Systems and methods for considering confounding factors in determining physiological parameters or conditions

Abstract

A system and method for determining a target physiological parameter of a subject includes sensing the subject using first, second, and third sensor devices configured to sense a first physiological parameter, a second physiological parameter, and a target physiological parameter over a period of time and generate a first physiological data signal, a second physiological data signal, and a target physiological data signal, respectively, determining the presence or absence of a confounding factor that adversely affects the determination of the target physiological parameter, forwarding the first physiological data signal, the second physiological data signal, and the target physiological data signal generated in the absence of the confounding factor for further processing and excluding the first physiological data signal, the second physiological data signal, and the target physiological data signal generated in the presence of the confounding factor, and determining a value of the target physiological parameter using the first data signal, the second data signal, and the target data signal generated in the absence of the confounding factor.

Description

[Technical Field]

[0001] FIELD OF THE DISCLOSURE The present disclosure relates generally to medical devices and methods, and more particularly to medical devices and methods for considering confounding factors in the determination of physiological parameters or conditions. [Background technology]

[0002] The determination of physiological parameters or states often depends on the determination of other physiological parameters. Very often, physiological parameters can be subject to extraneous influences (referred to herein as "confounding factors"). For example, a first physiological parameter may increase or decrease due to the influence of a confounding factor. Using that first physiological parameter to determine a second physiological parameter can adversely affect or contaminate the determination of the second physiological parameter, potentially reducing the accuracy of the determination as a result of the influence of the confounding factor. Autoregulatory state is an example of a physiological state that can be influenced by factors independent of a subject's autoregulatory system. Multiple factors (e.g., arterial stiffening with aging) can alter the characteristics of the vasoreactive response, and these factors can also alter the associated autoregulatory characteristics. Therefore, the range of blood flow autoregulation due to changes in blood pressure can vary between subjects and cannot be considered constant. Furthermore, physiological parameter data used to determine or measure a subject's autoregulatory state can be influenced by factors independent of the subject's autoregulatory system. For example, one or more NIRS indices (e.g., tissue oxygen saturation (StO2), relative total hemoglobin concentration per tissue (rTHb), differential change between oxyhemoglobin (OHb) and deoxyhemoglobin (HHb), HbD (i.e., OHb-HHb)) may be at levels that are not attributable to autoregulation. In these situations, autoregulation determinations or measurements made using these values ​​may adversely affect the accuracy of the autoregulation determinations or measurements. As another example, if a subject's blood carbon dioxide levels exceed the normal range (normoclysis), this may adversely affect the accuracy of the autoregulation determinations or measurements. Summary of the Invention [Problem to be solved by the invention]

[0003] There is a need for devices and methods that take into account one or more confounding factors during the determination of a physiological parameter or condition. [Means for solving the problem]

[0004] According to one aspect of the present disclosure, there is provided a method for determining a target physiological parameter for a subject, the method including the steps of: a) sensing the subject using a first sensor device configured to sense a first physiological parameter, the first sensor device generating a first physiological data signal representative of the first physiological parameter over a period of time; b) sensing the subject using a second sensor device configured to sense a second physiological parameter, the second sensor device generating a second physiological data signal representative of the second physiological parameter over a period of time; and c) sensing the subject using a third sensor device configured to sense the target physiological parameter, the third sensor device generating a target physiological data signal representative of the target physiological parameter over a period of time. d) determining the presence or absence of a confounding factor that adversely affects the determination of the target physiological parameter, the determination using the first physiological data signal, the second physiological data signal, and the target physiological data signal; e) carrying forward for further processing the first physiological data signal, the second physiological data signal, and the target physiological data signal generated in the absence of the confounding factor, and excluding the first physiological data signal, the second physiological data signal, and the target physiological data signal generated in the presence of the confounding factor; and f) determining the value of the target physiological parameter using the first physiological data signal, the second physiological data signal, and the target physiological data signal generated in the absence of the confounding factor.

[0005] In any aspect or embodiment described above and herein, the first physiological data signal, the second physiological data signal, and the target physiological data signal generated in the presence of a confounding factor are not used in determining the value of the target physiological parameter.

[0006] In any aspect or embodiment described above and herein, determining the presence or absence of a confounding factor may include comparing the first physiological data signal, the second physiological data signal, and a processed signal representative of the target physiological data signal.

[0007] In any aspect or embodiment described above and herein, determining the presence or absence of a confounder may use frequency domain methods.

[0008] In any aspect or embodiment described above and herein, determining the presence or absence of a confounding factor may include determining a first coherence between the first physiological data signal and the target physiological data signal, and a second coherence between the second physiological data signal and the target physiological data signal.

[0009] In any aspect or embodiment described above and herein, the first coherence may be based on a single frequency band.

[0010] In any aspect or embodiment described above and herein, the first coherence may represent coherence values ​​at different individual frequencies within a single frequency band.

[0011] In any aspect or embodiment described above and herein, the first coherence may be based on multiple frequency bands.

[0012] In any aspect or embodiment described above and herein, the first coherence may collectively represent a respective coherence value from each frequency band of the plurality of frequency bands.

[0013] In any aspect or embodiment described above and herein, the method may further include determining a first trend of the first physiological parameter, a second trend of the second physiological parameter, and a third trend of the target physiological parameter, and comparing the first trend, the second trend, and the third trend to each other.

[0014] In any aspect or embodiment described above and herein, determining the presence or absence of a confounder may utilize one or more polar filters configured to evaluate a first trend, a second trend, and a third trend.

[0015] In any aspect or embodiment described above and herein, the step of determining the presence or absence of a confounder may use an index table.

[0016] In any aspect or embodiment described above and herein, the step of determining the presence or absence of a confounder may use correlation methods.

[0017] In any aspect or embodiment described above and herein, the excluding step may include discarding the first physiological data signal, the second physiological data signal, and the target physiological data signal generated when the confounding factor is present.

[0018] In any aspect or embodiment described above and herein, steps a-f may be performed continuously for a certain period of time.

[0019] In any aspect or embodiment described above and herein, the target physiological parameter may be related to the total hemoglobin concentration (THb) per volume of sensed tissue.

[0020] In any aspect or embodiment described above and herein, the target physiological parameter may be relative total hemoglobin concentration (rTHb) per volume of sensed tissue.

[0021] In any aspect or embodiment described above and herein, the third sensor device may be a near-infrared spectroscopy (NIRS) tissue oximeter.

[0022] In any aspect or embodiment described above and herein, the first physiological parameter may relate to blood pressure of the subject and the first sensor device is a blood pressure sensor device.

[0023] In any aspect or embodiment described above and herein, the second physiological parameter may be related to the subject's heart rate.

