Patent Publication Number: US-2022218291-A1

Title: Optical patient monitor

Description:
PRIORITY CLAIM TO RELATED PROVISIONAL APPLICATIONS 
     Any and all applications for which a foreign or domestic priority claim is identified in the Application Data Sheet are hereby incorporated by reference under 37 CFR 1.57 
     FIELD OF THE DISCLOSURE 
     The present disclosure relates to a sensor for measuring physiological parameters and, in particular, relates to using measured physiological parameters to generate an indicator. 
     BACKGROUND 
     Pulse oximetry is a widely accepted noninvasive procedure for measuring the oxygen saturation level of arterial blood, an indicator of a person&#39;s oxygen supply. Early detection of a low blood oxygen level is critical in the medical field, for example in critical care and surgical applications, because an insufficient supply of oxygen can result in brain damage and death in a matter of minutes. A typical pulse oximetry system utilizes a sensor applied to a patient&#39;s finger. The sensor has an emitter configured with both red and infrared LEDs that project light through the finger to a detector so as to determine the ratio of oxygenated and deoxygenated hemoglobin light absorption. In particular, the detector generates first and second intensity signals responsive to the red and IR wavelengths emitted by the LEDs after absorption by constituents of pulsatile blood flowing within a fleshy medium, such as a finger tip. A pulse oximetry sensor is described in U.S. Pat. No. 6,088,607 titled  Low Noise Optical Probe,  which is assigned to Masimo Corporation, Irvine, Calif. and incorporated by reference herein. 
     Capnography comprises the continuous analysis and recording of carbon dioxide concentrations in the respiratory gases of patients. The device used to measure the CO 2  concentrations is referred to as a capnometer. CO 2  monitoring can be performed on both intubated and non-intubated patients. With non-intubated patients, a nasal cannula is used. Capnography helps to identify situations that can lead to hypoxia if uncorrected. Moreover, it also helps in the swift differential diagnosis of hypoxia before hypoxia can lead to irreversible brain damage. Pulse oximetry is a direct monitor of the oxygenation status of a patient. Capnography, on the other hand, is an indirect monitor that helps in the differential diagnosis of hypoxia so as to enable remedial measures to be taken expeditiously before hypoxia results in an irreversible brain damage. 
     Early detection of low blood oxygen is critical in a wide variety of medical applications. For example, when a patient receives an insufficient supply of oxygen in critical care and surgical applications, brain damage and death can result in just a matter of minutes. Because of this danger, the medical industry developed pulse oximetry, a noninvasive procedure for measuring the oxygen saturation of the blood. A pulse oximeter interprets signals from a sensor attached to a patient in order to determine that patient&#39;s blood oxygen saturation. 
     A conventional pulse oximetry sensor has a red emitter, an infrared emitter, and a photodiode detector. The sensor is typically attached to a patient&#39;s finger, earlobe, or foot. For a finger, the sensor is configured so that the emitters project light from one side of the finger, through the outer tissue of the finger, and into the blood vessels and capillaries contained inside. The photodiode is positioned at the opposite side of the finger to detect the emitted light as it emerges from the outer tissues of the finger. The photodiode generates a signal based on the emitted light and relays that signal to the pulse oximeter. The pulse oximeter determines blood oxygen saturation by computing the differential absorption by the arterial blood of the two wavelengths (red and infrared) emitted by the sensor. 
     SUMMARY 
     Multiple physiological parameters, combined, provide a more powerful patient condition assessment tool than when any physiological parameter is used by itself. For example, a combination of parameters can provide greater confidence if an alarm condition is occurring. More importantly, such a combination can be used to give an early warning of a slowly deteriorating patient condition as compared to any single parameter threshold, which may not indicate such a condition for many minutes. Conditions such as hypovolemia, hypotension, and airway obstruction may develop slowly over time. A physiological parameter system that combines multiple parameters so as to provide an early warning could have a major effect on the morbidity and mortality outcome in such cases. Parameters can include ECG, EKG, blood pressure, temperature, SpO 2 , pulse rate, HbCO, HbMet, Hbt, SpaO2, HbO2, Hb, blood glucose, water, the presence or absence of therapeutic drugs (aspirin, dapson, nitrates, or the like) or abusive drugs (methamphetamine, alcohol, or the like), concentrations of carbon dioxide (“CO2”), oxygen (“O”), ph levels, bilirubin, perfusion quality, signal quality, albumin, cyanmethemoglobin, and sulfhemoglobin (“HbSulf”) respiratory rate, inspiratory time, expiratory time, inspiratory to expiratory ratio, inspiratory flow, expiratory flow, tidal volume, minute volume, apnea duration, breath sounds—including rales, rhonchi, or stridor, changes in breath sounds, heart rate, heart sounds—including S1, S2, S3, S4, or murmurs, or changes in heart sounds, or the like. Some references that have common shorthand designations are referenced through such shorthand designations. For example, as used herein, HbCO designates carboxyhemoglobin, HbMet designates Methemoglobin, and Hbt designates total hemoglobin. Other shorthand designations such as COHb, MetHb, and tHb are also common in the art for these same constituents. These constituents are generally reported in terms of a percentage, often referred to as saturation, relative concentration or fractional saturation. Total hemoglobin is generally reported as a concentration in g/dL. The use of the particular shorthand designators presented in this application does not restrict the term to any particular manner in which the designated constituent is reported. 
     Further, a greater emphasis has been put on decreasing the pain level of patients on the ward. Accordingly, patients are often given an IV setup that enables the patient to increase the level of analgesia at will. In certain situations, however, the patient&#39;s input must be ignored so as to avoid over medication. Complications from over sedation may include hypotension, tachycardia, bradycardia, hypoventilation and apnea. A physiological parameter system that uses pulse oximetry monitoring of SO 2  and pulse rate in conjunction with patient controlled analgesia (PCA) can aid in patient safety. Utilization of conventional pulse oximetry in conjunction with PCA, however, can result in the patient being erroneously denied pain medication. Conventional monitors are susceptible to patient motion, which is likely to increase with rising pain. Further, conventional monitors do not provide an indication of output reliability. 
