob  50  that may be utilized to operate the display control feature. The display control feature may facilitate a user&#39;s observation of certain events (e.g., metrics and indications) by eliminating or reducing the time and effort required for a user to find the events by scanning through the data (e.g., trend data). For example, the display control feature may enable a user to turn the knob  50  or to use some other activation mechanism to cause the view provided by the monitor  10  to automatically snap or jump to certain events. In other words, present embodiments may allow a user to snap or jump directly to screens displaying certain events (e.g., alarms, detected patterns, maximum values, minimum values) by activating the display control feature. Indeed, a user may select a particular type of event or particular types of events to jump to and/or skip over. In one embodiment, a user can turn the knob  50  to scroll through various options and then push the knob  50  to select a particular option (e.g., jump to the latest detected desaturation pattern) that causes the display to jump to certain events. In some embodiments, the knob  50  may be replaced by other activation mechanisms. For example, a user may activate the display control feature by pressing a button and/or maneuvering a roller ball. It should be noted that the data to which the monitor  10  snaps or jumps may be displayed by the monitor  10  on the display screen  16  and/or the separate screen  28 . Features related to identifying events and then jumping or snapping to the identified events will be discussed in further detail below. 
     In one embodiment, the monitor  10  may detect and label certain events that can later be readily accessed using the display control feature. Indeed, the events may be continuously detected and labeled by a detection feature of the monitor  10 . Additionally, a user may designate certain data points, time periods, and so forth as events. For example, a user may select certain data points for review by highlighting and manually labeling the data. Once such events have been identified, a user may jump or cycle to displays that illustrate the detected events by activating (e.g., depressing, or rotating) the activation mechanism (e.g., knob  50 ) of the display control feature. 
     In a specific example, as illustrated in the exemplary display  100  in  FIG. 3 , the monitor  10  may automatically label the moment at which an alarm  102  was initiated by designating the alarm  102  with a timestamp  104  and/or graphic indicator  106 , for example, at the corresponding location of the alarm  102  on a trend  108 . Deactivation of the alarm  102  may also be designated on the trend  108 . It should be noted that the alarm  102  may correspond to detected physiological data (e.g., high temperature or low saturation) or any other type of alarm condition (e.g., low battery or sensor off). A user may also manually designate an event, as illustrated by user designated event  112 . As with automatically detected events (e.g., alarm  102 ), such user designated events may also be automatically timestamped. 
     In some embodiments, the monitor  10  may detect patterns in data (e.g., physiological data) that correspond to certain conditions. For example, present embodiments may detect a cluster of desaturation data or a desaturation pattern that is indicative of ventilatory instability in the patient being monitored. In some embodiments, ventilatory instability may be defined as a significant cyclical reduction in airflow, as measured by a nasal airflow sensor, accompanied by a reduction in chest and/or abdomen wall movement. Such reductions in airflow may cause a patient&#39;s SpO 2  to cyclically rise and fall as the patient begins to desaturate due to lack of oxygen and then subsequently recover (i.e., re-saturate). Thus, such SpO 2  cycles may be indicative of ventilatory instability. One example of ventilatory instability is sleep apnea. 
     Upon detecting such patterns, the monitor  10  may label (e.g., timestamp, textually indicate, highlight, or flash) the graphical representation of the initial portion of the pattern and the end portion of the pattern. In other words, the monitor may  10  provide an indication of the pattern data from where the pattern begins to where it ends once the pattern has been determined to exist. For example, in one embodiment, a pattern portion of a trend may be displayed in reverse video (e.g., flashing or highlighted) or indicated with a particular color (e.g., highlighted or colored with red to indicate high relevance, yellow to indicate medium relevance, and green to indicate low relevance). In another embodiment, the pattern portion of the trend may be displayed with a line having a distinguishing thickness or color. Further, the monitor  10  may essentially diagnose the pattern by labeling it with specific text or other graphical features based on a database of correlations between labels and detected patterns. 
       FIG. 4  is a representation of a display  180  that includes a trend  182  of oxygen saturation over time. As illustrated in  FIG. 4 , the monitor  10  may detect a cluster or pattern  184  of desaturation data, which the monitor  10  may determine is likely indicative of sleep apnea or some other issue. The monitor  10  may then label the pattern  184  with a textual graphic  186  and a timestamp  188  indicating a beginning and end of the detected pattern  184 . Further, the monitor  10  may highlight or flash the pattern, as indicated by block  190 , or utilize some other graphical indicator. Such labeling and/or indication may facilitate rapid diagnosis of a patient by a clinician. For example, the clinician may use present embodiments to simply snap or jump to a display including the pattern  184  (e.g., indication of sleep apnea or ventilation instability) by activating the display control feature (e.g., pressing a button), and the graphic indicators may draw the users attention to facilitate diagnosis. 
     In order to graphically or textually indicate the patterns in SpO 2  trend data (e.g., saturation patterns indicative of ventilatory instability), as discussed above, the patterns must first be detected. Accordingly, present embodiments may include code stored on a tangible, computer-readable medium (e.g., a memory) and/or hardware capable of detecting the presence of a saturation pattern in a series of physiologic data. For example,  FIG. 5  is a block diagram of an electronic device or pattern detection feature in accordance with present embodiments. The electronic device is generally indicated by the reference number  200 . The electronic device  200  (e.g., an SpO 2  monitor and/or memory device) may comprise various subsystems represented as functional blocks in  FIG. 5 . Those of ordinary skill in the art will appreciate that the various functional blocks shown in  FIG. 5  may comprise hardware elements (e.g., circuitry), software elements (e.g., computer code stored on a hard drive) or a combination of both hardware and software elements. For example, each functional block may represent software code and/or hardware components that are configured to perform portions of an algorithm in accordance with present embodiments. Specifically, in the illustrated embodiment, the electronic device  200  includes a reciprocation detection (RD) feature  202 , a reciprocation qualification (RQ) feature  204 , a cluster determination (CD) feature  206 , a saturation pattern detection index (SPDi) calculation feature  208 , and a user notification (UN) feature  210 . Each of these components and the coordination of their functions will be discussed in further detail below. 
     The RD feature  202  may be capable of performing an algorithm for detecting reciprocations in a data trend. Specifically, the algorithm of the RD feature  202  may perform a statistical method to find potential reciprocation peaks and nadirs in a trend of SpO 2  data. A nadir may be defined as a minimum SpO 2  value in a reciprocation. The peaks may include a rise peak (e.g., a maximum SpO 2  value in a reciprocation that occurs after the nadir) and/or a fall peak (e.g., a maximum SpO 2  value in a reciprocation that occurs before the nadir). Once per second, the RD feature  202  may calculate a 12 second rolling mean and standard deviation of the SpO 2  trend. Further, based on these mean and standard deviation values, an upper band  220  and lower band  222  with respect to an SpO 2  trend  224 , as illustrated by the graph  226  in  FIG. 6 , may be calculated as follows: 
       Upper Band=mean+standard deviation; 
       Lower Band=mean−standard deviation. 
     Once the upper band  220  and lower band  222  have been determined, potential reciprocation peaks and nadirs may be extracted from the SpO 2  trend  224  using the upper band  220  and the lower band  224 . Indeed, a potential peak may be identified as the highest SpO 2  point in a trend segment which is entirely above the upper band  220 . Similarly, a potential nadir may be identified as the lowest SpO 2  point in a trend segment that is entirely below the lower band  222 . In other words, peaks identified by the RD feature  202  may be at least one standard deviation above the rolling mean, and nadirs identified by the RD feature  202  may be at least one standard deviation below the mean. If there is more than one minimum value below the lower band  222 , the last (or most recent) trend point may be identified as a nadir. If more than one maximum value is above the upper band  220 , the point identified as a peak may depend on where it is in relation to the nadir. For example, regarding potential peaks that occur prior to a nadir (e.g., fall peaks), the most recent maximum trend point may be used. In contrast, for peaks that occur subsequent to a nadir (e.g., rise peaks), the first maximum point may be used. In the example trend data represented in  FIG. 6 , a peak and nadir is detected approximately every 30-60 seconds. 
     In one embodiment, a window size for calculating the mean and standard deviation may be set based on historical values (e.g., average duration of a set number of previous reciprocations). For example, in one embodiment, a window size for calculating the mean and standard deviation may be set to the average duration of all qualified reciprocations in the last 6 minutes divided by 2. In another embodiment, a dynamic window method may be utilized wherein the window size may be initially set to 12 seconds and then increased as the length of qualified reciprocations increases. This may be done in anticipation of larger reciprocations because reciprocations that occur next to each other tend to be of similar shape and size. If the window remained at 12 seconds, it could potentially be too short for larger reciprocations and may prematurely detect peaks and nadirs. The following equation or calculation is representative of a window size determination, wherein the output of the filter is inclusively limited to 12-36 seconds, and the equation is executed each time a new reciprocation is qualified: 
     