[0024] According to one aspect of the present disclosure, a system for determining a target physiological parameter of a subject is provided. The system includes a first sensor device, a second sensor device, a third sensor device, and a system controller. The first sensor device is configured to continuously sense a first physiological parameter for a certain period of time and generate a first physiological data signal representative of the first physiological parameter for the certain period of time. The second sensor device is configured to continuously sense a second physiological parameter for the certain period of time and generate a second physiological data signal representative of the second physiological parameter for the certain period of time. The third sensor device is configured to continuously sense a target physiological parameter for the certain period of time and generate a target physiological data signal representative of the target physiological parameter for the certain period of time. The system controller is in communication with the first sensor device, the second sensor device, and the target sensor device. The system controller includes at least one processor and a memory device configured to store instructions that, when executed, cause the system controller to: a) determine the presence or absence of a confounding factor using the first physiological data signal, the second physiological data signal, and the target physiological data signal that adversely affects the determination of the target physiological parameter; b) carry forward for further processing the first physiological data signal, the second physiological data signal, and the target physiological data signal that are generated in the absence of the confounding factor, and exclude the first physiological data signal, the second physiological data signal, and the target physiological data signal that are generated in the presence of the confounding factor; and c) determine a value of the target physiological parameter using the first physiological data signal, the second physiological data signal, and the target physiological data signal that are generated in the absence of the confounding factor.

[0025] In any aspect or embodiment described above and herein, when the stored instructions are executed, the system controller may determine the value of the target physiological parameter without using the first physiological data signal, the second physiological data signal, and the target physiological data signal generated in the presence of a confounding factor.

[0026] In any aspect or embodiment described above and herein, when the stored instructions are executed, the system controller may determine the presence or absence of a confounding factor using a comparison of the first physiological data signal, the second physiological data signal, and the processed signal representative of the target physiological data signal.

[0027] In any aspect or embodiment described above and herein, when the stored instructions are executed, the system controller may determine the presence or absence of a confounding factor using frequency domain methods.

[0028] In any aspect or embodiment described above and herein, when the stored instructions are executed, the system controller determines the presence or absence of a confounding factor, and the system controller may further determine a first coherence between the first physiological data signal and the target physiological data signal, and a second coherence between the second physiological data signal and the target physiological data signal.

[0029] In any aspect or embodiment described above and herein, when the stored instructions are executed, the system controller may determine a first trend for the first physiological parameter, a second trend for the second physiological parameter, and a third trend for the target physiological parameter, and compare the first trend, the second trend, and the third trend to each other.

[0030] In any aspect or embodiment described above and herein, when the stored instructions are executed, the system controller may determine the presence or absence of a confounder using one or more polar filters configured to evaluate a first trend, a second trend, and a third trend.

[0031] In any aspect or embodiment described above and herein, when the stored instructions are executed, the system controller may use the index table to determine the presence or absence of a confounding factor.

[0032] In any aspect or embodiment described above and herein, when the stored instructions are executed, the system controller may use correlation methods to determine the presence or absence of a confounding factor.

[0033] In any aspect or embodiment described above and herein, the excluded first physiological data signal, the second physiological data signal, and the target physiological data signal that were generated when a confounding factor was present may be discarded.

[0034] In any aspect or embodiment described above and herein, execution of the stored instructions may cause the system controller to perform functions a-c sequentially for a period of time.

[0035] According to another aspect of the present disclosure, there is provided a method for determining a target physiological parameter of a subject, the method comprising: a) sensing the subject for "N" physiological parameters, where "N" is an integer greater than or equal to three, wherein the sensing generates "N" sets of physiological data signals, each set of physiological data signals corresponding to a respective one of the "N" physiological parameters over a period of time, one of the "N" physiological parameters being a target physiological parameter; b) determining the presence or absence of a confounding factor that adversely affects the determination of the target physiological parameter, the determination using the "N" sets of physiological data signals including the target physiological parameter set of physiological data signals; c) carrying forward for further processing the "N" sets of physiological data signals generated in the absence of the confounding factor and excluding the "N" sets of physiological data signals generated in the presence of the confounding factor; and d) determining a value of the target physiological parameter using the "N" sets of physiological data signals generated in the absence of the confounding factor.

[0036] According to another aspect of the present disclosure, a non-transitory computer-readable medium is provided that stores executable instructions, the executable instructions, when executed, causing at least one processor to: a) control a first sensor device configured to sense a first physiological parameter to sense the subject and generate a first physiological data signal representative of the first physiological parameter over a period of time; b) control a second sensor device configured to sense a second physiological parameter to sense the subject and generate a second physiological data signal representative of the second physiological parameter over a period of time; and c) control a third sensor device configured to sense a target physiological parameter to sense the subject and generate a second physiological data signal representative of the target physiological parameter over a period of time. d) using the first physiological data signal, the second physiological data signal, and the target physiological data signal to determine the presence or absence of a confounding factor that adversely affects the determination of the target physiological parameter; e) carrying forward for further processing the first physiological data signal, the second physiological data signal, and the target physiological data signal generated when the confounding factor is not present, and excluding the first physiological data signal, the second physiological data signal, and the target physiological data signal generated when the confounding factor is present; and f) determining the value of the target physiological parameter using the first physiological data signal, the second physiological data signal, and the target physiological data signal generated when the confounding factor is not present.

[0037] The foregoing features and elements may be combined in various non-exclusive combinations unless expressly indicated otherwise. These features and elements, and their operation, will become more apparent in light of the following description and accompanying drawings. It should be understood, however, that the following description and drawings are illustrative in nature and not limiting. [Brief explanation of the drawings]

[0038] [Figure 1] 1 is a diagrammatic representation of a self-regulating system according to an embodiment of the present disclosure. [Figure 2] 1 is a diagrammatic representation of a self-regulating system according to an embodiment of the present disclosure. [Figure 3] 1 is a diagrammatic representation of an exemplary frequency domain method. [Figure 4] 1 is an illustrative flow chart of an embodiment of the present disclosure. [Figure 5] 1 is an embodiment of an exemplary index table that may be used in the present disclosure, showing example data from different cases. [Figure 6] 1 is an illustrative flow chart of an embodiment of the present disclosure. [Figure 7] 1 is an illustrative flow chart of an embodiment of the present disclosure. [Figure 8] 1 is a schematic flow chart of an embodiment of the present disclosure. [Figure 9] 1 is a schematic flow chart of an embodiment of the present disclosure. [Figure 10] 1 is an embodiment of an exemplary index table that may be used in the present disclosure, showing example data from different cases. [Figure 11] 1 is a graphical graph of physiological parameter values ​​versus time. [Figure 12] 1 is a graph of physiological parameter values ​​versus time. DETAILED DESCRIPTION OF THE INVENTION

[0039] The present disclosure provides a system 20 ("CFA system"), method, and computer-readable medium that consider one or more confounding factors during the determination of a physiological parameter. Non-limiting examples of physiological parameters that may be determined using the present disclosure include NIRS indices, autoregulatory status, pain, etc. Embodiments of the present disclosure may be implemented in various ways. In some embodiments, the present disclosure may be implemented to determine whether one or more confounding factors are present that may adversely affect the determination of a physiological parameter, and, if so, to consider sensory data collected when the one or more confounding factors are present. In this manner, the physiological parameter can be determined without being contaminated by confounding factors. Non-limiting examples of physiological parameter determinations that may be affected by one or more confounding factors include determining different types of total hemoglobin content, determining a subject's autoregulatory status, etc. As used herein, the term "confounding factor" refers to a physiological parameter or condition that, when present, may adversely affect or contaminate the determination of other physiological parameters. For example, the determination of a physiological parameter that is susceptible to a confounding factor may be less accurate as a result of the influence of the confounding factor. The present disclosure is configured to take into account confounding factors, if any, and reduce their influence on the aforementioned determinations, thereby leading to improved determination of physiological parameters.