     Advanced pulse oximetry is motion tolerant and also provides one or more indications of signal quality or data confidence. These indicators can be used as arbitrators in decision algorithms for adjusting the PCA administration and sedation monitoring. Further, advanced pulse oximetry can provide parameters in addition to oxygen saturation and pulse rate, such as perfusion index (PI). For example, hypotension can be assessed by changes in PI, which may be associated with changes in pulse rate. Motion tolerant pulse oximetry is described in U.S. Pat. No. 6,206,830 titled  Signal Processing Apparatus and Method;  signal quality and data confidence indicators are described in U.S. Pat. No. 6,684,090 titled  Pulse Oximetry Data Confidence Indicator,  both of which are assigned to Masimo Corporation, Irvine, Calif. and incorporated by reference herein. 
     One aspect of a physiological parameter system is a first parameter input responsive to a first physiological sensor and a second parameter input responsive to a second physiological sensor. A processor is adapted to combine the parameters and predetermined limits for the parameters so as to generate an indication of wellness. 
     Another aspect of a physiological parameter system is a parameter input responsive to a physiological sensor and a quality indicator input relating to confidence in the parameter input. A processor is adapted to combine the parameter input, the quality indicator input and predetermined limits for the parameter input and the quality indicator input so as to generate a control output. 
     A physiological parameter method comprises the steps of inputting a parameter responsive to a physiological sensor and inputting a quality indicator related to data confidence for the parameter. A control signal is output from the combination of the parameter and the quality indicator. The control signal is adapted to affect the operation of a medical-related device. 
     A method of improving the reporting of a physiological parameter in a physiological parameter system comprises obtaining measurements of a physiological parameter from a measurement site. At least some of the physiological parameter measurements are maintained. A change in the measurement site is detected. A measurement of the physiological parameter from a new measurement site is obtained. The measurement of the physiological parameter at the new measurement site is compared with the maintained physiological parameter measurements. The magnitude of the physiological parameter reported by the physiological parameter system at the new measurement site is adjusted to approximately match the magnitude of the maintained physiological parameter measurements. 
     A method of generating an indicator of patient wellness using a physiological parameter system includes receiving physiological parameter data from a sensor attached to the physiological parameter system. Physiological parameter preferences are provided to the physiological parameter system. The physiological parameter data is compared to the physiological parameter preferences. An indicator of patient wellness is generated by calculating a numerical wellness score based on the comparison. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates an embodiment of a physiological parameter measurement system. 
         FIG. 2A  illustrates an embodiment of a sensor assembly. 
         FIGS. 2B-C  illustrate alternative sensor embodiments. 
         FIG. 3A  illustrates an example chart of the value of a physiological parameter as measured by a sensor during a time when the sensor is moved from one measurement site to another. 
         FIG. 3B  illustrates a chart of a physiological parameter reported by a measurement system employing signal normalization techniques. 
         FIG. 3C  illustrates a chart of a MetHb reading which is smoothed to account for abnormal variations in the readings. 
         FIG. 3D  illustrates a MetHb smoothing flowchart. 
         FIG. 3E  illustrates a system of multiple different MetHb calculators which determine MetHb using different methods in order to calculate the most accurate MetHb reading. 
         FIG. 4  is a block diagram of a physiological parameter system having signal normalization capability. 
         FIG. 5  illustrates an embodiment of a method for normalizing a signal acquired by a sensor. 
         FIG. 6  is a general block diagram of a physiological parameter system having alarm, diagnostic and control outputs. 
         FIG. 6A  illustrates an embodiment of a physiological parameter system  600  similar to the system in  FIG. 6   
         FIG. 7  is a block diagram of a physiological parameter system combining pulse oximetry and capnography and providing alarm outputs. 
         FIG. 8  is a block diagram of a saturation limit alarm enhanced by ETCO 2  measurements. 
         FIG. 9  is a block diagram of a CO 2  waveform alarm enhanced by SpO 2  measurements. 
         FIG. 10  is a block diagram of a physiological parameter system combining pulse oximetry and capnography and providing a diagnostic output. 
         FIGS. 11A, 11B, 12  are block diagrams of a physiological parameter system utilizing pulse oximetry to control patient controlled analgesia (PCA). 
         FIGS. 13, 13A, 13B  illustrates an embodiment of a system that displays an indicator of the wellness of a patient. 
         FIG. 14  is a flowchart showing an example method of displaying an indicator of the wellness of a patient. 
     
    
    
     DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 
     Hereinafter, various example embodiments of the present disclosure will be described in detail with reference to the attached drawings such that the present disclosure can be put into practice by those skilled in the art. However, the present disclosure is not limited to the example embodiments, but may be embodied in various forms. 
     Some embodiments will be described in the context of computer-executable instructions, such as program modules, being executed by hardware devices, such as embedded processors, microcontrollers, and computer workstations. Program modules may include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular data types. Computer-executable instructions, associated data structures, and program modules represent examples of program code for executing steps of the methods disclosed herein. The particular sequence of executable instructions or arrangement of associated data structures represents examples of corresponding acts for implementing the functions described in such steps. A person of skill in the art would understand that other structures, arrangements, and executable instructions could be used with the present disclosure without departing from the spirit thereof. 
       FIG. 1  illustrates an embodiment of a physiological measurement system  100  having a monitor  101  and a sensor assembly  101 . The physiological measurement system  100  allows the monitoring of a person, including a patient. In particular, the multiple wavelength sensor assembly  101  allows the measurement of blood constituents and related parameters, including oxygen saturation, COHb, MetHb and pulse rate. 
