Method and device for non-invasive measurement of blood circulating hemoglobin

Abstract

A method and system for non-invasively determining continuous total hemoglobin data is provided herein. The method comprises a) continuously sensing tissue of a subject using a near infrared spectroscopy (NIRS) sensing device, the sensing resulting in an NIRS signal; and b) determining a continuous total hemoglobin (THb) using the generated NIRS signal.

Description

Technical Field

[0001] The present invention generally relates to methods and devices for determining blood circulation hemoglobin values, and more particularly to non-invasive methods and devices for determining blood circulation hemoglobin values using performance diagnostics.

[0002] Background Information

[0003] The molecule that carries oxygen in the blood is hemoglobin. Oxygenated hemoglobin (i.e., oxyhemoglobin or HbO2) and deoxygenated hemoglobin (i.e., deoxyhemoglobin or Hb) are the main types of hemoglobin present in the blood, but the 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 HbO 2 and Hb, and is proportional to the relative blood volume change, provided that the hematocrit or hemoglobin concentration of the blood remains constant.

[0004] Near-infrared spectroscopy (NIRS) is an optical spectroscopy method for continuously monitoring tissue parameters (e.g., oxygen saturation, hemoglobin level, etc.) to calculate clinical value parameters, which does not require pulsatile blood volume. NIRS spectroscopy is based on the principle that light in the near-infrared range (700 to 1,000 nm) can easily penetrate the skin, bone, and other tissues, where the light encounters hemoglobin mainly located within the microcirculation channels (e.g., capillaries, arterioles, and venules). Hemoglobin exposed to light in the near-infrared range has a specific absorption spectrum that varies according to its oxidation state. Oxygenated hemoglobin (HbO 2 ) and deoxygenated hemoglobin (Hb) each act as different chromophores. By using a light source that transmits near-infrared light at specific different wavelengths and measuring the change in the attenuation of the transmitted or reflected light, the concentration changes of oxygenated hemoglobin (HbO 2 ) and deoxygenated hemoglobin (Hb) and the oxygen saturation within the tissue can be monitored. U.S. Patent Nos. 6,456,862; 7,072,701; 8,078,250 describe NIRS spectroscopy devices and methods, and each of these patents is hereby incorporated by reference in its entirety.

[0005] A NIRS tissue oximeter can provide a non-invasively 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 sensed within the tissue (which volume may vary over time). Using an optically based sensor placed on the subject's skin, a NIRS tissue oximeter can be used to probe the tissue with light of different wavelengths (e.g., emit light into the tissue and detect the light emitted from the tissue), and then process the detected light to calculate the total hemoglobin value of the tissue, and if desired, also calculate the tissue oxygen saturation (StO 2 ) value. For example, the sensor portion of a NIRS tissue oximeter placed on a subject's forehead can be used to spectrophotometrically probe the subject's brain tissue and then determine the total hemoglobin and tissue oxygen saturation (StO2) values of the subject's brain tissue.

[0006] Historically, the circulating hemoglobin value (i.e., the hemoglobin value representing the hemoglobin within the circulating blood) has been determined using invasively drawn blood samples. An invasively drawn blood sample can be analyzed using a CO-oximeter or a blood gas analyzer. A CO-oximeter is a device that can be operated to measure one or more types of hemoglobin present within a blood sample; for example, HbO 2 , Hb, carboxyhemoglobin (COHb), methemoglobin (MetHb), etc. Most CO-oximeters are spectrophotometric measurement devices that can be operated to determine the presence and amount of the corresponding type of hemoglobin (e.g., HbO 2 , Hb, COHb, MetHb, etc.) within an invasively drawn blood sample by measuring the absorption of light passing through the blood sample at specific wavelengths. The relative absorption amounts at different wavelengths enable the measurement of the corresponding type of hemoglobin present within the blood sample. In contrast, most blood gas analyzers are electrochemical type analysis devices that use electrodes and changes in current or potential to detect and measure the components within an invasively drawn blood sample.

[0007] The main difference between existing NIRS tissue oximeters and CO oximeters or blood gas analyzers is that NIRS tissue oximeters are configured to determine parameter values (e.g., hemoglobin, oxygen saturation, etc.) within tissue, while CO oximeters or blood gas analyzers are configured to determine the same parameter values within a circulating blood sample (i.e., an invasively collected blood sample). Using total hemoglobin as an example parameter, the total hemoglobin value within tissue determined using an existing NIRS tissue oximeter may be affected by various 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, congestion, etc. The total hemoglobin value of a circulating blood sample determined using a CO oximeter or blood gas analyzer will not be affected by these physiological parameters, but requires an invasive collection step. In addition, invasive blood sampling for analysis purposes is typically performed periodically; for example, blood is sampled and then analyzed. Thus, the information obtainable from the blood is periodic rather than continuous. In contrast, continuous hemoglobin monitoring can provide an enhanced ability to identify rising or falling trends in blood components, as well as the accompanying ability to address such trends when necessary. Additionally, a stable trend in a blood component such as hemoglobin can provide continuous information indicative of a normal state, which can provide reassurance to a clinician.

[0008] What is needed is a method and device operable to non-invasively determine blood parameters (e.g., hemoglobin, etc.) that overcome the problems associated with current non-invasive techniques for determining blood parameters. Summary of the Invention

[0009] According to one aspect of the present disclosure, a method for non-invasively determining continuous total hemoglobin (THb) data is provided. The method includes a) continuously sensing tissue of a subject using a near-infrared spectroscopy (NIRS) sensing device, wherein an NIRS signal is generated from the sensing; and b) determining continuous total hemoglobin (THb) data using the generated NIRS signal.

