Abstract:
Systems and electronic processes for reducing electronic alarms in a medical patient monitoring system. For example, a system for reducing electronic alarms can include an optical sensor and one or more hardware processors in electronic communication with the optical sensor. The one or more hardware processors can be programmed to measure oxygen saturation values of a patient over a first period of time, determine if at least one oxygen saturation value obtained over the first period of time exceeds a first alarm threshold, determine whether a first alarm should be triggered based on the determination that the at least one oxygen saturation value obtained over the first period of time exceeds the first alarm threshold, determine a second alarm threshold to be applied during a second period of time subsequent to the first period of time, the second alarm threshold replacing the first alarm threshold.

Description:
PRIORITY CLAIM TO RELATED PROVISIONAL APPLICATIONS 
     The present application claims priority benefit under 35 U.S.C. §119(e) to U.S. patent application Ser. No. 13/037,184, filed Feb. 18, 2011 titled Adaptive Alarm System; Provisional Patent Application Ser. No. 61/309,419, filed Mar. 1, 2010 titled Adaptive Threshold Alarm System; and U.S. Provisional Patent Application Ser. No. 61/328,630, filed Apr. 27, 2010 titled Adaptive Alarm System; all of the above-cited provisional patent applications are hereby incorporated by reference herein. 
    
    
     BACKGROUND OF THE INVENTION 
     Pulse oximetry systems for measuring constituents of circulating blood have gained rapid acceptance in a wide variety of medical applications, including surgical wards, intensive care and neonatal units, general wards, home care, physical training, and virtually all types of monitoring scenarios. A pulse oximetry system generally includes an optical sensor applied to a patient, a monitor for processing sensor signals and displaying results and a patient cable electrically interconnecting the sensor and the monitor. A pulse oximetry sensor has light emitting diodes (LEDs), typically one emitting a red wavelength and one emitting an infrared (IR) wavelength, and a photodiode detector. The emitters and detector are typically attached to a finger, and the patient cable transmits drive signals to these emitters from the monitor. The emitters respond to the drive signals to transmit light into the fleshy fingertip tissue. The detector generates a signal responsive to the emitted light after attenuation by pulsatile blood flow within the fingertip. The patient cable transmits the detector signal to the monitor, which processes the signal to provide a numerical readout of physiological parameters such as oxygen saturation (SpO 2 ) and pulse rate. 
     SUMMARY OF THE INVENTION 
     Conventional pulse oximetry assumes that arterial blood is the only pulsatile blood flow in the measurement site. During patient motion, venous blood also moves, which causes errors in conventional pulse oximetry. Advanced pulse oximetry processes the venous blood signal so as to report true arterial oxygen saturation and pulse rate under conditions of patient movement. Advanced pulse oximetry also functions under conditions of low perfusion (small signal amplitude), intense ambient light (artificial or sunlight) and electrosurgical instrument interference, which are scenarios where conventional pulse oximetry tends to fail. 
     Advanced pulse oximetry is described in at least U.S. Pat. Nos. 6,770,028; 6,658,276; 6,157,850; 6,002,952; 5,769,785 and 5,758,644, which are assigned to Masimo Corporation (“Masimo”) of Irvine, Calif. and are incorporated by reference herein. Corresponding low noise optical sensors are disclosed in at least U.S. Pat. Nos. 6,985,764; 6,813,511; 6,792,300; 6,256,523; 6,088,607; 5,782,757 and 5,638,818, which are also assigned to Masimo and are also incorporated by reference herein. Advanced pulse oximetry systems including Masimo SET® low noise optical sensors and read through motion pulse oximetry monitors for measuring SpO 2 , pulse rate (PR) and perfusion index (PI) are available from Masimo. Optical sensors include any of Masimo LNOP®, LNCS®, SofTouch™ and Blue™ adhesive or reusable sensors. Pulse oximetry monitors include any of Masimo Rad-8®, Rad-5®, Rad®-5v or SatShare® monitors. 
     Advanced blood parameter measurement systems are described in at least U.S. Pat. No. 7,647,083, filed Mar. 1, 2006, titled Multiple Wavelength Sensor Equalization; U.S. Pat. No. 7,729,733, filed Mar. 1, 2006, titled Configurable Physiological Measurement System; U.S. Pat. Pub. No. 2006/0211925, filed Mar. 1, 2006, titled Physiological Parameter Confidence Measure and U.S. Pat. Pub. No. 2006/0238358, filed Mar. 1, 2006, titled Noninvasive Multi-Parameter Patient Monitor, all assigned to Masimo Laboratories, Irvine, Calif. (Masimo Labs) and all incorporated by reference herein. An advanced parameter measurement system that includes acoustic monitoring is described in U.S. Pat. Pub. No. 2010/0274099, filed Dec. 21, 2009, titled Acoustic Sensor Assembly, assigned to Masimo and incorporated by reference herein. 
     Advanced blood parameter measurement systems include Masimo Rainbow® SET, which provides measurements in addition to SpO 2 , such as total hemoglobin (SpHb™), oxygen content (SpOC™), methemoglobin (SpMet®), carboxyhemoglobin (SpCO®) and PVI®. Advanced blood parameter sensors include Masimo Rainbow® adhesive, ReSposable™ and reusable sensors. Advanced blood parameter monitors include Masimo Radical-7™, Rad-87™ and Rad-57™ monitors, all available from Masimo. Advanced parameter measurement systems may also include acoustic monitoring such as acoustic respiration rate (RRa™) using a Rainbow Acoustic Sensor™ and Rad-87™ monitor, available from Masimo. Such advanced pulse oximeters, low noise sensors and advanced physiological parameter measurement systems have also gained rapid acceptance in a wide variety of medical applications, including surgical wards, intensive care and neonatal units, general wards, home care, physical training, and virtually all types of monitoring scenarios. 
       FIGS. 1-3  illustrate problems and issues associated with physiological parameter measurement systems having fixed threshold alarm schemas.  FIG. 1  illustrates a lower-limit, fixed-threshold alarm schema with respect to an oxygen saturation (SpO 2 ) parameter. Two alarm thresholds, D L  (delay) and ND L  (no delay), are defined. If oxygen saturation falls below D L  for a time delay greater than TD, an alarm is triggered. If oxygen saturation falls below ND L  an alarm is immediately triggered. D L    120  is typically set around or somewhat above 90% oxygen saturation and ND L    130  is typically set at 5% to 10% below D L . For example, say a person&#39;s oxygen saturation  110  drops below D L    120  at t=t 1    162  and stays below D L  for at least a time delay TD  163 . This triggers a delayed alarm  140  at t=t 2    164 , where t 2 =t 1 +TD. The alarm  140  remains active until oxygen saturation  110  rises above D L    120  at t=t 3    166 . As another example, say that oxygen saturation  110  then drops below ND L    130 , which triggers an immediate alarm  150  at t=t 4   168 . The alarm  150  remains active until oxygen saturation  110  rises above D L    120  at t=t 5    169 . 
       FIG. 2  illustrates an upper-limit, fixed-threshold alarm schema with respect to an oxygen saturation (SpO 2 ) parameter. This alarm scenario is particularly applicable to the avoidance of ROP (retinopathy of prematurity). Again, two alarm thresholds, D U  (delay) and ND U  (no delay), are defined. D U    220  might be set at or around 85% oxygen saturation and ND U    230  might be set at or around 90% oxygen saturation. For example, a neonate&#39;s oxygen saturation  210  rises above D U    220  at t=t 1    262  and stays above D U  for at least a time delay TD  263 . This triggers a delayed alarm  240  at t=t 2    264 , where t 2 =t 1 +TD. The alarm  240  remains active until oxygen saturation  210  falls below D U    220  at t=t 3    166 . Oxygen saturation  210  then rises above ND U    230 , which triggers an immediate alarm  250  at t=t 4    268 . The alarm  250  remains active until oxygen saturation  210  falls below D U    220  at t=t 5    269 . 