     In an embodiment, the sensor assembly  101  is configured to plug into a monitor sensor port  103 . Monitor keys  105  provide control over operating modes and alarms, to name a few. A display  107  provides readouts of measured parameters, such as oxygen saturation, pulse rate, COHb and MetHb to name a few. 
       FIG. 2A  illustrates a multiple wavelength sensor assembly  201  having a sensor  203  adapted to attach to a tissue site, a sensor cable  205  and a monitor connector  201 . In an embodiment, the sensor  203  is incorporated into a reusable finger clip adapted to removably attach to, and transmit light through, a fingertip. The sensor cable  205  and monitor connector  201  are integral to the sensor  203 , as shown. In alternative embodiments, the sensor  203  can be configured separately from the cable  205  and connector  201 , although such communication can advantageously be wireless, over public or private networks or computing systems or devices, through intermediate medical or other devices, combinations of the same, or the like. 
       FIGS. 2B-C  illustrate alternative sensor embodiments, including a sensor  211  ( FIG. 2B ) partially disposable and partially reusable (resposable) and utilizing an adhesive attachment mechanism. Also shown is a sensor  213  being disposable and utilizing an adhesive attachment mechanism. In other embodiments, a sensor can be configured to attach to various tissue sites other than a finger, such as a foot or an ear. Also a sensor can be configured as a reflectance or transflectance device that attaches to a forehead or other tissue surface. The artisan will recognize from the disclosure herein that the sensor can include mechanical structures, adhesive or other tape structures, Velcro wraps or combination structures specialized for the type of patient, type of monitoring, type of monitor, or the like. 
     Certain physiological parameters and certain changes in physiological parameters may serve as indicators of an adverse condition affecting a patient. For example, an increase in blood methemoglobin (MetHb) concentration may be useful as a marker of the onset of sepsis or septic shock. As another example, measurements of high blood carboxyhemoglobin (COHb) concentration may indicate exposure to carbon monoxide (CO). Other physiological and related parameters to which techniques of the present disclosure may be applicable include respiration rate, respiration volume, oxygen saturation, pulse rate, ECG, blood glucose, blood pressure, temperature, perfusion index, exhaled carbon dioxide waveform, end tidal carbon dioxide, various signal quality indicators, data confidence indicators and trend data, among others. 
     A sensor measuring a physiological parameter (e.g., a physiological parameter measurement device) of a patient may, under certain circumstances, detect a change in the magnitude of a detected signal that does not correspond to a change in the value of the physiological parameter. Such changes in a detected signal may occur, for example, when the sensor is moved to a different measurement site. Sometimes, a sensor may be temporarily removed from a patient, and medical reasons may compel movement of the sensor to a different location. For example, a multiple wavelength sensor may need to be moved to a different finger of a patient about every 12 hours in order to maintain the sensor&#39;s measurement effectiveness and/or to avoid injury to the patient. When the measurement site of a multiple wavelength sensor is switched to a different location, the magnitudes of some of the signals detected by the sensor may change, even though no significant change in the patient&#39;s physiological parameters has occurred during the brief sensor relocation period. Signal normalization techniques described in the present disclosure may reduce changes in physiological parameters reported by a physiological parameter system that are unrelated to actual physiological parameter variation. 
     In some cases, the magnitude of a sensor measurement may be a less effective indicator of an adverse condition than a change in the magnitude of a sensor measurement. In such cases, a sensor may not need to be calibrated to report the absolute magnitude of a physiological parameter when changes in the magnitude of the parameter are more significant for purposes of condition detection. In other cases, the absolute magnitude of a physiological parameter is valuable, and a sensor signal must be analyzed and/or recalibrated to compensate for changes in the magnitude of the signal detected that do not correspond to changes in the value of the physiological parameter being measured. Signal normalization techniques may improve a physiological parameter system&#39;s reporting effectiveness for both types of parameters. 
       FIG. 3A  illustrates an example chart  300  of the value of a physiological parameter, such as, for example, MetHb, as measured by a sensor during a time when the sensor is moved from one measurement site to another. Chart  300  shows the magnitude of a signal measured by a sensor as a function of time before any analysis or manipulation of the signal occurs. A first axis  302  of chart  300  represents time, and a second axis  304  represents the magnitude of a signal, corresponding to a physiological parameter, detected at a point in time. The physiological parameter corresponding to the signal shown by way of example in  FIG. 3A  is blood MetHb concentration. 
     Curve  306  represents the magnitude of the signal detected by a sensor during a period when the sensor was at a first measurement site. The signal represented by curve  306  roughly oscillates about a nearly constant mean value of the signal. However, the signal may also follow any continuous increasing or decreasing trend and may also be nonoscillatory or contain a complex pattern of variation. 
     At time T 1  along axis  302 , the sensor is removed from the first measurement site. Curve  308  represents the magnitude of the signal detected by the sensor while it is disconnected from the patient, for example, while a care provider switches the sensor to a new measurement site. In chart  300 , the magnitude of the signal is about zero, but the sensor may continue to detect a signal of some nature (e.g., random noise, background interference, etc.) during a period when it is disconnected from a patient. 
     At time T 2  along axis  302 , the sensor is attached to a second measurement site on the patient. The second measurement site may be different than the first measurement site; for example, the second measurement site may be a different finger or a different position on a finger. Curve  310  represents the magnitude of the signal detected by the sensor during a period when the sensor is at the second measurement site. The signal represented by curve  310  roughly oscillates about a nearly constant mean value of the signal that is higher than the mean value of the portion of the signal represented by curve  306 . The difference between the magnitude of the signal shortly before time T 1  and the magnitude of the signal shortly after time T 2  is a shift in the magnitude of the signal that is related to the change in the measurement site. However, the shift in the signal may not correspond to an actual change in the value of a physiological parameter of the patient. In some cases, it may be safe to assume that the approximate value of a physiological parameter shortly before time T 1  and shortly after time T 2  is the same. In the absence of signal normalization, the signal shift may trigger a false alarm or cause a physiological parameter system to incorrectly report a change in a parameter. In the embodiment shown in  FIG. 3A , reporting the non-normalized signal may trigger an alarm for sepsis or septic shock at time T 2  due to an apparent increase in blood MetHb concentration. 
