Patent Publication Number: US-11026586-B2

Title: Determining changes to autoregulation

Description:
TECHNICAL FIELD 
     This disclosure relates to monitoring the autoregulation of blood pressure. 
     BACKGROUND 
     Cerebral autoregulation (CA) is the response mechanism by which an organism regulates cerebral blood flow over a wide range of systemic blood pressure changes through complex myogenic, neurogenic, and metabolic mechanisms. Autoregulation dysfunction may result from a number of causes including, stroke, traumatic brain injury, brain lesions, brain asphyxia, or infections of the central nervous system. Intact cerebral autoregulation function occurs over a range of blood pressures defined between a lower limit of autoregulation (LLA) and an upper limit of autoregulation (ULA). 
     SUMMARY 
     This disclosure describes example regional oximetry devices configured to determine a limit of autoregulation based on a previously determined estimate of the limit of autoregulation and a newly determined estimate of the limit of autoregulation, referred to herein as a previous value of the limit of autoregulation and a first estimate of the limit of autoregulation, respectively. The regional oximetry device may be configured to determine a weighted average of the previously determined estimate and the newly determined estimate, where a weighting factor for the newly determined estimate is based on the difference between the newly determined estimate and other estimates of the limit of autoregulation. The regional oximetry device may determine the other estimates of the limit of autoregulation based on signals such as blood-pressure signals, oxygen saturation signals, blood volume signals, and/or any other signals. 
     Clause 1: In some examples, a device comprises a display and processing circuitry configured to receive a first signal indicative of a first physiological parameter of a patient and a second signal indicative of a second physiological parameter of the patient. The processing circuitry is also configured to determine a first estimate of a limit of autoregulation of the patient based on the first signal and the second signal and determine a difference between the first estimate of the limit of autoregulation and one or more other estimates of the limit of autoregulation. The processing circuitry is further configured to determine a weighted average of the first estimate of the limit of autoregulation and a previous value of the limit of autoregulation based on the difference between the first estimate of the limit of autoregulation and the one or more other estimates of the limit of autoregulation. The processing circuitry is configured to output, for display via the display, an indication of the autoregulation status. 
     Clause 2: In some examples of clause 1, the processing circuitry is configured to determine the difference between the first estimate and the one or more other estimates by at least determining a mean of the first estimate of the limit of autoregulation and the one or more other estimates of the limit of autoregulation, determining a mean absolute difference between the first estimate of the limit of autoregulation and a mean of the one or more other estimates of the limit of autoregulation, and determining a normalized difference by at least dividing the mean absolute difference by the mean of the first estimate and the one or more other estimates. 
     Clause 3: In some examples of clause 2, the processing circuitry is further configured to determine a multiplier by at least subtracting the normalized difference from one. The processing circuitry is configured to determine the weighted average by at least determining a weighting factor for the first estimate of the limit of autoregulation by multiplying a predetermined maximum weighting factor by the multiplier. 
     Clause 4: In some examples of clause 3, the processing circuitry is further configured to determine a new value of the limit of autoregulation based on the weighted average and output, for display via the display, an indication of the new value of the limit of autoregulation. 
     Clause 5: In some examples of any of clauses 1-4, the processing circuitry is configured to determine the difference between the first estimate and the one or more other estimates by at least determining a standard deviation of the first estimate of the limit of autoregulation and the one or more other estimates of the limit of autoregulation. The processing circuitry is further configured to determine a weighting factor based on the standard deviation of the first estimate and the one or more other estimates. The processing circuitry is configured to determine the weighted average of the first estimate of the limit of autoregulation and a previous value of the limit of autoregulation based on the weighting factor. 
     Clause 6: In some examples of any of clauses 1-5, the processing circuitry is configured to determine the difference between the first estimate and the one or more other estimates by at least determining a mean of the one or more other estimates of the limit of autoregulation, determining a mean absolute difference between the first estimate of the limit of autoregulation and the mean of the one or more other estimates, and determining a normalized difference by at least dividing the mean absolute difference by the mean of the first estimate and the one or more other estimates. 
     Clause 7: In some examples of any of clauses 1-6, the processing circuitry is configured to determine the weighted average by at least determining a first weighting factor and a second weighting factor based on the difference between the first estimate of the limit of autoregulation and the one or more other estimates of the limit of autoregulation, determining a first weighted value of the first estimate of the limit of autoregulation based on the first weighting factor, determining a second weighted value of the previous value of the limit of autoregulation based on the second weighting factor, and determining a sum of the first weighted value and the second weighted value. 
     Clause 8: In some examples of clause 7, determining the first weighted value comprises multiplying the first weighting factor and the first estimate of the limit of autoregulation. Determining the second weighting factor comprises subtracting the first weighting factor from one. Determining the second weighted value comprises multiplying the second weighting factor and the previous value of the limit of autoregulation. 
     Clause 9: In some examples of any of clauses 1-8, the second physiological parameter comprises a blood pressure of the patient, and the processing circuitry is further configured to determine a mean arterial pressure of the patient based on the second signal. The processing circuitry is configured to determine the autoregulation status by at least determining whether the mean arterial pressure is greater than or equal to the weighted average. 
     Clause 10: In some examples of clause 9, the processing circuitry is further configured to determine that the mean arterial pressure is less than or equal to the weighted average for more than a predetermined period of time, generate a notification in response to determining that the mean arterial pressure is less than or equal to the weighted average for more than the predetermined period of time, and output the notification. 
     Clause 11: In some examples of any of clauses 1-10, the first physiological parameter comprises an oxygen saturation of the patient, the second physiological parameter comprises a blood pressure of the patient, and the sensing circuitry is further configured to receive a third signal indicative of a blood volume of the patient. The processing circuitry is configured to determine the one or more other estimates of the limit of autoregulation by at least determining a second estimate of the limit of autoregulation based on first signal, determining a third estimate of the limit of autoregulation based on second signal and the third signal, and determining a fourth estimate of the limit of autoregulation based on the third signal. 
     Clause 12: In some examples of clause 11, the processing circuitry is configured to determine the second estimate by at least determining a set of oxygen saturation values of the patient based on the first signal. The processing circuitry is configured to determine the third estimate by at least determining a set of hemoglobin volume values of the patient based on the second signal and the third signal. The processing circuitry is configured to determine the fourth estimate by at least determining a set of blood volume values of the patient based on the third signal. 
     Clause 13: In some examples of any of clauses 1-12, wherein the processing circuitry is configured to determine the first estimate by at least determining a set of oxygen saturation values based on the first signal, determining a set of mean arterial pressure values based on the second signal, determining a set of correlation coefficients based on the set of oxygen saturation values and the set of mean arterial pressure values, and determining the first estimate based on the set of correlation coefficients. 
     Clause 14: In some examples of any of clauses 1-13, the processing circuitry is configured to determine the first estimate by at least determining a set of blood volume values based on the first signal, determining a set of mean arterial pressure values based on the second signal, determining a set of correlation coefficients based on the set of blood volume values and the set of mean arterial pressure values, and determining the first estimate based on the set of correlation coefficients. 
     Clause 15: In some examples of any of clauses 1-14, the processing circuitry is further configured to receive updated data for the first signal, receive updated data for the second signal, and set the previous value of the limit of autoregulation equal to the weighted average. The processing circuitry is also configured to determine an updated first estimate of the limit of autoregulation of the patient based on the updated data for the first signal and the updated data for the second signal. The processing circuitry is configured to determine an updated difference between the updated first estimate and one or more other updated estimates of the limit of autoregulation. The processing circuitry is further configured to determine an updated weighted average of the updated first estimate and the previous value based on the updated difference, determine an updated autoregulation status based on the updated weighted average, and output, for display via the display, an indication of the updated autoregulation status. 
     Clause 16: In some examples of any of clauses 1-15, the device further comprises sensing circuitry configured to generate the first and second signals. 
     Clause 17: In some examples of any of clauses 1-16, the processing circuitry is further configured to determine that a rate of change of the weighted average exceeds a threshold rate for at least a threshold time duration. The processing circuitry is also configured to cease determining the autoregulation status in response to determining that the rate of change of the weighted average exceeds the threshold rate for at least the threshold time duration. 
     Clause 18: In some examples, a method comprises receiving, by processing circuitry and from sensing circuitry, a first signal indicative of a first physiological parameter of a patient and a second signal indicative of a second physiological parameter of the patient. The method also comprises determining, by the processing circuitry, a first estimate of a limit of autoregulation of the patient based on the first signal and the second signal and a difference between the first estimate of the limit of autoregulation and one or more other estimates of the limit of autoregulation. The method further comprises determining, by the processing circuitry, a weighted average of the first estimate of the limit of autoregulation and a previous value of the limit of autoregulation based on the difference between the first estimate of the limit of autoregulation and the one or more other estimates of the limit of autoregulation. The method comprises determining, by the processing circuitry, an autoregulation status based on the weighted average and outputting, by the processing circuitry for display via the display, an indication of the autoregulation status. 
     Clause 19: In some examples, a device comprises a display and processing circuitry configured to receive a first signal indicative of a first physiological parameter of a patient, a second signal indicative of a second physiological parameter of the patient, and a third signal indicative of a third physiological parameter of the patient. The processing circuitry is also configured to determine a first estimate of a limit of autoregulation of the patient and two or more estimates of the limit of autoregulation based on the first signal, the second signal, and the third signal. The processing circuitry is configured to determine a difference between the first estimate of the limit of autoregulation and two or more other estimates of the limit of autoregulation. The processing circuitry is further configured to determine a weighting factor based on the difference between the first estimate and the two or more other estimates and determine a weighted average of the first estimate of the limit of autoregulation and a previous value of the limit of autoregulation based on the weighting factor. The processing circuitry is configured to determine an autoregulation status based on the weighted average. 
     Clause 20: In some examples of clause 19, the processing circuitry is configured to determine the difference between the first estimate and the two or more other estimates by at least determining a mean of the first estimate of the limit of autoregulation and the two or more other estimates of the limit of autoregulation, determining a mean absolute difference between the first estimate of the limit of autoregulation and a mean of the two or more other estimates of the limit of autoregulation, and determining a normalized difference by at least dividing the mean absolute difference by the mean of the first estimate and the two or more other estimates. The processing circuitry is further configured to determine a multiplier based on the normalized difference. The processing circuitry is configured to determine the weighted average by at least determining a weighting factor for the first estimate of the limit of autoregulation by multiplying a predetermined maximum weighting factor by the multiplier. 
     Clause 21: In some examples, a device comprises sensing circuitry configured to receive a first signal indicative of a first physiological parameter of a patient and a second signal indicative of a second physiological parameter of the patient. The device also comprises processing circuitry configured to determine a first estimate of a limit of autoregulation of the patient based on the first signal and the second signal and determine a difference between the first estimate of the limit of autoregulation and one or more other estimates of the limit of autoregulation. The processing circuitry is further configured to determine a weighted average of the first estimate of the limit of autoregulation and a previous value of the limit of autoregulation based on the difference between the first estimate of the limit of autoregulation and the one or more other estimates of the limit of autoregulation. 