[0010] 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.

[0011] 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.

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

[0013] In any aspect or embodiment described above and herein, the reference absolute THb value can be obtained from a blood sample invasively collected from the subject.

[0014] In any aspect or embodiment described above and herein, the method can further include providing an indication to perform calibration of the NIRS sensing device based on the NIRS signal.

[0015] In any aspect or embodiment described above and herein, the step of providing the indication to perform calibration of the NIRS sensing device can be based on a determination of the acceptability of the NIRS signal in order to perform the determination of the continuous absolute THb data.

[0016] In any aspect or embodiment described above and herein, the determination of the acceptability of the NIRS signal can include evaluating the NIRS signal within a predetermined time period to determine the stability of the NIRS signal.

[0017] In any aspect or embodiment described above and herein, the determination of the acceptability of the NIRS signal can include evaluating the NIRS signal within a predetermined time period to assess the hemodynamic stability of the sensed tissue.

[0018] In any aspect or embodiment described above and herein, the determination of the acceptability of the NIRS signal can include evaluating the NIRS signal within a predetermined time period to assess hemodynamic changes in the sensed tissue.

[0019] In any aspect or embodiment described above and herein, the step of determining the continuous THb data can include using the oximetry characteristics based on the NIRS signal.

[0020] In any aspect or embodiment described above and herein, the oximetry characteristics based on the NIRS signal can include continuous relative tissue hemoglobin (ΔctHb) data.

[0021] In any aspect or embodiment described above and herein, the oximetry characteristics based on the NIRS signal include at least one of the following: skin temperature, the path length that photons travel between the NIRS transducer light source and the NIRS transducer light detector, deoxygenated tissue hemoglobin, oxygenated tissue hemoglobin, or tissue oxygen saturation.

[0022] In any aspect or embodiment described above and herein, the method can further include evaluating the NIRS signal to determine the acceptability of the NIRS signal in order to determine the continuous THb data.

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

[0024] In any of the aspects or embodiments described above and herein, the evaluation step of determining the acceptability of the NIRS signal may include evaluating the NIRS signal within a predetermined time period to assess the hemodynamic stability of the sensed tissue.

[0025] In any of the aspects or embodiments described above and herein, the evaluation step of determining the acceptability of the NIRS signal includes evaluating the NIRS signal within a predetermined time period to assess the hemodynamic changes of the sensed tissue.

[0026] 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.

[0027] In any of the aspects or embodiments described above and herein, the step of determining the continuous THb may utilize a trained machine learning method.

[0028] In any of the aspects or embodiments described above and herein, the method may further include providing an indication based on the NIRS signal indicating that calibration of the NIRS sensing device is permissible.

[0029] In any of the aspects or embodiments described above and herein, the indication indicating that calibration of the NIRS sensing device is permissible may be at least partially based on the stability of the NIRS signal.

[0030] 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 within a predetermined time period.

[0031] In any of the aspects or embodiments described above and herein, the NIRS signal may be in the form of a raw signal.

[0032] In any of the aspects or embodiments described above and herein, the indication indicating that calibration of the NIRS sensing device is permissible may be based on the stability of parameters (such as tissue oxygen saturation (StO2), relative tissue hemoglobin (ΔctHb), etc.) determined using the NIRS signal.

[0033] 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. The instructions, when executed, cause the controller to perform the following operations: a) continuously control the NIRS sensing device to 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.

[0034] In any aspect or embodiment described above and herein, the instructions, when executed, cause the controller to evaluate the NIRS signal to determine the acceptability of the NIRS signal for the determination of the continuous absolute THb data.

[0035] In any aspect or embodiment described above and herein, evaluating the NIRS signal to determine the acceptability of the NIRS signal may include evaluating the NIRS signal within a predetermined time period to determine the stability of the NIRS signal.

[0036] In any aspect or embodiment described above and herein, evaluating the NIRS signal to determine the acceptability of the NIRS signal may include evaluating the NIRS signal within a predetermined time period to assess the hemodynamic stability of the sensed tissue or the hemodynamic changes of the sensed tissue or both.

[0037] In any aspect or embodiment described above and herein, the instructions, when executed, may cause the controller to provide an indication to perform calibration of the NIRS sensing device based on the NIRS signal.

[0038] In any aspect or embodiment described above and herein, the indication to perform calibration of the NIRS sensing device may be based on the determination of the acceptability of the NIRS signal to determine the continuous THb data.

[0039] In any aspect or embodiment described above and herein, the determination of the acceptability of the NIRS signal may include evaluating the NIRS signal within a predetermined time period to determine the stability of the NIRS signal.

[0040] In any aspect or embodiment described above and herein, evaluating the NIRS signal to determine the acceptability of the NIRS signal may include evaluating the NIRS signal within a predetermined time period to assess the hemodynamic stability of the sensed tissue or the hemodynamic changes of the sensed tissue or both.

[0041] In any aspect or embodiment described above and herein, the determination of continuous THb can utilize a trained machine learning method.

[0042] In any aspect or embodiment described above and herein, when executed, the instruction can cause the controller to provide an indication based on the NIRS signal that calibration of the NIRS sensing device is permissible.

[0043] In any aspect or embodiment described above and herein, the NIRS sensing device can be configured to operate independently of the controller, and the NIRS sensing device and the controller can be independent of each other.

[0044] In any aspect or embodiment described above and herein, the NIRS sensing device and the controller can be integrated.