       FIG. 3  illustrates a baseline drift problem with the fixed threshold alarm schema described above. A person&#39;s oxygen saturation is plotted on an oxygen saturation (SpO 2 ) versus time graph  300 . In particular, during a first time interval T 1    362 , a person has an oxygen saturation  310  with a relatively stable “baseline”  312  punctuated by a shallow, transient desaturation event  314 . This scenario may occur after the person has been on oxygen so that baseline oxygen saturation is near 100%. Accordingly, with a fixed threshold alarm  330  set at, say, 90%, the transient event  314  does not trigger a nuisance alarm. However, the effects of oxygen treatments wear off over time and oxygen saturation levels drift downward  350 . In particular, during a second time interval T 2    364 , a person has an oxygen saturation  320  with a relatively stable baseline  322 . The later baseline  322  is established at a substantially lower oxygen saturation than the earlier baseline  312 . In this scenario, a shallow, transient desaturation event  324  now exceeds the alarm threshold  330  and results in a nuisance alarm. After many such nuisance alarms, a caregiver may lower the alarm threshold  330  to unsafe levels or turn off alarms altogether, significantly hampering the effectiveness of monitoring oxygen saturation. 
     A fixed threshold alarm schema is described above with respect to an oxygen saturation parameter, such as derived from a pulse oximeter. However, problematic fixed threshold alarm behavior may be exhibited in a variety of parameter measurement systems that calculate physiological parameters related to circulatory, respiratory, neurological, gastrointestinal, urinary, immune, musculoskeletal, endocrine or reproductive systems, such as the circulatory and respiratory parameters cited above, as but a few examples. 
     An adaptive alarm system, as described in detail below, advantageously provides an adaptive threshold alarm to solve false alarm and missed true alarm problems associated with baseline drift among other issues. For example, for a lower limit embodiment, an adaptive alarm system adjusts an alarm threshold downwards when a parameter baseline is established at lower values. Likewise, for an upper limit embodiment, the adaptive alarm system adjusts an alarm threshold upwards in accordance with baseline drift so as to avoid nuisance alarms. In an embodiment, the rate of baseline movement is limited so as to avoid masking of transients. In an embodiment, the baseline is established along upper or lower portions of a parameter envelop so as to provide a margin of safety in lower limit or upper limit systems, respectively. 
     One aspect of an adaptive alarm system is responsive to a physiological parameter so as to generate an alarm threshold that adapts to baseline drift in the parameter and reduce false alarms without a corresponding increase in missed true alarms. The adaptive alarm system has a parameter derived from a physiological measurement system using a sensor in communication with a living being. A baseline processor calculates a parameter baseline from an average value of the parameter. Parameter limits specify an allowable range of the parameter. An adaptive threshold processor calculates an adaptive threshold from the parameter baseline and the parameter limits. An alarm generator is responsive to the parameter and the adaptive threshold so as to trigger an alarm indicative of the parameter crossing the adaptive threshold. The adaptive threshold is responsive to the parameter baseline so as to increase in value as the parameter baseline drifts to a higher parameter value and to decrease in value as the parameter baseline drifts to a lower parameter value. 
     In various embodiments, the baseline processor has a sliding window that identifies a time slice of parameter values. A trend calculator determines a trend from an average of the parameter values in the time slice. A response limiter tracks only the relatively long-term transitions of the trend. A bias calculator deletes the highest parameter values in the time slice or the lowest parameter values in the time slice so as to adjust the baseline to either a lower value or a higher value, respectively. The adaptive threshold becomes less response to baseline drift as the baseline approaches a predefined parameter limit. A first adaptive threshold is responsive to lower parameter limits and a second adaptive threshold is responsive to upper parameter limits. The alarm generator is responsive to both positive and negative transients from the baseline according to the first adaptive threshold and the second adaptive threshold. The first adaptive threshold is increasingly responsive to negative transients and the second adaptive threshold is decreasingly responsive to positive transients as the baseline trends toward lower parameter values. 
     Another aspect of an adaptive alarm system measures a physiological parameter, establishes a baseline for the parameter, adjusts an alarm threshold according to drift of the baseline and triggers an alarm in response to the parameter measurement crossing the alarm threshold. In various embodiments, the baseline is established by biasing a segment of the parameter, calculating a biased trend from the biased segment and restricting the transient response of the biased trend. The alarm threshold is adjusted by setting a parameter limit and calculating a delta difference between the alarm threshold and the baseline as a linear function of the baseline according to the parameter limit. The delta difference is calculated by decreasing delta as the baseline drifts toward the parameter limit and increasing delta as the baseline drifts away from the parameter limit. A parameter limit is set by selecting a first parameter limit in relation to a delayed alarm and selecting a second parameter limit in relation to an un-delayed alarm. A segment of the parameter is biased by windowing the parameter measurements, removing a lower value portion of the windowed parameter measurements and averaging a remaining portion of the windowed parameter measurements. An upper delta difference between an upper alarm threshold and the baseline is calculated and a lower delta difference between a lower alarm threshold and the baseline is calculated. 
     A further aspect of an adaptive alarm system has a baseline processor that inputs a parameter and outputs a baseline according to a trend of the parameter. An adaptive threshold processor establishes an alarm threshold at a delta difference from the baseline. An alarm generator triggers an alarm based upon a parameter transient from the baseline crossing the alarm threshold. In various embodiments, a trend calculator outputs a biased trend and the baseline is responsive to the biased trend so as to reduce the size of a transient that triggers the alarm. A response limiter reduces baseline movement due to parameter transients. The adaptive threshold processor establishes a lower alarm threshold below the baseline and an upper alarm threshold above the baseline so that the alarm generator is responsive to both positive and negative transients from the baseline. The baseline processor establishes a lower baseline biased above the parameter trend and an upper baseline biased below the parameter trend. The lower alarm threshold is increasingly responsive to negative transients and the upper alarm threshold is decreasingly responsive to positive transients as the baseline trends toward lower parameter values. 
    