       
         
           
               
               
             
               
                   
                   
               
             
            
               
                   
                 If no qualified reciprocations in the last 6 minutes: 
               
               
                   
                     Window Size = 12 (initial value) 
               
               
                   
                 else: 
               
               
                   
                     RecipDur = ½ * current qualified recip duration + 
               
               
                   
                     ½ * previousRecipDur 
               
               
                   
                     Window Size = bound(RecipDur,12,36). 
               
               
                   
                   
               
            
           
         
       
     
     With regard to SpO 2  signals that are essentially flat, the dynamic window method may fail to find the three points (i.e., a fall peak, a rise peak, and a nadir) utilized to identify a potential reciprocation. Therefore, the RD feature  202  may limit the amount of time that the dynamic window method can search for a potential reciprocation. For example, if no reciprocations are found in 240 seconds plus the current dynamic window size, the algorithm of the RD feature  202  may timeout and begin to look for potential reciprocations at the current SpO 2  trend point and later. The net effect of this may be that the RD feature  202  detects potential reciprocations less than 240 seconds long. 
     Once potential peaks and nadirs are found using the RD feature  202 , the RQ feature  204  may pass the potential reciprocations through one or more qualification stages to determine if a related event is caused by ventilatory instability. A first qualification stage may include checking reciprocation metrics against a set of limits (e.g., predetermined hard limits). A second qualification stage may include a linear qualification function. In accordance with present embodiments, a reciprocation may be required to pass through both stages in order to be qualified. 
     As an example, in a first qualification stage, which may include a limit-based qualification, four metrics may be calculated for each potential reciprocation and compared to a set of limits. Any reciprocation with a metric that falls outside of these limits may be disqualified. The limits may be based on empirical data. For example, in some embodiments, the limits may be selected by calculating the metrics for potential reciprocations from sleep lab data where ventilatory instability is known to be present, and then comparing the results to metrics from motion and breathe-down studies. The limits may then be refined to filter out true positives. 
     The metrics referred to above may include fall slope, magnitude, slope ratio, and path length ratio. With regard to fall slope, it may be desirable to limit the maximum fall slope to filter out high frequency artifact in the SpO 2  trend, and limit the minimum fall slope to ensure that slow SpO 2  changes are not qualified as reciprocations. Regarding magnitude, limits may be placed on the minimum magnitude because of difficulties associated with deciphering the difference between ventilatory instability reciprocations and artifact reciprocations as the reciprocation size decreases, and on the maximum magnitude to avoid false positives associated with sever artifact (e.g., brief changes of more than 35% SpO 2  that are unrelated to actual ventilatory instability). The slope ratio may be limited to indirectly limit the rise slope for the same reasons as the fall slope is limited and because ventilatory instability patterns essentially always have a desaturation rate that is slower than the resaturation (or recovery) rate. The path length ratio may be defined as Path Length/((Fall Peak−Nadir)+(Rise Peak−Nadir)), where Path Length=Σ|Current SpO 2  Value−Previous SpO 2  value| for all SpO 2  values in a reciprocation, and the maximum path length ratio may be limited to limit the maximum standard deviation of the reciprocation, which limits high frequency artifact. The following table (Table I) lists the above-identified metrics along with their associated equations and the limits used in accordance with one embodiment: 
     
       
         
           
               
               
               
               
             
               
                 TABLE I 
               
               
                   
               
               
                 Metric 
                 Equation 
                 Minimum 
                 Maximum 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                 Fall Slope 
                 (Nadir − Fall Peak)/Time 
                 −1.6 
                 −0.08 
               
               
                   
                 between Fall Peak and Nadir 
                 (Fast 
                 (Fast 
               
               
                   
                   
                 Response 
                 Response 
               
               
                   
                   
                 Mode) 
                 Mode) 
               
               
                   
                   
                 −1 
                 −0.05 
               
               
                   
                   
                 (Normal 
                 (Normal 
               
               
                   
                   
                 Response 
                 Response Mode) 
               
               
                   
                   
                 Mode) 
               
               
                 Magnitude 
                 Max(Rise Peak, Fall Peak) − 
                 3 
                 35 
               
               
                   
                 Nadir 
               
               
                 Slope 
                 |Fall Slope/Rise Slope| 
                 0.05 
                 1.75 
               
               
                 Ratio 
               
               
                 Path 
                 Path Length =  Σ |Current 
                 N/A 
                 2 
               
               
                 Length 
                 SpO2 Value − Previous SpO2 
               
               
                 Ratio 
                 Value| for all SpO2 values in 
               
               
                   
                 a Reciprocation. 
               