     The details of one or more examples are set forth in the accompanying drawings and the description below. Other features, objects, and advantages will be apparent from the description and drawings, and from the claims. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a conceptual block diagram illustrating an example regional oximetry device. 
         FIG. 2  is a conceptual block diagram illustrating an example regional oximetry device for monitoring the autoregulation status of a patient. 
         FIG. 3  illustrates an example graphical user interface including autoregulation information presented on a display. 
         FIGS. 4A-4D  are example graphs of oxygen saturation (rSO 2 ), correlation coefficient (COx), blood volume under sensor (BVS), and hemoglobin volume index (HVx) versus mean arterial pressure. 
         FIG. 5  is a conceptual block diagram illustrating an example framework for determining estimates of a limit of autoregulation. 
         FIGS. 6-8  are flowcharts illustrating example techniques for determining a limit of autoregulation, in accordance with some examples of this disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     This disclosure describes devices, systems, and techniques for determining changes in cerebral autoregulation of a patient. A system may include a regional oximetry device that includes processing circuitry configured to determine the cerebral autoregulation status of the patient based on a limit of autoregulation, also referred to as a limit of cerebral autoregulation, such as the lower limit of autoregulation (LLA) and/or the upper limit of autoregulation (ULA). In order to determine the LLA and/or the ULA, the processing circuitry is configured to determine a weighted average of a previous value of the limit of autoregulation and a first estimate of the limit of autoregulation. The processing circuitry can then determine the cerebral autoregulation status of the patient by comparing the blood pressure of the patient to the weighted average in order to determine whether the blood pressure indicates that the patient has intact or impaired cerebral autoregulation. 
     The processing circuitry may be configured to use the most recent iteration of the weighted average as the previous value of the limit of autoregulation for the next iteration of the weighted average. The processing circuitry may also be configured to determine the first estimate of the limit of autoregulation based on two physiological signals received from the patient. The two physiological signals can be signals that indicate blood pressure and oxygen saturation, for example. In some examples, the processing circuitry determines the first estimate of the limit of autoregulation based on a correlation index (COx) of the blood pressure and oxygen saturation. 
     The processing circuitry may continually update the weighted average as part of determining the cerebral autoregulation status of the patient. The devices, systems, and techniques described herein may increase the accuracy of the determination of the cerebral autoregulation status of a patient by removing, dampening, or reducing the weighting of outlier estimates from the algorithm used to determine a limit of cerebral autoregulation. For example, if an estimate of a limit of autoregulation based on COx values is relatively far removed from other estimates of the limit of autoregulation, then the processing circuitry can reduce the weighting of the far-removed estimate of the limit of autoregulation. 
     A patient state, as indicated by sensed physiological signals, may change relatively rapidly over time. In response to a changing patient state, some estimates of a limit of autoregulation may change quickly, while other estimates may change more slowly. In some examples, the slowly changing estimate will be inaccurate during a time interval after the change in patient state. Other possible causes of large differences between estimates of limits of cerebral autoregulation include electrocautery, damping of blood pressures due to catherization of the patient, changes in sensed blood pressure due to probe (e.g., sensor) movement relative to the patient, and changes in sensed blood pressure due to line flushing. Processing circuitry that reduces the weighting of outlier estimates may determine a weighted average that is more accurate, as compared to processing circuitry that does not reduce the weighting of outlier estimates. 
     The processing circuitry may determine a first estimate of a limit of autoregulation based on a specific parameter or correlation coefficient. This disclosure primarily describes processing circuitry configured to determine a first estimate based on COx values, but, alternatively or additionally, the processing circuitry may be configured to determine a first estimate based on other parameters or correlation coefficients. When the processing circuitry determines a current first estimate of the limit of autoregulation, the processing circuitry does not necessarily discard the previous estimate of the limit of autoregulation. Instead, the processing circuitry determines a weighted average of the current first estimate and the previous estimate of the limit of autoregulation. In some examples, the processing circuitry weighs the previous estimate more heavily than the current first estimate. 
     In response to determining that the current first estimate is an outlier relative to other estimates, then the processing circuitry reduces the weight of the current first estimate, possibly to zero. If the current first estimate has zero weight, then the processing circuitry effectively reuses the previous estimate of the limit of autoregulation. Thus, in response to determining a relatively large difference between the current first estimate and other estimates (e.g., based on other parameters), the processing circuitry may be configured to determine a small weighting factor for the current first estimate and a large weighting factor for the previous estimate. Reducing the weighting factor for the current first estimate based on the difference between the current first estimate and the other estimates may increase the accuracy and the stability of the determination of the limit of autoregulation. The reduced weighting factor may increase stability because the processing circuitry can filter out or dilute inaccurate estimates by reducing the weight of the inaccurate estimates in the determination of the weighted average. 
     The devices, systems, and techniques of this disclosure may increase the accuracy of the presentation of an estimate of a limit of autoregulation of a patient and the presentation of an indication of the autoregulation status of the patient. The presentation of more accurate and more stable information may result in increased confidence by a clinician viewing the presented information, which may lead to more informed decision making by the clinician. A clinician may lose confidence in the information presented by the processing circuitry if the information is unstable and/or inaccurate. 
     To quickly ascertain the cerebral autoregulation status, a clinician may seek a single robust value of a limit of autoregulation based on a known parameter, such as COx or HVx. The devices, systems, and techniques of this disclosure may provide a single determination of the autoregulation status of a patient, rather than multiple parameters with multiple estimates of a limit of autoregulation that may be confusing or impractical to a clinician, e.g., view of some existing cerebral autoregulation monitoring devices. The devices, systems, and techniques of this disclosure can avoid combining multiple parameters in a way that may result in a combined parameter unfamiliar to the clinician. 
     The autoregulation status of a patient may be an indication that the cerebral autoregulation control mechanism of the patient is intact (e.g., functioning properly) or impaired. Cerebral blood flow (CBF) may be regulated over a range of systemic blood pressures by the cerebral autoregulation control mechanism. This range may lie within the LLA and ULA, beyond which blood pressure drives CBF, and cerebral autoregulation function may be considered impaired. One method to determine the limits of autoregulation (the LAs) noninvasively using near-infrared spectroscopy (NIRS) technology may be via the COx measure: a moving correlation index between mean arterial pressure (MAP) and regional oxygen saturation (rSO 2 ). 
     When the cerebral autoregulation is intact for a patient, there is typically no correlation between MAP and rSO 2 . In contrast, MAP and rSO 2  typically directly correlate (e.g., the correlation index of COx is approximately 1) when the cerebral autoregulation is impaired. In practice, however, sensed data indicative of autoregulation may be noisy and/or there might be a slightly correlated relationship between variables (e.g., MAP and rSO 2 ) even when cerebral autoregulation is intact for the patient. 
     Some existing systems for monitoring autoregulation may determine a patient&#39;s autoregulation status based on various physiological parameter values (also referred to herein as physiological values). Such physiological values may be subject to various sources of error, such as noise caused by relative sensor and patient motion, operator error, poor quality measurements, drugs, or other anomalies. However, some existing systems for monitoring autoregulation may not reduce the various sources of error when utilizing the measured physiological values to determine the patient&#39;s autoregulation status. Furthermore, some existing systems may not determine and/or utilize a reliable metric to determine whether the autoregulation status calculated from the physiological values is reliable. Accordingly, the autoregulation status determined by such existing systems may be less accurate or less reliable. 
     In some examples, a regional oximetry device of this disclosure may include processing circuitry configured to determine estimates of a limit of cerebral autoregulation based on a patient&#39;s MAP and a patient&#39;s oxygen saturation. In particular, the processing circuitry may determine estimates of a limit of cerebral autoregulation based on oxygen saturation (LArSO 2 ) and based on a set of COx values (LACOx). In addition, the processing circuitry may monitor the patient&#39;s autoregulation by correlating measurements of the patient&#39;s blood pressure with measurements of the patient&#39;s blood volume (BVS) and by determining an estimate of the limit of cerebral autoregulation based on the BVS values (LABVS). The processing circuitry can determine a hemoglobin volume index (HVx) based at least in part on a linear correlation between the patient&#39;s blood pressure and blood volume. The processing circuitry can then determine an estimate of the limit of cerebral autoregulation based on the HVx values (LAHVx). The processing circuitry may also determine various other linear correlations to help evaluate a patient&#39;s autoregulation status, such as a linear correlation between measurements of a patient&#39;s blood pressure and measurements of a patient&#39;s cerebral blood flow known as a mean velocity index (Mx). The processing circuitry may also determine a linear correlation between measurements of a patient&#39;s blood pressure and measurements of a patient&#39;s intracranial pressure known as a pressure reactivity index (PRx). Mx may be a proxy for COx, and PRx may be a proxy for HVx. 
     Additional example details of the parameters that can be used for determining a limit of autoregulation may be found in commonly assigned U.S. Patent Application Publication No. 2017/0105631 filed on Oct. 18, 2016, entitled “Systems and Method for Providing Blood Pressure Safe Zone Indication During Autoregulation Monitoring,” commonly assigned U.S. patent application Ser. No. 15/184,305 filed on Jun. 6, 2016, entitled “Systems and Methods for Reducing Signal Noise When Monitoring Autoregulation,” and commonly assigned U.S. patent application Ser. No. 15/296,150 filed on Oct. 18, 2016, entitled “System and Method for Providing Blood Pressure Safe Zone Indication During Autoregulation Monitoring,” which are incorporated herein by reference in their entirety. 
       FIG. 1  is a conceptual block diagram illustrating an example regional oximetry device  100 . Regional oximetry device  100  includes processing circuitry  110 , memory  120 , user interface  130 , display  132 , sensing circuitry  140 - 142 , and sensing device(s)  150 - 152 . In some examples, regional oximetry device  100  may be configured to determine and display the cerebral autoregulation status of a patient, e.g., during a medical procedure or for more long-term monitoring, such as fetal monitoring. A clinician may receive information regarding the cerebral autoregulation status of a patient via display  132  and adjust treatment or therapy to the patient based on the cerebral autoregulation status information. 
     Processing circuitry  110 , as well as other processors, processing circuitry, controllers, control circuitry, and the like, described herein, may include one or more processors. Processing circuitry  110  may include any combination of integrated circuitry, discrete logic circuitry, analog circuitry, such as one or more microprocessors, digital signal processors (DSPs), application specific integrated circuits (ASICs), or field-programmable gate arrays (FPGAs). In some examples, processing circuitry  110  may include multiple components, such as any combination of one or more microprocessors, one or more DSPs, one or more ASICs, or one or more FPGAs, as well as other discrete or integrated logic circuitry, and/or analog circuitry. 
     Memory  120  may be configured to store measurements of physiological parameters, MAP values, rSO 2  values, COx values, BVS values, HVx values, and value(s) of an LLA and/or a ULA, for example. Memory  120  may also be configured to store data such as weighting factors, maximum weighting factors, threshold values, and/or threshold levels, which may be predetermined by processing circuitry  110 . The weighting factors, maximum weighting factors, threshold values, and/or threshold levels may stay constant throughout the use of device  100  and across multiple patients, or these values may change over time. 
     In some examples, memory  120  may store program instructions, which may include one or more program modules, which are executable by processing circuitry  110 . When executed by processing circuitry  110 , such program instructions may cause processing circuitry  110  to provide the functionality ascribed to it herein. The program instructions may be embodied in software, firmware, and/or RAMware. Memory  120  may include any volatile, non-volatile, magnetic, optical, or electrical media, such as a random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically-erasable programmable ROM (EEPROM), flash memory, or any other digital media. 