[0040] 1 and 2 illustrate non-limiting examples of embodiments of the disclosed system 20, including their respective system components. The illustrated system 20 embodiments are not limiting; for example, alternative system 20 embodiments may include additional, alternative, or different component implementations. In some embodiments, the system 20 includes system components such as a tissue oximeter 22 and a system controller 24, and may include other system components such as a blood pressure sensor device 26, a carbon dioxide (CO2) sensor (e.g., a transcutaneous blood gas monitor or a breath CO2 sensor), a heart rate monitor (e.g., an electrocardiogram—"ECG"), a device configured to sense hemodynamic parameters such as vascular reactivity, cardiac output, or blood flow, one or more output devices, and one or more input devices. In some embodiments, these system components may be integrated into a single system 20 device, such as a system controller 24 integrally connected with sensor hardware (e.g., hardware associated with the tissue oximeter 22, hardware associated with the BP sensor device 26, etc.). In another embodiment, system 20 includes a system controller 24 and may be configured to communicate with (e.g., receive signal data, transmit signal data, etc.) independent system components. In other words, in embodiments in which system 20 includes independent system components, system 20 may be configured to communicate with a tissue oximeter 22 that can function independently of system 20, a BP sensor device 26 that can function independently of system 20, a CO2 sensor that can function independently of system 20, a heart rate monitor that can function independently of system 20, etc. In other embodiments, system 20 may include some combination of these system components in an integrated and independent manner. In those embodiments in which one or more of the aforementioned system components is independent of system 20, the independent system component may communicate with system controller 24 in any manner.

[0041] The tissue oximeter 22 may be a device configured to continuously sense tissue oxygenation parameters (hereinafter referred to individually as "NIRS indices" or collectively as "NIRS indices") that vary in response to blood flow within a subject's tissue, such as tissue oxygen saturation (StO), total hemoglobin concentration per tissue volume (THb), relative total hemoglobin concentration per tissue volume (rTHb), deoxygenated hemoglobin (HHb), relative deoxygenated hemoglobin (rHHb), oxyhemoglobin (OHb), relative oxyhemoglobin (rOHb), deoxygenated hemoglobin (HHb), etc. For clarity, the present disclosure is not limited to these particular NIRS indices, and the various acronyms used herein (e.g., StO, THb, rTHb, HHb, rHHb, OHb, rOHb) are non-limiting examples of acronyms that one skilled in the art might use to refer to each NIRS indices. Those skilled in the art will recognize that the same NIRS indicator may be referred to by different acronyms.

[0042] An example of an acceptable tissue oximeter 22 is a near-infrared spectroscopy ("NIRS") type tissue oximeter ("NIRS tissue oximeter"). U.S. Patent Nos. 6,456,862, 7,072,701, 8,078,250, 8,396,526, 8,965,472, and 10,117,610, all of which are incorporated herein by reference in their entirety, disclose non-limiting examples of noninvasive NIRS tissue oximeters 22 that may be used within the present disclosure. As used herein, the term "continuously" (to describe tissue oximeter 22 continuously sensing tissue oxygenation parameters) means that tissue oximeter 22 senses and collects subject data periodically during the monitoring period, the periodicity being sufficiently frequent to be considered clinically continuous. For example, some tissue oximeters sample data every 10 seconds or less, and may be configured to sample data more frequently (e.g., every 2 seconds or less).

[0043] The tissue oximeter 22 includes one or more sensors in communication with the controller portion. Each sensor includes one or more light sources (e.g., light-emitting diodes, or "LEDs") and one or more photodetectors (e.g., photodiodes, etc.). The light sources are configured to emit light at different wavelengths, e.g., in the red or near-infrared range, 400-1000 nm. In some sensor embodiments, the sensor may be configured to include a light source, near-field detector(s), and far-field detector(s). The near-field detector(s) are positioned closer to the light source than the far-field detector(s). Non-limiting examples of such sensors are disclosed in U.S. Patent No. 8,965,472, which is incorporated by reference in its entirety as noted above. The tissue oximeter 22 is configured for communication with the system controller 24, e.g., to transmit signals representative of (or usable to determine) one or more NIRS indices to the system controller 24, and to receive control signals, etc., from the system controller 24. Communication between tissue oximeter 22 and system controller 24 may be by known means, such as hardwired or wireless.

[0044] The NIRS tissue oximeter 22 may utilize one or more algorithms to determine one or more NIRS indices. This disclosure is not limited to a particular NIRS tissue oximeter 22 or algorithm for determining the NIRS indices of the sensed tissue. U.S. Patent Nos. 9,913,601, 9,848,808, 9,456,773, 9,364,175, 9,923,943, 8,788,004, 8,396,526, 8,078,250, 7,072,701, and 6,456,862 all describe non-limiting examples of algorithms for determining NIRS indices that may be used in whole or in part with the present disclosure, and all are incorporated by reference herein in their entirety.

[0045] The blood pressure sensor device 26 ("BP sensor device 26") may be any sensor or device configured to continuously measure a subject's blood pressure (e.g., arterial blood pressure). For example, the BP sensor device 26 may be a device configured to provide continuous blood pressure measurements, such as an arterial catheter line, or a continuous noninvasive blood pressure device, or a pulse oximetry sensor. However, the present disclosure is not limited to the use of these particular blood pressure sensor / measuring / monitoring devices. The BP sensor device 26 is configured to generate a blood pressure value signal indicative of the subject's blood pressure (e.g., arterial blood pressure) over a period of time. The BP sensor device 26 is configured for communication with the system controller 24, e.g., to transmit blood pressure value signals to the system controller 24 and to receive control signals, etc., from the system controller 24. Communication between the BP sensor device 26 and the system controller 24 may be via known means, such as hardwired or wireless. As used herein, the term "continuously" (to describe a BP sensor device 26 that continuously measures a subject's blood pressure) means that the BP sensor device 26 senses and collects subject data periodically during a monitoring period, where the periodicity is frequent enough to be considered clinically continuous. For example, some BP sensor devices sample data every 10 seconds or less, and may be configured to sample data more frequently (e.g., every 2 seconds or less).

[0046] Either or both of the BP sensor device 26 or the tissue oximeter 22 may be further configured to measure other parameters such as respiratory rate, respiratory effort, heart rate (HR), etc. The BP sensor device 26 and the tissue oximeter 22 may be placed on the same or different parts of the patient's body.

[0047] As described above, system 20 includes a system controller 24 and may include one or more output devices and one or more input devices. Non-limiting examples of input devices include a keyboard, touchpad, or other device through which a user can input data, commands, or signal information, or a port configured to communicate with an external input device via a hardwired or wireless connection. Non-limiting examples of output devices include any type of display, printer, or other device configured to display or communicate information or data generated by system 20. System 20 may be configured to connect to input or output devices via a hardwired or wireless connection.

