Method and apparatus for non-invasively measuring circulating hemoglobin

Abstract

A method and system for noninvasively determining continuous total hemoglobin data is provided, the method including: a) continuously sensing tissue of a subject using a near-infrared spectroscopy (NIRS) sensing device, where the sensing generates a NIRS signal; and b) determining continuous total hemoglobin (THb) using the generated NIRS signal.

Description

[Technical Field]

[0001] The present invention relates generally to methods and devices for determining circulating hemoglobin levels, and more particularly to non-invasive methods and devices for determining circulating hemoglobin levels using performance diagnostics. [Background technology]

[0002] The oxygen-carrying molecule in blood is hemoglobin. Oxygenated hemoglobin (i.e., oxyhemoglobin or HbO2) and deoxygenated hemoglobin (i.e., deoxyhemoglobin or Hb) are the major types of hemoglobin present in blood, although blood may contain relatively small amounts of other types of hemoglobin (e.g., carboxyhemoglobin (COHb), methemoglobin (MetHb), etc.). Thus, as used herein, the term "total hemoglobin" refers to the sum of HbO2 and Hb and is proportional to changes in relative blood volume, provided that the hematocrit or hemoglobin concentration in the blood remains unchanged.

[0003] Near-infrared spectroscopy (NIRS) is an optical spectrophotometric method for continuously monitoring tissue parameters (e.g., oxygen saturation, hemoglobin levels) without requiring pulsatile blood flow to calculate clinically valuable parameters. NIRS spectroscopy is based on the principle that light in the near-infrared range (700–1,000 nm) easily penetrates skin, bone, and other tissues and encounters hemoglobin, which resides primarily within the microcirculatory pathways (e.g., capillaries, arterioles, and venules). Hemoglobin exposed to near-infrared light exhibits specific absorption spectra that vary depending on its oxidation state. Oxyhemoglobin (HbO2) and deoxyhemoglobin (Hb) act as distinct chromophores. By using a light source that transmits near-infrared light of specific wavelengths and measuring changes in the attenuation of the transmitted or reflected light, changes in the concentrations of oxyhemoglobin (HbO2) and deoxyhemoglobin (Hb) in tissue, as well as oxygen saturation, can be monitored. NIRS spectroscopy apparatus and methods are described in U.S. Patent Nos. 6,456,862, 7,072,701, and 8,078,250, each of which is incorporated herein by reference in its entirety.

[0004] A NIRS tissue oximeter can provide a noninvasively determined total hemoglobin value of a subject's tissue. As described herein, the total hemoglobin of the tissue is proportional to the relative blood volume in the sensed tissue, which may change over time. A NIRS tissue oximeter uses an optical-based sensor placed on the subject's skin to probe the tissue with various wavelengths of light (e.g., irradiate the tissue with light and detect the light emitted from the tissue), and process the detected light to calculate the total hemoglobin value of the tissue and, optionally, the tissue oxygen saturation (StO2) value. For example, the sensor portion of a NIRS tissue oximeter placed on the subject's forehead can be used to spectrophotometrically probe the subject's brain tissue, subsequently determining the total hemoglobin value and tissue oxygen saturation (StO2) value of the subject's brain tissue.

[0005] Historically, circulating blood hemoglobin values ​​(i.e., hemoglobin values ​​representative of hemoglobin in circulating blood) have been determined using invasively drawn blood samples. Invasively drawn blood sample specimens may be analyzed using a CO-oximeter or blood gas analyzer. A CO-oximeter is a device that can be operated to measure one or more types of hemoglobin (e.g., HbO2, Hb, carboxyhemoglobin (COHb), methemoglobin (MetHb), etc.) present in a blood sample. Most CO-oximeters are spectrophotometric devices that can be operated to determine the presence and amount of each type of hemoglobin (e.g., HbO2, Hb, COHb, MetHb, etc.) in an invasively drawn blood sample by measuring the absorption of specific wavelengths of light passing through the blood sample. The relative amount of absorption at different wavelengths allows the determination of each type of hemoglobin present in the blood sample. In contrast, most blood gas analyzers are electrochemical-type analyzers that utilize electrodes and changes in current or potential to detect and measure constituents in an invasively drawn blood sample.

[0006] The primary difference between prior art NIRS tissue oximeters and CO2-oximeters or blood gas analyzers is that NIRS tissue oximeters are configured to determine parameter values ​​(e.g., hemoglobin, oxygen saturation, etc.) in tissue, whereas CO2-oximeters or blood gas analyzers are configured to determine the same parameter values ​​in a circulating blood sample (i.e., an invasively collected blood sample). Using total hemoglobin as an example parameter, the total hemoglobin value determined in tissue using a prior art NIRS tissue oximeter can be affected by a variety of different physiological parameters, including circulating blood hemoglobin, hemoglobin concentration per tissue volume, vascular reactivity, cardiac output, blood flow, partial pressure of carbon dioxide in arterial blood (PaCO2), heart rate, blood volume, hematoma, hyperemia, etc. While the total hemoglobin value of a circulating blood sample determined using a CO2-oximeter or blood gas analyzer is not affected by these physiological parameters, it does require an invasive collection step. Furthermore, invasive blood sampling for analytical purposes is typically performed intermittently; for example, blood is sampled and subsequently analyzed. Therefore, blood provides intermittent, not continuous, information. In contrast, continuous hemoglobin monitoring can enhance the ability to identify rising or falling trends in blood constituents and address those trends as needed. Furthermore, a stable trend in a blood constituent such as hemoglobin can provide continuous information of normality, providing clinicians with peace of mind.

[0007] What is needed is a method and apparatus operable to non-invasively determine a blood parameter (e.g., hemoglobin) that overcomes the problems associated with current non-invasive techniques for determining blood parameters. Summary of the Invention

[0008] According to one aspect of the present disclosure, a method for non-invasively determining continuous total hemoglobin (THb) data is provided, the method including: a) continuously sensing tissue of a subject using a near-infrared spectroscopy (NIRS) sensing device, wherein a NIRS signal is generated from the sensing; and b) determining the continuous total hemoglobin (THb) data using the generated NIRS signal.

[0009] In any of the aspects or embodiments described above and herein, the continuous THb data may be continuous relative THb (ΔTHb) data or continuous absolute THb data.

[0010] In any of the aspects or embodiments described above and herein, the step of determining continuous absolute THb data may include calibrating using a reference absolute THb value obtained from the subject.

[0011] In any of the aspects or embodiments described above and herein, the baseline absolute THb value may be obtained non-invasively from the subject.

[0012] In any of the aspects or embodiments described above and herein, the reference absolute THb value may be obtained from a blood sample invasively taken from the subject.