     User interface  130  and/or display  132  may be configured to present information to a user (e.g., a clinician). User interface  130  and/or display  132  may be configured to present a graphical user interface to a user, where each graphical user interface may include indications of values of one or more physiological parameters of a subject. For example, processing circuitry  110  may be configured to present blood pressure values, physiological parameter values, and indications of autoregulation status (e.g., cerebral autoregulation status) of a patient via display  132 . In some examples, if processing circuitry  110  determines that the autoregulation status of the patient is impaired, then processing circuitry  110  may present a notification (e.g., an alert) indicating the impaired cerebral autoregulation status via display  132 . As another example, processing circuitry  110  may present, via display  132 , estimates of rSO 2  for a patient, an estimate of the blood oxygen saturation (SpO 2 ) determined by processing circuitry  110 , pulse rate information, respiration rate information, blood pressure, any other patient parameters, or any combination thereof. 
     User interface  130  and/or display  132  may include a monitor, cathode ray tube display, a flat panel display such as a liquid crystal (LCD) display, a plasma display, a light emitting diode (LED) display, and/or any other suitable display. User interface  130  and/or display  132  may be part of a personal digital assistant, mobile phone, tablet computer, laptop computer, any other suitable computing device, or any combination thereof, with a built-in display or a separate display. User interface  130  may also include means for projecting audio to a user, such as speaker(s). Processing circuitry  110  may be configured to present, via user interface  130 , a visual, audible, tactile, or somatosensory notification (e.g., an alarm signal) indicative of the patient&#39;s autoregulation status. User interface  130  may include or be part of any suitable device for conveying such information, including a computer workstation, a server, a desktop, a notebook, a laptop, a handheld computer, a mobile device, or the like. In some examples, processing circuitry  110  and user interface  130  may be part of the same device or supported within one housing (e.g., a computer or monitor). In other examples, processing circuitry  110  and user interface  130  may be separate devices configured to communicate through a wired connection or a wireless connection (e.g., communication interface  290  shown in  FIG. 2 ). 
     Sensing circuitry  140 - 142  may be configured to receive physiological signals sensed by respective sensing device(s)  150 - 152  and communicate the physiological signals to processing circuitry  110 . Sensing device(s)  150 - 152  may include any sensing hardware configured to sense a physiological parameter of a patient, such as, but not limited to, one or more electrodes, optical receivers, blood pressure cuffs, or the like. Sensing circuitry  140 - 142  may convert the physiological signals to usable signals for processing circuitry  110 , such that processing circuitry  110  is configured to receive signals generated by sensing circuitry  140 - 142 . Sensing circuitry  140 - 142  may receive signals indicating physiological parameters from a patient, such as, but not limited to, blood pressure, regional oxygen saturation, blood volume, heart rate, and respiration. Sensing circuitry  140 - 142  may include, but are not limited to, blood pressure sensing circuitry, oxygen saturation sensing circuitry, blood volume sensing circuitry, heart rate sensing circuitry, temperature sensing circuitry, electrocardiography (ECG) sensing circuitry, electroencephalogram (EEG) sensing circuitry, or any combination thereof. In some examples, sensing circuitry  140 - 142  and/or processing circuitry  110  may include signal processing circuitry such as an analog-to-digital converter. 
     In some examples, oxygen saturation sensing device  150  is a regional oxygen saturation sensor configured to generate an oxygen saturation signal indicative of blood oxygen saturation within the venous, arterial, and/or capillary systems within a region of the patient. For example, oxygen saturation sensing device  150  may be configured to be placed on the patient&#39;s forehead and may be used to determine the oxygen saturation of the patient&#39;s blood within the venous, arterial, and/or capillary systems of a region underlying the patient&#39;s forehead (e.g., in the cerebral cortex). 
     In such cases, oxygen saturation sensing device  150  may include emitter  160  and detector  162 . Emitter  160  may include at least two light emitting diodes (LEDs), each configured to emit at different wavelengths of light, e.g., red or near infrared light. In some examples, light drive circuitry (e.g., within sensing device  150 , sensing circuitry  140 , and/or processing circuitry  110 ) may provide a light drive signal to drive emitter  160  and to cause emitter  160  to emit light. In some examples, the LEDs of emitter  160  emit light in the range of about 600 nanometers (nm) to about 1000 nm. In a particular example, one LED of emitter  160  is configured to emit light at about 730 nm and the other LED of emitter  160  is configured to emit light at about 810 nm. Other wavelengths of light may also be used in other examples. 
     Detector  162  may include a first detection element positioned relatively “close” (e.g., proximal) to emitter  160  and a second detection element positioned relatively “far” (e.g., distal) from emitter  160 . Light intensity of multiple wavelengths may be received at both the “close” and the “far” detector  162 . For example, if two wavelengths are used, the two wavelengths may be contrasted at each location and the resulting signals may be contrasted to arrive at a regional saturation value that pertains to additional tissue through which the light received at the “far” detector passed (tissue in addition to the tissue through which the light received by the “close” detector passed, e.g., the brain tissue), when it was transmitted through a region of a patient (e.g., a patient&#39;s cranium). Surface data from the skin and skull may be subtracted out, to generate a regional oxygen saturation signal for the target tissues over time. Oxygen saturation sensing device  150  may provide the regional oxygen saturation signal to processing circuitry  110  or to any other suitable processing device to enable evaluation of the patient&#39;s autoregulation status. 
     In operation, blood pressure sensing device  151  and oxygen saturation sensing device  150  may each be placed on the same or different parts of the patient&#39;s body. For example, blood pressure sensing device  151  and oxygen saturation sensing device  150  may be physically separate from each other and may be separately placed on the patient. As another example, blood pressure sensing device  151  and oxygen saturation sensing device  150  may in some cases be part of the same sensor or supported by a single sensor housing. For example, blood pressure sensing device  151  and oxygen saturation sensing device  150  may be part of an integrated oximetry system configured to non-invasively measure blood pressure (e.g., based on time delays in a PPG signal) and regional oxygen saturation. One or both of blood pressure sensing device  151  or oxygen saturation sensing device  150  may be further configured to measure other parameters, such as hemoglobin, respiratory rate, respiratory effort, heart rate, saturation pattern detection, response to stimulus such as bispectral index (BIS) or electromyography (EMG) response to electrical stimulus, or the like. While an example regional oximetry device  100  is shown in  FIG. 1 , the components illustrated in  FIG. 1  are not intended to be limiting. Additional or alternative components and/or implementations may be used in other examples. 
     Blood pressure sensing device  151  may be any sensor or device configured to obtain the patient&#39;s blood pressure (e.g., arterial blood pressure). For example, blood pressure sensing device  151  may include a blood pressure cuff for non-invasively monitoring blood pressure or an arterial line for invasively monitoring blood pressure. In certain examples, blood pressure sensing device  151  may include one or more pulse oximetry sensors. In some such cases, the patient&#39;s blood pressure may be derived by processing time delays between two or more characteristic points within a single plethysmography (PPG) signal obtained from a single pulse oximetry sensor. 
     Additional example details of deriving blood pressure based on a comparison of time delays between certain components of a single PPG signal obtained from a single pulse oximetry sensor are described in commonly assigned U.S. Patent Application Publication No. 2009/0326386 filed Sep. 30, 2008, and entitled “Systems and Methods for Non-Invasive Blood Pressure Monitoring,” the entire content of which is incorporated herein by reference. In other cases, the patient&#39;s blood pressure may be continuously, non-invasively monitored via multiple pulse oximetry sensors placed at multiple locations on the patient&#39;s body. As described in commonly assigned U.S. Pat. No. 6,599,251, entitled “Continuous Non-invasive Blood Pressure Monitoring Method and Apparatus,” the entire content of which is incorporated herein by reference, multiple PPG signals may be obtained from the multiple pulse oximetry sensors, and the PPG signals may be compared against one another to estimate the patient&#39;s blood pressure. Regardless of its form, blood pressure sensing device  151  may be configured to generate a blood pressure signal indicative of the patient&#39;s blood pressure (e.g., arterial blood pressure) over time. Blood pressure sensing device  151  may provide the blood pressure signal to sensing circuitry  141 , processing circuitry  110 , or to any other suitable processing device to enable evaluation of the patient&#39;s autoregulation status. 
     Processing circuitry  110  may be configured to receive one or more signals generated by sensing devices  150 - 152  and sensing circuitry  140 - 142 . The physiological signals may include a signal indicating blood pressure, a signal indicating oxygen saturation, and/or a signal indicating blood volume of a patient (e.g., an isosbestic signal). Processing circuitry  110  may be configured to determine a first estimate of a limit of autoregulation based on two or more signals received by sensing devices  150 - 152  and sensing circuitry  140 - 142  and delivered to processing circuitry  110 . Sensing devices  150 - 152  and sensing circuitry  140 - 142  can deliver the physiological signals directly to processing circuitry  110  or sensing circuitry  140 - 142  can modify the physiological signals (e.g., through pre-processing) before delivering signals to processing circuitry  110 . 
     In some examples, processing circuitry  110  determines the first estimate of the limit of autoregulation based on a correlation index (e.g., COx, HVx), an oxygen saturation value, a blood volume value, a gradients measure, and/or another physiological parameter of the first and second physiological signals. Although described herein primarily as LACOx, the first estimate may also be based on other physiological parameters or physiological indices, such as LAHVx, LArSO2, LABVS, or an estimate of the limit of autoregulation based on Mx or PRx. For example, Equations (1)-(5) below use LACOx current  as the first estimate of the limit of autoregulation, but processing circuitry  110  can use other physiological parameters to determine the first estimate. 
     Processing circuitry  110  may use any of several techniques to determine the first estimate of the limit of autoregulation. In some examples, processing circuitry  110  determines a set of COx values based on MAP values and rSO 2  values. Processing circuitry  110  may then determine an estimate of a lower limit of autoregulation based on the lowest blood pressure value at which the expected value of COx is less than a threshold value, such as 0.5, 0.4, 0.3, 0.2, 0.1, or 0.0. Using this threshold value, processing circuitry  110  can determine where there is a distinct change in the gradient of a rSO 2 -MAP curve or a BVS-MAP curve. This distinct change may correspond to a distinct step down in the plot of COx or HVx versus MAP. Similarly, processing circuitry  110  may determine an estimate of an upper limit of autoregulation based on the highest blood pressure value at which the expected value of COx is less than a threshold value. Additional example details of determining LAs and cerebral autoregulation status may be found in commonly assigned U.S. Patent Application Publication No. 2018/0014791, filed on Jul. 13, 2017, the entire content of which is incorporated herein by reference. 
     Processing circuitry  110  is also configured to determine one or more other estimates of the limit of autoregulation, such as LArSO 2 , LABVS, LAHVx, and/or any other estimate of the limit of autoregulation. For example, LArSO 2 , LAHVx, and LABVS in Equations (1), (5), (6), and (11) below represent the other estimates of the limit of autoregulation, although processing circuitry  110  may determine different, greater than, or fewer than three other estimates of the limit of autoregulation in some examples. Processing circuitry  110  may determine the other estimates of the limit of autoregulation using example thresholds and/or other methods described with respect to  FIGS. 4A-4D . Additional example details of determining estimates of LAs may be found in commonly assigned U.S. Patent Application Publication No. 2018/0049649 filed on Aug. 1, 2017, and entitled “System and Method for Identifying Blood Pressure Zones During Autoregulation Monitoring,” and in commonly assigned U.S. Patent Application Publication No. 2016/0367197 filed on Dec. 22, 2016, and entitled “Systems and Methods of Reducing Signal Noise when Monitoring Autoregulation,” the entire contents of each of which are incorporated herein by reference. 
     Processing circuitry  110  may be configured to determine a difference between the first estimate of the limit of autoregulation and the one or more other estimates of the limit of autoregulation. The difference may indicate whether the first estimate of the limit of autoregulation is an outlier estimate of the limit of autoregulation relative to the other estimates, or whether the first estimate of the limit of autoregulation is closely aligned with the other estimates. One possible reason for the first estimate of the limit of autoregulation being an outlier estimate is that the first estimate of the limit of autoregulation is an inaccurate estimate. Another possible reason for the first estimate of the limit of autoregulation being an outlier estimate is that some or all of the other estimates are inaccurate estimates. 
     Equation (1) shows one example technique for determining a mean absolute difference (MADCOx) between the first estimate of the limit of autoregulation and the other estimates (LArSO 2 , LAHVx, and LABVS). In Equation (1), the mean absolute difference equals the absolute value of the difference between the first estimate of the limit of autoregulation and the mean of three other estimates.
 