[0045] According to another aspect of the present disclosure, there is provided a non-transitory computer-readable medium that includes software code segments adapted to execute a method for non-invasively determining continuous relative total hemoglobin (ΔTHb) data. The method includes the following steps: a) continuously controlling a near-infrared spectroscopy (NIRS) sensing device to sense tissue of a subject, the sensing generating a NIRS signal; and b) using the generated NIRS signal to determine continuous relative total hemoglobin (ΔTHb).

[0046] Unless otherwise explicitly indicated, the foregoing features and elements can be combined in various combinations without exclusivity. These features and elements and their operations will become more apparent from the following description and drawings. However, it should be understood that the following description and drawings are intended to be exemplary in nature and non-limiting. Description of the Drawings

[0047] Figure 1 is a schematic diagram of a NIRS sensing device applying a sensing transducer to the head of a subject.

[0048] Figure 2 is a schematic diagram of applying a NIRS sensing device transducer to the head of a subject.

[0049] Figure 3 is a planar schematic diagram of a NIRS sensing device transducer.

[0050] Figure 4 shows a first graph of the relative tissue hemoglobin value (ΔctHb) changing over time, and a second graph of the total hemoglobin (tHb) and continuous total hemoglobin (THb) of a blood sample changing over time, where each graph shows the corresponding value changing over the same time period.

[0051] Figure 5A graph showing the change in absolute total hemoglobin (THb) over time in the case of initial calibration is presented.

[0052] Figure 5A A graph showing the change in relative total hemoglobin (ΔTHb) over time without initial calibration is presented.

[0053] Figure 6 A graph showing the change in relative tissue hemoglobin value (ΔctHb) over time, indicating a two-minute window of marked data, is presented.

[0054] Figure 7 A first graph showing the change in relative tissue hemoglobin (ΔctHb) over time and a second graph showing the change in tissue oxygen saturation (StO2) over time are presented, where each graph shows the change in the corresponding value over the same time period.

[0055] Figure 8 A first graph showing the change in relative tissue hemoglobin (ΔctHb) over time and a second graph showing the change in tissue oxygen saturation (StO2) over time are presented, where each graph shows the change in the corresponding value over the same time period.

[0056] Figure 9A A graph showing the change in relative tissue hemoglobin (ΔctHb) over time, showing the values of ΔctHb and the reference variable "R" within a single evaluation period, is presented.

[0057] Figure 9B A graph showing the change in relative tissue hemoglobin (ΔctHb) over time, showing the values of ΔctHb and the reference variable "R" over multiple evaluation periods, is presented.

[0058] Figure 9C A graph showing the change in relative tissue hemoglobin (ΔctHb) over time, showing the values of ΔctHb and the reference variable "R" over multiple evaluation periods as well as recalibration markers, is presented.

[0059] Figure 10 A graph of THb versus time is presented, placed above the graph of ΔctHb versus time (same time period) to illustrate calibration / non - calibration. Detailed Description

[0060] The present disclosure relates to a near-infrared spectroscopy (NIRS) system 20 and a method for non-invasively measuring circulating hemoglobin using a near-infrared spectroscopy (NIRS) sensing device 22, including logic for determining whether a calibration is suitable for such a system 20, when a calibration can be performed, and techniques for performing such a calibration. In some embodiments, the system 20 of the present disclosure can be independent of and communicate with the NIRS sensing device 22. For example, the system 20 of the present disclosure can be configured to use the NIRS sensing device 22, which can operate independently and is configured to operate independently as a NIRS tissue oximeter. In these embodiments, the system 20 of the present disclosure can be configured to input and receive signal data from the NIRS sensing device 22 and process the signal data according to the functions described herein. In other embodiments of the present disclosure, the system 20 of the present disclosure and the NIRS sensing device 22 can be integrated with each other.

[0061] The NIRS sensing device 22 includes one or more transducers 24 and a system module, which generally includes a display and a system controller 40, as will be described in detail herein. Each transducer is capable of being operated to transmit an optical signal into a subject's tissue and, once the optical signal has passed through the subject's tissue by transmission or reflection, is capable of sensing the transmitted optical signal. According to aspects of the present disclosure, various types of NIRS sensing devices can be modified, and thus the present disclosure is not limited to any particular type of NIRS sensing device.

[0062] Reference Figure 1 , the NIRS sensing device 22 is shown diagrammatically and is configured to sense brain tissue. However, the present disclosure is not limited to brain tissue applications. The NIRS sensing device 22 includes a system module 26 in communication with a pair of transducers 24, which are configured to be attached to a subject; for example, on the subject's forehead. Figure 2 One of the transducers 24 applied to the skull is shown diagrammatically. Figure 3 An embodiment of the transducer 24 in a plan view is shown diagrammatically. The transducer 24 includes 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 can be eliminated (e.g., one or more cables are directly connected from the transducer 24 to the system module 26), or the transducer 24 can communicate with the system module 26 wirelessly. The transducer body 28 is generally a flexible structure that can be directly attached to a subject's body and includes one or more light sources and one or more light detectors. Figure 2 and Figure 3The transducer 24 embodiment shown includes a light source 34, a near-light detector 36, and a far-light detector 38, where the terms "near" and "far" indicate the relative distance from the light source 34. The transducer body 28 can be easily and firmly mounted to the subject's skin using a disposable adhesive envelope or pad. The light source 34 can include, but is not limited to, a light-emitting diode ("LED") that emits light with a narrow spectral bandwidth at a predetermined wavelength. The light detectors 36, 38 can each include one or more photodiodes or other light-detecting 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 both of which are hereby incorporated by reference in their entireties.