    
     
       DESCRIPTION OF THE DRAWINGS 
         FIGS. 1-3  are exemplar graphs illustrating problems and issues associated with physiological parameter measurement systems having fixed threshold alarm schemas; 
         FIGS. 4A-B  are general block diagrams of an adaptive alarm system having lower parameter limits; 
         FIGS. 5A-B  are a graph of a physiological parameter versus delta space and a graph of delta versus baseline, respectively, illustrating the relationship between a baseline, a lower-limit adaptive threshold and a variable difference delta between the baseline and the adaptive threshold; 
         FIG. 6  is an exemplar graph of a physiological parameter versus time illustrating an adaptive alarm system having a lower-limit adaptive threshold; 
         FIG. 7  is a graph of oxygen saturation versus time illustrating a baseline for determining an adaptive threshold; 
         FIG. 8  is a graph of oxygen saturation versus time comparing adaptive-threshold alarm performance with fixed-threshold alarm performance; 
         FIGS. 9A-B  are general block diagrams of an adaptive alarm system having upper parameter limits; 
         FIGS. 10A-B  are a graph of a physiological parameter versus delta space and a graph of delta versus baseline, respectively, illustrating the relationship between a baseline, an upper-limit adaptive threshold and a variable delta difference between the baseline and the adaptive threshold; 
         FIG. 11  is an exemplar graph of a physiological parameter versus time illustrating an adaptive alarm system having an upper-limit adaptive threshold; 
         FIGS. 12A-B  are general block diagrams of an adaptive alarm system having both lower alarm limits and upper alarm limits; 
         FIGS. 13A-E  are physiological parameter versus delta space graphs illustrating a lower-limit adaptive threshold, an upper-limit adaptive threshold, and a combined lower- and upper-limit adaptive threshold in various delta spaces; and 
         FIG. 14  is an exemplar graph of a physiological parameter versus time illustrating an adaptive alarm system having both lower and upper alarm limits. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
       FIGS. 4A-B  illustrate an adaptive alarm system  400  embodiment having lower parameter limits L 1  and L 2 . As shown in  FIG. 4A , the adaptive alarm system  400  has parameter  401 , first limit (L 1 )  403 , second limit (L 2 )  405  and maximum parameter value (Max)  406  inputs and generates a corresponding alarm  412  output. The parameter  401  input is generated by a physiological parameter processor, such as a pulse oximeter or an advanced blood parameter processor described above, as examples. The adaptive alarm system  400  has an alarm generator  410 , a baseline processor  420 , and an adaptive threshold processor  440 . The alarm generator  410  has parameter  401  and adaptive threshold (AT)  442  inputs and generates the alarm  412  output accordingly. A baseline processor  420  has the parameter  401  input and generates a parameter baseline (B)  422  output. The baseline processor  420 , is described in detail with respect to  FIG. 4B , below. An adaptive threshold processor  440  has parameter baseline (B)  422 , L 1    403 , L 2    405  and Max  406  inputs and generates the adaptive threshold (AT)  442 . The adaptive threshold processor  440  is described in detail with respect to  FIGS. 5A-B , below. 
     As shown in  FIG. 4A , in an embodiment L 1    403  and L 2    405  may correspond to conventional fixed alarm thresholds with and without an alarm time delay, respectively. For an adaptive threshold schema, however, L 1    403  and L 2    405  do not determine an alarm threshold per se, but are reference levels for determining an adaptive threshold (AT)  442 . In an embodiment, L 1    403  is an upper limit of the adaptive alarm threshold AT when the baseline is near the maximum parameter value (Max), and L 2    405  is a lower limit of the adaptive alarm threshold, as described in detail with respect to  FIGS. 5A-B , below. In an exemplar embodiment when the parameter is oxygen saturation, L 1    403  is set at or around 90% and L 2    405  is set at 5 to 10% below L 1 , i.e. at 85% to 80% oxygen saturation. Many other L 1  and L 2  values may be used for an adaptive threshold schema as described herein. 
     Also shown in  FIG. 4A , in an embodiment the alarm  412  output is triggered when the parameter  401  input falls below AT  442  and ends when the parameter  401  input rises above AT  442  or is otherwise cancelled. In an embodiment, the alarm  412  output is triggered after a time delay (TD), which may be fixed or variable. In an embodiment, the time delay (TD) is a function of the adaptive threshold (AT)  442 . In an embodiment, the time delay (TD) is zero when the adaptive threshold (AT) is at the second lower limit (L 2 )  405 . 
     As shown in  FIG. 4B , a baseline processor  420  embodiment has a sliding window  450 , a bias calculator  460 , a trend calculator  470  and a response limiter  480 . The sliding window  450  inputs the parameter  401  and outputs a time segment  452  of the parameter  401 . In an embodiment, each window incorporates a five minute span of parameter values. The bias calculator  460  advantageously provides an upward shift in the baseline (B)  422  for an additional margin of error over missed true alarms. That is, a baseline  422  is generated that tracks a higher-than-average range of parameter values, effectively raising the adaptive threshold AT slightly above a threshold calculated based upon a true parameter average, as shown and described in detail with respect to  FIGS. 7-8 , below. In an embodiment, the bias calculator  460  rejects a lower range of parameter values from each time segment  452  from the sliding window so as to generate a biased time segment  462 . 
     Also shown in  FIG. 4B , the trend calculator  470  outputs a biased trend  472  of the remaining higher range of parameter values in each biased segment  462 . In an embodiment, the biased trend  462  is an average of the values in the biased time segment  462 . In other embodiments, the biased trend  462  is a median or mode of the values in the biased time segment  462 . The response limiter  480  advantageously limits the extent to which the baseline  422  output tracks the biased trend  472 . Accordingly, the baseline  422  tracks only relatively longer-lived transitions of the parameter, but does not track (and hence mask) physiologically significant parameter events, such as oxygen desaturations for a SpO 2  parameter to name but one example. In an embodiment, the response limiter  480  has a low pass transfer function. In an embodiment, the response limiter  480  is a slew rate limiter. 
       FIGS. 5A-B  further illustrate an adaptive threshold processor  440  ( FIG. 4A ) having a baseline (B)  422  input and generating an adaptive threshold (AT)  442  output and a delta (Δ)  444  ancillary output according to parameter limits L 1    403 , L 2    405  and Max  406 , as described above. As shown in  FIG. 5A , as the baseline (B)  422  decreases (increases) the adaptive threshold (AT)  444  monotonically decreases (increases) between L 1    403  and L 2    405 . Further, as the baseline (B)  422  decreases (increases) the delta (Δ)  444  difference between the baseline (B)  422  and the adaptive threshold (AT)  442  monotonically decreases (increases) between Max−L 1  and zero. 
     As shown in  FIG. 5B , the relationship between the delta (Δ)  444  and the baseline (B)  444  may be linear  550  (solid line), non-linear  560  (small-dash lines) or piecewise-linear (large-dash lines), to name a few. In an embodiment, the adaptive threshold processor  440  ( FIG. 4A ) calculates an adaptive threshold (AT)  442  output in response to the baseline (B)  422  input according to a linear relationship. In a linear embodiment, the adaptive threshold processor  440  ( FIG. 4A ) calculates the adaptive threshold (AT)  442  according to EQS. 1-2: 
                   Δ   =         -     (       Max   -     L   1         Max   -     L   2         )       ⁢     (     Max   -   B     )       +     (     Max   -     L   1       )               (   1   )               AT   =     B   -   Δ             (   2   )               
where Δ=Max−L 1  @ B=Max; Δ=0 @ B=L 2  
 
and where AT=L 1  @ B=Max; AT=L 2  @ B=L 2 , accordingly.
 
       FIG. 6  illustrates the operational characteristics an adaptive alarm system  400  ( FIG. 4A ) having parameter limits Max  612 , L 1    614  and L 2    616  and an alarm responsive to a baseline (B)  622 ,  632 ,  642 ; an adaptive threshold (AT)  628 ,  638 ,  648 ; and a corresponding Δ  626 ,  636 ,  646  according to EQS. 1-2, above. In particular, a physiological parameter  610  is graphed versus time  690  for various time segments t 1 , t 2 , t 3    692 - 696 . The parameter range (PR)  650  is:
 
PR=Max− L   2   (3)
 
and the adaptive threshold range (ATR)  660  is:
 
ATR= L   1   −L   2   (4)
 