MADCOx=|LACOx current −⅓(LArSO 2 +LAHVx+LABVS)|  (1)
 
     The MADCOx value may serve as a quality metric for the currently computed LACOx value, such that a relatively large MADCOx can indicate a higher possibility of inaccuracies. Processing circuitry  110  may be configured to use this quality metric to weight the currently calculated LLACOx value when processing circuitry  110  adds the current estimate to the current reported value (e.g., the previous estimate) on display  132  to provide an updated reported value (see, e.g., Equation (2)). Processing circuitry  110  may use the other estimates to give or remove confidence from the first estimate. Processing circuitry  110  may look at the other estimates to determine whether the first estimate is an outlier estimate. If processing circuitry  110  determines that the first estimate is an outlier estimate, processing circuitry  110  may be configured to reduce the weighting of the first estimate in the determination of a weighted average. 
     Processing circuitry  110  is configured to then determine a weighted average of the first estimate of the limit of autoregulation and a previous value of the limit of autoregulation. To determine the weighted average, processing circuitry  110  can use the difference between the first estimate of the limit of autoregulation and the other estimates to determine a weighting factor for the first estimate of the limit of autoregulation. In some examples, processing circuitry  110  can reduce a magnitude of the weighting factor of the first estimate of the limit of autoregulation based on determining a relatively large difference between the first estimate of the limit of autoregulation and the other estimates. The relatively large difference may indicate that the first estimate of the limit of autoregulation is inaccurate. Thus, by reducing the weighting factor of the first estimate of the limit of autoregulation, processing circuitry  110  may insulate the weighted average, and consequently the determination of an autoregulation status of a patient, from a possibly inaccurate first estimate of the limit of autoregulation. Conversely, processing circuitry  110  may increase the weighting factor based on a relatively small difference between the first estimate of the limit of autoregulation and the other estimates. 
     Equations (2)-(9) show example techniques by which processing circuitry  110  may determine a weighted average of the first estimate of the limit of autoregulation and a previous value of the limit of autoregulation. As shown in Equation (2), processing circuitry  110  can determine a weighted average (LACOx new ) based on a previous value of the limit of autoregulation (LACOx previous ), which may be the most recently determined weighted average. Thus, if the newly determined weighted average is the Nth iteration of the weighted average, then processing circuitry  110  may set the previous value of the limit of autoregulation equal to the (N−1)th iteration of the weighted average. Processing circuitry  110  may multiply the first estimate of the limit of autoregulation (LACOx current ) by a first weighting factor (w) that is between zero and one. Processing circuitry  110  may also multiply the previous value of the limit of autoregulation by a second weighting factor that is equal to the one minus the first weighting factor. Thus, in the example of Equation (2), the sum of the two weighting factors is one. Processing circuitry  110  can use Equations (3) and (4) to determine the weighted values that make up the weighted average.
 
LACOx new =[(1− w )×LACOx previous ]( w ×LACOx current )  (2)
 
Weighted value of first estimate= w ×LACOx current   (3)
 
Weighted value of previous value=(1− w )×LACOx previous   (4)
 
     In Equation (2), LACOx new  may represent the new reported value, e.g., on a device screen. For example, processing circuitry  110  can output, to display  132 , LACOx new  for presentation to a user. LACOx previous  may represent the previously reported value by processing circuitry  110  on the device screen (e.g., display  132 ). LACOx current  may represent the first estimate of the limit of autoregulation based on currently calculated COx values by processing circuitry  110  of device  100  using the latest acquired signal portions from sensing devices  150 - 152 . Processing circuitry  110  may not necessarily output LACOx current  for display, because in some examples processing circuitry  110  may use LACOx current  only to determine LACOx new . 
     Processing circuitry  110  can determine the first weighting factor using the example techniques of Equations (5)-(9), although other techniques may be used in other examples. In some examples, processing circuitry  110  may determine a normalized difference (MADCOx divided by μ LA ) based on the difference between the first estimate of the limit of autoregulation and the other estimates. Processing circuitry  110  may be configured to normalize the difference by dividing the difference by the mean (μ LA ) of the first estimate of the limit of autoregulation and the other estimates. Processing circuitry  110  may determine the mean of the estimates μ LA  as shown in Equation (5). By normalizing the difference using Equation (7), processing circuitry  110  may determine a percentage difference rather than a difference in absolute terms. In some examples, processing circuitry  110  may determine the mean based on only the other estimates by excluding or leaving out the first estimate, as shown in Equation (6). 
     