[0013] In any of the aspects or embodiments described above and herein, the method may further include providing instructions for calibrating the NIRS sensing device based on the NIRS signal.

[0014] In any of the aspects or embodiments described above and herein, the step of providing instructions to calibrate the NIRS sensing device may be based on a determination of the acceptability of the NIRS signal for purposes of determining continuous absolute THb data.

[0015] In any of the aspects or embodiments described above and herein, determining the acceptability of the NIRS signal may include evaluating the NIRS signal over a predetermined period of time to determine the stability of the NIRS signal.

[0016] In any of the aspects or embodiments described above and herein, determining the acceptability of the NIRS signal may include evaluating the NIRS signal over a predetermined period of time to determine the hemodynamic stability of the sensed tissue.

[0017] In any of the aspects or embodiments described above and herein, determining the acceptability of the NIRS signal may include evaluating the NIRS signal over a predetermined period of time to determine hemodynamic changes in the sensed tissue.

[0018] In any of the aspects or embodiments described above and herein, determining the continuous THb data may include using oximetry features based on the NIRS signal.

[0019] In any of the aspects or embodiments described above and herein, the oximetry features based on the NIRS signal may include continuous relative tissue hemoglobin (ΔctHb) data.

[0020] In any of the aspects or embodiments described above and herein, the oximetry features based on the NIRS signal may include at least one of skin temperature, a path length traveled by photons between the NIRS transducer light source and the NIRS transducer photodetector, deoxygenated tissue hemoglobin, oxygenated tissue hemoglobin, or tissue oxygen saturation.

[0021] In any of the aspects or embodiments described above and herein, the method may further include evaluating the NIRS signal to determine acceptability of the NIRS signal for purposes of determining continuous THb data.

[0022] In any of the aspects or embodiments described above and herein, the evaluating step to determine the acceptability of the NIRS signal may include evaluating the NIRS signal over a predetermined period of time to determine the stability of the NIRS signal.

[0023] In any of the aspects or embodiments described above and herein, the evaluating step to determine the acceptability of the NIRS signal may include evaluating the NIRS signal over a predetermined period of time to determine the hemodynamic stability of the sensed tissue.

[0024] In any of the aspects or embodiments described above and herein, the evaluating step to determine the acceptability of the NIRS signal includes evaluating the NIRS signal over a predetermined period of time to determine hemodynamic changes in the sensed tissue.

[0025] In any of the aspects or embodiments described above and herein, the method may further include estimating the blood volume fraction (BVF) of the sensed tissue.

[0026] In any of the aspects or embodiments described above and herein, the step of determining continuous THb may utilize trained machine learning methods.

[0027] In any of the aspects or embodiments described above and herein, the method may further include providing an indication indicating that calibration of the NIRS sensing device is permissible based on the NIRS signal.

[0028] In any of the aspects or embodiments described above and herein, the indication that calibration of the NIRS sensing device is permissible may be based at least in part on the stability of the NIRS signal.

[0029] In any of the aspects or embodiments described above and herein, the stability of the NIRS signal may be determined by evaluating the NIRS signal generated over a predetermined period of time.

[0030] In any of the aspects or embodiments described above and herein, the NIRS signal may be in the form of a raw signal.

[0031] In any of the aspects or embodiments described above and herein, the indication that calibration of the NIRS sensing device is permissible may be based on the stability of a parameter determined using the NIRS signal, such as tissue oxygen saturation (StO2), relative tissue hemoglobin (ΔctHb), etc.

[0032] According to another aspect of the present disclosure, a system for determining continuous total hemoglobin data from a subject is provided. The system includes a near-infrared spectroscopy (NIRS) sensing device and a controller. The NIRS sensing device is configured to sense a tissue region of the subject and generate an NIRS signal from the sensing. The controller is in communication with the NIRS sensing device. The controller includes at least one processor and a memory device configured to store instructions. When executed, the instructions cause the controller to a) control the NIRS sensing device to continuously sense the tissue of the subject and generate an NIRS signal from the sensing, and b) determine continuous total hemoglobin (THb) data using the generated NIRS signal.

[0033] In any of the aspects or embodiments described above and herein, the instructions, when executed, may cause the controller to evaluate the NIRS signal to determine the acceptability of the NIRS signal for purposes of said determining said continuous absolute THb data.

[0034] In any of the aspects or embodiments described above and herein, evaluating the NIRS signal to determine the acceptability of the NIRS signal may include evaluating the NIRS signal over a predetermined period of time to determine stability of the NIRS signal.

[0035] In any of the aspects or embodiments described above and herein, evaluating the NIRS signal to determine acceptability of the NIRS signal may include evaluating the NIRS signal over a predetermined period of time to determine hemodynamic stability of the sensed tissue, or hemodynamic changes in the sensed tissue, or both.

[0036] In any of the aspects or embodiments described above and herein, the instructions, when executed, may cause the controller to provide instructions to calibrate the NIRS sensing device based on the NIRS signal.

[0037] In any of the aspects or embodiments described above and herein, the instruction to calibrate the NIRS sensing device may be based on a determination of the acceptability of the NIRS signal for the determination of continuous THb data.

[0038] In any of the aspects or embodiments described above and herein, determining the acceptability of the NIRS signal may include evaluating the NIRS signal over a predetermined period of time to determine the stability of the NIRS signal.

[0039] In any of the aspects or embodiments described above and herein, evaluating the NIRS signal to determine acceptability of the NIRS signal may include evaluating the NIRS signal over a predetermined period of time to determine hemodynamic stability of the sensed tissue, or hemodynamic changes in the sensed tissue, or both.

[0040] In any of the aspects or embodiments described above and herein, the determination of continuous THb may utilize trained machine learning methods.

[0041] In any of the aspects or embodiments described above and herein, the instructions, when executed, may cause the controller to provide an indication indicating that calibration of the NIRS sensing device is permissible based on the NIRS signal.

[0042] In any of the aspects or embodiments described above and herein, the NIRS sensing device may be configured to operate independently of the controller, and the NIRS sensing device and the controller may be independent of each other.

[0043] In any of the aspects or embodiments described above and herein, the NIRS sensing device and the controller may be integrated.

[0044] According to another aspect of the present disclosure, there is provided a non-transitory computer-readable medium including software code portions adapted to perform a method for non-invasively determining continuous relative total hemoglobin (ΔTHb) data, the method including: a) continuously controlling a near-infrared spectroscopy (NIRS) sensing device to sense tissue of a subject, said sensing generating a NIRS signal; and b) determining continuous relative total hemoglobin (ΔTHb) using the generated NIRS signal.