[0063] The system controller 40 can include any type of computing device, computing circuit, or any type of process or processing circuit capable of executing a series of instructions stored in the memory device 42. The system controller 40 can include multiple processors and / or multi-core CPUs, and can 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, etc. and any combination thereof. The instructions stored in the memory can represent one or more algorithms for controlling the system 20, and the stored instructions are not limited to any particular form (e.g., program files, system data, buffers, drivers, utilities, system programs, etc.), as long as they can be executed by the system controller 40. These instructions are configured to perform the methods and functions described herein. The system controller 40 can be configured (e.g., via circuitry) to process various received signals (e.g., received from the transducer 24), and can be configured to generate certain signals thereto; for example, signals configured to control the operation of the transducer 24.

[0064] The memory device 42 can 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 certain functions or cause the performance of certain functions. The memory device 42 can be a single memory device or multiple memory devices. The memory device 42 can be a non-transitory device and can include a storage area network, network-attached storage device, and 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 the present disclosure, those skilled in the art should understand that the implementation of the system controller 40 can be achieved via the use of hardware, software, firmware, or any combination thereof.

[0065] In some embodiments, the system 20 of the present disclosure may include one or more input devices and one or more output devices. Non-limiting examples of input devices include a keyboard, a touchpad, or other devices through which a user may input data, commands, or signal information, or ports configured to communicate with an external input device via a hardwired or wireless connection, etc. Non-limiting examples of output devices include any type of display (e.g., as shown in Figure 1 ), a printer, or other devices configured to display or convey information or data generated by the system 20. The system 20 may be configured to connect to an input device or an output device via a hardwired connection or a wireless connection.

[0066] The system controller 40 (or other controller within the system 20) may be adapted to determine blood parameter values, including oxygen saturation values (which may be referred to as "SnO 2 ", "StO 2 ", "SctO 2 ", "CrSO 2 ", "rSO 2 ", etc.) and hemoglobin concentration values (e.g., HbO 2, 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. WO 2018 / 187510 (each of which is hereby incorporated by reference in its entirety) each disclose methods for spectroscopic 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 non-subject-dependent 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 methods for determining NIRS tissue blood parameter values that take into account the specific physical characteristics of the tissue of a particular subject being sensed; i.e., methods based on non-subject-dependent algorithms, such as the algorithms disclosed in U.S. Patent Nos. 6,456,862 and 7,072,701, to make them subject-dependent. PCT Patent Publication No. WO 2018 / 187510 discloses a method and system for non-invasively measuring circulating hemoglobin, and U.S. Provisional Patent Application No. 63 / 218,684 discloses a method and system for non-invasively measuring circulating hemoglobin that takes into account hemodynamic confounding factors - both are commonly assigned to the applicant of the present application. U.S. Provisional Patent Application No. 63 / 218,684 is hereby incorporated by reference in its entirety. Aspects of the present disclosure may include, but are not limited to, the methods described in the above patents and applications. The present disclosure described herein provides methods and techniques for modifying such methods or using them in conjunction with other NIRS methods to enable determination of NIRS circulating THb values. Embodiments of the present disclosure may provide significant additional utility for the methods and systems disclosed in the patents and patent applications cited above and for other methods and systems for non-invasively measuring circulating hemoglobin. Accordingly, the present disclosure is not limited to use with the methods and systems disclosed in the patents and patent applications cited above. Additionally, as described above, the present disclosure contemplates that the system 20 of the present disclosure may be independent of and communicate with the NIRS sensing device 22 or be integral with the NIRS sensing device 22. Accordingly, aspects of the functionality described herein may be performed independently of the NIRS sensing device 22 in the system 20 of the present disclosure or integrally within the NIRS sensing device 22, or in any combination thereof.

[0067] The present disclosure relates to methods and systems for non-invasively 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 may affect the accuracy of circulating hemoglobin measurements generated using the NIRS sensing device. Hemodynamic changes may occur slowly or rapidly over time. The present disclosure provides embodiments of methods and systems for facilitating non-invasive measurement of circulating hemoglobin parameters with higher accuracy. Aspects of the present disclosure include logic / techniques for determining whether calibration or re-calibration of the NIRS system is appropriate (e.g., taking into account hemodynamic instability and / or changes), when calibration / re-calibration is appropriate, and techniques for performing such calibration. Aspects of the present disclosure further include methods for estimating BVF using NIRS data and blood gas data.

[0068] The NIRS sensing device 22 can be used to continuously and non-invasively determine the hemoglobin value of a subject's tissue (e.g., relative tissue hemoglobin value, hereinafter referred to as "ΔctHb"). As used herein, the term "continuously" (used to describe the NIRS sensing device 22 that senses continuously) can be a NIRS sensing device 22 that senses and collects subject data on a periodic basis during a monitoring period, the periodic basis being frequent 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 methods are known, and the NIRS sensing device 22 can employ these methods to determine the relative tissue hemoglobin value. The patents and patent applications cited above provide examples of methods that can be used, but the present disclosure is not limited thereto.

[0069] The relative tissue hemoglobin can in turn be used to determine the continuous relative total blood hemoglobin (ΔTHb). The following Equation 1 shows a non-limiting example of how the relative tissue hemoglobin (ΔctHb) can be used to determine the relative total blood hemoglobin (ΔTHb):

[0070]

[0071] Equation 1 can alternatively be expressed as shown in Equation 1A to illustrate the change in relative total blood hemoglobin over time.