       
         
           
             
               
                 
                   
                     μ 
                     LA 
                   
                   = 
                   
                     
                       1 
                       4 
                     
                     ⁢ 
                     
                       ( 
                       
                         
                           LACOx 
                           current 
                         
                         + 
                         
                           LArSO 
                           2 
                         
                         + 
                         LAHVx 
                         + 
                         LABVS 
                       
                       ) 
                     
                   
                 
               
               
                 
                   ( 
                   5 
                   ) 
                 
               
             
             
               
                 
                   
                     μ 
                     LA 
                   
                   = 
                   
                     
                       1 
                       3 
                     
                     ⁢ 
                     
                       ( 
                       
                         
                           LArSO 
                           2 
                         
                         + 
                         LAHVx 
                         + 
                         LABVS 
                       
                       ) 
                     
                   
                 
               
               
                 
                   ( 
                   6 
                   ) 
                 
               
             
             
               
                 
                   
                     Normalized 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     difference 
                   
                   = 
                   
                     MADCOx 
                     
                       μ 
                       LA 
                     
                   
                 
               
               
                 
                   ( 
                   7 
                   ) 
                 
               
             
             
               
                 
                   m 
                   = 
                   
                     1 
                     - 
                     
                       MADCOx 
                       
                         μ 
                         LA 
                       
                     
                   
                 
               
               
                 
                   ( 
                   8 
                   ) 
                 
               
             
             
               
                 
                   w 
                   = 
                   
                     
                       
                         w 
                         s 
                       
                       × 
                       m 
                     
                     = 
                     
                       
                         w 
                         s 
                       
                       - 
                       
                         
                           
                             w 
                             s 
                           
                           × 
                           MADCOx 
                         
                         
                           μ 
                           LA 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   9 
                   ) 
                 
               
             
           
         
       
     