     As shown in  FIG. 6 , during a first time period t 1    692 , a parameter segment  620  has a baseline (B)  622  at about Max  612 . As such, Δ  626 =Max−L 1  and the adaptive threshold (AT)  628  is at about L 1    614 . Accordingly, a transient  624  having a size less than Δ  626  does not trigger the alarm  412  ( FIG. 4A ). 
     Also shown in  FIG. 6 , during a second time period t 2    694 , a parameter segment  630  has a baseline (B)  632  at about L 1    614 . As such, Δ  636  is less than Max−L 1  and the adaptive threshold (AT)  638  is between L 1  and L 2 . Accordingly, a smaller transient  634  will trigger the alarm as compared to a transient  624  in the first time segment. 
     Further shown in  FIG. 6 , during a third time period t 3    696 , a parameter segment  640  has a baseline (B)  642  at about L 2    616 . As such, Δ  646  is about zero and the adaptive threshold (AT)  648  is at about L 2 . Accordingly, even a small negative transient will trigger the alarm. As such, the behavior of the alarm threshold AT  628 ,  638 ,  648  advantageously adapts to higher or lower baseline values so as to increase or decrease the size of negative transients that trigger or do not trigger the alarm  412  ( FIG. 4A ). 
       FIG. 7  is a parameter versus time graph  700  illustrating the characteristics of an adaptive alarm system  400  ( FIGS. 4A-B ), as described with respect to  FIGS. 4-6 , above, where the parameter is oxygen saturation (SpO 2 ). The graph  700  has a SpO 2  trace  710  and a superimposed baseline trace  720 . The graph  700  also delineates tracking periods  730 , where the baseline  720  follows the upper portions of SpO 2  values, and lagging periods  740 , where the baseline  720  does not follow transient SpO 2  events. The tracking time periods  730  illustrate that the baseline  720  advantageously tracks at the higher range of SpO 2  values  710  during relatively stable (flat) periods, as described above. Lagging time periods  740  illustrate that the baseline  720  is advantageously limited in response to transient desaturation events so that significant desaturations fall below an adaptive threshold (not shown) and trigger an alarm accordingly. 
       FIG. 8  is a parameter versus time graph  800  illustrating characteristics of an adaptive alarm system  400  ( FIGS. 4A-B ), as described with respect to  FIGS. 4-6 , above, where the parameter is oxygen saturation (SpO 2 ). Vertical axis (SpO 2 ) resolution is 1%. The time interval  801  between vertical hash marks is five minutes. The graph  800  has a SpO 2  trace  810  and a baseline trace  820 . The graph  800  also has a fixed threshold trace  830 , a first adaptive threshold (AT) trace  840  and a second AT trace  850 . The graph  800  further has a fixed threshold alarm trace  860 , a first adaptive threshold alarm trace  870  and a second adaptive threshold alarm trace  880 . In this example, L 1  is 90% and L 2  is 85% for the first AT trace  840  and first AT alarm trace  870 . L 2  is 80% for a second AT trace  850  and a second AT alarm trace  880 . The fixed threshold  830  results in many nuisance alarms  860 . By comparison, the adaptive threshold alarm with L 2 =85% has just one time interval of alarms  872  during a roughly 6% desaturation period (from 92% to 86%). The adaptive threshold alarm with L 2 =80%, has no alarms during the 1 hour 25 minute monitoring period. 
       FIGS. 9A-B  illustrate an adaptive alarm system  900  embodiment having upper parameter limits U 1  and U 2 . As shown in  FIG. 9A , the adaptive alarm system  900  has parameter  901 , first limit (U 1 )  903 , second limit (U 2 )  905  and minimum parameter value (Min)  906  inputs and generates a corresponding alarm  912  output. The parameter  901  input is generated by a physiological parameter processor, such as a pulse oximeter or an advanced blood parameter processor described above, as examples. The adaptive alarm system  900  has an alarm generator  910 , a baseline processor  920 , and an adaptive threshold processor  940 . The alarm generator  910  has parameter  901  and adaptive threshold (AT)  942  inputs and generates the alarm  912  output accordingly. A baseline processor  920  has the parameter  901  input and generates a parameter baseline (B)  922  output. The baseline processor  920 , is described in detail with respect to  FIG. 9B , below. An adaptive threshold processor  940  has parameter baseline (B)  922 , U 1    903 , U 2    905  and Min  906  inputs and generates the adaptive threshold (AT)  942 . The adaptive threshold processor  940  is described in detail with respect to  FIGS. 10A-B , below. 