[0048] In some embodiments, system controller 24 may be configured to process various received signals (received from integrated or separate components) and generate specific signals for the same, e.g., signals configured to control one or more components in system 20. Alternatively, system 20 may be configured such that signals from each component are transmitted to one or more intermediate processing devices, which may provide processed signals or data to system controller 24. As described below, system controller 24 may also be configured to execute stored instructions (e.g., algorithmic instructions) that cause system 20 to perform the steps and functions described herein and generate and communicate data (e.g., determining physiological parameter values ​​that take into account one or more confounding factors).

[0049] The system controller 24 may include any type of computing device, computing circuit, or processing circuit capable of executing a sequence of instructions stored in memory. The controller may include multiple processors and / or multi-core CPUs, and may include any type of processor, such as a microprocessor, digital signal processor, coprocessor, microcontroller, microcomputer, central processing unit, field programmable gate array, programmable logic device, state machine, logic circuit, analog circuit, digital circuit, or any combination thereof. For example, in those embodiments of the system 20 described above, if the system 20 includes multiple integrated components (e.g., BP sensor device 26, tissue oximeter 22, CO2 sensor, etc.), the controller may include multiple processors, e.g., independent processors dedicated to each component, and any and all of these processors may communicate with a central processor of the system 20 that coordinates the functioning of the system 20. The instructions stored in memory may represent one or more algorithms for controlling the system 20, and the stored instructions are not limited to a particular format (e.g., program files, system data, buffers, drivers, utilities, system programs, etc.), as long as they are executable by the controller. The instructions are configured to perform the methods and functions described herein.

[0050] The memory may be a machine-readable storage medium configured to store instructions that, when executed by one or more processors, cause the one or more processors to perform or execute particular functions. The memory may be a single memory device or multiple memory devices. The memory devices may be non-transitory devices and may include storage area networks, network-attached storage, disk drives, read-only memory, random-access memory, volatile memory, non-volatile memory, static memory, dynamic memory, flash memory, cache memory, and / or any device that stores digital information. One skilled in the art will understand, based on a review of this disclosure, that implementation of the system controller can be achieved using hardware, software, firmware, or any combination thereof.

[0051] The techniques, blocks, steps, and means described herein can be implemented in various ways. For example, these techniques, blocks, steps, and means can be implemented in hardware, software, or a combination thereof. In the case of a hardware implementation, a processing device configured to perform the described functions and steps (e.g., by executing stored instructions) may be implemented within one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, or other electronic units designed to perform the functions described herein, and / or any combination thereof.

[0052] Embodiments of the present disclosure may be described herein as a process that is depicted as a flowchart, flow diagram, block diagram, or the like. While any of these structures may describe operations as a sequential process, many operations may be performed in parallel and / or concurrently. Additionally, the order of operations may be rearranged. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, or the like.

[0053] The system components are configured to continuously sense a plurality of physiological parameters (e.g., tissue oximetry data associated with one or more NIRS indices, blood pressure data, heart rate, CO2 data, etc.) and generate signal data representative thereof in real time. The particular capabilities (e.g., sampling rate, etc.) of the system components that generate the physiological signal data may be configured as appropriate for the operation of system 20, and the present disclosure is not limited to any particular component configuration.

[0054] System 20 may be configured to process this “input” physiological signal data using algorithms based on frequency-domain methods to generate a coherence (“COHZ”) analysis, using algorithms based on correlation / regression techniques, or a combination thereof, as described in more detail below. For ease of explanation, the present disclosure is described below utilizing frequency-domain methods to generate coherence (“COHZ”) values, although the present disclosure is not limited to the use of frequency-domain methods. Referring to FIG. 3 , a frequency-domain method converts input physiological parameter signal data pairs from the time domain to the frequency domain (e.g., via a Fourier transform). FIG. 3 shows respective plots of a first physiological parameter (“C1”) versus frequency and a second physiological parameter (“Cn,” where “n” is an integer greater than 1) versus frequency. The converted data is further analyzed to determine the degree of coherence within a single band of frequencies (i.e., a single frequency band). The degree of coherence is indicated on an arbitrarily assigned scale of zero to one (0-1), with the degree of coherence increasing from zero to one (shown as a plot of coherence value versus frequency). A coherence value of one represents a strong relationship between C1 and Cn, while a coherence value approaching zero indicates an increasingly weak relationship between C1 and Cn. The process of determining the degree of coherence (COHZ) is performed for at least two different physiological parameter pairs (e.g., C1 and Cn, C2 and Cn) and is not limited to a specific number of physiological parameter pairs. For example, the process of determining the degree of coherence (COHZ) may be performed for two or more different physiological parameter pairs (e.g., C1 and Cn, C2 and Cn, C3 and Cn, etc.).

[0055] 4, the determined coherence values ​​for each physiological parameter pair are evaluated to determine whether the collected physiological parameter data is affected by confounding factors. Such evaluation may be performed in a variety of ways, including, but not limited to, using an index table, a flat filter, a polar filter, etc.

[0056] An example of an index table that may be used is one that allows the coherence value of each physiological parameter pair to be compared against the coherence value of another physiological parameter pair. The index table may include empirically collected data that allows characterization of the coherence values ​​of the physiological parameter pairs. The table may take any data structure format that allows such comparison. The table shown in FIG. 5 illustrates four example coherence value scenarios for the C1 / Cn physiological parameter pair and the C2 / Cn physiological parameter pair. In Example 1, both the C1 / Cn and C2 / Cn physiological parameter pairs are shown as having low coherence values. The instructions stored in memory may include information (e.g., thresholds) that can be used to determine "low" coherence values, "high" coherence values, or indeterminate coherence values. Based on the empirical data stored in the index table, the agreement of the coherence values ​​(i.e., both low) of the C1 / Cn and C2 / Cn physiological parameter pairs indicates that the collected physiological data is valid, not contaminated by confounding factors, and can be used to determine the next physiological parameter (e.g., binning or other processing). In Example 2, the C1 / Cn and C2 / Cn physiological parameter pairs are both shown to have high coherence values. Based on the empirical data stored in the index table, the agreement of the coherence values ​​(i.e., both high) of the C1 / Cn and C2 / Cn physiological parameter pairs indicates that the collected physiological data is valid, not contaminated by confounding factors, and can be used to determine the next physiological parameter (e.g., binning or other processing). In Examples 3 and 4, the C1 / Cn and C2 / Cn physiological parameter pairs are shown to have different coherence values ​​(i.e., one high and the other low). Based on the empirical data stored in the index table, discrepancies in the coherence values ​​(both high) of the physiological parameter pairs C1 / Cn and C2 / Cn indicate that the collected physiological data are likely contaminated by confounding factors and therefore should not be used (e.g., not binned) in determining the next physiological parameter.In these cases, collected physiological data used to determine the coherence value (determined to be contaminated by confounders) may be frozen (e.g., excluded) or discarded. It should be noted that the present disclosure provides continuous, real-time information. Thus, physiological data collected during a first time period may be deemed acceptable and used to determine a physiological parameter, while during a second time period, the physiological data may be deemed unacceptable and therefore not used to determine the physiological parameter. The present disclosure contemplates that the process of distinguishing between confounded and unconfounded physiological data may be a continuous, real-time process during the sensing period. The stored instructions may cause the unconfounded physiological data to be binned or otherwise processed into a format (e.g., values ​​representing the physiological parameter organized into bins of MAP ranges) that can then be used to determine a physiological parameter (e.g., THb, AR, etc.) as a function of time. In this manner, the present disclosure facilitates the real-time determination of a physiological parameter based on data uncontaminated by confounders. As shown in Figure 4, physiological data contaminated by one or more confounding factors is not binned or otherwise processed and therefore is not used to determine physiological parameters in the same manner as uncontaminated physiological data. In some cases, contaminated physiological data that has been frozen (e.g., excluded) from the process of determining normal physiological parameters may be discarded, but discarding the contaminated physiological data is not required. The contaminated physiological data is not used for real-time determination of physiological parameters.