[0045] 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]

[0046] [Figure 1] FIG. 1 is a schematic diagram of a NIRS sensing device with sensing transducers worn on the subject's head. [Figure 2]FIG. 1 is a schematic diagram of a NIRS sensing device transducer worn on the subject's head. [Figure 3] FIG. 1 is a schematic plan view of a NIRS sensing device transducer. [Figure 4] Shown is a first graph of relative tissue hemoglobin values ​​(ΔctHb) as a function of time and a second graph of blood sample total hemoglobin (tHb) and continuous total hemoglobin (THb) as a function of time, each graph displaying the respective values ​​as a function of the same time period. [Figure 5] 1 shows a graph of absolute blood total hemoglobin (THb) as a function of time with initial calibration. [Figure 5A] 1 shows a graph of relative blood total hemoglobin (ΔTHb) as a function of time without initial calibration. [Figure 6] Graph of relative tissue hemoglobin values ​​(ΔctHb) as a function of time, showing 2-minute windows where data was flagged. [Figure 7] Shown is a first graph of relative tissue hemoglobin (ΔctHb) as a function of time and a second graph of tissue oxygen saturation (StO2) as a function of time, each graph displaying its respective value as a function of the same time period. [Figure 8] Shown is a first graph of relative tissue hemoglobin (ΔctHb) as a function of time and a second graph of tissue oxygen saturation (StO2) as a function of time, each graph displaying its respective value as a function of the same time period. [Figure 9A] A graph of relative tissue hemoglobin (ΔctHb) as a function of time is shown, displaying ΔctHb and reference variable "R" values ​​for a single evaluation period. [Figure 9B] A graph of relative tissue hemoglobin (ΔctHb) as a function of time is shown, displaying ΔctHb and reference variable "R" values ​​for multiple evaluation periods. [Figure 9C] A graph of relative tissue hemoglobin (ΔctHb) as a function of time is shown, displaying ΔctHb and reference variable "R" values ​​and recalibration flags for multiple evaluation periods. [Figure 10] To illustrate the with / without calibration, a THb vs. time graph (for the same period) is shown placed above a ΔctHb vs. time graph. DETAILED DESCRIPTION OF THE INVENTION

[0047] The present disclosure relates to a near-infrared spectroscopy (NIRS) system 20 and a method for noninvasively measuring circulating hemoglobin using a near-infrared spectroscopy (NIRS) sensing device 22, including logic for determining whether calibration is appropriate for such a system 20, when calibration can be performed, and techniques for performing such calibration. In some embodiments, the presently disclosed system 20 may be independent of or in communication with the NIRS sensing device 22. For example, the presently disclosed system 20 may be configured to use an independently operable NIRS sensing device 22 or may be configured to operate independently as a NIRS tissue oximeter. In these embodiments, the presently disclosed system 20 may be configured to input and receive signal data from the NIRS sensing device 22 and process the signal data according to the functionality described herein. In other embodiments of the present disclosure, the present system 20 and the NIRS sensing device 22 may be integrated with each other.

[0048] The NIRS sensing device 22 includes one or more transducers 24 and a system module that typically includes a display and a system controller 40, as described in detail herein. Each transducer is operable to transmit an optical signal into the subject's tissue and to sense the transmitted optical signal as it passes through the subject's tissue via transmission or reflection. Various NIRS sensing device types can be modified in accordance with aspects of the present disclosure, and thus the present disclosure is not limited to any particular type of NIRS sensing device.

[0049] Referring to FIG. 1, a NIRS sensing device 22 configured to sense brain tissue is diagrammatically shown. However, the present disclosure is not limited to applications to brain tissue. The NIRS sensing device 22 includes a system module 26 in communication with a pair of transducers 24 configured to be attached to a subject, such as the subject's forehead. FIG. 2 diagrammatically illustrates one of the transducers 24 applied to the skull. FIG. 3 diagrammatically illustrates an embodiment of the transducers 24 in a plan view. The transducers 24 include a transducer body 28 and may include a cable connector 30. A first connector cable 32A extends between the transducer body 28 and the cable connector 30. One or more second connector cables 32B extend between the cable connector 30 and the system module 26. In alternative embodiments, the cable connector 30 may be eliminated (e.g., one or more cables connect directly from the transducers 24 to the system module 26), or the transducers 24 may communicate with the system module 26 via wireless means. The transducer body 28 is typically a flexible structure that can be attached directly to the subject's body and includes one or more light sources and one or more photodetectors. The embodiment of the transducer 24 shown in Figures 2 and 3 includes a light source 34, a near photodetector 36, and a far photodetector 38, where the terms "near" and "far" refer to the relative distance from the light source 34. Disposable adhesive bandages or pads may be used to easily and securely attach the transducer body 28 to the subject's skin. The light source 34 may include, but is not limited to, a light-emitting diode ("LED") that emits light in a narrow spectral bandwidth at a predetermined wavelength. The photodetectors 36, 38 may each include one or more photodiodes or other photodetecting devices. Non-limiting examples of acceptable NIRS sensing device transducers 24 are described in U.S. Patent Nos. 9,988,873 and 8,428,674, both of which are commonly assigned to the assignee of the present application and are incorporated herein by reference in their entireties.

[0050] The system controller 40 may include any type of computing device, calculation circuit, or any type of process or processing circuit capable of executing a sequence of instructions stored in the memory device 42. The system controller 40 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. 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 file, system data, buffer, driver, utility, system program, etc.) as long as they are executable by the system controller 40. The instructions are configured to perform the methods and functions described herein. The system controller 40 is configured (e.g., via electrical circuitry) to process various received signals (e.g., received from the transducer 24) and to generate specific signals for the same transducer 24, such as signals configured to control the operation of the transducer 24.

[0051] Memory device 42 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 perform a particular function. Memory device 42 may be a single memory device or multiple memory devices. Memory device 42 may be a non-transitory device and may include a storage area network, network-attached storage, as well as 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. Based on a review of this disclosure, an artisan will understand that implementation of system controller 40 can be achieved using hardware, software, firmware, or any combination thereof.

[0052] In some embodiments, the disclosed system 20 may include one or more input devices and one or more output 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 (e.g., as shown in FIG. 1 ), a printer, or other device configured to display or communicate information or data generated by the system 20. The system 20 may be configured to connect to the input or output devices via a hardwired or wireless connection.