[0072]

[0073] The term "local blood volume fraction" refers to the blood volume fraction (BVF) in tissue sensed by the NIRS sensing device 22. The BVF may vary among subjects (e.g., inter-patient variability; different subjects have different BVF), and may vary over time (e.g., intra-patient variability). Regarding the latter, the BVF of a subject may vary over time with various physiological conditions, including but not limited to vasoconstriction / dilation, venous congestion, etc. In some embodiments of the present disclosure, sensed data (i.e., stored empirical data representing a clinically sufficient amount of data) non-invasively generated by the NIRS sensing device 22 and hemoglobin data can be used to estimate the local BVF. Empirical hemoglobin data can be determined by analyzing invasively collected blood samples using a blood gas analyzer or a CO-oximeter, but the present disclosure is not limited to hemoglobin data generated from invasively collected blood samples.

[0074] In some embodiments, artificial intelligence (AI) / machine learning (ML) techniques, algorithmic techniques, etc. can be used in conjunction with empirical hemoglobin data to correlate NIRS relative tissue hemoglobin (ΔctHb) and total hemoglobin in circulation THb; e.g., to determine the estimated BVF. As described herein, such correlations can be used to eliminate the need for initial calibration using the total hemoglobin in circulation THb value. In some embodiments, the correlation can take the form of a calibration parameter ("k"). Non-limiting examples of how such calibration parameters can be determined include plotting tissue (e.g., mapping) total hemoglobin in circulation THb data relative to the NIRS total tissue hemoglobin value for analysis. A trend line can be determined based on the plotted data points (e.g., using linear regression techniques), which represents the best fit to the data points. The trend line has a slope value and an intercept value, and the slope value and the intercept value can be used to determine the calibration parameter. The embodiments herein for determining the calibration parameter based on the plotted ΔctHb and total hemoglobin in circulation THb values are used to illustrate how the calibration parameter can be determined, and the present disclosure is not limited thereto. As described above, various techniques can be used with empirical data points to determine the calibration parameter. The following expression is an example of how such a calibration parameter can be used to determine relative total hemoglobin (ΔTHb) using relative tissue hemoglobin (ΔctHb).

[0075] ΔTHb = ΔctHb * k (Equation 2)

[0076] ΔTHb t =(ΔctHb t -ΔctHb t0 ) * k (Equation 2A)

[0077] PCT Publication No. WO 2018 / 187510 (which is hereby incorporated by reference in its entirety) provides examples of how empirical data can be used to determine calibration parameters.

[0078] Once the continuous relative total hemoglobin in blood (ΔTHb) is determined, the total hemoglobin value determined from an invasively collected blood sample can be used to determine the absolute total hemoglobin (THb). Figure 4 Including a first graph sensing the change of relative tissue hemoglobin (ΔctHb – μmol) over time and a second graph of the absolute total hemoglobin in blood (THb) determined based on the change of relative tissue hemoglobin (ΔctHb) over time (alternatively, the continuous relative total hemoglobin in blood (ΔTHb) that can be determined from ΔctHb can be used). In this example, the absolute total hemoglobin (THb) is initially calibrated (at the approximately 10:00 min mark) using the total hemoglobin value determined from an invasively collected blood sample, as indicated by the dot at the start of the data graph. The conversion from relative total hemoglobin in blood (ΔTHb) to absolute total hemoglobin in blood can be determined using Equation 3:

[0079] THb(t) = ΔTHb + THb(t0) (Equation 3)

[0080] where THb(t0) is the total hemoglobin THb at the calibration point, such as Figure 4 shown, and ΔTHb is determined as described above. The above method provides a means for non-invasively providing continuous absolute total hemoglobin in blood information with only minimally invasive blood collection.

[0081] In the determination of total hemoglobin (relative or absolute), some system embodiments may consider multiple factors (referred to herein as "oximetry characteristics"). These oximetry characteristics may be related to: the physiological characteristics of the subject (e.g., StO2 determined in a single band or multiple bands, tissue perfusion index or "TPI", ΔctHb, skin temperature, etc.), or intermediate characteristics (e.g., tissue optical properties or "TOP", or an analysis-determined constant reflecting the characteristics of an individual subject - e.g., C n *StO2, etc.), or NIRS oximetry characteristics (e.g., the length of the path traveled by photons between the transducer light source and the light detector, optical density, gain, etc.), or statistical characteristics (e.g., the mean, average, median, standard deviation, etc. of different data windows), etc., including any combination thereof. Examples of tissue optical properties or "TOP" include skin pigmentation, muscle and bone density, etc. These oximetry characteristics can be interpreted in the expression of total hemoglobin in blood (absolute or relative). Equation 4 below shows a non-limiting example of how oximetry characteristics can be interpreted in the expression of absolute total hemoglobin in blood (THb).

[0082]

[0083] where "BG(t0)" is the total hemoglobin value determined from an invasively collected blood sample (e.g., blood gas), "k" is a calibration parameter (as described herein), "C 0 " is a constant, "f" is an oximetry characteristic, "Cn" is a derived constant for each oximetry characteristic, and the sum represents one to five oximetry characteristics being considered.

[0084] Alternatively, in some embodiments, continuous absolute blood total hemoglobin information may be provided without using an initial total hemoglobin value determined from an invasively collected blood sample for calibration.

[0085] Figure 5 is a graph showing the change over time of continuous absolute blood total hemoglobin (THb) in grams per deciliter (g / dL). Figure 5 The g / dL scale (on the Y-axis) shown in Figure 4 is from about 8 g / dL to about 12 g / dL. Similar to Figure 5 the THb data shown in Figure 5 is initially calibrated using the total hemoglobin value determined from an invasively collected blood sample. Figure 5 The calibrated THb data shown in Figure 5 starts at about the 8:40 minute mark, where the mark indicates calibration.