     As shown in  FIG. 9A , in an embodiment U 1    903  and U 2    905  may correspond to conventional fixed alarm thresholds with and without an alarm time delay, respectively. For an adaptive threshold schema, however, U 1    903  and U 2    905  do not determine an alarm threshold per se, but are reference levels for determining an adaptive threshold (AT)  942 . In an embodiment, U 1    903  is a lower limit of the adaptive alarm threshold AT when the baseline is near the minimum parameter value (Min), and U 2    905  is an upper limit of the adaptive alarm threshold, as described in detail with respect to  FIGS. 10A-B , below. In an exemplar embodiment when the parameter is oxygen saturation, U 1    903  is set at or around 85% and U 2    905  is set at or around 90% oxygen saturation. Many other U 1  and U 2  values may be used for an adaptive threshold schema as described herein. 
     Also shown in  FIG. 9A , in an embodiment the alarm  912  output is triggered when the parameter  901  input rises above AT  942  and ends when the parameter  901  input falls below AT  942  or is otherwise cancelled. In an embodiment, the alarm  912  output is triggered after a time delay (TD), which may be fixed or variable. In an embodiment, the time delay (TD) is a function of the adaptive threshold (AT)  942 . In an embodiment, the time delay (TD) is zero when the adaptive threshold (AT) is at the second upper limit (U 2 )  905 . 
     As shown in  FIG. 9B , a baseline processor  920  embodiment has a sliding window  950 , a bias calculator  960 , a trend calculator  970  and a response limiter  980 . The sliding window  950  inputs the parameter  901  and outputs a time segment  952  of the parameter  901 . In an embodiment, each window incorporates a five minute span of parameter values. The bias calculator  960  advantageously provides a downward shift in the baseline (B)  922  for an additional margin of error over missed true alarms. That is, a baseline  922  is generated that tracks a lower-than-average range of parameter values, effectively lowering the adaptive threshold AT slightly below a threshold calculated based upon a true parameter average. In an embodiment, the bias calculator  960  rejects an upper range of parameter values from each time segment  952  from the sliding window so as to generate a biased time segment  962 . 
     Also shown in  FIG. 9B , the trend calculator  970  outputs a biased trend  972  of the remaining lower range of parameter values in each biased segment  962 . In an embodiment, the biased trend  962  is an average of the values in the biased time segment  962 . In other embodiments, the biased trend  962  is a median or mode of the values in the biased time segment  962 . The response limiter  980  advantageously limits the extent to which the baseline  922  output tracks the biased trend  972 . Accordingly, the baseline  922  tracks only relatively longer-lived transitions of the parameter, but does not track (and hence mask) physiologically significant parameter events, such as oxygen desaturations for a SpO 2  parameter to name but one example. In an embodiment, the response limiter  980  has a low pass transfer function. In an embodiment, the response limiter  980  is a slew rate limiter. 
       FIGS. 10A-B  further illustrate an adaptive threshold processor  940  ( FIG. 9A ) having a baseline (B)  922  input and generating an adaptive threshold (AT)  942  output and a delta (Δ)  944  ancillary output according to parameter limits U 1    903 , U 2    905  and Min  906 , as described above. As shown in  FIG. 10A , as the baseline (B)  922  decreases (increases) the adaptive threshold (AT)  944  monotonically decreases (increases) between U 1    903  and U 2    905 . Further, as the baseline (B)  922  decreases (increases) the delta (Δ)  944  difference between the baseline (B)  922  and the adaptive threshold (AT)  942  monotonically decreases (increases) between Min−U 1  and zero. 
     As shown in  FIG. 10B , the relationship between the delta (Δ)  944  and the baseline (B)  944  may be linear  550  (solid line), non-linear  560  (small-dash lines) or piecewise-linear (large-dash lines), to name a few. In an embodiment, the adaptive threshold processor  940  ( FIG. 9A ) calculates an adaptive threshold (AT)  942  output in response to the baseline (B)  922  input according to a linear relationship. In a linear embodiment, the adaptive threshold processor  940  ( FIG. 9A ) calculates the adaptive threshold (AT)  942  according to EQS. 5-6: 
                   Δ   =         -     (         U   1     -   Min         U   2     -   Min       )       ⁢     (     B   -   Min     )       +     (       U   1     -   Min     )               (   5   )               AT   =     B   +   Δ             (   6   )               
where Δ=U 1 −Min @ B=Min; Δ=0 @ B=U 2  
 