     Processing circuitry  110  can then use the normalized difference to determine the first weighting factor (w) by multiplying a predetermined maximum weighting factor (w s ) by one minus the normalized difference, as shown in Equations (7)-(9). Processing circuitry  110  may determine the first weighting factor based on the similarity between the COx value of the limit of autoregulation and estimates from the other method. The magnitude of the weighting factor effectively represents a trade-off between the amount of trust given to previous values of the limit of autoregulation versus the current value of the limit of autoregulation weighted average. In some examples, the predetermined maximum weighting factor may be set or determined based on empirical data. 
     Processing circuitry  110  may store the predetermined maximum weighting factor to memory  120  as a value between zero and one, such as 0.1, 0.2, 0.3, 0.4, or any other suitable value. The weighting factor may be scaled version of the predetermined maximum weighting factor, where a maximum value of the weighting factor is the value of the predetermined maximum weighting factor. Processing circuitry  110  may also store an initial weighting factor value to memory  120  for use before processing circuitry  110  has determined a multiplier. 
     Thus, in response to determining a relatively large difference using Equation (1), processing circuitry  110  may determine a relatively small multiplier (e.g., an “m” factor) using Equation (8) and a relatively small first weighting factor using Equation (9). In some examples, the multiplier may be a simple metric multiplier equal to unity (e.g., one) if all points agree and less than unity if the points differ from LACOx. Processing circuitry  110  may be configured to determine the multiplier by subtracting the normalized difference from one. If the value of the multiplier is less than zero, then processing circuitry  110  may set the value of the multiplier to zero. Processing circuitry  110  may then produce the weighting factor from a standard weight (e.g., a predetermined maximum weighting factor) and the multiplier using Equation (9). A small value of the first weighting factor may result in processing circuitry  110  determining a weighted average based on a heavily weighted previous value of the limit of autoregulation and a lightly weighted first estimate of the limit of autoregulation using Equation (2). In this manner, processing circuitry  110  may be configured to dampen or reduce the change in the weighted average from one iteration to the next iteration based on determining a relatively large difference between the first estimate of the limit of autoregulation and the other estimates because the relatively large difference may result from an inaccurate first estimate of the limit of autoregulation. 
     In some examples, processing circuitry  110  is configured to determine an autoregulation status based on the weighted average of the first estimate of the limit of autoregulation and a previous value of the limit of autoregulation. For example, processing circuitry  110  can determine that a patient has intact autoregulation in response to determining that the blood pressure of the patient is greater than a lower limit of autoregulation and less than an upper limit of autoregulation (e.g., the blood pressure is between the limits of autoregulation). 
     Once the autoregulation status has been determined, processing circuitry  110  outputs, such as for display via display  132  of user interface  130 , an indication of the autoregulation status. Display  132  may present a graphical user interface such as graphical user interface  300  shown in  FIG. 3 . As described in further detail below, graphical user interface  300  includes an indicator of autoregulation status  350 . The indication of autoregulation status may include text, colors, and/or audio presented to a user. Processing circuitry  110  may be further configured to present an indication of one or more limits of autoregulation (e.g., indicators  360  and  370 ). 
     Although other example techniques are possible, regional oximetry device  100  may be configured to determine the first estimate of the limit of autoregulation based on COx values derived from MAP values and rSO 2  values. For example, processing circuitry  110  may determine the first estimate of the limit of autoregulation based on HVx values, BVS values, and/or rSO 2  values in order to determine a robust value of any of the other parameters. Regional oximetry device  200  of  FIG. 2  includes additional detail on how processing circuitry  110  can determine rSO 2  values based on a physiological signal received from sensing device  150 . 
       FIG. 2  is a conceptual block diagram illustrating an example regional oximetry device  200  for monitoring the autoregulation status of a patient. In the example shown in  FIG. 2 , regional oximetry device  200  includes sensing device  250  and regional oximetry device  200 , which each generate and process physiological signals of a subject. In some examples, sensing device  250  and regional oximetry device  200  may be part of an oximeter. As shown in  FIG. 2 , regional oximetry device  200  includes back-end processing circuitry  214 , user interface  230 , light drive circuitry  240 , front-end processing circuitry  216 , control circuitry  245 , and communication interface  290 . Regional oximetry device  200  may be communicatively coupled to sensing device  250 . Regional oximetry device  200  is an example of regional oximetry device  100  shown in  FIG. 1  and regional oximetry device  200  shown in  FIG. 2 . In some examples, regional oximetry device  200  may also include a blood pressure sensor and/or a blood volume sensor (e.g., sensing devices  151  and  152 ). 
     In the example shown in  FIG. 2 , sensing device  250  includes light source  260 , detector  262 , and detector  263 . In some examples, sending device  250  may include more than two detectors. Light source  260  may be configured to emit photonic signals having two or more wavelengths of light (e.g., red and infrared (IR)) into a subject&#39;s tissue. For example, light source  260  may include a red light emitting light source and an IR light emitting light source, (e.g., red and IR light emitting diodes (LEDs)), for emitting light into the tissue of a subject to generate physiological signals. In some examples, the red wavelength may be between about 600 nm and about 700 nm, and the IR wavelength may be between about 800 nm and about 1000 nm. Other wavelengths of light may be used in other examples. Light source  260  may include any number of light sources with any suitable characteristics. In examples in which an array of sensors is used in place of sensing device  250 , each sensing device may be configured to emit a single wavelength. For example, a first sensing device may emit only a red light while a second sensing device may emit only an IR light. In some examples, light source  260  may be configured to emit two or more wavelengths of near-infrared light (e.g., wavelengths between 600 nm and 1000 nm) into a subject&#39;s tissue. In some examples, light source  260  may be configured to emit four wavelengths of light (e.g., 724 nm, 770 nm, 810 nm, and 850 nm) into a subject&#39;s tissue. In some examples, the subject may be a medical patient. 
     As used herein, the term “light” may refer to energy produced by radiative sources and may include one or more of ultrasound, radio, microwave, millimeter wave, infrared, visible, ultraviolet, gamma ray or X-ray electromagnetic radiation. Light may also include any wavelength within the radio, microwave, infrared, visible, ultraviolet, or X-ray spectra, and that any suitable wavelength of electromagnetic radiation may be appropriate for use with the present techniques. Detectors  262  and  263  may be chosen to be specifically sensitive to the chosen targeted energy spectrum of light source  260 . 
     In some examples, detectors  262  and  263  may be configured to detect the intensity of multiple wavelengths of near-infrared light. In some examples, detectors  262  and  263  may be configured to detect the intensity of light at the red and IR wavelengths. In some examples, an array of detectors may be used and each detector in the array may be configured to detect an intensity of a single wavelength. In operation, light may enter detector  262  after passing through the subject&#39;s tissue, including skin, bone, and other shallow tissue (e.g., non-cerebral tissue and shallow cerebral tissue). Light may enter detector  263  after passing through the subject&#39;s tissue, including skin, bone, other shallow tissue (e.g., non-cerebral tissue and shallow cerebral tissue), and deep tissue (e.g., deep cerebral tissue). Detectors  262  and  263  may convert the intensity of the received light into an electrical signal. The light intensity may be directly related to the absorbance and/or reflectance of light in the tissue. That is, when more light at a certain wavelength is absorbed or reflected, less light of that wavelength is received from the tissue by detectors  262  and  263 . 
     After converting the received light to an electrical signal, detectors  262  and  263  may send the detection signals to regional oximetry device  200 , which may process the detection signals and determine physiological parameters (e.g., based on the absorption of the red and IR wavelengths in the subject&#39;s tissue at both detectors). In some examples, one or more of the detection signals may be preprocessed by sensing device  250  before being transmitted to regional oximetry device  200 . Additional example details of determining oxygen saturation based on light signals may be found in commonly assigned U.S. Pat. No. 9,861,317, which issued on Jan. 9, 2018, and is entitled “Methods and Systems for Determining Regional Blood Oxygen Saturation,” the entire content of which is incorporated herein by reference. 
     Control circuitry  245  may be coupled to light drive circuitry  240 , front-end processing circuitry  216 , and back-end processing circuitry  214 , and may be configured to control the operation of these components. In some examples, control circuitry  245  may be configured to provide timing control signals to coordinate their operation. For example, light drive circuitry  240  may generate one or more light drive signals, which may be used to turn on and off light source  260 , based on the timing control signals provided by control circuitry  245 . Front-end processing circuitry  216  may use the timing control signals to operate synchronously with light drive circuitry  240 . For example, front-end processing circuitry  216  may synchronize the operation of an analog-to-digital converter and a demultiplexer with the light drive signal based on the timing control signals. In addition, the back-end processing circuitry  214  may use the timing control signals to coordinate its operation with front-end processing circuitry  216 . 
     Light drive circuitry  240 , as discussed above, may be configured to generate a light drive signal that is provided to light source  260  of sensing device  250 . The light drive signal may, for example, control the intensity of light source  260  and the timing of when light source  260  is turned on and off. In some examples, light drive circuitry  240  provides one or more light drive signals to light source  260 . Where light source  260  is configured to emit two or more wavelengths of light, the light drive signal may be configured to control the operation of each wavelength of light. The light drive signal may comprise a single signal or may comprise multiple signals (e.g., one signal for each wavelength of light). 
     Front-end processing circuitry  216  may perform any suitable analog conditioning of the detector signals. The conditioning performed may include any type of filtering (e.g., low pass, high pass, band pass, notch, or any other suitable filtering), amplifying, performing an operation on the received signal (e.g., taking a derivative, averaging), performing any other suitable signal conditioning (e.g., converting a current signal to a voltage signal), or any combination thereof. The conditioned analog signals may be processed by an analog-to-digital converter of circuitry  216 , which may convert the conditioned analog signals into digital signals. Front-end processing circuitry  216  may operate on the analog or digital form of the detector signals to separate out different components of the signals. Front-end processing circuitry  216  may also perform any suitable digital conditioning of the detector signals, such as low pass, high pass, band pass, notch, averaging, or any other suitable filtering, amplifying, performing an operation on the signal, performing any other suitable digital conditioning, or any combination thereof. Front-end processing circuitry  216  may decrease the number of samples in the digital detector signals. In some examples, front-end processing circuitry  216  may also remove dark or ambient contributions to the received signal. 
     Back-end processing circuitry  214  may include processing circuitry  210  and memory  220 . Processing circuitry  210  may include an assembly of analog or digital electronic components and may be configured to execute software, which may include an operating system and one or more applications, as part of performing the functions described herein with respect to, e.g., processing circuitry  110 . Processing circuitry  210  may receive and further process physiological signals received from front-end processing circuitry  216 . For example, processing circuitry  210  may determine one or more physiological parameter values based on the received physiological signals. For example, processing circuitry  210  may compute one or more of regional oxygen saturation, blood oxygen saturation (e.g., arterial, venous, or both), pulse rate, respiration rate, respiration effort, blood pressure, hemoglobin concentration (e.g., oxygenated, deoxygenated, and/or total), any other suitable physiological parameters, or any combination thereof. 
     Processing circuitry  210  may perform any suitable signal processing of a signal, such as any suitable band-pass filtering, adaptive filtering, closed-loop filtering, any other suitable filtering, and/or any combination thereof. Processing circuitry  210  may also receive input signals from additional sources not shown. For example, processing circuitry  210  may receive an input signal containing information about treatments provided to the subject from user interface  230 . Additional input signals may be used by processing circuitry  210  in any of the determinations or operations it performs in accordance with back-end processing circuitry  214  or regional oximetry device  200 . 
     Processing circuitry  210  is an example of processing circuitry  110  and is configured to perform the techniques of this disclosure. For example, processing circuitry  210  is configured to receive signals indicative of physiological parameters. Processing circuitry  210  is also configured to determine an autoregulation status based on a weighted average of a first estimate of a limit of autoregulation and a previous value of the limit of autoregulation. Processing circuitry  210  may be configured to determine a weighting factor for the determination of the weighted average based on a difference between the first estimate of the limit of autoregulation and one or more other estimates of the limit of autoregulation. 
     Memory  220  may include any suitable computer-readable media capable of storing information that can be interpreted by processing circuitry  210 . In some examples, memory  220  may store reference absorption curves, reference sets, determined values, such as blood oxygen saturation, pulse rate, blood pressure, fiducial point locations or characteristics, initialization parameters, any other determined values, or any combination thereof, in a memory device for later retrieval. Memory  220  may also store a previously determined estimate of the limit of autoregulation, weighting factors, weighted values, and so on. Back-end processing circuitry  214  may be communicatively coupled with user interface  230  and communication interface  290 . 