[0057] Referring to FIG. 6, in determining a coherence value within a single frequency band between two physiological parameters, the coherence value may vary as a function of a particular frequency within the frequency band; see, e.g., FIG. 3. In some embodiments, the present disclosure may operate to select a value representative of the coherence values ​​at different frequencies within the frequency band. For example, some embodiments of the present disclosure may operate to select a peak coherence value (i.e., the largest magnitude) from the coherence values ​​at each frequency within the frequency band as a representative coherence value. The peak coherence value may then be used to determine the acceptability of the physiological parameter signal data for determining the physiological parameter value. In some embodiments, the selected coherence values ​​within the single frequency band may be processed to generate a collective coherence parameter (hereinafter referred to as a "CP index") that represents the coherence values ​​within the frequency band. The CP index may then be used to determine the acceptability of the physiological parameter signal data for determining the physiological parameter value. A first example of how a CP index value may be determined is by averaging (or an averaging-like process) the coherence values ​​at selected frequencies within the frequency band. A second example of how a CP index value may be determined is by multiplying the coherence values ​​at selected frequencies within the frequency band and taking the square root of the product to generate the CP index value. This disclosure is not limited to these examples of determining a CP index, and alternative processes can be used to determine the CP index value. This disclosure is also not limited to these examples of selecting values ​​representative of coherence values ​​at different frequencies within the frequency band (i.e., peak COHZ, CP index).

[0058] In the above examples illustrating embodiments of the present disclosure configured to evaluate physiological parameter data to assess the presence of confounding factors, coherence values ​​(e.g., peaks, CP indices, etc.) for a pair of physiological parameters (e.g., C1 and Cn or C2 and Cn) are determined within a single frequency band (see FIG. 3). Referring to FIGS. 7 and 8, in some embodiments, coherence values ​​for a particular pair of physiological parameters may be determined within multiple different frequency bands. Sensing physiological parameter data in different frequency bands can generate more robust information about a particular physiological parameter. For example, a first frequency band having a first duration sampling window may be useful for providing coherence data related to rapid changes in a physiological parameter, a second frequency band having a second duration sampling window may be useful for providing coherence data related to changes in the physiological parameter that occur more slowly than changes that may be sensed in the first frequency band (e.g., changes that are not readily determinable in the first frequency band), and a third frequency band having a third duration sampling window may be useful for providing coherence data related to changes in the physiological parameter that occur more slowly than changes that may be sensed in the second frequency band. Other frequency bands may be selected based on their ability to assess a physiological characteristic of the subject and identify coherence between that physiological characteristic and the physiological parameter under consideration.

[0059] If a coherence value (e.g., COHZ at a particular frequency, peak COHZ, CP index, etc.) is determined for a particular physiological parameter pair within each of a plurality of different frequency bands, the determined coherence values ​​for the particular physiological parameter pair may be further processed to determine an aggregate coherence value (e.g., peak COHZ, CP index, etc.) based on the determined coherence values ​​from the different frequency bands. By way of example (see FIG. 7 ), selected coherence values ​​from each frequency band for each particular physiological parameter pair may be processed in the manner described above to generate an aggregate coherence value for all frequency bands for that physiological parameter pair (e.g., peak COHZ or CP index, etc.).

[0060] FIG. 8 illustrates an embodiment utilizing a combination of the processing described and illustrated in FIGS. 6 and 7. More specifically, for each frequency band of a plurality of frequency bands, selected coherence values ​​within each frequency band may be processed to generate an aggregate coherence parameter (e.g., peak COHZ, CP index, etc.) representing the coherence values ​​within that frequency band. This process is repeated for each frequency band for each physiological parameter pair, e.g., as described in FIG. 6. The aggregate coherence values ​​for each frequency band for a particular physiological pair are then processed in a similar manner to provide aggregate coherence values ​​(e.g., peak COHZ, CP index, etc.) for all frequency bands of that physiological pair. This process is performed for each physiological parameter pair, and each aggregate coherence value (e.g., peak COHZ, CP index, etc.) is then passed to the evaluation of confounders step.

[0061] In some embodiments, the present disclosure may determine coherence values ​​or values ​​representing coherence values ​​(e.g., peak COHZ, CP index, etc.) over a period of time and process those values ​​to determine trend information, such as whether a physiological parameter is trending upward over the period of time, trending downward over the period of time, or stable over the period of time. In some applications, the trend information may be used to determine whether the collected physiological parameter data is acceptable and therefore can be used to determine a physiological parameter, or unacceptable and therefore should not be used to determine a physiological parameter. For example, a physiological parameter that increases over a period of time may be characterized as an increasing trend (“Incr”), a physiological parameter that decreases over a period of time may be characterized as a decreasing trend (“Decr”), and a physiological parameter that stabilizes over a period of time may be characterized as stable (“N / C”). Relative trends between different physiological parameters may provide information about the usefulness of the collected physiological parameter data. In some embodiments, the trend information may be used in a polar filter. For example, if a first physiological parameter is trending upward and a second physiological parameter is trending downward, the fact that the two physiological parameters have opposite trend “polarities” (one decreasing and one increasing) may provide a basis for determining whether the collected physiological parameter data is acceptable or unacceptable. As another example, if both physiological parameters are trending in the same direction (i.e., similar polarity), this may provide a basis for determining whether the collected physiological parameter data is acceptable or unacceptable. In some embodiments, the disclosed process (e.g., in the stored instructions) may include a polarity filter that operates to determine whether the collected physiological parameter data is acceptable or unacceptable. The stored instructions may provide a means for determining whether the physiological parameter is trending upward, downward, or stable, such as a threshold value for the rate of change or a threshold value for the magnitude of change.

[0062] As described above, the present disclosure relates to a process for distinguishing between physiological data contaminated by confounding factors and physiological data that is not contaminated by confounding factors, which may be a real-time, continuous process. The uncontaminated physiological data may then be used to determine a physiological parameter as a function of time. Thus, the present disclosure, including the embodiments disclosed above, allows for a physiological parameter to be determined with greater certainty than if confounding factors were not considered. The flowcharts in FIGS. 4 and 6-9 illustrate the present disclosure in a continuous manner; for example, the process steps may be performed continuously while the subject is sensed and physiological data is generated. The sensed physiological data is processed (e.g., "Input Processing of Physiological Data"), and the processed data may then be subjected to various additional steps, as indicated in each flow step, including evaluating the data to determine whether it is contaminated by confounding factors (if so, the data is frozen or discarded) or not (if not, it is carried forward for further processing). The final step ("determining physiological parameter" or "determining THb" - i.e., further processing) shown in the exemplary embodiment involves displaying the determined values ​​as a function of time, for example, in a manner such as that shown in FIG. 12. As described herein, the present disclosure can be used with a variety of methods for determining and / or displaying physiological parameter values, and thus is not limited to any particular method for determining and / or displaying physiological parameter values.