[0086] Figure 5A is a graph showing the change over time of relative blood total hemoglobin (ΔTHb) in grams per deciliter (g / dL). Figure 5A The ΔTHb data shown in Figure 5 is the data shown in Figure 5A this time generated without initial calibration. Figure 5A The g / dL scale (on the Y-axis) shown in Figure 5A is from about 0 g / dL to about -4 g / dL.

[0087]

[0088] Figure 5 and 5AThe THb and ΔTHb data shown in [reference] track with very high consistency. The difference between the two is an offset in the THb value (i.e., approximately 8 to 12 g / dL in the calibrated data versus approximately -4 to 0 g / dL). Clearly, Figure 5A The data graph in [reference] shows the same data trend over time as the data graph in Figure 5 but does not require invasive sample data and thus provides relative THb data without the need for initial calibration. The ability of embodiments of the present disclosure to provide such information non-invasively is considered beneficial in many cases.

[0089] As described above, the present disclosure includes a calibration method for ensuring that the absolute total blood hemoglobin information generated is not incorrect or otherwise compromised, and a method for estimating BVF using NIRS data and blood gas data generated by invasive measurements (e.g., based on AI / machine learning).

[0090] A first example of a calibration method is designed to indicate when it is appropriate to calibrate the NIRS sensing system so that it can accurately provide non-invasive continuous absolute total blood hemoglobin information. Certain factors (when present) may affect the accuracy of calibration. Thus, the system 20 can 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 the case where the NIRS sensing device 22 generates relative tissue hemoglobin (ΔctHb) data that varies over time, the generated relative tissue hemoglobin (ΔctHb) data may be unstable; for example, the variable exceeds a predetermined threshold range. In such a case, the system 20 of the present disclosure can interpret the ΔctHb instability as an indication that the relative tissue hemoglobin data is suspect. Figure 6 A graph showing the relative tissue hemoglobin (ΔctHb) data varying over time is presented. The method can, for example, evaluate the ΔctHb data within a rolling predetermined window; for example, a two-minute "evaluation" window. If the amplitude of the collected ΔctHb data exceeds a predetermined threshold range, the system 20 can flag the change to indicate that the ΔctHb data collected within the evaluation window should not be used to calibrate the absolute total blood hemoglobin of the NIRS sensing system. Figure 6 It is indicated that the two-minute window between 11:24 and 11:26 is flagged.

[0091] In a second example of a calibration method, the present disclosure can utilize the NIRS sensing device 22 to determine the tissue oxygen saturation value (StO2). The tissue oxygen saturation value (StO2) generated by the NIRS sensing device 22 can be used to evaluate whether the transducer 24 of the NIRS sensing device 22 is properly placed on the subject or otherwise evaluate the operation of the transducer 24. Figure 7Displays a graph of ΔctHb versus time, which is placed above the graph of StO2 versus time (for the same time period). Both graphs include a "1" line placed on the upper edge of the respective graph and a "0" line placed on the bottom edge of the respective graph. The "1" line of the StO2 versus time graph indicates that the NIRS sensed data (i.e., StO2) is acceptable / valid, while the "0" line of the StO2 versus time graph indicates that the NIRS sensed data (i.e., StO2) is unacceptable / invalid. The "1" line of the ΔctHb versus time graph indicates that the ΔctHb data is unacceptable / invalid for calibration. It should be noted that the StO2 value can be based on the raw signal with signal quality and variability, and in some embodiments, the acceptable / unacceptable characteristics of the StO2 data and the ΔctHb data can consider the signal quality and variability of the raw signal.

[0092] In some embodiments, the StO2 sensed data can be further evaluated over time. For example, the StO2 data may naturally change from an acceptable value to an unacceptable value (e.g., due to system issues, rapid instantaneous changes in StO2, signal quality / variability, etc.). These fluctuations may occur rarely or frequently, and the durations may vary. The further evaluation can include evaluating the StO2 fluctuations within the evaluation period. The further evaluation can consider the amplitude of the fluctuations and / or the total duration of the fluctuations within the evaluation period. For example, the total duration of unacceptable fluctuations within a given evaluation period can be continuously evaluated relative to a predetermined total threshold (e.g., a percentage of the evaluation window duration, such as 10%). If the total duration of unacceptable fluctuations exceeds the total threshold, all StO2 data collected at that point in the evaluation window may be considered unacceptable, and the corresponding ΔctHb data can be marked as unacceptable for calibration. In some embodiments, once the total threshold is reached, a new evaluation period can be initiated and the total duration of unacceptable fluctuations can be set to zero.

[0093] In a third calibration method example, the NIRS sensing device 22 can be used to determine the tissue oxygen saturation value (StO2). Figure 8Displays a graph of ΔctHb versus time, positioned above a graph of StO2 versus time (same time period). In this example, the parameters (ΔctHb and StO2) are evaluated based on the raw signal strength, where the raw signal strength is depicted in the graph by the system via a proxy (such as amplification gain). The raw signal strength can be an indicator of tissue changes sensed by the NIRS sensing device 22 (e.g., changes in blood volume within the tissue, changes in oxygen saturation within the tissue, etc.), and can be used to determine the acceptability of the data for calibration purposes. Tissue changes can be caused by various events, such as hemodilution generated by a cardiopulmonary bypass pump. Changes in the raw signal strength relative to a predetermined threshold can be used to determine whether the NIRS sensing data (i.e., StO2) is stable / acceptable or unstable / unacceptable / invalid. In Figure 8 it, the graph of StO2 versus time indicates that the raw signal strength (via the amplification gain proxy) is represented by the value "30" on any scale at the first level during the period from shortly before 11:30 to approximately 11:33. At approximately 11:33, the graph of StO2 versus time indicates that the raw signal strength (via the amplification gain proxy) is represented by the value "45" on any scale at the second level during the period from just about 11:33 to after 11:37. In this example, the graph of ΔctHb versus time (same time period as the graph of StO2 versus time) indicates that the uncalibrated marker is raised (via a line placed at the "1" line between approximately 11:33 and approximately 11:34).