     User interface  230  may include input device  234 , display  232 , and speaker  236 . User interface  230  is an example of user interface  130  shown in  FIG. 1 , and display  232  is an example of display  132  shown in  FIG. 1 . User interface  230  may include, for example, any suitable device such as one or more medical devices (e.g., a medical monitor that displays various physiological parameters, a medical alarm, or any other suitable medical device that either displays physiological parameters or uses the output of back-end processing  214  as an input), one or more display devices (e.g., monitor, personal digital assistant (PDA), mobile phone, tablet computer, clinician workstation, any other suitable display device, or any combination thereof), one or more audio devices, one or more memory devices, one or more printing devices, any other suitable output device, or any combination thereof. 
     Input device  234  may include any type of user input device such as a keyboard, a mouse, a touch screen, buttons, switches, a microphone, a joy stick, a touch pad, or any other suitable input device or combination of input devices. Input device  234  may also receive inputs to select a model number of sensing device  250 , blood pressure sensor  250  ( FIG. 2 ), or blood pressure processing equipment. In some examples, processing circuitry  210  may determine a predetermined maximum weighting factor based on user inputs received by input device  234 . 
     In some examples, the subject may be a medical patient and display  232  may exhibit a list of values which may generally apply to the subject, such as, for example, an oxygen saturation signal indicator, a blood pressure signal indicator, a COx signal indicator, a COx value indicator, and/or an autoregulation status indicator. Display  232  may also be configured to present additional physiological parameter information. Graphical user interface  300  shown in  FIG. 3  is an example of an interface that can be presented via display  232  of  FIG. 2 . Additionally, display  232  may present, for example, one or more estimates of a subject&#39;s regional oxygen saturation generated by regional oximetry device  200  (referred to as an “rSO2” measurement). Display  232  may also present indications of the upper and lower limits of autoregulation. Speaker  236  within user interface  230  may provide an audible sound that may be used in various examples, such as for example, sounding an audible alarm in the event that a patient&#39;s physiological parameters are not within a predefined normal range. 
     Communication interface  290  may enable regional oximetry device  200  to exchange information with external devices. Communication interface  290  may include any suitable hardware, software, or both, which may allow regional oximetry device  200  to communicate with electronic circuitry, a device, a network, a server or other workstations, a display, or any combination thereof. For example, regional oximetry device  200  may receive MAP values and/or oxygen saturation values from an external device via communication interface  290 . 
     The components of regional oximetry device  200  that are shown and described as separate components are shown and described as such for illustrative purposes only. In some examples the functionality of some of the components may be combined in a single component. For example, the functionality of front end processing circuitry  216  and back-end processing circuitry  214  may be combined in a single processor system. Additionally, in some examples the functionality of some of the components of regional oximetry device  200  shown and described herein may be divided over multiple components. For example, some or all of the functionality of control circuitry  245  may be performed in front end processing circuitry  216 , in back-end processing circuitry  214 , or both. In other examples, the functionality of one or more of the components may be performed in a different order or may not be required. In some examples, all of the components of regional oximetry device  200  can be realized in processor circuitry. 
       FIG. 3  illustrates an example graphical user interface  300  including autoregulation information presented on a display.  FIG. 3  is an example of a presentation by processing circuitry  110  on display  132  shown in  FIG. 1  or by processing circuitry  210  on display  232  shown in  FIG. 2 . Graphical user interface  300  may be configured to display various information related to blood pressure, oxygen saturation, the COx index, limits of autoregulation, and/or autoregulation status. As shown, graphical user interface  300  may include oxygen saturation signal indicator  310 , blood pressure signal indicator  320 , and COx signal indicator  330 . Graphical user interface  300  may include COx value indicator  340 , autoregulation status indicator  350 , and limit of autoregulation indicators  360  and  370 . 
     Blood pressure signal indicator  320  may present a set of MAP values determined by processing circuitry  110  of regional oximetry device  100 . In some examples, blood pressure signal indicator  320  may present MAP values as discrete points over time or in a table. Blood pressure signal indicator  320  may also present MAP values as a moving average or waveform of discrete points. Blood pressure signal indicator  320  may present MAP values as a single value (e.g., a number) representing a current MAP value. Oxygen saturation signal indicator  310  and COx signal indicator  330  may also present rSO2 values and COx values, respectively, as discrete points, in a table, as a moving average, as a waveform, and/or as a single value. 
     COx signal indicator  330  may present a set of correlation coefficients determined by processing circuitry  110 . Processing circuitry  110  may determine the correlation coefficients as a function of the oxygen saturation values presented in oxygen saturation signal indicator  310  and the MAP values presented in blood pressure signal indicator  320 . In some examples, a COx value at or near one indicates the autoregulation status of a patient is impaired, as shown in autoregulation status indicator  350 . 
     COx value indicator  340  shows a COx value of 0.8, which may result in a determination by processing circuitry  110  that the autoregulation status of the patient is impaired. Processing circuitry  110  may be configured to present, as the COx value in COx value indicator  340 , the most recently determined COx value. In order to determine the autoregulation status of a patient for presentation in autoregulation status indicator  350 , processing circuitry  110  may determine whether the most recent MAP value shown in blood pressure signal indicator  320  is between the limits of autoregulation presented in limit of autoregulation indicators  360  and  370 . 
     Processing circuitry  110  may present limit of autoregulation indicators  360  and/or  370  in terms of blood pressure, for example, millimeters of mercury (mmHg). Processing circuitry  110  may determine a weighted average of a first estimate and a previous value in order to determine a lower limit of autoregulation presented in indicator  360  or an upper limit of autoregulation presented in indicator  370 . If processing circuitry  110  determines the lower limit of autoregulation based on a weighted average and determines that a MAP value is less than or equal to the weighted average, processing circuitry  110  may be configured to generate a notification in response to determining that the MAP value is less than or equal to the weighted average for more than a predetermined period of time. In response to determining that the MAP value is less than or equal to the weighted average for more than the predetermined period of time, processing circuitry  110  may output the notification in autoregulation status indicator  350  as text, color, blinking, and/or any other suitable visible or audible manner. 
       FIGS. 4A-4D  are example graphs of rSO 2 , COx, BVS, and HVx versus mean arterial pressure. Processing circuitry  110  is configured to determine one or more other estimates of the limit of autoregulation, such as LArSO 2 , LABVS, LAHVx, and/or any other estimate of the limit of autoregulation. For example, LArSO 2 , LAHVx, and LABVS in Equations (1), (5), and (6) above and Equation (11) below represent the other estimates of the limit of autoregulation, although processing circuitry  110  may determine different, more, or fewer than three other estimates of the limit of autoregulation in some examples. 
     Estimates  410 A- 410 D represent four computed limits of autoregulation using respective methods. Each of estimates  410 A- 410 D may not necessarily be equal to the other three of estimates  410 A- 410 D. To determine an estimate of a limit of autoregulation, processing circuitry  110  may use different algorithms for each physiological parameter (e.g., rSO 2  and BVS) and for each correlation coefficient (e.g., COx and HVx). In some examples, processing circuitry  110  may be configured to determine four estimates of the limit of autoregulation based on the values of the two physiological parameters and the values of the two correlation coefficients. Although  FIGS. 4A-5  are described with respect to processing circuitry  110  of regional oximetry device  100  ( FIG. 1 ), in other examples, processing circuitry  210 ,  214 , and/or  216  ( FIG. 2 ), alone or in combination with processing circuitry  110 , may perform any part of the techniques of  FIGS. 4A-5 . 
     Processing circuitry  110  may determine estimate  410 A of the lower limit of autoregulation based on the oxygen saturation values shown in  FIG. 4A . Processing circuitry  110  may determine estimate  410 B of the lower limit of autoregulation based on the correlation coefficients shown in  FIG. 4B . Processing circuitry  110  may determine estimate  410 C of the lower limit of autoregulation based on the blood volume values shown in  FIG. 4C . Processing circuitry  110  may determine estimate  410 D of the lower limit of autoregulation based on the hemoglobin volume values shown in  FIG. 4D . Processing circuitry  110  may be configured to use one of estimates  410 A- 410 D as a “first estimate of the limit of autoregulation” and the remaining one or more of estimates  410 A- 410 D as “other estimates of the limits of autoregulation.” 
     For example, processing circuitry  110  may be configured to determine estimate  410 A of the limit of autoregulation based on a set of oxygen saturation values by determining trendlines  400 A and  402 A. Processing circuitry  110  may determine estimate  410 A at the MAP value where the trendlines shift from a positive slope (e.g., trendline  400 A) to a negative slope or a near-zero slope (e.g., trendline  402 A). Processing circuitry  110  may use a similar technique for determining estimate  410 C based on a set of blood volume values. Processing circuitry  110  may determine estimate  410 C at the MAP value where the trendlines shift from a positive slope (e.g., trendline  400 C) to a negative slope or a near-zero slope (e.g., trendline  402 C). 
     Processing circuitry  110  may be configured to determine estimates  410 B and  410 D of the limit of autoregulation based on a set of correlation coefficients by determining the MAP values where the mean or median correlation coefficient values are less than or equal to threshold  400 B or  400 D, respectively. In the example of determining estimate  410 B of the lower limit of autoregulation, processing circuitry  110  may be configured to determine the mean or median of COx values for each bin of a plurality of bins, which each bin includes the COx value between two MAP values, such as 50 mmHg and 55 mmHg. Processing circuitry  110  may be configured to determine estimate  410 B as the lowest MAP value at which the mean of median of the COx values in a corresponding bin is less than or equal to threshold  400 B. 
     Each of estimates  410 A- 410 D may differ from the other estimates of estimates  410 A- 410 D. Processing circuitry  110  may be configured to determine “the first estimate” of the lower limit of autoregulation as estimate  410 A. However, in other examples, processing circuitry  110  may determine “the first estimate” as one of estimates  410 B- 410 D. If processing circuitry  110  determines estimates  410 A as the first estimate, then processing circuitry  110  may determine the mean absolute difference between estimate  410 A and the mean of estimates  410 B- 410 D, as shown in Equation (1). Processing circuitry  110  may also determine the mean of all four of estimates  410 A- 410 D, as shown in Equation (5). Processing circuitry  110  may then determine a weighting factor for estimate  410 A using Equations (8) and (9). Using the weighting factor and a previous value of estimate  410 A, processing circuitry can determine a weighted average of estimate  410 A and the previous value. 
       FIG. 5  is a conceptual block diagram illustrating an example framework for determining estimates of a limit of autoregulation. In the example of  FIG. 5 , processing circuitry  110  receives a first signal indicative of oxygen saturation of a patient ( 500 ). Processing circuitry  110  can determine a set of oxygen saturation values based on the first signal. Processing circuitry may then determine an estimate of a limit of autoregulation (LArSO 2 ) based on the set of oxygen saturation values ( 530 ). 
     In the example of  FIG. 5 , processing circuitry  110  receives a second signal indicative of the MAP of a patient ( 510 ). Processing circuitry  110  can determine a set of MAP values based on the second signal and determine a set of COx values based on the set of oxygen saturation values and the set of MAP values. Processing circuitry  110  may then determine an estimate of a limit of autoregulation (LACOx) based on the set of COx values ( 540 ). 
     In the example of  FIG. 5 , processing circuitry  110  receive a third signal indicative of blood volume of a patient ( 520 ). Processing circuitry  110  can determine a set of blood volume values based on the third signal. Processing circuitry  110  may then determine a set of hemoglobin volume index values based on the set of blood volume values and the set of MAP values. Processing circuitry  110  determines an estimate of a limit of autoregulation (LAHVx) based on the set of blood volume values and the set of MAP values ( 550 ). Processing circuitry  110  may also determine an estimate of a limit of autoregulation (LABVS) based on the set of blood volume values ( 560 ). Processing circuitry  110  may use any of LArSO 2 , LACOx, LAHVx, or LABVS as the first estimate of the limit of autoregulation. 
       FIGS. 6-8  are flowcharts illustrating example techniques for determining a limit of autoregulation, in accordance with some examples of this disclosure. Although  FIGS. 6-8  are described with respect to processing circuitry  110  of regional oximetry device  100  ( FIG. 1 ), in other examples, processing circuitry  210 ,  214 , and/or  216  ( FIG. 2 ), alone or in combination with processing circuitry  110 , may perform any part of the techniques of  FIGS. 6-8 . In the example of  FIG. 6 , processing circuitry  110  determines a first estimate of a limit of autoregulation based on COx values ( 600 ). Processing circuitry  110  also determines one or more other estimates of the limit of autoregulation based on other physiological parameters ( 610 ). 
     In the example of  FIG. 6 , processing circuitry  110  determines whether the first estimate of the limit of autoregulation is close to the one or more other estimates of the limit of autoregulation ( 620 ). If processing circuitry  110  determines that the first estimate is relatively close to the other estimates, then processing circuitry  110  may determine a higher weighting factor for the first estimate, for example, using Equation (9) above or (10) below ( 630 ). Relative closeness between the first estimate and the other estimates may indicate a higher likelihood that the first estimate is accurate. The higher weighting factor may increase the effect that the first estimate has on the determination of the weighted average and the autoregulation status by processing circuitry  110 . Processing circuitry  110  may determine relative closeness using a normalized difference, for example, shown in Equation (7). 
     Equation (10) shows an alternative technique for processing circuitry  110  to determine a weighting factor based on a multiplier. Using Equation (10), processing circuitry  110  can determine the weighting factor for the first estimate of the limit of autoregulation by multiplying a predetermined maximum weighting factor by an exponential function of the multiplier. The exponential function of Equation (10), as compared to Equation (9), increases the effect of the multiplier on the determination of the weighting factor.
 