[0063] To facilitate understanding of the scope and usefulness of the present disclosure, a specific example of the present disclosure is provided below. This example provides the process shown in Figure 9 for evaluating collected physiological data to determine whether there are any hemodynamic confounding factors (e.g., heart rate changes, blood pressure changes, hemodilution, etc.) that may adversely affect total hemoglobin (THb) data and THb determination. In this example, three physiological parameters (e.g., THb, BP, HR) are sensed, and two paired physiological parameters are utilized.

[0064] In this example, the disclosed system 20 includes a tissue oximeter 22 configured to continuously generate data representative of the subject's THb, a BP sensor device configured to continuously generate data representative of the subject's blood pressure, and an HR monitor configured to generate data representative of the subject's heart rate. Alternatively, system 20 may be configured to generate heart rate data using tissue oximeter 22 or the BP sensor device. The tissue oximeter 22, BP sensor device, and HR monitor may be integrated into system 20 or may be separate devices in communication with system controller 24.

[0065] The system 20 is operated to generate signal data representing the subject's THb, BP, and HR. The signal data representing the subject's THb, BP, and HR are processed by the disclosed system 20 using an algorithm based on a frequency domain method to generate a coherence analysis. The frequency domain method converts input physiological parameter signal data pairs from the time domain to the frequency domain (e.g., via a Fourier transform) (see FIG. 3). In this example, the physiological parameter signal data pairs are BP vs. THb and HR vs. THb. The process then determines the degree of coherence within a single frequency band or multiple frequency bands, as described above. If the system 20 is configured to determine coherence in a single frequency band, a single coherence value for the BP vs. THb pair and a single coherence value for the HR vs. THb pair may be determined. These coherence values ​​are then evaluated to determine whether the THb data is contaminated by confounding factors. If system 20 is configured to determine coherence in multiple frequency bands, a coherence value may be determined for each frequency band, and an aggregate coherence value (e.g., peak COHZ) or a value collectively representing the coherence values ​​of the frequency bands (e.g., CP index) may be determined. The aggregate coherence value may then be evaluated to determine whether the THb data is contaminated by confounding factors.

[0066] As described above, the disclosed system 20 may be configured to evaluate coherence values ​​in various ways to determine whether THb data is contaminated by a confounding factor. In some embodiments, an index table containing empirically collected data characterizing each coherence value may be used. For example, as described above and shown in FIG. 5, coherence values ​​may be characterized as low, high, or indeterminate. The index table may be configured to generate an output indicating whether the input physiological parameter data is contaminated by a confounding factor. The output may be binary, i.e., if the input physiological parameter data (e.g., THb, BP, HR) is determined to be contaminated by a confounding factor, the data is excluded (frozen) or discarded, or if the input physiological parameter data is determined not to be contaminated by a confounding factor, the data is advanced for use in determining the subject's THb.

[0067] Referring to FIG. 10, as another example, system 20 may be configured to determine the coherence values ​​described above and evaluate them over a period of time. Physiological parameter (e.g., THb, BP, HR) data may be stored, and trend data for each physiological parameter may be created. The trend data may then be used as a basis for determining whether the physiological parameter data of interest (i.e., THb) is contaminated by a confounding factor. The input table shown in FIG. 10 shows trend data for each physiological parameter in ten different cases. For each case, the input table includes a binary decision column indicating whether the physiological parameter data of interest (i.e., THb) is valid for use based on the coherence and polarity of the physiological parameter pair. In cases 1, 2, and 7-10, the input physiological parameter data (e.g., THb, BP, HR) is determined to be uncontaminated by a confounding factor, and the data may be passed on for use in determining the subject's THb. In cases 3–6, input physiological parameter data (e.g., THb, BP, HR) are determined to be contaminated by one or more confounding factors, and the data are either excluded (“frozen”) or discarded, and therefore not passed on to be used in determining the subject's THb.

[0068] As noted above, assessing whether the physiological parameter data of interest (THb in this example) is valid for use in determining the physiological parameter of interest can be done in a variety of ways and is therefore not limited to the use of an index table.

[0069] For clarity, the above examples are presented in the context of physiological parameter data collected for the purpose of generating a determination of THb. The present disclosure is not limited to determining and evaluating the impact of confounding factors on physiological parameter data collected for the purpose of generating a determination of THb. Rather, the present disclosure may be used to evaluate the impact of confounding factors on various physiological parameters.

[0070] As described above, the present disclosure relates to a process for distinguishing between physiological data contaminated by confounders and physiological data not contaminated by confounders (which may be a real-time, continuous process), thereby enabling a physiological parameter to be determined solely based on uncontaminated physiological data. In some instances, including confounded physiological data in determining a physiological parameter may result in a physiological parameter value that differs from a value determined solely based on uncontaminated physiological data. FIG. 11 illustrates a graph of physiological parameter value versus time. The graph illustrates two data curves, one solid and one dashed. The solid curve represents the physiological parameter value as a function of time based on physiological data not contaminated by confounders. The dashed curve represents the physiological parameter value as a function of time based on physiological data not checked for confounders. Initially (from t0 to t1), the two data curves are parallel and substantially equal (visibly separated on the graph). The physiological data sensed between t1 and t2 is contaminated by confounders. Data contamination by a confounding factor causes the determined physiological parameter to have a different data value than if the physiological parameter value were determined based solely on uncontaminated physiological data. The difference between the contaminated and uncontaminated physiological parameter value is identified on the graph as an offset resulting from the confounding factor. When a physiological parameter value is determined based on binned physiological data, the effect of the confounding physiological data (if included) can have an effect even after the period in which the confounding event occurs, e.g., after t2.

[0071] FIG. 12 is a graph of physiological parameter values ​​(e.g., THb) versus time. The graph illustrates a confounder event beginning at approximately 12 minutes (12 min) and continuing until approximately 21 minutes (21 min). As previously discussed, in accordance with the present disclosure, physiological sensed data may be continuously evaluated to determine the presence of a confounder. Once the presence of a confounder is determined (e.g., at the 12 minute mark), the sensed physiological data is frozen / discarded and not used in determining the physiological parameter (e.g., THb). Data displayed after the "frozen" data point (e.g., during the confounder event) may be displayed in various ways, for example, the data may be displayed in a manner indicating the presence of contaminated physiological sensed data, displayed as a constant value based on the last uncontaminated physiological parameter value, or otherwise displayed. The present disclosure is not limited to these display options. Once the sensed physiological data is determined to be uncontaminated by a confounder (e.g., at the 21 minute mark), the system 20 may again use the sensed physiological data (now uncontaminated) in determining the physiological parameter and re-display the determined physiological parameter value.