               
                   
                 Path Length Ratio = Path 
               
               
                   
                 Length/((Fall Peak − Nadir) + 
               
               
                   
                 (Rise Peak − Nadir)) 
               
               
                   
               
            
           
         
       
     
     As indicated in Table I above, an oximetry algorithm in accordance with present embodiments may operate in two response modes: Normal Response Mode or Fast Response Mode. The selected setting may change the SpO 2  filtering performed by the oximetry algorithm, which in turn can cause changes in SpO 2  patterns. Therefore a saturation pattern detection feature may also accept a response mode so that it can account for the different SpO 2  filtering. Table I indicates values associated with both types of response mode with regard to the Fall Slope values. 
     A second qualification stage of the RQ feature  204  may utilize a object reciprocation qualification feature. Specifically, the second qualification stage may utilize a linear qualification function based on ease of implementation, efficiency, and ease of optimization. The equation may be determined by performing a least squares analysis. For example, such an analysis may be performed with MATLAB®. The inputs to the equation may include the set of metrics described below. The output may be optimized to a maximum value for patterns where ventilatory instability is known to be present. The equation may be optimized to output smaller values (e.g., 0) for other data sets where potential false positive reciprocations are abundant. 
     To simplify optimization, the equation may be factored into manageable sub-equations. For example, the equation may be factored into sub-equation 1, sub-equation D, and sub-equation 2, as will be discussed below. The output of each sub-equation may then be substituted into the qualification function to generate an output. The outputs from each of the sub-equations may not be utilized to determine whether a reciprocation is qualified in accordance with present embodiments. Rather, an output from a full qualification function may be utilized to qualify a reciprocation. It should be noted that the equations set forth in the following paragraphs describe one set of constants. However, separate sets of constants may be used based on the selected response mode. For example, a first set of constants may be used for the Normal Response Mode and a second set of constants may be used for the Fast Response Mode. 
     Preprocessing may be utilized in accordance with present embodiments to prevent overflow for each part of the qualification function. The tables (Tables II-VII) discussed below, which relate to specific components of the qualification function may demonstrate this overflow prevention. Each row in a table contains the maximum value of term which is equal to the maximum value of the input variable multiplied by the constant, wherein the term “maximum” may refer to the largest possible absolute value of a given input. Each row in a table contains the maximum intermediate sum of the current term and all previous terms. For example, a second row may contain the maximum output for the second term calculated, as well as the maximum sum of terms 1 and 2. It should be noted that the order of the row may match the order that the terms are calculated by the RQ feature  204 . Further, it should be noted that in the tables for each sub-equation below, equations may be calculated using temporary signed 32-bit integers, and, thus, for each row in a table where the current term or intermediate term sum exceeds 2147483647 or is less than −2147483647 then an overflow/underflow condition may occur. 
     A first sub-equation, sub-equation 1, may use metrics from a single reciprocation. For example, sub-equation 1 may be represented as follows: 
         Eq 1Score=SlopeRatio* SrCf +PeakDiff* PdCf +FallSlope* FsCf +PathRatio* PrCf+Eq 1Offset, 
     where SrCf, PdCf, FsCf, PrCf, and Eq1 Offset may be selected using least squares analysis (e.g., using MATLAB®). PeakDiff may be defined as equal to |Recip Fall Peak−Recip Rise Peak|. It should be noted that PeakDiff is typically not considered in isolation but in combination with other metrics to facilitate separation. For example, a true positive reciprocation which meets other criteria but has a high peak difference could be an incomplete recovery. That is, a patient&#39;s SpO 2  may drop from a baseline to a certain nadir value, but then fail to subsequently recover to the baseline. However, when used in combination with other metrics in the equation, PeakDiff may facilitate separation of two classifications, as large peak differences are more abundant in false positive data sets. 
     With regard to sub-equation 1, the tables (Tables II and III) set forth below demonstrate that the inputs may be preprocessed to prevent overflow. Further, the tables set forth below include exemplary limits that may be utilized in sub-equation 1 in accordance with present embodiments. It should be noted that Table II includes Fast Response Mode constants and Table III includes Normal Response Mode constants. 
     
       
         
           
               
               
               
               
               
               
               
               
             
               
                 TABLE II 
               
               
                   
               
               
                   
                   
                   
                   
                   
                   
                 Maximum 
                   
               
               
                   
                   
                 Maximum 
                   
                   
                   
                 Intermediate Sum 
               
               
                   
                 Variable 
                 Variable 
                   
                 Constant Value 
                 Maximum Term 
                 (sum of all previous 
               
               
                 Term 
                 Type 
                 Value (a) 
                 Variable Preprocessing 
                 (b) (Fast Mode) 
                 Value (a * b) 
                 rows) 
                 Overflow 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                 PeakDiff * PdCf 
                 U8 
                 100 
                 None. This value may 
                 −29282 
                 −2928200 
                 −2928200 
                 NO 
               
               
                   
                   
                   
                 not exceed 100 since 
               
               
                   
                   
                   
                 the maximum SpO 2   
               
               
                   
                   
                   
                 value accepted is 100 
               
               
                 SlopeRatio * SrCf 
                 U8 
                 255 
                 None 
                 −1534 
                 −391170 
                 −3319370 
                 NO 
               
               
                 FallSlope * FsCf 
                 S16 
                 −32768 
                 None 
                 −19 
                 622592 
                 −2696778 
                 NO 
               
               
                 PathRatio * PrCf 
                 U16 
                 65535 
                 None 
                 −7982 
                 −523100370 
                 −525797148 
                 NO 
               
               
                 Eq1Offset 
                 N/A 
                 N/A 
                 N/A 
                 809250 
                 809250 
                 −524987898 
                 NO 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
               
               
               
               
               
               
               
             
               
                 TABLE III 
               
               
                   
               
               
                   
                   
                   
                   
                   
                   
                 Maximum 
                   
               
               
                   
                   
                 Maximum 
                   
                 Constant 
                   
                 Intermediate 
               
               
                   
                 Variable 
                 Variable 
                   
                 Value (b) 
                 Maximum Term 
                 Sum (sum of all 
               
               
                 Term 
                 Type 
                 Value (a) 
                 Variable Preprocessing 
                 (Normal Mode) 
                 Value (a * b) 
                 previous rows) 
                 Overflow 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                 PeakDiff * PdCf 
                 U8 
                 100 
                 None. This value may not 
                 −33311 
                 −3331100 
                 −3331100 
                 NO 
               
               
                   
                   
                   
                 exceed 100 since the 
               
               
                   
                   
                   
                 maximum SpO2 value 
               
               
                   
                   
                   
                 accepted is 100 
               
               
                 SlopeRatio * SrCf 
                 U8 
                 255 
                 None 
                 −2151 
                 −548505 
                 −3879605 
                 NO 
               
               
                 FallSlope * FsCf 
                 S16 
                 −32768 
                 None 
                 −706 
                 23134208 
                 19254603 
                 NO 
               
               
                 PathRatio * PrCf 
                 U16 
                 65535 
                 None 
                 −6178 
                 −404875230 
                 −385620627 
                 NO 
               
               
                 Eq1Offset 
                 N/A 
                 N/A 
                 N/A 
                 576330 
                 576330 
                 −385044297 
                 NO 
               