       FIG. 3B  illustrates a chart  350  of a physiological parameter reported by a measurement system employing signal normalization techniques. In the situation corresponding to chart  350 , it is assumed that the approximate value of the physiological parameter shortly before time T 1  is the same as the approximate value of the physiological parameter shortly after time T 2 . A first axis  352  of chart  350  represents time, and a second axis  354  represents the value of a physiological parameter reported by a physiological parameter system at a point in time. The physiological parameter shown by way of example in  FIG. 3B  is blood MetHb concentration. 
     In chart  350 , curve  356  represents the value of the physiological parameter reported while the sensor is at the first measurement site. Curve  358  represents the value of the physiological parameter reported while the sensor is not connected to the patient. In alternative embodiments, a physiological parameter system may not report a parameter or may shut off the sensor when the system detects that the sensor is not at a measurement site. Curve  360  represents the value of the physiological parameter reported while the sensor is at the second measurement site. The physiological parameter data in chart  350  is normalized because the value of the physiological parameter reported just before T 1  is adjusted to match the value of the physiological parameter just after T 2 . Various methods of matching may exist, including adjusting the values before and after the measurement site change to be approximately equal, using data points before T 1  to generate a trend line and fixing the data point at T 2  to the trend line, or any other method known in the art of projecting or approximating the value of the physiological parameter at T 2  based on data prior to T 1 . 
     In some embodiments, sensor measurements that are received after time T 2 , as shown in curve  310  of chart  300  ( FIG. 3A ), may be normalized by adding an offset to the magnitudes of the measurements. The offset may be calculated by subtracting the magnitude of the non-normalized sensor measurement at time T 2  from the magnitude of the normalized sensor measurement at T 2 . The offset may be a negative number. Similar methods of normalizing data points involving, for example, subtraction of an offset and other known methods may also be used. One result of signal normalization is that, given a relatively constant physiological parameter over time, the mean value of curve  360  will more closely approximate the mean value of curve  356 . Signal normalization may reduce the incidence of false alarms and reports of changes in physiological parameters that have not in fact changed. 
       FIG. 3C  illustrates a further example of normalizing a signal with erratic noise, such as, for example, motion induced noise. As illustrated, a physiological parameter signal  370 , such as a signal indicative of MetHb, is illustrated. The physiological parameter signal  370  includes various inconsistencies, such as, for example, erratic noises  371 , probe off conditions  373 , and cite change conditions  375 . In order to deal with these inconsistencies, processing is used to determine a normalization  377  or trend of the signal. The normalization  377  uses various methods in order to determine a relatively stable physiological parameter reading  377 . 
       FIG. 3D  illustrates a flow chart of a normalization procedure  380 . For ease in discussion,  FIGS. 3D and 3E  will be discussed with respect to a MetHb reading, however, it should be understood that any physiological parameter can be used with the present disclosure. The normalization procedure begins with the data signal  381 . As show, the normalization feature  380  includes Met calculator  382 ; smoother  384 , Met signal extractor  385 ; signal quality  387  and distortion  388 . In an embodiment, a data signal  381  responsive to an intensity signal is input into the Met calculator  382 , and a current value  383  of Met is calculated. The current value  383  of Met, which in an embodiment is subject to noise, distortion, and site movements in the data signal  381 , is input into the smoother  384 , which reduces an error between the current value  383  of Met and actual MetHb conditions. For example, the smoother  384  may advantageously determine a Met trend, and depending upon an indication of some or all of an amount of distortion, noise, signal quality, and/or waveform quality in the data signal  383 , substitute or combine the MetHb trend for or with the current value  383  to generate an output MetHb measurement. 
     In an embodiment, the distortion signal  388  may comprise a Boolean value indicating whether the data signal  383  includes, for example, motion-induced noise. Although an artisan will recognize from the disclosure herein a number of methodologies for deriving the distortion signal  388 , derivation of a Boolean distortion signal is disclosed in U.S. Pat. No. 6,606,511, incorporated herein by reference. Alternatively, or in addition to, the signal quality signal  387  may comprise a Boolean value indicating whether the data signal  383  meets various waveform criteria Although an artisan will recognize from the disclosure herein a number of methodologies for deriving the signal quality signal  387 , derivation of a Boolean distortion signal is disclosed in the &#39;511 patent. Alternatively, or in addition to, a feature extractor  385  may advantageously produce waveform quality outputs  386 , indicative of waveform quality or waveform shape. Although an artisan will recognize from the disclosure herein a number of methodologies for deriving the waveform quality signal  386 , derivation thereof is disclosed in U.S. Pat. No. 6,334,065, also incorporated herein by reference. 
     Thus, the smoother  384  accepts one or more or different indicators of the quality of the data signal  381 , and determines how to smooth or normalize the output to reduce errors between data trends and actual MetHb conditions. In an embodiment, the smoothing may advantageously comprise statistical weighting, other statistical combinations, or simply passing the MetHb measurement  383  through to the output, depending upon one or more of the quality signals  386 ,  387 ,  388 , or logical combinations thereof. 
     Upon the output of the normalized MetHb measurement, a monitor may advantageously audibly and/or visually presents the measurement to a caregiver, and when the measurement meets certain defined thresholds or behaviors, the monitor may advantageously audibly and/or visually alert the caregiver. In other embodiments, the monitor may communicate with other computing devices to alert the caregiver, may compare longer term trend data before alarming, or the like. 