[0053] System controller 40 (or other controllers within system 20) may be adapted to determine blood parameter values, including oxygen saturation values ​​(which may be referred to as “SnO2,” “StO2,” “SctO2,” “CrSO2,” “rSO2,” etc.) and hemoglobin concentration values ​​(e.g., HbO2, Hb, THb, etc.). U.S. Patent Nos. 6,456,862, 7,072,701, 8,396,526, 8,923,943, 9,456,773, and 10,117,610, and PCT Publication No. WO2018 / 187510 (each of which is incorporated herein by reference in its entirety), each disclose methods for spectrophotometric blood parameter monitoring. The methods for determining blood parameters disclosed in U.S. Patent Nos. 6,456,862 and 7,072,701 represent acceptable examples of determining subject-independent NIRS tissue-blood parameter values. The methods disclosed in U.S. Patent Nos. 8,396,526, 8,923,943, 9,456,773, and 10,117,610 represent acceptable examples of determining NIRS tissue-blood parameter values ​​that take into account the specific physical properties of the particular subject's tissue being sensed, i.e., methods that build upon subject-independent algorithms such as those disclosed in U.S. Patent Nos. 6,456,862 and 7,072,701 to make them subject-dependent. PCT Patent Publication No. WO2018 / 187510 discloses a method and system for noninvasively measuring circulating hemoglobin, and U.S. Provisional Patent Application No. 63 / 218,684 discloses a method and system for noninvasively measuring circulating hemoglobin while accounting for hemodynamic confounding factors, both of which are commonly assigned to the present applicant. U.S. Provisional Patent Application No. 63 / 218,684 is incorporated herein by reference in its entirety. Aspects of the present disclosure include, but are not limited to, the methods described in the above-identified patents and applications. The disclosure described herein provides methods and techniques for modifying such methods or for use in conjunction with other NIRS techniques to enable the determination of NIRS circulating THb values.Embodiments of the present disclosure may provide significant additional utility over the methods and systems disclosed in the above-referenced patents and patent applications, as well as other methods and systems for noninvasively measuring circulating hemoglobin. Accordingly, the present disclosure is not limited to use with the methods and systems disclosed in the above-referenced patents and patent applications. Furthermore, as noted above, the present disclosure contemplates that the presently disclosed system 20 may be independent of, in communication with, or integrated with the NIRS sensing device 22. Accordingly, aspects of the functionality described herein may be performed in the present system 20 independently of, or integrated within, the NIRS sensing device 22, or any combination thereof.

[0054] The present disclosure relates to methods and systems for noninvasively measuring circulating hemoglobin using a NIRS sensing device 22, including logic for determining the presence of hemodynamic instability and / or hemodynamic changes in a subject's tissue. The presence of hemodynamic instability and / or changes can affect the accuracy of circulating hemoglobin measurements made using a NIRS sensing device. Hemodynamic changes can occur slowly over time or rapidly. The present disclosure provides embodiments of techniques and systems that facilitate noninvasive measurement of circulating hemoglobin parameters with improved accuracy. Aspects of the present disclosure include logic / techniques for determining whether and when calibration or recalibration of the NIRS system is appropriate (e.g., in light of hemodynamic instability and / or changes), and techniques for performing such calibration. Aspects of the present disclosure further include techniques for estimating BVF using NIRS data and blood gas data.

[0055] The NIRS sensing device 22 may be used to continuously and noninvasively determine a subject's tissue hemoglobin value (e.g., a relative tissue hemoglobin value, hereinafter referred to as "ΔctHb"). The term "continuously," as used herein (to describe that the NIRS sensing device 22 continuously senses), may mean that the NIRS sensing device 22 senses and collects subject data periodically during a monitoring period, where "periodic" is frequently enough to be considered clinically continuous. The term "relative tissue hemoglobin value" is used herein to refer to the change in tissue hemoglobin between time points (e.g., t1, t2, etc.). Various techniques are known and may be employed by the NIRS sensing device 22 to determine the relative tissue hemoglobin value. The patents and patent applications referenced above provide examples of techniques that may be used, but the present disclosure is not limited thereto.

[0056] The relative tissue hemoglobin can then be used to determine the continuous relative total blood hemoglobin (ΔTHb). A non-limiting example of how relative tissue hemoglobin (ΔctHb) can be used to determine the relative total blood hemoglobin (ΔTHb) is shown in Equation 1 below.

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[0057] In some embodiments, artificial intelligence (AI) / machine learning (ML) techniques, algorithmic techniques, etc., can be utilized in conjunction with empirical blood hemoglobin data to correlate NIRS relative tissue hemoglobin (ΔctHb) with circulating THb, for example, to determine an estimated BVF. As described herein, such correlations can be used to eliminate the need for initial calibration using circulating THb values. In some embodiments, the correlations can take the form of a calibration parameter (“k”). A non-limiting example of a method for determining such a calibration parameter is to collate (e.g., plot) and analyze circulating THb data and NIRS total tissue hemoglobin values. From the plotted data points, a trend line representing a best fit to the data points can be determined (e.g., using linear regression techniques). The trend line has slope and intercept values, which may be used to determine the calibration parameter. While embodiments that determine calibration parameters from plotted ΔctHb and circulating THb values ​​are used herein to illustrate how calibration parameters may be determined, the disclosure is not limited thereto. As discussed above, various techniques may be used to determine calibration parameters using empirical data points. The following equation is an example of how such calibration parameters may be used to determine relative total blood hemoglobin (ΔTHb) using relative tissue hemoglobin (ΔctHb): ΔTHb=ΔctHb*k (Equation 2) ΔTHb t =(ΔctHb t -ΔctHb t0 )*k (Equation 2A) PCT Publication No. WO2018 / 187510, disclosed above and incorporated by reference, provides examples of how empirical data can be used to determine calibration parameters.

[0058] Once the continuous relative blood total hemoglobin (ΔTHb) is determined, absolute total hemoglobin (THb) can be determined using the total blood hemoglobin value determined from the invasively collected blood sample. Figure 4 includes a first graph of the sensed relative tissue hemoglobin (ΔHb-μmol) as a function of time and a second graph of the absolute blood total hemoglobin (THb) determined as a function of time based on the relative tissue hemoglobin (ΔHb). (Alternatively, continuous relative blood total hemoglobin (ΔTHb) determinable from ΔHb may be used.) In this example, absolute total hemoglobin (THb) is first calibrated (at approximately the 10:00 time mark) using the total blood hemoglobin value determined from the invasively collected blood sample, as indicated by the dot at the beginning of the data plot. The conversion of relative blood total hemoglobin (ΔTHb) to absolute blood total hemoglobin may be determined using Equation 3. THb(t)=ΔTHb+THb(t0) (Equation 3) where THb(t0) is the total blood hemoglobin THb at the calibration point as shown in Figure 4, and ΔTHb is determined as described above. The above-described technique minimizes the invasive blood draw required and provides a means to provide continuous absolute blood total hemoglobin information primarily non-invasively.