and where AT=U 1  @ B=Min; AT=U 2  @ B=U 2 , accordingly.
 
       FIG. 11  illustrates the operational characteristics an adaptive alarm system  900  ( FIG. 9A ) having parameter limits Min  1112 , U 1    1114  and U 2    1116  and an alarm responsive to a baseline (B)  1122 ,  1132 ,  1142 ; an adaptive threshold (AT)  1128 ,  1138 ,  1148 ; and a corresponding Δ  1126 ,  1136 ,  1146  according to EQS. 5-6, above. In particular, a physiological parameter  1110  is graphed versus time  1190  for various time segments t 1 , t 2 , t 3    1192 - 1196 . The parameter range (PR)  1150  is:
 
PR= U   2 −Min  (7)
 
and the adaptive threshold range (ATR)  1160  is:
 
ATR= U   2   −U   1   (8)
 
     As shown in  FIG. 11 , during a first time period t 1    1192 , a parameter segment  1120  has a baseline (B)  1122  at about Min  1112 . As such, Δ  1126 =U 1 −Min and the adaptive threshold (AT)  1128  is at about U 1    1114 . Accordingly, a transient  1124  having a size less than Δ  1126  does not trigger the alarm  912  ( FIG. 9A ). 
     Also shown in  FIG. 11 , during a second time period t 2    1194 , a parameter segment  1130  has a baseline (B)  1132  at about U 1    1114 . As such, Δ  1136  is less than U 1 −Min and the adaptive threshold (AT)  1138  is between U 1  and U 2 . Accordingly, a smaller transient  1134  will trigger the alarm as compared to a transient  1124  in the first time segment. 
     Further shown in  FIG. 11 , during a third time period t 3    1196 , a parameter segment  1140  has a baseline (B)  1142  at about U 2    1116 . As such, Δ  1146  is about zero and the adaptive threshold (AT)  1148  is at about U 2 . Accordingly, even a small positive transient will trigger the alarm. As such, the behavior of the alarm threshold AT  1128 ,  1138 ,  1148  advantageously adapts to higher or lower baseline values so as to increase or decrease the size of positive transients that trigger or do not trigger the alarm  912  ( FIG. 9A ). 
       FIGS. 12A-B  illustrate an adaptive alarm system  1200  embodiment having lower limits L 1 , L 2    1203 , such as described with respect to  FIGS. 4A-B  above, or upper limits U 1 , U 2    1205  such as described with respect to  FIGS. 9A-B  above, or both. As shown in  FIG. 12A , the adaptive alarm system  1200  has parameter  1201 , lower limit  1203  and upper limit  1205  inputs and generates a corresponding alarm  1212  output. The parameter  1201  input is generated by a physiological parameter processor, such as a pulse oximeter or an advanced blood parameter processor described above, as examples. The adaptive alarm system  1200  has an alarm generator  1210 , a baseline processor  1220  and an adaptive threshold processor  1240 . The alarm generator  1210  has parameter  1201  and adaptive threshold (AT)  1242  inputs and generates the alarm  1212  output accordingly. A baseline processor  1220  has the parameter  1201  input and generates one or more parameter baseline  1222  outputs. The baseline processor  1220 , is described in detail with respect to  FIG. 12B , below. An adaptive threshold processor  1240  has parameter baseline  1222 , lower limit L 1 , L 2    1203  and upper limit U 1 , U 2    1205  inputs and generates lower and upper adaptive threshold AT l , AT u    1242  outputs. The adaptive threshold processor  1240  also generates ancillary upper and lower delta  1244  outputs. The adaptive threshold processor  1240  is described in detail with respect to  FIGS. 13A-E , below. 
     As shown in  FIG. 12A , in an embodiment L 1 , L 2    1203  and U 1 , U 2    1205  may correspond to conventional fixed alarm thresholds with an alarm delay (L 1 , U 1 ) and without an alarm delay (L 2 , U 2 ). For an adaptive threshold schema, however, these limits  1203 ,  1205  do not determine an alarm threshold per se, but are reference levels for determining lower and upper adaptive thresholds AT l , AT u    1242 . 
     Also shown in  FIG. 12A , in an embodiment the alarm  1212  output is triggered when the parameter  1201  input falls below AT l    1242  and ends when the parameter  1201  input rises above AT l    1242  or the alarm is otherwise cancelled. Further, the alarm  1212  output is triggered when the parameter  1201  input rises above AT u    1242  and ends when the parameter  1201  input falls below AT u    1242  or the alarm is otherwise cancelled. In an embodiment, the alarm  1212  output is triggered after a time delay (TD), which may be fixed or variable. In an embodiment, the time delay (TD) is a function of the adaptive thresholds (AT l , AT u )  1242 . In an embodiment, the time delay (TD) is zero when the lower adaptive threshold (AT l )  1242  is at the second lower limit (L 2 )  1203  or when the upper adaptive alarm threshold AT u    1242  is at the second upper limit (U 2 )  1205 . 