[0094] Some embodiments of the present disclosure may include a recalibration algorithm based on the cumulative change in ΔctHb; for example, another measure of ΔctHb stability / NIRS sensing device 22 performance. The cumulative change in ΔctHb can be based on cumulative ΔctHb variance data. The following is a non-limiting example of the recalibration algorithm.

[0095] The algorithm may include a reference variable "R", which is initially (e.g., at the start of the THb monitoring period) set in step 1 to:

[0096] R = ΔctHb(t 0 ) (Equation 5)

[0097] For each new tissue sample analysis, the algorithm accumulates the ΔctHb deviation data of a second reference value "CumDev", which is initially set to zero in step 1. Equation 6 shows a non-limiting example of how the reference value CumDev can be populated in step 2:

[0098] [ΔctHb(t n ) - R > 5 μM → CumDev = CumDev + (△ctHb(t n ) - R) (Equation 6)

[0099] For each new analysis of an organizational sample, the algorithm can include evaluating a reference value CumDev to determine whether the cumulative ΔctHb deviation data represented by CumDev exceeds a predetermined threshold. If the cumulative ΔctHb deviation data represented by the reference value CumDev exceeds the predetermined threshold, a "recalibration" flag can be triggered. If the recalibration flag is triggered, the reference variable R can be reset as shown in Equation 5 above, and when the recalibration flag is triggered, the reference value CumDev can be set to zero, and the process can be restarted as described. If the cumulative ΔctHb deviation data represented by the reference value CumDev does not exceed the predetermined threshold, the recalibration flag is not triggered. If the "recalibration" flag is not triggered after a certain predetermined period (e.g., a 5-minute evaluation period), the reference variable R is reset as shown in Equation 5 above, and then the reference value CumDev is set to zero at the end of the current evaluation period, and the process is repeated. Figure 9A A graph of ΔctHb versus time is shown, with ΔctHb data and "R" values for the first five-minute evaluation period, and Figure 9B shows the same graph of ΔctHb versus time, now with ΔctHb data and "R" values for each of a plurality of five-minute evaluation periods. Figure 9A and Figure 9B neither shows a recalibration flag. Figure 9C A graph of ΔctHb versus time is shown, showing ΔctHb data and "R" values for a plurality of five-minute evaluation periods. At approximately 10:15, the recalibration flag was triggered. Once the recalibration flag was triggered, the reference variable R was reset, and the reference value CumDev was set to zero, and the process was started again. Figure 9C Shows ΔctHb data and "R" values for a plurality of five-minute evaluation periods after the recalibration flag has been triggered. In this way, embodiments of the present disclosure maintain continuous evaluation of the ΔctHb deviation. The above method is an example of how ΔctHb deviation data can be monitored to identify when recalibration may be needed, and the present disclosure is not limited to this example.

[0100] Another non-limiting example of a recalibration algorithm can utilize statistical parameters based on ΔctHb values collected within an evaluation window that occurred within a certain period of time prior to the current time point; for example, ΔctHb values collected within the previous "X" minutes. For example, the ΔctHb data collected within the evaluation window can be processed to determine the median. Then, the determined ΔctHb median and a threshold can be used to evaluate the current ΔctHb value. For example, the evaluation can determine the absolute difference between the current ΔctHb value and the ΔctHb median and compare that difference to the threshold, as shown in Equation 7 below.

[0101] |ΔctHb 当前 - Intermediate ΔctHb 评估窗口 | > Threshold (Equation 7)

[0102] If the absolute difference between the current ΔctHb value and the median value of ΔctHb exceeds the threshold, a "recalibration" flag can be triggered.

[0103] In some embodiments, the above algorithm can be configured to select a calibration measure other than triggering a "recalibration" flag, or a calibration measure in addition to the "recalibration" flag. The present disclosure is not limited to the above recalibration algorithm.

[0104] In some embodiments, the present disclosure may include a calibration algorithm that utilizes machine learning or other artificial intelligence techniques. In these embodiments, the function "f" can be used to represent a multivariate machine learning model that uses oximetry data. Equation 8 represents a general function that represents the method.

[0105] THb(t) = BG(t0) + f(oximetry data) (Equation 8)

[0106] 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 as above. A more specific example of an algorithm that can be used is shown in Equation 9:

[0107] THb(t) = BG(t0) + (ΔctHb(t) - ΔctHb(t 0 )) * k (Equation 9)

[0108] The variable "ΔctHb(t 0 )" represents the relative tissue hemoglobin concentration at calibration; for example, calibration is performed using blood gas THb as described above. The variable "k" represents a correlation factor (as described above), which can be calculated using linear regression techniques with a machine learning training dataset. The machine learning training dataset contains a clinically significant amount of clinical data.

[0109] The algorithms utilized by machine learning can 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 can be split into a training dataset portion and one or more test / validation dataset portions; for example, a training dataset portion, a cross-validation dataset portion, and a final validation dataset portion.