[0059] In determining total hemoglobin (relative or absolute), some system embodiments may consider various factors (referred to herein as "oximetry features"). These oximetry features may include physiological characteristics of the subject (e.g., StO2 determined in a single frequency band or multiple frequency bands, tissue perfusion index or "TPI," ΔctHb, skin temperature, etc.), or intermediate features (e.g., tissue optical properties "TOP"), or analytically determined constants, e.g., C, that reflect the characteristics of an individual subject. n*StO2, etc.), or NIRS oximetry features (e.g., path length traveled by photons between the transducer light source and photodetector, optical density, gain, etc.), or statistical features (e.g., mean, mean, median, standard deviation, etc. of different data windows), including any combination thereof. Examples of tissue optical properties or "TOPs" include skin pigmentation, muscle and bone density, etc. These oximetry features can be described in the equation for total blood hemoglobin (absolute or relative). A non-limiting example of how oximetry features are described in the equation for absolute total blood hemoglobin (THb) is shown in Equation 4 below.

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[0060] Alternatively, in some embodiments, continuous absolute blood total hemoglobin information may be provided without using an initial total blood hemoglobin value determined from an invasively taken blood sample for calibration purposes.

[0061] FIG. 5 is a graph of continuous absolute blood total hemoglobin (THb) in grams per deciliter (g / dL) as a function of time. The g / dL scale (Y-axis) shown in FIG. 5 ranges from approximately 8 g / dL to approximately 12 g / dL. As with FIG. 4, the THb data shown in FIG. 5 is first calibrated using total blood hemoglobin values ​​determined from invasively collected blood samples. The calibrated THb data shown in FIG. 5 begins at approximately 8:40, where the calibration dot is located. The data shown in FIG. 5 also shows other invasively collected total blood hemoglobin values ​​at approximately 10:50, 11:40, and 12:35. The blood hemoglobin value at approximately 10:50 is also used for calibration. The THb data values ​​range from just under 8 g / dL to approximately 12 g / dL. The THb data shown in FIG. 5 may be generated using an equation such as that shown in Equation 4 above.

[0062] FIG. 5A is a graph of relative blood total hemoglobin (ΔTHb), also shown in units of grams per deciliter (g / dL) as a function of time. The ΔTHb data shown in FIG. 5A was generated from that shown in FIG. 5, this time without initial calibration. The g / dL scale (Y-axis) shown in FIG. 5A ranges from approximately 0 g / dL to approximately −4 g / dL. The ΔTHb data values ​​in FIG. 5A vary from just over 0 g / dL to approximately −4 g / dL. The ΔTHb data shown in FIG. 5A may be generated using a modification of Equation 4 above, as shown in Equation 4A below.

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[0063] As discussed above, the present disclosure includes calibration techniques to ensure that the generated absolute blood total hemoglobin information is free of errors or corruption, as well as techniques (e.g., based on AI / machine learning) for estimating BVF using NIRS data and blood gas data generated from invasive measurements.

[0064] The first calibration technique relates to indicating when calibration is appropriate to enable the NIRS sensing system to accurately provide noninvasive, continuous, absolute blood total hemoglobin information. The presence of certain factors can affect the accuracy of calibration. Therefore, the system 20 may be configured to identify the presence or absence of such factors and, if present, flag or prevent the user from performing calibration. For example, in a situation where the NIRS sensing device 22 generates relative tissue hemoglobin (ΔHb) data as a function of time, the generated relative tissue hemoglobin (ΔHb) data may be unstable, e.g., fluctuating beyond a predetermined threshold range. Under such circumstances, the disclosed system 20 may interpret the ΔHb instability as an indication that the relative tissue hemoglobin data is suspect. Figure 6 shows a graph of relative tissue hemoglobin (ΔHb) data as a function of time. For example, the technique may evaluate the ΔHb data within a rolling, predetermined window (e.g., a two-minute "evaluation" window). If the collected ΔctHb data fluctuates in magnitude outside a predetermined threshold range, the system 20 may flag the fluctuation to indicate that the ΔctHb data collected within the evaluation window should not be used for calibration purposes of the NIRS sensing system for absolute blood total hemoglobin. Figure 6 shows that the two-minute window between 11:24 and 11:26 is flagged.

[0065] In a second exemplary calibration technique, the present disclosure may utilize the NIRS sensing device 22 to determine a tissue oxygen saturation value (StO2). The tissue oxygen saturation value (StO2) generated by the NIRS sensing device 22 may be used to evaluate whether the transducer 24 of the NIRS sensing device 22 is properly positioned on the subject or to evaluate the performance of the transducer 24. FIG. 7 shows a ΔctHb vs. time graph positioned above a StO2 vs. time graph (for the same time period). Both graphs include a "1" line positioned at the top of each graph and a "0" line positioned at the bottom of each graph. The "1" line on the StO2 vs. time graph is an indication that the NIRS sensing data (i.e., StO2) is acceptable / valid, while the "0" line on the StO2 vs. time graph is an indication that the NIRS sensing data (i.e., StO2) is unacceptable / invalid. The "1" line on the ΔctHb vs. time graph is an indication that the ΔctHb data is not acceptable / valid for calibration. It should be noted that the StO2 value may be based on a raw signal having signal quality and variability, and in some embodiments, the acceptable / unacceptable characteristics of the StO2 and ΔctHb data may take into account the signal quality and variability of the raw signal.

[0066] In some embodiments, the StO2 sensed data may undergo further evaluation as a function of time. For example, StO2 data may naturally fluctuate from acceptable to unacceptable values ​​(e.g., due to system issues, sudden transient changes in StO2, signal quality / variability, etc.). These increases and decreases may occur infrequently or frequently and may be of varying duration. The further evaluation includes evaluating the increase or decrease in StO2 over an evaluation period. The further evaluation may consider the magnitude of the increase or decrease and / or the total duration of the increase or decrease within the evaluation period. For example, the total duration of unacceptable increases or decreases within a given evaluation period may be continuously evaluated against a predetermined overall threshold (e.g., a percentage of the evaluation window duration, such as 10%). If the total duration of unacceptable increases or decreases exceeds the overall threshold, all StO2 data collected up to that point in the evaluation window may be deemed unacceptable, and the corresponding ΔctHb data may be flagged as unacceptable for use in calibration. In some embodiments, once the overall threshold is reached, a new evaluation period may begin and the total duration of the unacceptable increase or decrease may be set to zero.