               
                   
               
            
           
         
       
     
     A second sub-equation, sub-equation D, may correspond to a difference between two consecutive reciprocations which have passed the hard limit qualifications checks, wherein consecutive reciprocations include two reciprocations that are separated by less than a defined time span. For example, consecutive reciprocations may be defined as two reciprocations that are less than 120 seconds apart. The concept behind sub-equation D may be that ventilatory instability tends to be a relatively consistent event, with little change from one reciprocation to the next. Artifact generally has a different signature and tends to be more random with greater variation among reciprocations. For example, the following equation may represent sub-equation D: 
         EqD =SlopeRatioDiff* SrDCf +DurationDiff* DDCf +NadirDiff* NdCf +PathLengthRatioDiff* PrDCf   —   EqD Offset, 
     where, SrDCf, DDCf, NdCf, PrDCf, and EqDOffset may be selected using least squares analysis (e.g., using MATLAB®). With regard to other variables in sub-equation D, SlopeRatioDiff may be defined as |Current Recip Slope Ratio−Slope Ratio of last qualified Recipi|; DurationDiff may be defined as |Current Recip Duration−Duration of last qualified Recip|; NadirDiff may be defined as |Current Recip Nadir−Nadir value of last qualified Recip|; and PathLengthRatioDiff may be defined as |Current Recip Path Length Ratio−Path Length Ratio of last qualified Recip|. 
     With regard to sub-equation D, the tables (Tables IV and V) set forth below demonstrate that the inputs may be preprocessed to prevent overflow. Further, the tables set forth below include exemplary limits that may be utilized in sub-equation D in accordance with present embodiments. It should be noted that Table IV includes Fast Response Mode constants and Table V includes Normal Response Mode constants. 
     
       
         
           
               
               
               
               
               
               
               
               
             
               
                 TABLE IV 
               
               
                   
               
               
                   
                   
                   
                   
                 Constant 
                   
                 Maximum 
                   
               
               
                   
                   
                 Maximum 
                   
                 Value 
                   
                 Intermediate Sum 
               
               
                   
                 Variable 
                 Variable 
                 Variable 
                 (b) 
                 Maximum Term 
                 (sum of all previous 
               
               
                 Term 
                 Type 
                 Value (a) 
                 Preprocessing 
                 (Fast Mode) 
                 Value (a * b) 
                 rows) 
                 Overflow 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                 EqDOffset 
                 N/A 
                 N/A 
                 N/A 
                 885030 
                 885030 
                 885030 
                 NO 
               
               
                 SlopeRatioDiff * 
                 U8 
                 255 
                 None 
                 −2809 
                 −716295 
                 168735 
                 NO 
               
               
                 SrDCf 
               
               
                 DurationDiff * DDCf 
                 U16 
                 240 
                 The Recip detection 
                 −2960 
                 −710400 
                 −541665 
                 NO 
               
               
                   
                   
                   
                 module may only 
               
               
                   
                   
                   
                 detect recips less than 
               
               
                   
                   
                   
                 or equal to 240 
               
               
                   
                   
                   
                 seconds long 
               
               
                 NadirDiff * NdCf 
                 U8 
                 100 
                 This value may not 
                 −13237 
                 −1323700 
                 −1865365 
                 NO 
               
               
                   
                   
                   
                 exceed 100 since the 
               
               
                   
                   
                   
                 maximum SpO2 value 
               
               
                   
                   
                   
                 accepted is 100 
               
               
                 PathLengthRatioDiff * 
                 U16 
                 65535 
                 None 
                 −7809 
                 −511762815 
                 −513628180 
                 NO 
               
               
                 PrDCf 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
               
               
               
               
               
               
               
             
               
                 TABLE V 
               
               
                   
               
               
                   
                   
                   
                   
                   
                   
                 Maximum 
                   
               
               
                   
                   
                 Maximum 
                   
                 Constant  
                 Maximum 
                 Intermediate 
               
               
                   
                 Variable 
                 Variable 
                   
                 Value (b) 
                 Term Value 
                 Sum (sum of all 
               
               
                 Term 
                 Type 
                 Value (a) 
                 Variable Preprocessing 
                 (Normal Mode) 
                 (a * b) 
                 previous rows) 
                 Overflow 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                 EqDOffset 
                 N/A 
                 N/A 
                 N/A 
                 847650 
                 847650 
                 847650 
                 NO 
               
               
                 SlopeRatioDiff * 
                 U8 
                 255 
                 None 
                 −2629 
                 −670395 
                 177255 
                 NO 
               
               
                 SrDCf 
               
               
                 DurationDiff * DDCf 
                 U16 
                 240 
                 The Recip detection 
                 −4282 
                 −1027680 
                 −850425 
                 NO 
               
               
                   
                   
                   
                 module may only detect 
               
               
                   
                   
                   
                 recips less than or equal 
               
               
                   
                   
                   
                 to 240 seconds long 
               
               
                 NadirDiff * NdCf 
                 U8 
                 100 
                 This value may not 
                 −11705 
                 −1170500 
                 −2020925 
                 NO 
               
               
                   
                   
                   
                 exceed 100 since the 
               
               
                   
                   
                   
                 maximum SpO2 value 
               
               
                   
                   
                   
                 accepted is 100 
               
               
                 PathLengthRatioDiff * 
                 U16 
                 65535 
                 None 
                 −7844 
                 −514056540 
                 −516077465 
                 NO 
               
               
                 PrDCf 
               
               
                   
               
            
           
         
       
     
     A third sub-equation, sub-equation 2, may combine the output of sub-equation D with the output of sub-equation 1 for a reciprocation (e.g., a current reciprocation) and a previous reciprocation. For example, the following equation may represent sub-equation 2: 
         Eq 2Score= EqD Score* DCf+Eq 1ScoreCurrent*Curr Eq 1 Cf+Eq 1ScorePrev*Prev Eq 1 Cf,    
     where DCf, N1Cf, PrevEq1Cf, and Eq2Offset may be selected using least squares analysis (e.g., using MATLAB®). With regard to other variables in sub-equation 2, EqDScore may be described as the output of sub-equation D; Eq1ScoreCurrent may be described as the output of sub-equation 1 for a current reciprocation; and Eq1ScorePrev may be described as the output of sub-equation 1 for the reciprocation previous to the current reciprocation. 
     With regard to sub-equation 2, the tables (Tables VI and VII) set forth below demonstrate that the inputs may be preprocessed to prevent overflow. Further, the tables set forth below include exemplary limits that may be utilized in sub-equation 2 in accordance with present embodiments. It should be noted that Table VI includes Fast Response Mode constants and Table VII includes Normal Response Mode constants. 
     