       FIG. 3E  illustrates a simplified block diagram of an embodiment of a MetHb determination system  390  using multiple Met calculation techniques. As shown, data  391  is input into the system. The data  391  is then routed to at least two different Met calculators  392 ,  393 . In an embodiment, more than two different types of calculation techniques can be used. The at least two Met calculators  392 ,  393  output Met indications for input into the Met selector  395 . The Met selector  395  determines a Met value to output. The Met selector chooses the output based on which Met calculator works best for a given condition of the signal or based on which Met calculation fits the trend of Met readings. Other methods of selecting the best Met value can also be made as would be understood by a person of skill in the art from the present disclosure. 
       FIG. 4  is a block diagram of a physiological parameter system having signal normalization capability. A physiological parameter system may include a sensor signal analysis subsystem  400  that implements signal normalization techniques. Signal analysis subsystem  400  receives a signal  402  from a physiological parameter measurement device output. Signal  402  may be, for example, an electrical signal produced by an optical transducer within a pulse oximeter or a capnometer. 
     In the embodiment shown in  FIG. 4 , signal  402  is communicated to a sensor event module  404 . Sensor event module  404  includes program code for detecting events that occur based on a pattern recognized in signal  402 . Detected events may include a change in measurement site, movement of the sensor, interference in the signal, etc. For example, sensor event module  404  may determine that a measurement site of the sensor has been exchanged if a normal physiological parameter pattern ceases for a short period of time and then resumes. Alternatively, sensor event module  404  may detect a measurement site switch when signal  402  is interrupted by an interval of random noise and/or a relatively large discontinuity in the signal. Alternatively, an operator can indicate an event, such as a location change, by, for example, pressing a predetermined function button. As another example, sensor event module  404  may determine that signal normalization may not be appropriate when a sensor has been disconnected from a measurement site for a sufficiently long period of time (e.g., when an assumption that a signal trend will continue is no longer sound). Sensor event module  404  may communicate signal  402  and/or event information to a sensor memory  406  to store sensor signal pattern data for later use. Sensor event module  404  may also communicate signal  402  and event information to signal normalization module  408 . 
     Sensor memory  406  may retain a certain number of signal  402  samples or may retain signal  402  samples for a certain period. Retained samples may be used by program code in signal normalization module  408  and/or sensor event module  404 . Samples from signal  402  may be stored in a queue data structure, for example. In some embodiments, sensor event module  404  may instruct sensory memory  406  to cease storing new samples when it determines that the sensor is not connected to a measurement site so that signal data for potential future signal normalization may be retained. Signal memory  406  may also retain signal offset or calibration data. 
     Signal normalization module  408  comprises program code for converting a signal  402  from a sensor output into a normalized measure of a physiological parameter. Program code in module  408  may, for example, add or subtract a value from signal  402  in order to eliminate shifts in the magnitude of signal  402  that are not related to variation in a patient&#39;s physiological parameters. Signal normalization module  408  may determine an offset that counterbalances a shift in signal  402  that results from a change in sensor measurement site. Module  408  may include program code for calculating a trend line from data stored in sensor memory  406 . A trend line may be used to determine an appropriate value for a patient parameter when measurement resumes after an interruption in signal  402 . Module  408  may also employ pattern recognition or signal transforms to help it determine how signal  402  should be normalized. Sensor event module  404  may trigger signal normalization module  408  to reset its signal normalization when a certain signal events are detected. In some embodiments, sensor event module  404  may communicate to signal normalization module  408  the retained signal data from sensor memory  406  it should use to calculate a new offset. Signal normalization module  408  passes a normalized signal  450  out of signal normalization subsystem  400 . 
     Normalized signal  450  may then be passed to other components of a physiological parameter system for further analysis and/or display. For example, normalized signal  450  may be communicated to a comparator  454  that compares signal  450  to one or more parameter limits  452 . In some embodiments, comparator  454  may generate an alarm signal  456  if normalized signal  450  falls outside of parameter limits  452 . 
       FIG. 5  illustrates an embodiment of a method for normalizing a signal acquired by a sensor when the measurement site of the sensor is changed. At step  502 , sensor memory  406  ( FIG. 4 ) maintains recent physiological parameter measurements received from sensor output  402 . Sensor signal data may be passed directly to sensor memory  406  for storage, or sensor event module  404 , for example, may select which signal samples will be retained and pass them to sensor memory  406 . Retained signal sample data may include the magnitude of the signal as well as an indicator of the time that the sample was taken and/or the order in which the sample was received. Alternatively, sensor memory  406  may simply maintain signal data in chronological order in a queue, purging old sample data as new sample data is received. Data may be retained only for a certain time interval, such several seconds, a fraction of a minute, a minute, two minutes, or longer. The interval of retention may vary depending on the physiological parameter associated with signal  402 . This step may continue until sensor event module  404  detects a sensor measurement site change. 
     In step  504  of  FIG. 5 , sensor event module  404  detects a change in the sensor measurement site. In some embodiments, sensor event module  404  may detect the change in measurement site by one of the methods described with respect to the description of program code within sensor event module  404  above. Alternatively, a user of a physiological parameter system may indicate that a change in sensor measurement site has occurred by means of a hardware or software interface. For example, the sensor may include a hardware switch that activates when the measurement site is changed. The system may also include a manual switch or button that a user can activate to cause sensor event module  404  to register a change in the sensor measurement site. When sensor event module  404  determines that sampling at the new measurement site has begun, the method proceeds to step  506 . 