[0072] In some embodiments, the executed system instructions can cause the controller to determine a display value curve from the onset of the confounder event to its termination using valid physiological parameter values ​​immediately prior to the onset of the confounder event (i.e., at 12 minutes) and valid physiological parameter values ​​immediately after the confounder event has ended (i.e., at 21 minutes). For example, the system instructions can determine the difference between the physiological parameter value at the time of the confounder onset (i.e., at 12 minutes) and the physiological parameter value at the time of the confounder's termination (i.e., at 21 minutes) and use that difference to generate a display value curve from the onset of the confounder event to its termination, where, for example, the display value may be a line or have some other shape. The present disclosure is not limited to any particular method for displaying physiological parameter values ​​determined in accordance with the present disclosure.

[0073] While the principles of the disclosure have been described above with reference to specific devices and methods, it should be clearly understood that this description is illustrative only and is not intended to be a limitation on the scope of the disclosure. Specific details are set forth in the above description to provide a thorough understanding of the embodiments. However, it will be understood that embodiments may be practiced without these specific details.

[0074] The embodiments may be described as a process that is depicted as a flowchart, flow diagram, block diagram, etc. While any of these structures may describe operations as a sequential process, many operations may be performed in parallel or concurrently. Additionally, the order of operations may be rearranged. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.

[0075] The singular forms "a," "an," and "the" refer to one or more than one, unless the context clearly dictates otherwise. For example, the term "comprising a specimen" includes one or more specimens and is considered equivalent to the phrase "comprising at least one specimen." The term "or" refers to a single element of the stated alternative elements or a combination of two or more elements, unless the context clearly dictates otherwise. As used herein, "comprises" means "includes." Thus, "comprising A or B" means "including A, B, or A and B," without excluding additional elements.

[0076] It is noted that various connections between elements in this description and in the drawings are shown, the contents of which are incorporated by reference into this disclosure. These connections are general and, unless otherwise specified, may be direct or indirect, and this specification is not intended to be limiting in this respect. References to attached, fixed, connected, or the like may include permanent, detachable, temporary, partial, complete, and / or other possible attachment options.

[0077] No element, component, or method step in this disclosure is intended to be dedicated to the public, regardless of whether the element, component, or method step is expressly recited in a claim. No claim element described herein is to be construed under the provisions of 35 U.S.C. 112(f) unless expressly recited using the phrase "means for." As used herein, "comprises," "comprising," or any other variation thereof, is intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus comprising a list of elements does not include only those elements, but may include other elements not expressly listed or inherent in such process, method, article, or apparatus.

[0078] While various inventive aspects, concepts, and features in the present disclosure may be described and illustrated as being embodied in combination in exemplary embodiments, these various aspects, concepts, and features may be used individually or in various combinations and subcombinations in many alternative embodiments. Unless expressly excluded herein, all such combinations and subcombinations are intended to be within the scope of the present application. Furthermore, while various alternative embodiments (e.g., alternative materials, structures, configurations, methods, devices, components, etc.) for various aspects, concepts, and features of the disclosure may be described herein, these descriptions are not intended to be a complete or exhaustive list of all available alternative embodiments, whether currently known or later developed. Those skilled in the art may readily employ one or more of the inventive aspects, concepts, and features in additional embodiments or applications within the scope of the present application, even in embodiments not explicitly disclosed herein. For example, in the example embodiments described above, elements may be described as individual units within the "Detailed Description" section of this specification and shown separately from each other for ease of description. In alternative embodiments, such elements may be configured as combined elements. It is also noted that various method or process steps related to embodiments of the present disclosure are described herein. The description may present method and / or process steps in a particular order. However, unless the method or process depends on the particular order of steps set forth herein, the method or process should not be limited to the particular order of steps described. As one of ordinary skill in the art would understand, other orders of steps may be possible. Thus, the particular order of steps set forth in the description should not be construed as limiting.

[0079] Furthermore, even if some features, concepts, or aspects of the disclosure are described herein as preferred arrangements or methods, such description does not imply that the feature is necessary or essential unless expressly stated. Furthermore, while example or representative values ​​or ranges may be included to aid in understanding the current application, such values ​​or ranges should not be construed in a limiting sense, and only when explicitly stated are significant values ​​or ranges intended.

[0080] The treatment techniques, methods, and procedures described or suggested herein or incorporated by reference may be performed in live animals or cadavers, non-biological simulations such as cadaver hearts, anthropomorphic ghosts, or simulators (e.g., where body parts or tissues are simulated).

[0081] Various systems, devices, apparatus, etc. included in this disclosure may be sterilized (e.g., heat, radiation, ethylene oxide, hydrogen peroxide) to ensure safety for use on patients, and the methods described herein may include sterilization (e.g., heat, radiation, ethylene oxide, hydrogen peroxide) of the associated systems, devices, apparatus, etc.

Claims

1. 1. A method for determining a target physiological parameter of a subject, comprising: a) sensing a subject using a first sensor device configured to sense a first physiological parameter, the first sensor device generating a first physiological data signal representative of the first physiological parameter over a period of time; b) sensing the subject using a second sensor device configured to sense a second physiological parameter, the second sensor device generating a second physiological data signal representative of the second physiological parameter during the period of time; c) sensing the subject using a third sensor device configured to sense a target physiological parameter, the third sensor device generating a target physiological data signal representative of the target physiological parameter during the period of time; d) determining the presence or absence of confounding factors that adversely affect the determination of the target physiological parameter, said determining using the first physiological data signal, the second physiological data signal, and the target physiological data signal; e) carrying forward for further processing the first physiological data signal, the second physiological data signal, and the target physiological data signal generated in the absence of the confounding factor, and excluding the first physiological data signal, the second physiological data signal, and the target physiological data signal generated in the presence of the confounding factor; f) determining a value of the target physiological parameter using the first physiological data signal, the second physiological data signal, and the target physiological data signal generated in the absence of the confounding factor.

2. 2. The method of claim 1, wherein the first physiological data signal, the second physiological data signal, and the target physiological data signal generated in the presence of the confounding factor are not used in determining the value of the target physiological parameter.

3. 10. The method of claim 1, wherein the step of determining the presence or absence of the confounding factor comprises comparing a processed signal representative of the first physiological data signal, the second physiological data signal, and the target physiological data signal.

4. The method of claim 1 , wherein the step of determining the presence or absence of the confounder uses frequency domain methods.

5. 5. The method of claim 4, wherein the step of determining the presence or absence of the confounding factor comprises determining a first coherence between the first physiological data signal and the target physiological data signal, and a second coherence between the second physiological data signal and the target physiological data signal.

6. The method of claim 5 , wherein the first coherence is based on a single frequency band.

7. The method of claim 6 , wherein the first coherence represents a coherence value at different individual frequencies within the single frequency band.

8. The method of claim 5 , wherein the first coherence is based on multiple frequency bands.

9. The method of claim 8 , wherein the first coherence collectively represents a respective coherence value from each frequency band of the plurality of frequency bands.

10. 6. The method of claim 5, further comprising determining a first trend of the first physiological parameter, a second trend of the second physiological parameter, and a third trend of the target physiological parameter, and comparing the first trend, the second trend, and the third trend to one another.