       
         
           
               
               
               
               
               
               
               
               
             
               
                 TABLE VI 
               
               
                   
               
               
                   
                   
                   
                   
                   
                   
                 Maximum 
                   
               
               
                   
                   
                 Maximum 
                   
                   
                   
                 Intermediate Sum 
               
               
                   
                 Variable 
                 Variable 
                   
                 Constant Value 
                 Maximum Term 
                 (sum of all 
               
               
                 Term 
                 Type 
                 Value (a) 
                 Variable Preprocessing 
                 (b) (Fast Mode) 
                 Value (a * b) 
                 previous rows) 
                 Overflow 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                 Eq2Offset 
                 N/A 
                 N/A 
                 N/A 
                 −203800 
                 −203800 
                 −203800 
                 NO 
               
               
                 EqDScore * DCf 
                 S32 
                 −501590 
                 The largest output for sub- 
                 529 
                 −265341110 
                 −265544910 
                 NO 
               
               
                   
                   
                   
                 equation D may be −513628180 
               
               
                   
                   
                   
                 (see Table IV). 
               
               
                   
                   
                   
                 The input value may be 
               
               
                   
                   
                   
                 scaled by dividing the value 
               
               
                   
                   
                   
                 by 1024. Therefore the 
               
               
                   
                   
                   
                 largest input value may be −501590 
               
               
                 Eq1ScorePrev * 
                 S32 
                 −512683 
                 The largest output for sub- 
                 333 
                 −170723439 
                 −436268349 
                 NO 
               
               
                 PrevEq1Cf 
                   
                   
                 equation 1 may be −524987898 
               
               
                   
                   
                   
                 (see Table II). 
               
               
                   
                   
                   
                 The input value may be 
               
               
                   
                   
                   
                 scaled by dividing the value 
               
               
                   
                   
                   
                 by 1024. Therefore the 
               
               
                   
                   
                   
                 largest input value may be −512683 
               
               
                 Eq1ScoreCurrent * 
                 S32 
                 −512683 
                 Same as previous row 
                 617 
                 −316325411 
                 −752593760 
                 NO 
               
               
                 CurrEq1Cf 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
               
               
               
               
               
               
               
             
               
                 TABLE VII 
               
               
                   
               
               
                   
                   
                   
                   
                   
                   
                 Maximum 
                   
               
               
                   
                   
                   
                   
                 Constant 
                   
                 Intermediate 
               
               
                   
                   
                 Maximum 
                   
                 Value (b) 
                 Maximum 
                 Sum (sum of 
               
               
                   
                 Variable 
                 Variable 
                   
                 (Normal 
                 Term Value 
                 all previous 
                 Over- 
               
               
                 Term 
                 Type 
                 Value (a) 
                 Variable Preprocessing 
                 Mode) 
                 (a * b) 
                 rows) 
                 flow 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
            
               
                 Eq2Offset 
                 N/A 
                 N/A 
                 N/A 
                 −194550 
                 −194550 
                 −194550 
                 NO 
               
               
                 EqDScore * DCf 
                 S32 
                 −503981 
                 The largest output for sub-equation D 
                 532 
                 −268117892 
                 −268312442 
                 NO 
               
               
                   
                   
                   
                 may be −516077465 (see Table V). The 
               
               
                   
                   
                   
                 input value may be scaled by dividing the 
               
               
                   
                   
                   
                 value by 1024. Therefore the largest input 
               
               
                   
                   
                   
                 value may be −503981 
               
               
                 Eq1ScorePrev * 
                 S32 
                 −376000 
                 The largest output for sub-equation 1 may 
                 496 
                 −186496000 
                 −454808442 
                 NO 
               
               
                 PrevEq1Cf 
                   
                   
                 be −385024297 (see Table III). The input 
               
               
                   
                   
                   
                 value may be scaled by dividing the value 
               
               
                   
                   
                   
                 by 1024. Therefore the largest input value 
               
               
                   
                   
                   
                 may be −376000 
               
               
                 Eq1ScoreCurrent * 
                 S32 
                 −376000 
                 Same as previous row 
                 406 
                 −152656000 
                 −607464442 
                 NO 
               
               
                 CurrEq1Cf 
               
               
                   
               
            
           
         
       
     
     A qualification function may utilize the output of each of the equations discussed above (i.e., sub-equation 1, sub-equation D, and sub-equation 2) to facilitate qualification and/or rejection of a potential reciprocation. For example, the output of the qualification function may be filtered with an IIR filter, and the filtered output of the qualification function may be used to qualify or reject a reciprocation. An equation for an unfiltered qualification function output in accordance with present embodiments is set forth below: 
       QFUnfiltered= Eq 1Score*SingleRecipWt* Eq 2 Cf+N 2Score*MultipleRecipWt* Eq 2 Cf+N ConsecRecip*Consec Cf +RecipMax*Max Cf +Artifact %*Art Cf +QFOffset, 
     where Eq2Cf, ConsecCf, MaxCf, ArtCf, and QFOffset may be selected using least squares analysis (e.g., using MATLAB®), and, as indicated above, Eq1Score may be defined as the output of sub-equation 1. 
     Other metrics in the unfiltered qualification function include SingleRecipWt, MultipleRecipWt, NConsecRecip, RecipMax, and Artifact %. With regard to SingleRecipWt and MultipleRecipWt, when there are two or more consecutive qualified reciprocations (e.g., qualified reciprocations that are less than 120 seconds apart) present, SingleRecipWt may equal 0 and MultipleRecipWt may equal 1. However, when only a single reciprocation is present, SingleRecipWt may equal 1 and MultipleRecipWt may equal 0. 
     NConseRecip, which may be defined as equal to max(NConsecRecip′,QFConsecMax), may include a count of the number of consecutive reciprocations (e.g., reciprocations that are less than or equal to 120 seconds apart) that have passed the hard limit checks. The value for NConsecRecip may be reset to 0 whenever a gap between any two partially qualified reciprocations exceeds 120 seconds. This may be based on the fact that ventilatory instability is a relatively long lasting event as compared to artifact. Therefore, as more reciprocations pass the hard limit checks, the qualification function may begin qualifying reciprocations that were previously considered marginal. However, to guard against a situation where something is causing a longer term artifact event (e.g., interference from nearby equipment), the value may be clipped to a maximum value to limit the metrics influence on the qualification function output. 
     RecipMax, which may be defined as equal to max(Fall Peak, Rise Peak), may facilitate making decisions about marginal reciprocations. Indeed, marginal reciprocations with higher maximum SpO 2  values may be more likely to get qualified than marginal reciprocations with lower SpO 2  values. It should be noted that this metric works in tandem with the NConsecRecip metric, and multiple marginal reciprocations with lower maximum SpO 2  values may eventually, over a long period of time, get qualified due to the NConsecRecip metric. 
     The metric Artifact % may be defined as an artifact percentage that is equal to 100*Total Artifact Count/Recip Duration, where Total Artifact Count is the number of times and artifact flag was set during the reciprocation. Present embodiments may include many metrics and equations that are used to set the artifact flag. Because of this it is a generally reliable indication of the amount of artifact present in the oximetry system as a whole. Marginal reciprocations with a high Artifact % are less likely to be qualified than marginal reciprocations with a low (or 0) artifact percentage. 
     A last component of the qualification function may include an infinite impulse response (IIR) filter that includes coefficients that may be tuned manually using a tool (e.g., a spreadsheet) that models algorithm performance. The filtered qualification function may be represented by the following equation, which includes different constants for different modes (e.g., Fast Response Mode and Normal Response Mode): 
       QFFiltered=SingleRecipWt*QFUnfiltered+((1 −a )*QFUnfiltered+ a *PrevQFFiltered)*MultipleRecipWt, 
     where QFUnfiltered may be defined as the current unfiltered qualification function output; PrevQFFiltered may be defined as the previous filtered qualification function output; and where the constant “a” may be set to 0.34 for Fast Response Mode and 0.5 for Normal Response Mode. 
     The filtered output of the qualification function may be compared to a threshold to determine if the current reciprocation is the result of RAF or artifact. The optimum threshold may theoretically be 0.5. However, an implemented threshold may be set slightly lower to bias the output of the qualification function towards qualifying more reciprocations, which may result in additional qualification of false positives. The threshold may be lowered because, in accordance with present embodiments, a cluster determination portion of the algorithm, such as may be performed by the CD feature  206 , may require a certain number (e.g., 5) of fully qualified reciprocations before an index may be calculated, and a certain number (e.g., at least 2) of consecutive qualified reciprocations (with no intervening disqualified reciprocations) within the set of fully qualified reciprocations. Since multiple reciprocations may be required, the clustering detection method may be biased toward filtering out false positives. Accordingly, the reciprocation qualification function threshold may be lowered to balance the two processes. 
     The CD feature  206  may be capable of performing an algorithm that maintains an internal reciprocation counter that keeps track of a number of qualified reciprocations that are currently present. When the reciprocation counter is greater than or equal to a certain value, such as 5, the clustering state may be set to “active” and the algorithm may begin calculating and reporting the SPDi. When clustering is not active (e.g., reciprocation count&lt;5) the algorithm may not calculate the SPDi. The SPDi may be defined as a scoring metric associated with the identification of a saturation trend pattern generated in accordance with present embodiment and may correlate to ventilatory instability in a population of sleep lab patients. 
     The CD feature  206  may utilize various rules to determine the reciprocation count. For example, when the clustering state is inactive, the following rules may be observed:
         1.) If the distance between qualified reciprocation exceeds 120 seconds, then the reciprocation count=0;   2.) If the current reciprocation is qualified, and the time from the start of the current reciprocation to the end of the last qualified reciprocation is &lt;=120 seconds, then the reciprocation count=reciprocation count+1;   3.) If the current reciprocation is not qualified, then the reciprocation count=max(reciprocation count−2, 0).
 