[0067] In a third exemplary calibration technique, the NIRS sensing device 22 may be used to determine tissue oxygen saturation values ​​(StO2). FIG. 8 shows a ΔctHb vs. time graph placed above a StO2 vs. time graph (for the same time period). In this example, the parameters (ΔctHb and StO2) are evaluated with respect to raw signal strength, which is plotted on the graph via a surrogate measure, such as the system's amplification gain. The raw signal strength is indicative of changes in the tissue being sensed by the NIRS sensing device 22 (e.g., changes in blood volume in the tissue, changes in blood oxygen saturation in the tissue, etc.) and may be used to determine the acceptability of the data for calibration purposes. Changes in the tissue can result from a variety of events, such as hemodilution caused by a cardiopulmonary bypass pump. The change in raw signal strength relative to a predetermined threshold may be used to determine whether the NIRS sensing data (i.e., StO2) is stable / acceptable or unstable / unacceptable / invalid. In Figure 8, the StO2 vs. time graph shows a first level of raw signal strength (via an amplification gain surrogate) indicated by a value of "30" on an arbitrary scale between just before 11:30 and approximately 11:33. At approximately 11:33, the StO2 vs. time graph shows a second level of raw signal strength (via an amplification gain surrogate) indicated by a value of "45" on an arbitrary scale for the time between just after 11:33 and 11:37. In this example, the ΔctHb vs. time graph (for the same period as the StO2 vs. time graph) indicates that a no calibration flag has been set (via a line placed at the "1" line between approximately 11:33 and approximately 11:34).

[0068] Some embodiments of the present disclosure may include a recalibration algorithm based on cumulative ΔctHb change, for example, ΔctHb stability / another metric of performance of the NIRS sensing device 22. The cumulative ΔctHb change may be based on cumulative ΔctHb variance data. The following are non-limiting examples of recalibration algorithms:

[0069] The algorithm may include a reference variable "R" that is initialized in step 1 below (eg, at the beginning of the THb monitoring period). R=ΔctHb(t0) (Equation 5) For each new tissue sample analysis, the algorithm accumulates ΔctHb deviation data about a second reference value, "CumDev," which was initialized to zero in Step 1. Equation 6 shows a non-limiting example of how the reference value, CumDev, may be set in Step 2. |ΔctHb(t n )-R|>5μM→CumDev= CumDev+(ΔctHb(t n )-R) (Equation 6) For each new tissue sample analysis, the algorithm may include evaluating the reference value CumDev to determine whether the cumulative ΔctHb deviation data represented by the reference value CumDev exceeds a predetermined threshold. If the cumulative ΔctHb deviation data represented by the reference value CumDev exceeds a predetermined threshold, a "recalibrate" flag may be raised. If the recalibrate flag is raised, the reference variable R may be reset as shown in Equation 5 above, and the reference value CumDev may be set to zero when the recalibrate flag is raised and the process begins again as described above. If the cumulative ΔctHb deviation data represented by the reference value CumDev does not exceed a predetermined threshold, the recalibrate flag is not raised. If a predetermined period of time (e.g., a five-minute evaluation period) has elapsed without the "recalibrate" flag being raised, the reference variable R is reset as shown in Equation 5 above, the reference value CumDev is set to zero at the end of the current evaluation period, and the process is repeated. FIG. 9A shows a graph of ΔctHb versus time, along with ΔctHb data and "R" values, for the first 5-minute evaluation period, and FIG. 9B shows the same graph, this time along with ΔctHb data and "R" values ​​for each of several 5-minute evaluation periods. Neither FIG. 9A nor FIG. 9B shows a recalibration flag. FIG. 9C is a graph of ΔctHb versus time, showing ΔctHb data and "R" values ​​for several 5-minute evaluation periods. At approximately 10:15, a recalibration flag is raised. When the recalibration flag is raised, the reference variable R is reset, the reference value CumDev is set to zero, and the process begins again. FIG. 9C shows ΔctHb data and "R" values ​​for several 5-minute evaluation periods after the recalibration flag is raised. In this manner, embodiments of the present disclosure maintain a continuous assessment of ΔctHb deviation. The above-described technique is one example of how ΔctHb deviation data may be monitored to identify when recalibration is warranted, and the disclosure is not limited to this example.

[0070] Another non-limiting example of a recalibration algorithm may utilize statistical parameters based on ΔctHb values ​​collected within an evaluation window that occurred during a period prior to the time point, e.g., ΔctHb values ​​collected within the previous "X" minutes. For example, the ΔctHb data collected within the evaluation window may be processed to determine a median value. The ΔctHb value at the time point may be evaluated using the determined median ΔctHb value and a threshold value. For example, the evaluation may determine the absolute value of the difference between the ΔctHb value at the time point and the median ΔctHb value and compare that difference to a threshold value, as shown in Equation 7 below. |ΔctHb 電流 - Median ΔctHb 評価ウィンドウ |>Threshold (Equation 7) If the absolute difference between the current ΔctHb value and the median ΔctHb exceeds a threshold, a "recalibrate" flag may be set.

[0071] In some embodiments, the algorithms described above may be configured to select corrective actions other than or in addition to raising a "recalibrate" flag. The present disclosure is not limited to the recalibration algorithms described above.

[0072] In some embodiments, the present disclosure may include calibration algorithms that utilize machine learning or other artificial intelligence techniques. In these embodiments, the function "f" may be used to represent a multivariate machine learning model that uses oximetry data. Equation 8 represents a general function that represents this approach. THb(t) = BG(t0) + f(oximetry data) (Equation 8) The variable "BG" represents the THb value obtained using a technique such as a blood gas analyzer (or the like). The term "oximetry data" is defined above. A more specific example of an algorithm that can be used is shown in Equation 9. THb(t)=BG(t0)+(ΔctHb(t)-ΔctHb(t0))*k (Equation 9) The variable "ΔctHb(t)" represents the relative tissue hemoglobin concentration at the time of calibration, for example, the calibration described above using blood gas THb. The variable "k" represents the correlation coefficient (described above), which may be calculated using a linear regression method using a machine learning training dataset. The machine learning training dataset contains a clinically significant amount of clinical data.

[0073] Algorithms utilized in machine learning may be developed in a variety of different ways. As an example, in a first step, a training dataset containing a clinically significant amount of clinical data may be divided into a training dataset portion and one or more test / validation dataset portions, such as a training dataset portion, a cross-validation dataset portion, and a final validation dataset portion.

[0074] The second step in the algorithm development process may involve selecting a training approach for developing the THb computational model. A first example of a training approach that may be used is a direct approach that estimates the change in THb since the last device calibration, e.g., functionally represented in Equation 10: BG (t) -BG (t0) = f(oximetry data) (Equation 10) A second and third example of a training approach that can utilize a boosting approach is, for example, an approach that estimates the error of the model represented by Equation 9, which is represented by Equation 11 below. BG (t) -BG (t0) -(k*(ΔctHb (t) -ΔctHb (t0) )= f(oximetry data) (Equation 11) Alternatively, one approach is to estimate the error of a multivariate model, as expressed in Equation 12 below. BG (t) -BG (t0) -modelX=f(oximetry data) (Equation 12) where the variable "modelX" represents the multivariate model. The present disclosure is not limited to the above example training approaches.