     As shown in  FIG. 12B , a baseline processor  1220  embodiment has a sliding window  1250 , an over-bias calculator  1260 , an under-bias calculator  1265 , trend calculators  1270  and response limiters  1280 . The sliding window  1250  inputs the parameter  1201  and outputs a time segment  1252  of the parameter  1201 . In an embodiment, each window incorporates a five minute span of parameter  1201  values. 
     Also shown in  FIG. 12B , the over-bias calculator  1260  advantageously provides an upward shift in the lower baseline (B l )  1282  for an additional margin of error over missed lower true alarms. That is, a lower baseline (B l )  1282  is generated that tracks a higher-than-average range of parameter values, effectively raising the lower adaptive threshold AT l  slightly above a threshold calculated based upon a true parameter average. In an embodiment, the over-bias calculator  1260  rejects a lower range of parameter values from each time segment  1252  of the sliding window  1250  so as to generate an over-biased time segment  1262 . 
     Further shown in  FIG. 12B , the under-bias calculator  1265  advantageously provides a downward shift in the upper baseline (B u )  1287  for an additional margin of error over missed upper true alarms. That is, an upper baseline (B u )  1287  is generated that tracks a lower-than-average range of parameter values, effectively lowering the upper adaptive threshold AT u  slightly below a threshold calculated based upon a true parameter average. In an embodiment, the under-bias calculator  1267  rejects an upper range of parameter values from each time segment  1252  of the sliding window  1250  so as to generate an under-biased time segment  1267 . 
     Additionally shown in  FIG. 12B , the trend calculator  1270  outputs an over-biased trend  1272  of the remaining higher range of parameter values in each over-biased segment  1262 . Further, the trend calculator  1270  outputs an under-biased trend  1277  of the remaining lower range of parameter values in each under-biased segment  1267 . In an embodiment, the biased trends  1272 ,  1277  are each an average of the values in the corresponding biased time segments  1262 ,  1267 . In other embodiments, the biased trends  1272 ,  1277  are each a median or mode of the values in the corresponding biased time segments  1262 ,  1267 . The response limiter  1280  advantageously limits the extent to which the baseline  1222  outputs track the biased trends  1272 ,  1277 . Accordingly, the baseline  1222  outputs track only relatively longer-lived transitions of the parameter  1201 , but do not track (and hence mask) physiologically significant parameter events. In an embodiment, the response limiter  1280  has a low pass transfer function. In an embodiment, the response limiter  1280  is a slew rate limiter. 
       FIGS. 13A-E  illustrate parameter (P) operating ranges and ideal ranges in view of both lower and upper parameter limits. As shown in  FIG. 13A , as the baseline (B l )  1317  decreases (increases) the adaptive threshold (AT l )  1318  monotonically decreases (increases) between L 1  and L 2 . Further, as the baseline (B l )  1317  decreases (increases) the delta (Δ l )  1319  difference between the baseline (B l )  1317  and the adaptive threshold (AT l )  1318  monotonically decreases (increases) between Max−L 1  and 0. 
     As shown in  FIG. 13B , as the baseline (B u )  1327  increases (decreases) the adaptive threshold (AT u )  1328  monotonically increases (decreases) between U 1  and U 2 . Further, as the baseline (B u )  1327  increases (decreases) the delta (Δ u )  1329  difference between the adaptive threshold (AT u )  1328  and the baseline (B u )  1327  monotonically decreases (increases) between Min−U 1  and 0. 
     As shown in  FIG. 13C , combining  FIGS. 13A-B , the parameter (P) operating range is bounded by the overlapping regions of  13 A and  13 B  1330  having an upper bound of U 2  and a lower bound of L 2 . In particular, L 1 , L 2  are the upper and lower limits of the lower adaptive alarm threshold AT l ; and U 2 , U 1  are the upper and lower limits of the upper adaptive alarm threshold AT u . 
       FIG. 13D  illustrates parameter (P) versus the overlapping independent delta domains F u , F l  for upper and lower baselines B u , B l ; adaptive thresholds AT u , AT l  and deltas Δ u , Δ l , based upon  FIGS. 13A-C .  FIG. 13E  illustrates parameter (P) versus the overlapping independent delta domains F u , F l  (reversed); for upper and lower baselines B u , B l ; adaptive thresholds AT u , AT l  and deltas Δ u , Δ l , 
     As shown in  FIG. 13E , the equations for bi-lateral adaptive thresholds are: 
                     Δ   u     =         -     (         U   1     -     L   2           U   2     -     L   2         )       ⁢     (     B   -     L   2       )       +     (       U   1     -     L   2       )               (   9   )                 AT   u     =     B   +     Δ   u               (   10   )               
where Δ u =U 1 −L 2  @ B=L 2 ; and Δ u =0 @ B=U 2 ; and
 