     At step  506 , signal normalization module  408  compares the magnitude of the signal sampled at the new measurement site with the magnitude of the retained signal that was obtained at the old measurement site. Signal normalization module  408  may use pattern recognition or signal transform techniques to attempt to compare an oscillatory signal at similar points in its cycle to obtain a more accurate comparison. In some embodiments, module  408  uses the comparison to calculate an offset that adjusts the signal at the time that measurement at the new measurement site begins to conform to a trend line fitted to signal data acquired from the old measurement site. Retained signal data from the old measurement site may be retrieved from sensor memory  406  and analyzed for the purpose of calibrating the sensor signal at the new measurement site. After the initial physiological parameter value is projected when the sensor begins sampling at the new measurement site, the method proceeds to step  508 . 
     In step  508 , signal normalization module  408  adjusts the magnitude of the signal measured at the new measurement site in order to output a normalized signal  450 . In some embodiments, adjusting the magnitude of the signal measured comprises modifying the magnitude of a signal measure measurement by adding or subtracting an offset. For example, the offset may be calculated by subtracting the magnitude of the signal sampled just after the sensor begins measurements at the new measurement site from the magnitude of the signal sampled just before the sensor was removed from the old measurement site. Alternatively, the offset may be defined as the difference between (1) a projected value of the magnitude of the signal just after the sensor begins measurements at the new measurement site, the projection based on measurements at the old measurement site, and (2) the actual measured value of the magnitude of the signal just after the sensor begins measurements at the new measurement site. Any other known means for calculating an offset may also be used. Signal normalization module  408  continues to add or subtract the calculated offset until another normalization step is required. At the conclusion of the method shown in  FIG. 5 , the steps shown may be repeated as many times as changes in the measurement site of the sensor may require. 
     Various embodiments of signal normalization techniques have been shown and described. Some alternative embodiments and combinations of embodiments disclosed herein have already been mentioned. Additional embodiments comprise various other combinations or alterations of the embodiments described. 
       FIG. 6  illustrates a physiological parameter system  600 , which may comprise an expert system, a neural-network or a logic circuit, for example. The physiological parameter system  600  has as inputs  601  from one or more parameters from one or more physiological measurement devices, such as a pulse oximeter  610  and/or a capnometer  620 . Pulse oximeter parameters may include oxygen saturation (SpO 2 ), perfusion index (PI), pulse rate (PR), various signal quality and/or data confidence indicators (Qn) and trend data, to name a few. Capnography parameter inputs may include, for example, an exhaled carbon dioxide waveform, end tidal carbon dioxide (ETCO 2 ) and respiration rate (RR). Signal quality and data confidence indicators are described in U.S. Pat. No. 6,108,090 cited above. The physiological parameter system  600  may also have parameter limits  606 , which may be user inputs, default conditions or otherwise predetermined thresholds within the system  600 . 
     The inputs  601  are processed in combination to generate one or more outputs  602  comprising alarms, diagnostics and controls. Alarms may be used to alert medical personnel to a deteriorating condition in a patient under their care. Diagnostics may be used to assist medical personnel in determining a patient condition. Controls may be used to affect the operation of a medical-related device. Other measurement parameters  630  that can be input to the monitor may include or relate to one or more of ECG, blood glucose, blood pressure (BP), temperature (T), HbCO, MetHb, respiration rate and respiration volume, to name a few. 
       FIG. 6A  illustrates an embodiment of a physiological parameter system  600  similar to the system in  FIG. 6 . The physiological parameter system  600  has as inputs  601  from one or more parameters from one or more physiological measurement devices, such as, for example a pulse oximeter  610 , an acoustic respiratory monitor  640 , an ECG monitor  650 , an invasive or non-invasive blood pressure monitor  650 , a thermometer, or any other invasive or noninvase physiological monitoring devices or the like. 
       FIG. 7  illustrates one embodiment of a physiological parameter system  700  combining pulse oximetry parameter inputs  710  and capnography parameter inputs  720  so as to generate alarm outputs  702 . Parameter limits  705  may be user inputs, default conditions or otherwise predetermined alarm thresholds for these parameters  710 ,  720 . The alarms  702  are grouped as pulse oximetry related  730 , capnography related  740  and a combination  750 . For example, a pulse oximetry alarm  730  may be related to percent oxygen saturation and trigger when oxygen saturation falls below a predetermined percentage limit. A capnography alarm  740  may be related to ETCO 2  and trigger when ETCO 2  falls below or rises above a predetermined mm Hg pressure limit. A combination alarm  750  may indicate a particular medical condition related to both pulse oximetry and capnography or may indicate a malfunction in either instrument. 
       FIG. 8  illustrates a SpO 2  alarm embodiment  800  that is responsive to ETCO 2 . In particular, a SpO 2  alarm  805  may be triggered sooner and may indicate a high priority if ETCO 2    803  is falling. That is, if ETCO 2    803  is trending down above a certain rate, the SpO 2  alarm  805  is triggered at a higher percentage oxygen saturation threshold and alerts a caregiver to the possibility of a serious condition, e.g. a pulmonary embolism. 
     As shown in  FIG. 8 , a slope detector  810  determines the slope  812  of the ETCO 2  input  803 . A slope comparator  820  compares this slope  812  to a predetermined slope limit  804 . If the downward trend of ETCO 2    803  is great enough, a delta value  803  is added  840  to the SpO 2  lower limit  802  to generate a variable threshold  842 . A threshold comparator  850  compares this variable threshold  842  to the SpO 2  input  801  to generate a trigger  852  for the SpO 2  alarm  805 . The alarm volume, modulation or tone may be altered to indicate priority, based upon the slope comparator output  822 . 
       FIG. 9  illustrates a CO 2  alarm embodiment  900  that is responsive to SpO 2 . In particular, morphology of the input CO 2  waveform  901  is utilized to trigger an alarm  905 , and that alarm is also responsive to a falling SpO 2    902 . That is, if a pattern in the CO 2  waveform is detected and SpO 2  is trending down above a certain rate, then an alarm is triggered. For example, an increasing slope of the CO 2  plateau in combination with a downward trend of SpO 2  may trigger an alarm and alert a caregiver to the possibility of an airway obstruction. 