11. 11. The method of claim 10, wherein the step of determining the presence or absence of the confounding factor utilizes one or more polar filters configured to evaluate the first trend, the second trend, and the third trend.

12. The method of claim 1 , wherein the step of determining the presence or absence of the confounding factor uses an index table.

13. The method of claim 1 , wherein the step of determining the presence or absence of the confounding factor uses correlation methods.

14. 2. The method of claim 1, wherein the excluding step comprises discarding the first physiological data signal, the second physiological data signal, and the target physiological data signal that were generated when the confounding factor was present.

15. The method of claim 1 , wherein steps a to f are performed continuously during the fixed period.

16. The method of claim 1 , wherein the target physiological parameter is related to total hemoglobin concentration (THb) per volume of sensed tissue.

17. 17. The method of claim 16, wherein the target physiological parameter is relative total hemoglobin concentration (rTHb) per volume of sensed tissue.

18. 18. The method of claim 17, wherein the third sensor device is a near-infrared spectroscopy (NIRS) tissue oximeter.

19. 17. The method of claim 16, wherein the first physiological parameter is related to the subject's blood pressure and the first sensor device is a blood pressure sensor device.

20. 17. The method of claim 16, wherein the second physiological parameter is related to the subject's heart rate.

21. 1. A system for determining a target physiological parameter of a subject, comprising: a first sensor device configured to continuously sense a first physiological parameter during a period of time and generate a first physiological data signal representative of the first physiological parameter during the period of time; a second sensor device configured to continuously sense a second physiological parameter during the period of time and generate a second physiological data signal representative of the second physiological parameter during the period of time; a third sensor device configured to continuously sense a target physiological parameter during the period of time and generate a target physiological data signal representative of the target physiological parameter during the period of time; a system controller in communication with the first sensor device, the second sensor device, and the target sensor device; Including, The system controller includes at least one processor and a memory device configured to store instructions, the stored instructions, when executed, causing the system controller to: a) determining the presence or absence of confounding factors that adversely affect the determination of the target physiological parameter using the first physiological data signal, the second physiological data signal, and the target physiological data signal; b) carrying forward for further processing the first physiological data signal, the second physiological data signal, and the target physiological data signal generated in the absence of the confounding factor, and excluding the first physiological data signal, the second physiological data signal, and the target physiological data signal generated in the presence of the confounding factor; c) determining a value of the target physiological parameter using the first physiological data signal, the second physiological data signal, and the target physiological data signal generated in the absence of the confounding factor.

22. 22. The system of claim 21, wherein the stored instructions, when executed, cause the system controller to determine the value of the target physiological parameter without using the first physiological data signal, the second physiological data signal, and the target physiological data signal generated in the presence of the confounding factor.

23. 22. The system of claim 21, wherein the stored instructions, when executed, cause the system controller to determine the presence or absence of the confounding factor using a comparison of the first physiological data signal, the second physiological data signal, and a processed signal representative of the target physiological data signal.

24. 22. The system of claim 21, wherein the stored instructions, when executed, cause the system controller to determine the presence or absence of the confounder using frequency domain methods.

25. 25. The system of claim 24, wherein the stored instructions, when executed, cause the system controller to determine the presence or absence of the confounding factor, and further cause the system controller to determine a first coherence between the first physiological data signal and the target physiological data signal, and a second coherence between the second physiological data signal and the target physiological data signal.

26. 26. The system of claim 25, wherein the first coherence is based on a single frequency band.

27. 27. The system of claim 26, wherein the first coherence represents a coherence value at different individual frequencies within the single frequency band.

28. 26. The system of claim 25, wherein the first coherence is based on multiple frequency bands.

29. 30. The system of claim 28, wherein the first coherence collectively represents a respective coherence value from each frequency band of the plurality of frequency bands.

30. 26. The system of claim 25, wherein, when the stored instructions are executed, the system controller determines a first trend for the first physiological parameter, a second trend for the second physiological parameter, and a third trend for the target physiological parameter, and compares the first trend, the second trend, and the third trend to one another.

31. 31. The system of claim 30, wherein, when the stored instructions are executed, the system controller determines the presence or absence of the confounding factor using one or more polar filters configured to evaluate the first trend, the second trend, and the third trend.

32. 22. The system of claim 21, wherein the stored instructions, when executed, cause the system controller to determine the presence or absence of the confounding factor using an index table.

33. 22. The system of claim 21, wherein the stored instructions, when executed, cause the system controller to determine the presence or absence of the confounding factor using a correlation method.

34. 22. The system of claim 21, wherein the excluded first physiological data signal, the second physiological data signal, and the target physiological data signal produced when the confounding factor is present are discarded.

35. 22. The system of claim 21, wherein the stored instructions, when executed, cause the system controller to sequentially perform functions a through c during the period of time.

36. 22. The system of claim 21, wherein the target physiological parameter is related to total hemoglobin concentration (THb) per volume of sensed tissue.

37. 37. The system of claim 36, wherein the target physiological parameter is relative total hemoglobin concentration (rTHb) per volume of sensed tissue.

38. 38. The system of claim 37, wherein the third sensor device is a near-infrared spectroscopy (NIRS) tissue oximeter.

39. 37. The system of claim 36, wherein the first physiological parameter is related to the subject's blood pressure and the first sensor device is a blood pressure sensor device.

40. 37. The system of claim 36, wherein the second physiological parameter is related to the subject's heart rate.

41. 1. A method for determining a target physiological parameter of a subject, comprising: sensing the subject for "N" physiological parameters, "N" being an integer greater than or equal to three, said sensing producing "N" sets of physiological data signals, each set of physiological data signals corresponding to a respective one of said "N" physiological parameters over a period of time, one of said "N" physiological parameters being a target physiological parameter; determining the presence or absence of confounding factors that adversely affect the determination of said target physiological parameters, said determining using said "N" sets of physiological data signals comprising said target physiological parameter set of physiological data signals; forwarding for further processing the "N" sets of physiological data signals generated when the confounding factor is not present and excluding the "N" sets of physiological data signals generated when the confounding factor is present; and determining a value of said target physiological parameter using said "N" sets of physiological data signals generated in the absence of said confounding factor.

42. A non-transitory computer-readable medium storing executable instructions that, when executed, cause at least one processor to: controlling a first sensor device configured to sense a first physiological parameter to sense the subject and generate a first physiological data signal representative of the first physiological parameter over a period of time; controlling a second sensor device configured to sense a second physiological parameter to sense the subject and generate a second physiological data signal representative of the second physiological parameter during the period of time; controlling a third sensor device configured to sense a target physiological parameter to sense the subject and generate a target physiological data signal representative of the target physiological parameter during the period of time; using the first physiological data signal, the second physiological data signal, and the target physiological data signal to determine the presence or absence of confounding factors that adversely affect the determination of the target physiological parameter; carrying forward for further processing the first physiological data signal, the second physiological data signal, and the target physiological data signal that were generated when the confounding factor was not present, and excluding the first physiological data signal, the second physiological data signal, and the target physiological data signal that were generated when the confounding factor was present; A non-transitory computer-readable medium for determining a value of the target physiological parameter using the first physiological data signal, the second physiological data signal, and the target physiological data signal generated in the absence of the confounding factor.

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