[0075] As indicated above, "oximetry data" may include a variety of different data types that may be considered in the development of machine learning algorithms. AI / ML techniques (e.g., correlation, linear regression, coherence, decision trees, etc.) may be used to identify the most appropriate oximetry data type. Once applicable oximetry data has been identified and further identified as being most suitable for machine learning, deeper algorithmic formulations, such as those shown in Equation 4 above, may be used.

[0076] FIG. 10 shows a THb vs. time graph placed over a ΔctHb vs. time graph (for the same time period) to illustrate calibration versus no calibration. The ΔctHb vs. time graph includes a first continuous line 44 depicting "ΔctHb LB" (where "LB" refers to NIRS data acquired from the left hemisphere of the subject's brain) and a second continuous line 46 depicting "ΔctHb RB" (where "RB" refers to NIRS data acquired from the right hemisphere of the subject's brain). The ΔctHb vs. time graph also includes a marker "X" identifying an indication of "no calibration" and a marker "*" identifying an indication of recalibration. The THb vs. time graph shown in FIG. 10 includes a line 48 depicting THb data, a marker 50 indicating the blood gas (BG)-derived THb value, and a marker 52 indicating the blood gas (BG)-derived THb value used for calibration. The data shown in the graph of FIG. 10 includes a region of initial noise at approximately 13:00 and a region of rapid instability (e.g., due to cardiopulmonary bypass, etc.) just before 15:30. As shown in the THb versus time graph, a blood gas (BG)-derived THb value (labeled 50) is provided just after 13:00, but is not used for calibration due to unstable ΔctHb data at this time. Shortly thereafter, another blood gas (BG)-derived THb value (labeled 52) is provided to be used for calibration. Just before 15:30 on the ΔctHb versus time graph, a recalibration flag 54 is indicated. At approximately 15:30 on the THb versus time graph, a blood gas (BG)-derived THb value (labeled 50) is provided, but is not used for calibration due to unstable ΔctHb data at that time. Shortly after 15:30, another blood gas (BG)-derived THb value (labeled 52) is provided to be used for recalibration. The subsequent THb data is in good agreement with the blood gas (BG) derived THb value (labeled 50) from 16:30 onwards. The ΔctHb versus time graph includes an "X" (labeled 56) which is used to indicate no calibration, i.e., a situation where calibration should not be performed at that time according to the present disclosure.

[0077] As noted above, the functionality described herein may be implemented, for example, in hardware, software tangibly embodied in a computer-readable medium, firmware, or any combination thereof. In some embodiments, at least a portion of the functionality described herein may be implemented in one or more computer programs. Each such computer program may be implemented in a computer program product tangibly embodied in a non-transitory signal in a machine-readable storage device for execution by a computer processor. The method steps of the present disclosure may be performed by a computer processor executing a program tangibly embodied on a computer-readable medium, performing operations on inputs and generating outputs to perform the functions of the present disclosure. Each computer program within the scope of the present claims may be implemented in any programming language, such as assembly language, machine language, a high-level procedural programming language, or an object-oriented programming language. The programming language may be, for example, a compiled or interpreted programming language.

[0078] While the present invention has been described with reference to exemplary embodiments, those skilled in the art will recognize that various modifications may be made and equivalents substituted for elements thereof without departing from the scope of the invention. For example, the term "total hemoglobin" is described herein as the sum of HbO2 and Hb. The present disclosure contemplates embodiments in which the total hemoglobin value may include contributions from one or more other types of hemoglobin, such as carboxyhemoglobin (COHb), methemoglobin (MetHb), etc. Additionally, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is not intended that the invention be limited to the particular embodiments disclosed, but rather that the invention will include all embodiments falling within the scope of the appended claims.

[0079] It should be noted that the embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a block diagram, or the like. While any one of these structures may describe operations as a sequential process, many of the 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, or the like.

[0080] 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 an analyte" includes one or more analytes and is considered equivalent to the phrase "comprising at least one analyte." 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.

[0081] It should be noted that various connections are shown between elements in the description and drawings herein, the contents of which are incorporated by reference into this disclosure. It should be noted that these connections are general and, unless otherwise specified, may be direct or indirect, and that the 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.

[0082] No element, component, or method step in this disclosure is intended to be publicly disclosed, regardless of whether that element, component, or method step is expressly recited in a claim. No element of any claim described herein is to be construed under 35 U.S.C. 112(f) unless that element is 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 that includes a list of elements does not include only those elements, but may include elements not expressly listed and other elements not inherent in such process, method, article, or apparatus.

[0083] While various inventive aspects, concepts, and features in this disclosure may be described and shown as 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 this application. Furthermore, although various alternative embodiments (alternative structures, configurations, methods, devices, components, etc.) of 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 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 and applications within the scope of this application, even in embodiments not explicitly disclosed herein. For example, in the example embodiments described above, within the detailed description section of this specification, elements may be described as individual units and shown separately from each other for ease of description. In alternative embodiments, such elements may be configured as combined elements.

[0084] Furthermore, while some features, concepts, or aspects of the disclosure may be described herein as preferred configurations or methods, such description does not imply that the feature is necessary or essential unless expressly stated. Furthermore, to aid in understanding the present application, example or representative values ​​and ranges may be included, but such values ​​and ranges should not be construed in a limiting sense, and only when explicitly stated are significant values ​​or ranges intended.

[0085] The treatment techniques, methods, and steps described or suggested herein or incorporated by reference may be performed in a non-biological simulation, such as a live animal or cadaver, a cadaver heart, an anthropomorphic ghost, or a simulator (e.g., where body parts or tissues are simulated).

[0086] 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 non-invasively determining continuous total hemoglobin data, comprising: sensing tissue of the subject continuously using a near-infrared spectroscopy (NIRS) sensing device, wherein a NIRS signal is generated from the sensing; determining continuous total hemoglobin (THb) data using the generated NIRS signal; A method comprising:

2. The method of claim 1 , wherein the continuous THb data is continuous relative THb (ΔTHb) data.

3. The method of claim 1 , wherein the continuous THb data is continuous absolute THb data.

4. 4. The method of claim 3, wherein determining continuous absolute THb data comprises calibrating using a reference absolute THb value obtained from the subject.

5. 5. The method of claim 4, wherein the baseline absolute THb value is obtained non-invasively from the subject.

6. 5. The method of claim 4, wherein the reference absolute THb value is obtained from an invasively taken blood sample from the subject.