Once clustering is active, it may remain active until the time between two qualified reciprocations exceeds 120 seconds. The following table (Table VIII) illustrates an example of how the reciprocation count rules may be applied to determine a clustering state.
       

     
       
         
           
               
               
               
               
             
               
                 TABLE VIII 
               
               
                   
               
               
                 Current 
                 Time 
                   
                   
               
               
                 Reciprocation 
                 Since Last Qualified 
                 Reciprocation 
                 Clustering 
               
               
                 Qualified 
                 Reciprocation (seconds) 
                 Count 
                 State 
               
               
                   
               
             
            
               
                 TRUE 
                 N/A 
                 1 
                 INACTIVE 
               
               
                 FALSE 
                 60 
                 0 
                 INACTIVE 
               
               
                 TRUE 
                 N/A 
                 1 
                 INACTIVE 
               
               
                 FALSE 
                 60 
                 0 
                 INACTIVE 
               
               
                 TRUE 
                 N/A 
                 1 
                 INACTIVE 
               
               
                 TRUE 
                 30 
                 2 
                 INACTIVE 
               
               
                 TRUE 
                 120 
                 3 
                 INACTIVE 
               
               
                 FALSE 
                 60 
                 1 
                 INACTIVE 
               
               
                 TRUE 
                 10 
                 2 
                 INACTIVE 
               
               
                 TRUE 
                 20 
                 3 
                 INACTIVE 
               
               
                 TRUE 
                 40 
                 4 
                 INACTIVE 
               
               
                 FALSE 
                 30 
                 2 
                 INACTIVE 
               
               
                 FALSE 
                 60 
                 0 
                 INACTIVE 
               
               
                 TRUE 
                 N/A 
                 1 
                 INACTIVE 
               
               
                 TRUE 
                 20 
                 2 
                 INACTIVE 
               
               
                 TRUE 
                 120 
                 3 
                 INACTIVE 
               
               
                 TRUE 
                 10 
                 4 
                 INACTIVE 
               
               
                 FALSE 
                 90 
                 2 
                 INACTIVE 
               
               
                 TRUE 
                 120 
                 3 
                 INACTIVE 
               
               
                 TRUE 
                 60 
                 4 
                 INACTIVE 
               
               
                 TRUE 
                 20 
                 5 
                 ACTIVE 
               
               
                 TRUE 
                 30 
                 6 
                 ACTIVE 
               
               
                 FALSE 
                 50 
                 6 
                 ACTIVE 
               
               
                 FALSE 
                 100 
                 6 
                 ACTIVE 
               
               
                 TRUE 
                 121 
                 1 
                 INACTIVE 
               
               
                 FALSE 
                 50 
                 0 
                 INACTIVE 
               
               
                 TRUE 
                 N/A 
                 1 
                 INACTIVE 
               
               
                 TRUE 
                 30 
                 2 
                 INACTIVE 
               
               
                 TRUE 
                 121 
                 1 
                 INACTIVE 
               
               
                 TRUE 
                 10 
                 2 
                 INACTIVE 
               
               
                 TRUE 
                 20 
                 3 
                 INACTIVE 
               
               
                 TRUE 
                 40 
                 4 
                 INACTIVE 
               
               
                 TRUE 
                 40 
                 5 
                 ACTIVE 
               
               
                   
               
            
           
         
       
     