     As shown in  FIG. 9 , a pattern extractor  910  identifies salient features in the CO 2  waveform and generates a corresponding feature output  912 . A pattern memory  920  stores one or more sets of predetermined waveform features to detect in the CO 2  input  901 . The pattern memory  920  is accessed to provide a feature template  922 . A feature comparator  930  compares the feature output  912  with the feature template  922  and generates a match output  932  indicating that a specific shape or pattern has been detected in the CO 2  waveform  901 . In addition, a slope detector  940  determines the slope  942  of the SpO 2  input  902 . A slope comparator  950  compares this slope  942  to a predetermined slope limit  904 . If the downward trend of SpO 2    902  is great enough, a slope exceeded output  952  is generated. If both the match output  932  and the slope exceeded output  952  are each asserted or “true,” then a logical AND  960  generates a trigger output  96  to the alarm  970 , which generates an alarm output  905 . 
       FIG. 10  illustrates a combination embodiment  1000  having a diagnostic output  1005  responsive to both SpO 2    1001  and CO 2    1003  inputs. A SpO 2  slope detector  100  determines the slope  102  of the SpO 2  input  1001  and can be made responsive to a negative slope, a positive slope or a slope absolute value. A first comparator  1020  compares this slope  102  to a predetermined SpO 2  slope limit  1002 . If the trend of SpO 2    1001  is great enough, a SpO 2  slope exceeded output  1022  is asserted. Likewise, an CO 2  slope detector  1030  determines the slope  1032  of the CO 2  input  1003 . A second comparator  1040  compares this slope  1032  to a predetermined CO 2  slope limit  1004 . If the downward trend of CO 2    1001  is great enough, an CO 2  slope exceeded output  1042  is asserted. If both slope exceeded outputs  1022 ,  1042  are asserted or “true,” a diagnostic output  1005  is asserted. 
     In one embodiment, the slope detectors  610 ,  1030  are responsive to a negative trend in the SpO 2    1001  and CO 2    1003  inputs, respectively. Accordingly, the diagnostic output  1005  indicates a potential embolism or cardiac arrest. In another embodiment, the SpO 2  slope detector  610  is responsive to negative trends in the SpO 2    1001  input, and the CO 2  slope detector  1030  is responsive to a positive trend in the CO 2    1003  input. Accordingly, the diagnostic output  1005  indicates a potential airway obstruction. The diagnostic output  1005  can trigger an alarm, initiate a display, or signal a nursing station, to name a few. 
       FIGS. 11A-B  illustrate a physiological parameter system  1100  utilizing pulse oximetry to control patient controlled analgesia (PCA). In particular embodiments, a control output  1108  is responsive to pulse oximetry parameters  1101  only if signal quality  1103  is above a predetermined threshold  1104 . In  FIG. 11A , the control output  1108  can be used to lock-out patient controlled analgesia (PCA) if pulse oximetry parameter limits have been exceeded. If signal quality is so low that those parameters are unreliable, however, PCA is advantageously allowed. That is, the pulse oximeter parameters are not allowed to lock-out PCA if those parameters are unreliable. By contrast, in  FIG. 11B , the control output  1108  can be used to advantageously lock-out or disable patient controlled analgesia (PCA) if pulse oximetry parameter limits have been exceeded or if signal quality is so low that those parameters are unreliable. 
     As shown in  FIG. 11A , pulse oximetry parameters  1101  and corresponding limits  1102  for those parameters are one set of inputs and a signal quality measure  1103  and a corresponding lower limit  1104  for signal quality are another set of inputs. The parameters  1101  and corresponding limits  1102  generate a combined output  1202  that is asserted if any of the pulse oximetry parameter limits are exceeded. A comparator  1110  compares the signal quality  1103  input with a lower limit  1104  generating a quality output  1112  that is asserted if the signal quality  1103  drops below that limit  1104 . An AND logic  1120  generates a reset  1122  if the combined output  1202  is asserted and the quality output  1112  is not asserted. The reset  1122  resets the timer  1130  to zero. A comparator  1140  compares the timer output  1132  to a predetermined time limit  1106  and generates a trigger  1142  if the time limit is exceeded. The trigger  1142  causes the control  1150  to generate the control output  1108 , enabling a patient controlled analgesia (PCA), for example. In this manner, the PCA is enabled if all monitored parameters are within set limits and signal quality is above its lower limit for a predetermined period of time. 
     As shown in  FIG. 11B , the combined output  1202 , quality output  1112 , reset  1122 , timer  1130 , comparator  1140  and control  1150  are generated as described with respect to  FIG. 11A , above. An OR logic  1121  generates a reset  1122  if either the combined output  1202  or the quality output  1112  is asserted. In this manner, the PCA is disabled for a predetermined period of time if any of the monitored parameters are outside of set limits or the signal quality is below its lower limit. 
       FIG. 12  illustrates combined limits  1200  having SpO 2  parameters  1101  and corresponding thresholds  1102  as inputs and providing a combination output  1202 . In particular, if any parameter  1101  exceeds its corresponding limit  1102 , the output of the corresponding comparator  1210 ,  1220 ,  1240  is asserted. An OR logic  1250  is responsive to any asserted output  1212 ,  1222 ,  1242  to asserted the combined output  1202 . For example, the combined output  1202  may be asserted if SpO 2    1201  falls below a lower limit  1209 , pulse rate (PR)  1203  rises above an upper limit  1204  or PR  1203  falls below a lower limit  120 . 
     A physiological parameter 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. For example, the control output  1108  ( FIG. 11B ) can be used to control (titrate) delivered, inspired oxygen levels to patients based upon pulse oximetry parameters, unless signal quality is so low that those parameters are unreliable. One of ordinary skill in the art will also recognize that the control output  1108  ( FIG. 11B ) can be used to control patient delivery of any of various pharmacological agents and/or medical gases. 