 w=w   s   ×e   m   (10)
 
     Equations (11) and (12) show alternative techniques processing circuitry  110  may be configured to use in some examples to determine a multiplier. Using Equations (11) and (12), processing circuitry  110  may reduce the first weighting factor as the difference between the computed parameters increases. In the example of Equation (11), processing circuitry  110  determines a standard deviation of the first estimate of the limit of autoregulation and the other estimates of the limit of autoregulation. Processing circuitry  110  can set the multiplier equal to the standard deviation or to a function of the standard deviation, which can normalize the distance in Equation (1) by the observed variation of all of the estimates. The standard deviation may represent the difference, or the relative closeness, between the first estimate and the other estimates. Processing circuitry  110  can determine a weighting factor based on the standard deviations using Equation (10) or (11). 
     
       
         
           
             
               
                 
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     In the example of Equation (12), processing circuitry  110  determines the multiplier using a normalizing parameter (T) to have a large weight if the other three estimates do not agree. In some examples, where the first estimate is relatively close to the mean of the other estimates, but the other estimates include one or more outliers, the multiplier of Equations (11) and (12) may have a larger value than the multiplier of Equation (8). Thus, processing circuitry  110  can detect the possibility of outlier estimates using Equations (11) and (12) that processing circuitry  110  may not necessarily be able to detect using Equation (8). 
     In some examples, processing circuitry  110  is configured to determine an autoregulation status for a patient based on the weighted average. Processing circuitry  110  may use the weighted average as an estimate of the limit of autoregulation to determine if a blood pressure or mean arterial pressure of the patient is within an intact region of autoregulation. For example, processing circuitry  110  may use the weighted average as an estimate of the lower limit of autoregulation and determine whether the patient has intact autoregulation by determining whether the mean arterial pressure of the patient is greater than or equal to the weighted average. By determining a weighted average based on a first estimate of the limit of autoregulation and other estimates, processing circuitry  110  may more accurately determine autoregulation status, as compared to another device that does not implement the techniques of this disclosure. 
     In some examples, processing circuitry  110  can use the following techniques to determine and output a weighted average for determining an autoregulation status. At a first step, processing circuitry  110  determines a first estimate and other estimates of a limit of autoregulation. Processing circuitry  110  can then determine a mean of all of the estimates, for example, using Equation (5). Processing circuitry  110  can also determine a difference between the first estimate and the other estimates, for example, using Equation (1). To determine the difference, processing circuitry  110  can first determine a mean of the other estimates and then subtract the mean of the other estimates from first estimate. In some examples, the difference is an absolute value of the difference of first estimate and the mean of the other estimates. 
     Processing circuitry  110  may be configured to then determine a normalized difference, for example, using Equation (7) to divide the difference by the mean. Processing circuitry  110  can use the normalized difference to determine a multiplier, as shown in Equation (8). Alternatively, in some examples processing circuitry  110  can use Equation (11) or (12) to determine the multiplier without first determining a normalized difference. Processing circuitry  110  may be configured to determine a weighting factor based on the normalized difference and a maximum predetermined weighting factor, for example, using Equations (9) and (10). 
     Processing circuitry  110  can use the first weighting factor to determine a weighted value of the first estimate, for example, using Equation (3). Processing circuitry  110  may determine a weighted value by multiplying the first estimate and the first weighting factor. Processing circuitry  110  can also use the first weighting factor to determine a weighted value of the previous value of the limit of autoregulation, for example, using Equation (4). Processing circuitry  110  may determine the weighted value of the previous value by multiplying the previous value and a second weighting factor. Processing circuitry  110  may determine the second weighting factor by subtracting the first weighting factor from one. Thus, both weighting factors may have values between zero and one, and the sum of the weighting factors may be equal to one. Processing circuitry  110  may then determine the weighted average of the weighted value of the first estimate and the weighted value of the other estimates, for example, using Equation (2) to determine a sum of the weighted values. Processing circuitry  110  determines previous value as the previous iteration of the weighted average. Processing circuitry  110  can determine an autoregulation status based on each iteration of the weighted average. 
     If processing circuitry  110  determines that the first estimate is not relatively close to the other estimates, processing circuitry  110  may determine a lower weighting factor for the first estimate ( 640 ). Lack of relative closeness between the first estimate and the other estimates may indicate a lower likelihood that the first estimate is accurate. The lower weighting factor may decrease the effect that the first estimate has on the determination of the weighted average and the autoregulation status by processing circuitry  110 . In this manner, processing circuitry  110  can determine a more accurate weighted average by reducing the weighting factor when the estimates of a limit of autoregulation indicate a higher likelihood for error (e.g., when the estimates are not relatively close to each other). 
     In the example of  FIG. 7 , processing circuitry  110  receives a first signal and a second signal from sensing circuitry  140  and  141  ( 700 ). The signals may be indicative of physiological parameters such as mean arterial pressure, oxygen saturation, and/or blood volume. Processing circuitry  110  then determines a first estimate of the limit of autoregulation based on the first signal and the second signal ( 702 ). Processing circuitry  110  also determines one or more other estimates of the limit of autoregulation based on the signals received from sensing circuitry  140 - 142 . Processing circuitry  110  determines a difference between the first estimate of the limit of autoregulation and the one or more estimates of the limit of autoregulation ( 704 ). For example, processing circuitry  110  may use Equation (1) to determine a mean absolute difference. 
     In the example of  FIG. 7 , processing circuitry  110  determines a weighted average of the first estimate of the limit of autoregulation and a previous value of the limit of autoregulation based on the difference between the first estimate of the limit of autoregulation and the one or more estimates of the limit of autoregulation ( 706 ). Processing circuitry  110  can use Equation (2) to determine the weighted average. Processing circuitry  110  can set the previous value of the limit of autoregulation equal to a previous version of the weighted average. Processing circuitry  110  determines an autoregulation status of the patient based on the weighted average ( 708 ). Processing circuitry  110  can output, for display by user interface  130 , an indication of the autoregulation status. An example of an indication of the autoregulation status is element  350  of graphical user interface  300  shown in  FIG. 3 . 
     In the example of  FIG. 8 , processing circuitry  110  receives or acquires signals from sensing circuitry  140 - 142  ( 800 ) and determines or calculates correlation coefficients (e.g., COx values, HVx values) based on the signals ( 802 ). Processing circuitry  110  then determines an estimate of a limit of autoregulation based on the correlation coefficients ( 804 ) and determines a weighting factor for the estimate of the limit of autoregulation based on other estimates of the limit of autoregulation ( 806 ). Processing circuitry  110  can determine the weighting factor using, e.g., Equations (9) or (10). Processing circuitry  110  determines a weighted average of the first estimate of the limit of autoregulation and a previous value of the limit of autoregulation ( 808 ). Processing circuitry  110  may determine the weighted average by adding a calculated LACOx value to a previously reported value in the weighted average (e.g., a previous iteration of the weighted average). 
     In the example of  FIG. 8 , processing circuitry  110  presents, via display  132 , the weighted average of the first estimate of the limit of autoregulation and the previous value of the limit of autoregulation ( 810 ). Processing circuitry  110  may display the new value of LACOx along with an indication of the autoregulation status of the patient. Processing circuitry  110  can then set the weighted average determined during a first iteration as the previous value of the limit of autoregulation for the second iteration of  FIG. 8  ( 812 ). Thus, during the second iteration of  FIG. 8 , processing circuitry  110  may use the previous weighted average as the previous value of the limit of autoregulation. Thus, processing circuitry  110  may make the new value of the limit of autoregulation into the next previous value of the limit of autoregulation. 
     In the second iteration of  FIG. 8 , processing circuitry  110  receives updated data for the first signal and the second signal ( 800 ). Processing circuitry  110  determines an updated first estimate based on the updated data for the first and the second signals ( 804 ). Processing circuitry  110  also determines an updated difference between the updated first estimate and other updated estimates. Processing circuitry  110  then determines an updated weighting factor ( 806 ) and an updated weighted average of the updated first estimate and the previous value based on the updated difference ( 808 ). Processing circuitry  110  determines an updated autoregulation status based on the updated weighted average and outputs, for display, an indication of the updated autoregulation status. Processing circuitry  110  can also present the updated weighted average via display  132  ( 810 ). 
     Processing circuitry  110  may monitor the weighted average to determine if the weighted average is varying significantly over time. Substantial variations in the weighted average over a substantial time duration may indicate that the weighted average is no longer reliable. For example, processing circuitry  110  may determine that the rate of change of the weighted average (e.g., change divided by time) exceeds a threshold rate for at least a threshold time duration. Processing circuitry  110  may store the threshold rate and the threshold time duration to memory  120 . In response to determining that the rate of change of the weighted average exceeds the threshold rate for at least the threshold time duration, processing circuitry  110  can cease performing one or more of the following functions: determining the weighted average, determining the autoregulation status, or outputting an indication of the autoregulation status for display. 
     The disclosure contemplates computer-readable storage media comprising instructions to cause a processor to perform any of the functions and techniques described herein. The computer-readable storage media may take the example form of any volatile, non-volatile, magnetic, optical, or electrical media, such as a RAM, ROM, NVRAM, EEPROM, or flash memory. The computer-readable storage media may be referred to as non-transitory. A programmer, such as patient programmer or clinician programmer, or other computing device may also contain a more portable removable memory type to enable easy data transfer or offline data analysis. 
     The techniques described in this disclosure, including those attributed to devices  100  and  200 , processing circuitry  110 ,  210 ,  214 , and  216 , memories  120  and  220 , displays  132  and  232 , sensing circuitries  140 - 142 , circuitries  240  and  245 , sensing devices  150 - 152  and  250 , and various constituent components, may be implemented, at least in part, in hardware, software, firmware or any combination thereof. For example, various aspects of the techniques may be implemented within one or more processors, including one or more microprocessors, DSPs, ASICs, FPGAs, or any other equivalent integrated or discrete logic circuitry, as well as any combinations of such components, embodied in programmers, such as physician or patient programmers, stimulators, remote servers, or other devices. The term “processor” or “processing circuitry” may generally refer to any of the foregoing logic circuitry, alone or in combination with other logic circuitry, or any other equivalent circuitry. 
     As used herein, the term “circuitry” refers to an ASIC, an electronic circuit, a processor (shared, dedicated, or group) and memory that execute one or more software or firmware programs, a combinational logic circuit, or other suitable components that provide the described functionality. The term “processing circuitry” refers one or more processors distributed across one or more devices. For example, “processing circuitry” can include a single processor or multiple processors on a device. “Processing circuitry” can also include processors on multiple devices, wherein the operations described herein may be distributed across the processors and devices. 
     Such hardware, software, firmware may be implemented within the same device or within separate devices to support the various operations and functions described in this disclosure. For example, any of the techniques or processes described herein may be performed within one device or at least partially distributed amongst two or more devices, such as between devices  100  and  200 , processing circuitry  110 ,  210 ,  214 , and  216 , memories  120  and  220 , sensing circuitries  140 - 142 , and/or circuitries  240  and  245 . In addition, any of the described units, modules or components may be implemented together or separately as discrete but interoperable logic devices. Depiction of different features as modules or units is intended to highlight different functional aspects and does not necessarily imply that such modules or units must be realized by separate hardware or software components. Rather, functionality associated with one or more modules or units may be performed by separate hardware or software components, or integrated within common or separate hardware or software components. 
     The techniques described in this disclosure may also be embodied or encoded in an article of manufacture including a non-transitory computer-readable storage medium encoded with instructions. Instructions embedded or encoded in an article of manufacture including a non-transitory computer-readable storage medium encoded, may cause one or more programmable processors, or other processors, to implement one or more of the techniques described herein, such as when instructions included or encoded in the non-transitory computer-readable storage medium are executed by the one or more processors. Example non-transitory computer-readable storage media may include RAM, ROM, programmable ROM (PROM), erasable programmable ROM (EPROM), electronically erasable programmable ROM (EEPROM), flash memory, a hard disk, a compact disc ROM (CD-ROM), a floppy disk, a cassette, magnetic media, optical media, or any other computer readable storage devices or tangible computer readable media. 
     In some examples, a computer-readable storage medium comprises non-transitory medium. The term “non-transitory” may indicate that the storage medium is not embodied in a carrier wave or a propagated signal. In certain examples, a non-transitory storage medium may store data that can, over time, change (e.g., in RAM or cache). Elements of devices and circuitry described herein, including, but not limited to, devices  100  and  200 , processing circuitry  110 ,  210 ,  214 , and  216 , memories  120  and  220 , displays  132  and  232 , sensing circuitries  140 - 142 , circuitries  240  and  245 , sensing devices  150 - 152  and  250  may be programmed with various forms of software. The one or more processors may be implemented at least in part as, or include, one or more executable applications, application modules, libraries, classes, methods, objects, routines, subroutines, firmware, and/or embedded code, for example. 
     Where processing circuitry  110  is described herein as determining that a value is less than or equal to another value, this description may also include processing circuitry  110  determining that a value is only less than the other value. Similarly, where processing circuitry  110  is described herein as determining that a value is less than another value, this description may also include processing circuitry  110  determining that a value is less than or equal to the other value. The same properties may also apply to the terms “greater than” and “greater than or equal to.” 
     Various examples of the disclosure have been described. Any combination of the described systems, operations, or functions is contemplated. These and other examples are within the scope of the following claims.