     When the clustering state is active, the SPDi calculation feature  208  may calculate an unfiltered SPDi for each new qualified reciprocation. The following formula may be used by the SPDi calculation feature  208 : 
       Unfiltered  SPDi=a *Magnitude+ b *PeakDelta+ c *NadirDelta;         wherein a=1.4, b=2.0, c=0.2;   wherein Magnitude=average magnitude of all reciprocations in the last 6 minutes;   wherein PeakDelta average of the three highest qualified reciprocation rise peaks in the last 6 minutes minus the average of the three lowest qualified reciprocation rise peaks in the last 6 minutes; and   wherein NadirDelta=average of the three highest qualified reciprocation nadirs in the last 6 minutes minus the average of the three lowest qualified reciprocation nadirs in the last 6 minutes.   Wherein SPDi&lt;=31.       
     The above formula may be utilized to quantify the severity of a ventilatory instability pattern. The constants and metrics used may be based on input from clinical team members. It should be noted that the PeakDelta parameter may be assigned the largest weighting constant since the most severe patterns generally have peak reciprocation values that do not recover to the same baseline. 
     The unfiltered SPDi may be updated whenever clustering is active and a new qualified reciprocation is detected. Non-zero SPDi values may be latched for a period of time (e.g., 6 minutes). The unfiltered SPDi may then be low pass filtered to produce the final output SPDi value. The following IIR filter with a response time of approximately 40 seconds may be used: 
         SPDi =Unfiltered  SPDi/a +Previous Filtered  SPDi *( a− 1)/ a;    
     wherein a=40. 
       FIG. 7  is an exemplary graph  260  including an SpO 2  trend  262  that contains a ventilatory instability SpO 2  pattern and a trend of the resulting SPDi  264 . In the illustrated example, it should be noted that the SPDi is sensitive to the decreasing peaks (incomplete recoveries) starting at approximately t=6000. 
     The UN feature  210  may be capable of determining if a user notification function should be employed to notify a user (e.g., via a graphical or audible indicator) of the presence of a detected patterns such as ventilatory instability. The determination of the UN feature  210  may be based on a user configurable tolerance setting and the current value of the SPDi. For example, the user may have four choices for the sensitivity or tolerance setting: Off, Low, Medium, and High. When the sensitivity or tolerance setting is set to Off, an alarm based on detection of a saturation pattern may never be reported to the user. The other three tolerance settings (i.e., Low, Medium, and High) may each map to an SPDi threshold value. For example, Low may map to an SPDi threshold of 6, Medium may map to an SPDi threshold of 15, and High may map to an SPDi threshold of 24. The thresholds may be based on input from users. When the SPDi is at or above the threshold for a given tolerance setting, the user may be notified that ventilatory instability is present. As discussed below, the indication to the user may include a graphical designation of the trend data corresponding to the detected pattern. For example, the trend data utilized to identify a ventilatory instability pattern may be highlighted, flashing, or otherwise indicated on a user interface of a monitor in accordance with present embodiments. Similarly, parameters such as the SPDi value and the tolerance settings may be graphically presented on a display. 
     It should be noted that, in order to detect certain data patterns, embodiments of the present disclosure may utilize systems and methods such as those disclosed in U.S. Pat. No. 6,760,608, U.S. Pat. No. 6,223,064, U.S. Pat. No. 5,398,682, U.S. Pat. No. 5,605,151, U.S. Pat. No. 6,748,252, U.S. application Ser. No. 11/455,408 filed Jun. 19, 2006, U.S. application Ser. No. 11/369,379 filed Mar. 7, 2006, and U.S. application Ser. No. 11/351,787 filed Feb. 10, 2006. Accordingly, U.S. Pat. No. 6,760,608, U.S. Pat. No. 6,223,064, U.S. Pat. No. 5,398,682, U.S. Pat. No. 5,605,151, U.S. Pat. No. 6,748,252, U.S. application Ser. No. 11/455,408 filed Jun. 19, 2006, U.S. application Ser. No. 11/369,379 filed Mar. 7, 2006, and U.S. application Ser. No. 11/351,787 filed Feb. 10, 2006 are each incorporated herein by reference. 
     Embodiments of the present disclosure may facilitate user observation and analysis of data, such as the detected patterns discussed above, by establishing a distinction between data of interest (e.g., data having certain notable characteristics, recent data) and other data (e.g., standard data, old data). For example, present embodiments may include graphical features that make a clear distinction between data detected within a designated time period (e.g., within 15 minutes) from a present time and data that is older (e.g., 15 minutes old or older). This may be beneficial in preventing a user (e.g., a clinician) from improperly diagnosing a current situation based on past data. Further, in another example, data of concern (e.g., data exhibiting a pattern of desaturation) may be distinguished from other data. The graphical features may include timestamps  104 , graphic indicators  106 , color changes in graphic features, flashing graphics, highlighting, blinking text, and so forth. 
     For example, as illustrated in  FIG. 8 , portions of a trend  270  in a trend display  272  that represent old data  270 A (or data acquired over fifteen minutes before a present time) may be displayed as inverted, while current data  270 B (or data acquired within fifteen minutes from the present time) may be displayed as normal. In another example, as illustrated in  FIG. 9 , detected patterns  280  in a trend  282  may be highlighted (or flashing) on a trend display  284  to distinguish the patterns  280  from other trend data. In some embodiments, if a particular pattern is of substantial interest it may flash, while other patterns may be simply highlighted. In yet other embodiments, the trend may be displayed in different colors or having varying line thicknesses depending on the nature (e.g., age and/or pattern) of the associated portions of trend data. Accordingly, when a user reviews trend data in accordance with present embodiments (e.g., snaps back or forward to an event), the user may readily discern the time period in which the event was recorded by observing the indicative graphical feature. It should be noted that in  FIG. 9 , an arrow  286  indicates that a particular pattern  280  has been selected and the time stamp  288  associated with the event is being displayed. In another embodiment, a vertical cursor line is used. In some embodiments, as will be discussed further below, a time scale may be presented along the trend  282  to facilitate identification of event occurrence times. 
     As suggested above, in addition to graphic identification of areas of interest in trend data, various other graphical and/or textual features may also facilitate user review of trend data. For example, as illustrated in the display screen  298  of FIG.  10 , a time scale  300  may be displayed with respect to SpO 2  trend data  302  to avoid ambiguity as to when an event occurred. The time scale  300  may indicate the onset time  304  and the offset time  306  for the section of trend data being displayed. In some embodiments, onset and offset times may be displayed specifically for designated areas of interest within the trend data being displayed. For example, a highlighted portion  308  of the trend may have an onset time  310  and an offset time  312  at the beginning and end of the highlighted portion, respectively. It should be noted that in other embodiments, the time scale  300  may be utilized for different physiologic data trends (e.g., heart rate). Another feature that may facilitate user examination of monitor data is a status indicator  314  for pattern detection and/or severity, as illustrated in  FIG. 10 . In the illustrated embodiment, the status indicator  314  is represented as a triangle that may graphically fill from top to bottom as a monitored and/or calculated value increases. For example, in one embodiment, the status indicator  314  may gradually fill as the SPDi calculated by the SPDi calculation feature  208  increases. Further, the status indicator  314  may include a sensitivity level indicator  316  that displays a 1, 2, or 3, respectively, for sensitivity settings of High, Medium, and Low for the SPDi calculation feature  208 . 
     As indicated above, various events in a trend of physiological data may be designated as being areas of interest by a device in accordance with present embodiments. For example, as discussed above, the monitor  10  may automatically detect and identify alarm events, saturation patterns in SpO 2  trend data, and so forth. Further, a user may utilize features of the monitor  10  to manually designate certain events. In view of the various events that may be designated in a data trend on the monitor  10 , present embodiments may facilitate viewing these events without requiring a user to scroll through data that has not been identified as an area of interest. For example, a display control feature may be utilized to jump a display of a data trend to areas in the trend that have been automatically or manually designated as being of interest. 