       FIG. 13  illustrates an embodiment of a system  1300  that displays an indicator of the wellness of a patient. Various sensors  1302   a - 1302   n  communicate with a parameter analysis module  1306 . Sensors  1302   a - 1302   n  may include pulse oximeters and capnometers, among other physiological parameter measurement devices. Sensor  1302   n  outputs a signal that may be sampled, normalized, and/or analyzed by modules that are not shown in system  1300  before being passed to parameter analysis module  1306 . As described above, normalization of sensor signals before comparison of the signals to parameter limits  452  ( FIG. 4 ) and/or parameter preferences  1304  may have certain benefits, such as decreased incidence of false alarms and/or more effective determination of the wellness of the patient. 
     In the embodiment shown, a user may provide parameter preferences  1304  to parameter analysis module  1306  through a user interface. Parameter preferences  1304  may include preferred ranges, less preferred ranges, least preferred ranges, upper limits, lower limits, preferred rates of increase or decrease, preferred patterns or trends, preferred states, or any combination of such preferences or other standards for evaluating the desirability of various physiological parameter values and signals. In some cases, a user of system  1300  may provide custom preferences to override a default set of physiological parameter preferences  1304  preprogrammed into system  1300 . In some embodiments, parameter analysis module  1306  may include program code for dynamically changing or suggesting changes to various parameter preferences as a function of certain physiological parameters or related sensor performance data. 
     Parameter analysis module  1306  compares at least some of the signal data received from sensors  1302   a - 1302   n  to parameter preferences  1304  in order to calculate an indicator of the wellness of a patient. In some embodiments, the indicator calculated is a numerical indicator; for example, a number between one and ten, where a ten corresponds to a patient with a high level of wellness, and a one corresponds to a patient with a very low level of wellness as depicted in  FIG. 13B . Other ranges, such as one to 100, −100 to 100, etc., and scales, such as an alphabetic A-F scale or a color scale, may also be used including the scale depicted in  FIG. 13A . Other indicators that may be generated by parameter analysis module  1306  include graphical indicators of potential trouble areas, gauges, charts, level meters, and the like may also be used. Parameter analysis module  1306  communicates the indicator to a display  1308 , which may display the indicator in any suitable graphical or textual form that is known in the art. For example, display  1308  may show a number of bars or a level meter, the number of which may correspond to one of the numerical indicator scales discussed above. 
       FIG. 14  is a flowchart showing an example method of displaying an indicator of the wellness of a patient. At step  1402 , parameter analysis module  1306  ( FIG. 13 ) receives signal data from one or more sensors  1302   a - 1302   n.  As discussed previously, such signal data may be normalized or otherwise modified from its raw form before being passed to parameter analysis module  1306 . Parameter analysis module  1306  may continuously update an indicator as new data is received and may calculate averages, variances, and/or other analytical measures of various physiological parameters over time. In some embodiments, parameter analysis module  1306  may update the indicator of patient wellness only periodically, sporadically, or by request rather than continuously, thus requiring only occasional reception of data from sensors  1302   a - 1302   n.    
     In step  1404 , parameter analysis module  1306  receives parameter preferences  1304 . Preferences  1304  may by received only once or sporadically as a user supplies custom preferences. Preferences  1304  may also be received and/or updated continuously when, for example, parameter preferences  1304  are functions of various physiological or sampling parameters. 
     At step  1406 , parameter analysis module  1306  compares the data received from sensors  1302   a - 1302   n  to parameter preferences  1304 . Individual sensor measurements may be compared to parameter preferences  1304 , or parameter analysis module may compare parameter preferences  1304  to a moving average of sensor measurements, for example. Comparison of various other known analytical measures of sensor data is also possible and within the scope of the present disclosure. The comparison performed by parameter analysis module  1306  may include magnitude comparisons, pattern analysis, and/or trend analysis. Historical sensor data may also be used in the comparison. 
     In step  1408  of  FIG. 14 , parameter analysis module  1306  generates an indicator of the wellness of the patient based on the comparison performed in step  1406 . The indicator may be in any of the forms discussed previously. For example, module  1306  may increase a wellness score (e.g., a numerical indicator of wellness) when physiological parameters fall within preferred ranges or when sensor signals follow preferred patterns and/or trends. The indicator may comprise a simple or a more detailed textual and/or graphical summary of the patient&#39;s wellness as interpreted from parameters measured by sensors  1302   a - 1302   n.  In some embodiments, the indicator may be a scaled number in combination with a textual description of the patient&#39;s wellness score and/or conditions that may be affecting the score. In addition, particular a particular condition affecting the patient can also be generated for communication to a healthcare provider, such as, for example, sepsis, septic shock, apnea, heart failure, airway obstruction, carbon monoxide poisoning, low oxygen content, etc. 
     After parameter analysis module  1306  generates the wellness indicator, it sends the indicator to display  1308  at step  1410 . Display  1308  may be integrated with physiological parameter system  1300  or may be a separate display device. The display may also include auditory sounds, such as for example, beeps, voices, words, etc., to indicate a particular event or condition occurring. 
     Although the foregoing invention has been described in terms of certain preferred embodiments, other embodiments will be apparent to those of ordinary skill in the art from the disclosure herein. Additionally, other combinations, omissions, substitutions and modifications will be apparent to the skilled artisan in view of the disclosure herein. It is contemplated that various aspects and features of the invention described can be practiced separately, combined together, or substituted for one another, and that a variety of combination and subcombinations of the features and aspects can be made and still fall within the scope of the invention. Furthermore, the systems described above need not include all of the modules and functions described in the preferred embodiments. Accordingly, the present invention is not intended to be limited by the recitation of the preferred embodiments, but is to be defined by reference to the appended claims.