7. The method of claim 4 , further comprising providing instructions for calibrating the NIRS sensing device based on the NIRS signal.

8. 8. The method of claim 7, wherein the step of providing the instruction to perform the calibration of the NIRS sensing device is based on a determination of the acceptability of an NIRS signal for purposes of the determination of the continuous absolute THb data.

9. The method of claim 8 , wherein the determining the acceptability of the NIRS signal comprises evaluating the NIRS signal over a predetermined period of time to determine stability of the NIRS signal.

10. 10. The method of claim 8, wherein the determining the acceptability of the NIRS signal comprises evaluating the NIRS signal over a predetermined period of time to determine hemodynamic stability of the sensed tissue.

11. 10. The method of claim 8, wherein the determining the acceptability of the NIRS signal comprises evaluating the NIRS signal over a predetermined period of time to determine hemodynamic changes in the sensed tissue.

12. The method of claim 1 , wherein determining the continuous THb data includes using oximetry features based on the NIRS signal.

13. 13. The method of claim 12, wherein the oximetry features based on the NIRS signal include continuous relative tissue hemoglobin (ΔctHb) data.

14. 13. The method of claim 12, wherein the oximetry features based on the NIRS signal include at least one of skin temperature, a path length traveled by photons between a NIRS transducer light source and a NIRS transducer photodetector, deoxygenated tissue hemoglobin, oxygenated tissue hemoglobin, or tissue oxygen saturation.

15. The method of claim 1 , further comprising evaluating the NIRS signal to determine its acceptability for purposes of the determination of the continuous THb data.

16. 16. The method of claim 15, wherein the evaluating step to determine the acceptability of the NIRS signal comprises evaluating the NIRS signal over a predetermined period of time to determine stability of the NIRS signal.

17. 16. The method of claim 15, wherein the evaluating step to determine the acceptability of the NIRS signal comprises evaluating the NIRS signal over a predetermined period of time to determine hemodynamic stability of the sensed tissue.

18. 16. The method of claim 15, wherein the evaluating step to determine the acceptability of the NIRS signal comprises evaluating the NIRS signal over a predetermined period of time to determine hemodynamic changes in the sensed tissue.

19. The method of claim 1 , further comprising estimating a blood volume fraction (BVF) of the sensed tissue.

20. The method of claim 1 , wherein determining the continuous THb utilizes a trained machine learning method.

21. The method of claim 1 , further comprising providing an indication based on the NIRS signal that calibration of the NIRS sensing device is permissible.

22. 22. The method of claim 21, wherein the indication that the calibration of the NIRS sensing device is permissible is based at least in part on stability of the NIRS signal.

23. 23. The method of claim 22, wherein the stability of the NIRS signal is determined by evaluating the NIRS signal generated over a predetermined period of time.

24. 23. The method of claim 22, wherein the NIRS signal is in raw signal form.

25. 22. The method of claim 21, wherein the indication that the calibration of the NIRS sensing device is permissible is based on stability of a parameter determined using the NIRS signal.

26. 26. The method of claim 25, wherein the parameter is tissue oxygen saturation (StO2).

27. 26. The method of claim 25, wherein the parameter is relative tissue hemoglobin (ΔctHb).

28. 1. A system for determining continuous total hemoglobin data from a subject, the system comprising: a near-infrared spectroscopy (NIRS) sensing device configured to sense a tissue region of the subject and generate a NIRS signal from the sensing; a controller in communication with the NIRS sensing device, the controller including at least one processor and a memory device configured to store instructions that, when executed, cause the controller to: controlling the NIRS sensing device to continuously sense tissue of the subject and generate an NIRS signal from the sensing; determining continuous total hemoglobin (THb) data using the generated NIRS signal; system.

29. 29. The system of claim 28, wherein the continuous THb data is continuous relative total hemoglobin (ΔTHb).

30. 29. The system of claim 28, wherein the continuous THb data is continuous absolute THb.

31. 31. The system of claim 30, wherein the instructions, when executed, cause the controller to evaluate the NIRS signal to determine its acceptability for purposes of the determination of the continuous absolute THb data.

32. 32. The system of claim 31, wherein the evaluating the NIRS signal to determine the acceptability of the NIRS signal comprises evaluating the NIRS signal over a predetermined period of time to determine stability of the NIRS signal.

33. 32. The system of claim 31, wherein evaluating the NIRS signal to determine acceptability of the NIRS signal comprises evaluating the NIRS signal over a predetermined period of time to determine hemodynamic stability of the sensed tissue, or hemodynamic changes in the sensed tissue, or both.

34. 30. The system of claim 28, wherein the instructions, when executed, cause the controller to provide instructions to calibrate the NIRS sensing device based on the NIRS signal.

35. 35. The system of claim 34, wherein the instruction to perform the calibration of the NIRS sensing device is based on a determination of acceptability of the NIRS signal for determining the continuous THb data.

36. 36. The system of claim 35, wherein determining the acceptability of the NIRS signal comprises evaluating the NIRS signal over a predetermined period of time to determine stability of the NIRS signal.

37. 36. The system of claim 35, wherein evaluating the NIRS signal to determine acceptability of the NIRS signal comprises evaluating the NIRS signal over a predetermined period of time to determine hemodynamic stability of the sensed tissue, or hemodynamic changes in the sensed tissue, or both.

38. 30. The system of claim 28, wherein the determination of continuous THb utilizes a trained machine learning method.

39. 30. The system of claim 28, wherein the instructions, when executed, cause the controller to provide an indication indicating that calibration of the NIRS sensing device is permissible based on the NIRS signal.

40. 30. The system of claim 28, wherein the NIRS sensing device is configured to operate independently from the controller, and the NIRS sensing device and the controller are independent of each other.

41. 30. The system of claim 28, wherein the NIRS sensing device and the controller are integral.

42. 1. A non-transitory computer readable medium comprising software code portions adapted to perform a method for non-invasively determining continuous relative total hemoglobin data, the method comprising: continuously controlling a near-infrared spectroscopy (NIRS) sensing device to sense tissue of the subject, said sensing producing an NIRS signal; determining continuous relative total hemoglobin (ΔTHb) using the generated NIRS signal; 1. A non-transitory computer-readable medium, comprising:

43. 1. A non-transitory computer readable medium comprising software code portions adapted to perform a method for non-invasively determining continuous absolute total hemoglobin data, the method comprising: continuously controlling a near-infrared spectroscopy (NIRS) sensing device to sense tissue of the subject, said sensing producing an NIRS signal; and determining continuous absolute total hemoglobin (THb) using the generated NIRS signal. Non-transitory computer-readable medium.

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