where AT u =U 1  @ B=L 2 ; and AT u =U 2  @ B=U 2 .
 
Further:
 
                     Δ   l     =       (         U   2     -     L   1           U   2     -     L   2         )     ⁢     (     B   -     L   2       )               (   11   )                 AT   l     =     B   -     Δ   l               (   12   )               
where Δ l =U 2 −L 1  @ B=U 2 ; and Δ l =0 @ B=L 2 ; and
 
where AT l =L 1  @ B=U 2 ; AT l =L 2  @ B=L 2 .
 
     Although shown as a linear relationship, in general:
 
Δ l   =f   1 ( B );Δ u   =f   2 ( B )
 
That is, Δ l  and Δ u  can each be a linear function of B, a non-linear function of B or a piecewise linear function of B, to name a few, in a manner similar to that described with respect to  FIGS. 5B and 10B , above.
 
       FIGS. 14A-B  illustrate the operational characteristics an adaptive alarm system  1200  ( FIGS. 12A-B ) having upper limits U 1 , U 2    1412 ,  1414  and lower limits L 1 , L 2    1422 ,  1424 . An alarm  1212  ( FIG. 12A ) output is responsive to a baseline (B)  1432 ,  1442 ,  1452 ,  1462 ; an upper delta (Δ u )  1437 ,  1447 ,  1457 ,  1467 ; and a corresponding upper adaptive threshold (AT u )  1439 ,  1449 ,  1459 ,  1469 , according to EQS. 9-10, above. Further, the alarm  1212  ( FIG. 12A ) output is responsive to a lower delta (Δ l )  1436 ,  1446 ,  1456 ,  1466  and a corresponding lower adaptive threshold (AT l )  1438 ,  1448 ,  1458 ,  1468 , according to EQS. 11-12, above. 
     As shown in  FIGS. 14A-B , a physiological parameter  1410  is graphed versus time  1490  for various time segments t 1 , t 2 , t 3 , t 4    1492 - 1498 . The parameter range (PR)  1480  is:
 
PR= U   2   −L   2   (13)
 
the lower adaptive threshold AT l  range is:
 
ATR l   =L   1   −L   2   (14)
 
the upper adaptive threshold AT U  range is:
 
ATR l   =U   2   −U   1   (15)
 
     As shown in  FIG. 14A , during a first time period t 1    1492 , a parameter segment  1430  has a baseline (B)  1432  at about U 2    1414 . As such, Δ l    1436 =U 2 −L 1 ; Δ u    1437 =0; AT l    1438 =L 1 ; AT u    1439 =U 2 . Accordingly, a negative transient  1434  having a size less than U 2 −L 1  does not trigger an alarm. 
     Also shown in  FIG. 14A , during a second time period t 2    1494 , a parameter segment  1440  has a baseline (B)  1442  less than U 2 . As such, Δ l    1446  is less than U 1 −L 1  and the adaptive threshold (AT u )  1447  is between U 1  and U 2 . Accordingly, a smaller negative transient  1444  will trigger the alarm as compared to the negative transient  1434  in the first time segment  1430 . 
     Further shown in  FIG. 14A , during a third time period t 3    1496 , a parameter segment  1450  has a baseline (B)  1452  less than U 1    1412 . As such, a smaller negative transient  1454  will trigger the alarm as compared to the negative transient  1444  in the second time segment  1440 . However, a larger positive transient  1455  is needed to trigger the alarm as compared to the positive transient  1445  in the second time segment  1440 . 
     Additionally shown in  FIG. 14A , during a fourth time period t 4    1460 , a parameter segment  1460  has a baseline (B)  1462  at about L 2    1424 . As such, Δ l    1466 =0; Δ u    1467 =U 1 −L 2 ; AT l    1468 =L 2 ; AT u    1469 =U 1 . Accordingly, a positive transient  1465  having a size less than U 1 −L 2  does not trigger an alarm. 
     An adaptive alarm system has been disclosed in detail in connection with various embodiments. These embodiments are disclosed by way of examples only and are not to limit the scope of the claims that follow. One of ordinary skill in the art will appreciate many variations and modifications.