     Activation of the display control feature during normal operation of the monitor  10  may cause the monitor  10  to jump or automatically scroll to a display of the most recent detected event. For example, in one embodiment, where no particular event type is designated, a user may press a button or the knob  50  to sequentially jump to all detected events in a set of historical data. Specifically, for example, with reference to  FIG. 3 , if no events are detected between the alarm  102  and when the display control feature is activated, activation of the display control feature may cause the monitor  10  to automatically display historical data of the trend  108  associated with the alarm  102 . However, if events are detected between the time of the alarm and the time of activating the display control feature, the user may use the display control feature to cycle through the events to get to a display of data associated with the alarm  102 . For example, a user may create the user designated event  112  by marking a certain portion of data at a point on the trend  108  after the alarm  102  occurred for later review. Such marking may be incorporated as an event by the monitor  10 . Accordingly, activation of the display control feature from a current display may cause the monitor  10  to display the user designated event  112  (i.e., the marked data) before proceeding to display the data associated with the alarm  102 , which would occur upon additional activation of the display control feature. Indeed, present embodiments may enable a user to cycle through all or a selected subset of events stored by the monitor  10 . 
     A user may select different types of events for the display control feature to cycle through or jump to in accordance with present embodiments. In other words, the display control feature may be configured or programmed by the user such that activation of the display control feature causes the monitor&#39;s display to jump to specific types of events and to bypass others. This improves efficiency in viewing and analyzing data by allowing a user to skip over data that is irrelevant or not of interest. For example, a user may only be interested in alarms associated with recognized physiological patterns in the data (e.g., a pattern indicative of sleep apnea). Accordingly, the user may choose to view only labels that include alarms based on recognized physiological patterns and not labels based on equipment alarms (e.g., low battery alarms, sensor disconnected alarms), user markers, or other event types. 
     In some embodiments, a user may select particular types of events to snap or jump to when the display control feature is activated. For example, a user may turn the knob  50  to select between various soft menu features  402  that represent different types of events (e.g., events, data pattern types) in a display  404 , as illustrated by the front view of a control panel  406  in  FIG. 11 . Turning the knob  50  may allow the user to navigate a menu or grouping of menu features  402  (e.g., buttons) and select the event type for the display control feature to seek out or jump to when it is activated. For example, a particular event type or set of event types may be selected by pressing the knob  50  when the button or menu item corresponding to the particular event type is highlighted or designated. In a specific example, a user may turn the knob  50  to guide a graphic arrow  408  such that it designates a desired one of the menu features  402 , and the user may then depress the knob  50  to select the feature. If the user desires to deselect the feature, the process may be repeated to remove it as a selected feature. Once the event type or types are designated, the knob  50  may be utilized to navigate to a browsing menu  410 , as illustrated in  FIG. 12 , which allows a user to select soft browsing buttons  412  by rotating the knob  50  to highlight the appropriate button and depressing the knob  50 . The selection of the soft browsing buttons  412  may activate the display control feature and cause the display to jump to the most recent designated event type in the indicated direction within a trend  414  of historical data. 
       FIG. 13  is a front view of a control panel  500  in accordance with an exemplary embodiment of the present disclosure. Specifically, the control panel  500  includes a display screen  502  disposed adjacent a plurality of display control mechanisms  504 . In the illustrated embodiment, the display screen  502  is displaying a trend  506  of data in an X-Y plot format. In other embodiments, different representations (e.g., bar graph, numerals, text) of the data may be employed. The control mechanisms  504  may include a dial  508 , a find-forward button  510 , a find-backward button  512 , a select button  514 , and/or a plurality of event designator buttons  516 . The buttons may be actual buttons or soft buttons. While the illustrated embodiment shows the control mechanisms  504  on the faceplate of an actual monitor, in other embodiments, the control mechanisms  504  may be icons on a display screen and/or features disposed on a remote control that communicates with the actual monitor. In one embodiment, the entire control panel  500  may be a virtual control panel (e.g., a functional graphic) on a display presented on the display screen  502 . It should be noted that if the display control feature is configured to only snap or jump to one type of event (e.g., detected desaturation patterns, or all detected events), the find-forward  510  and find-backward buttons  512  could be utilized without other features to simplify navigation of the historical data (e.g., trend  506 ). 
     The control mechanisms  504  may facilitate navigation through the history of the data (e.g., trend  506 ) represented on the display screen  502 . For example, a user may rotate the dial  508  to slowly scroll through historical data recorded as the trend  506 . The display of data may scroll in the direction that the dial  508  is rotated (i.e., counter-clockwise rotation of the dial scrolls the display back in time and clockwise rotation of the dial scrolls the display forward in time). The dial  508  may be substantially flush with the control panel  500 , with a circular indentation  518  on the outer perimeter that facilitates rotation by allowing a user to insert a finger tip into the indentation  518  to control movement. In another example, the user may forgo scrolling through historical data by pressing the find-forward button  510  or the find-backward button  512 , which may cause the display to jump to a certain event. In one embodiment, the view changes to include the most recent recognized event or selected event type in the direction indicated by the selected control mechanism  504  (e.g., find-backward button  512 ). For example, the monitor  10  may cause the screen  502  to display the last detected alarm when the find-backward button  512  is depressed or toggled from a real-time or standard operational display of the trending data  506 . In another example, pressing the find-forward button  510  from a location in the historical data may cause the display to jump to the next recognized event or selected event type toward the present. If no events are identified between the location being observed and a real-time display, the display may simply jump to the real-time display. 
     The display control feature may be configured for selective viewing of labels using the event designator buttons  516  or similar input features. For example, a user may select one or more event designator buttons  516  that are associated with particular events of interest (e.g., alarms, alarm types, detected patterns, pattern types, user marks). In a specific example, a user may want the display control feature to operate such that when activated it cycles through sleep apnea patterns detected in a trend of physiological data. Accordingly, the user may select the event designator button  516  corresponding to detected sleep apnea patterns, thus causing the monitor  10  to jump directly to the display of these detected events when the display control feature is activated. In other examples, multiple event types may be selected for such observation. For example, multiple event designator buttons  516  may be activated such that the display control feature snaps to various alarm types and pattern types. Controlling the types of events that the monitor  10  automatically displays upon activation of the display control feature allows for efficient use of the monitor  10 . 
     While the embodiments of the present disclosure may be susceptible to various modifications and alternative forms, specific embodiments have been shown by way of example in the drawings and will be described in detail herein. However, it should be understood that the present embodiments are not intended to be limited to the particular forms disclosed. Rather, present embodiments are to cover all modifications, equivalents and alternatives falling within the spirit and scope of present embodiments as defined by the following appended claims.