[0110] A second step in the algorithm development process can involve selecting a training method for developing a THb calculation model. A first example of a training method that can be used is a direct method for estimating the change in THb since the last device calibration; for example, it is represented in functional form in Equation 10:

[0111] BG (t) -BG (t0) = f(oximetry data) (Equation 10)

[0112] The second and third instances of the training method using the enhancement method can be used; for example, a method for estimating the error of the model represented in Equation 9, as represented in Equation 11 below:

[0113] BG (t) –BG (t0) -(k*(ΔctHb (t) -ΔctHb (t0) )) = f(oximetry data) (Equation 11) or a method for estimating the error of a multivariate model, as represented in Equation 12 below:

[0114] BG (t) -BG (t0) - Model X = f(oximetry data) (Equation 12)

[0115] Where the variable model X represents a multivariate model. The present disclosure is not limited to the above instances of the training method.

[0116] As described above, "oximetry data" can include various different data types that can be considered in the development of machine learning algorithms. AI / ML techniques (e.g., correlation, linear regression, consistency, decision tree, etc.) can be used to identify the most suitable type of oximetry data. Considering the identification of suitable oximetry data and the further identification of oximetry data most suitable for machine learning, more in-depth algorithmic expressions, such as the algorithmic expression shown in Equation 4 above, can be used.

[0117] Figure 10 A graph of THb versus time is shown, placed above the graph of ΔctHb versus time (for the same period) to illustrate calibration / no calibration. The graph of ΔctHb versus time includes a first line 44 depicting "ΔctHb LB" (where "LB" refers to NIRS data obtained from the left hemisphere of the subject's brain) and a second line 46 depicting "ΔctHb RB" (where "RB" refers to NIRS data obtained from the right hemisphere of the subject's brain). The graph of ΔctHb versus time also includes a marker "X" identifying the "no calibration" indication and a marker "*" identifying the recalibration indication. Figure 10 The graph of THb versus time shown in includes a line 48 depicting THb data, a marker 50 indicating the THb value derived from blood gas (BG), and a marker 52 indicating the THb value derived from blood gas (BG) used for calibration. Figure 10The data shown in the graphs includes an initial noise region at approximately 13:00 and a sharp instability region shortly before 15:30 (e.g., caused by cardiopulmonary bypass, etc.). As shown in the THb vs. time graph, shortly after 13:00, a blood gas (BG)-derived THb value (labeled 50) was provided, which was not used for calibration due to the instability of the ΔctHb data at that time point. Shortly thereafter, another blood gas (BG)-derived THb value for calibration (labeled 52) was provided. Shortly before 15:30 in the ΔctHb vs. time graph, a recalibration marker 54 was indicated. At approximately 15:30 in the THb vs. time graph, a blood gas (BG)-derived THb value (labeled 50) was provided, which was not used for calibration due to the instability of the ΔctHb data at that time point. Shortly after 15:30, another blood gas (BG)-derived THb value for recalibration (labeled 52) was provided. The subsequent THb data shows a very good match with the blood gas (BG)-derived THb value (labeled 50) after 16:30. The ΔctHb vs. time graph includes a symbol "X" (labeled 56) for indicating no calibration; that is, according to the present disclosure, the situation at that time then made it such that calibration should not be performed.

[0118] As indicated above, the functions described herein can 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 functions described herein can be implemented in one or more computer programs. Each such computer program can 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 can be performed by a computer processor executing a program tangibly embodied on a computer-readable medium to perform the functions of the present disclosure by operating on inputs and generating outputs. Each computer program within the scope of the appended claims can be implemented in any programming language, such as assembly language, machine language, high-level procedural programming language, or object-oriented programming language. For example, the programming language can be a compiled or interpreted programming language.

[0119] Although the present invention has been described with reference to exemplary embodiments, those skilled in the art will understand that various changes can be made and elements of the present invention can be replaced with equivalents without departing from the scope of the present 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 can include the contribution of one or more other types of hemoglobin; for example, carboxyhemoglobin (COHb), methemoglobin (MetHb), etc. Additionally, many modifications can be made to adapt a particular situation or material to the teachings of the present invention without departing from the basic scope of the present invention. Therefore, the present invention is not intended to be limited to the particular embodiments disclosed, but rather the present invention will include all embodiments falling within the scope of the appended claims

[0120] It should be noted that embodiments can be described as processes depicted in a flowchart, a flow diagram, a block diagram, etc. Although any of these structures can describe the operations as a sequential process, many operations can be performed in parallel or simultaneously. In addition to this, the order of these operations can be rearranged. A process can correspond to a method, a function, a program, a subroutine, a subprogram, etc

[0121] The singular forms "a or an" and "the" refer to one or more than one, unless the context clearly dictates otherwise. For example, the term "comprising a sample" includes a single or multiple samples and is considered equivalent to the phrase "comprising at least one sample". The term "or" refers to a single one of the stated alternative elements or a combination of two or more elements, unless the context clearly indicates otherwise. As used herein, "comprising" means "including". Thus, "including A or B" means "comprising A, or B, or A and B", without excluding additional elements

[0122] Note that various connections between elements are set forth in this specification and the accompanying drawings (the content of which is incorporated herein by reference). Note that these connections are general and, unless otherwise specified, can be direct or indirect, and this specification is not intended to be limiting in this regard. Any reference to attachment, fixation, connection, etc. can include permanent, removable, temporary, partial, complete, and / or any other possible attachment options

[0123] No element, component, or method step in this disclosure is dedicated to the public, whether or not the element, component, or method step is expressly recited in the claims. No claim element herein shall be construed under the provisions of 35 U.S.C. 112(f) unless the element is expressly recited using the phrase "means for." As used herein, the term "comprises / comprising" or any other variant thereof is intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0124] While the various inventive aspects, concepts, and features of this disclosure may be described and illustrated herein as being embodied in combination in exemplary embodiments, these various aspects, concepts, and features may be used alone or in various combinations and sub-combinations in many alternative embodiments. All such combinations and sub-combinations are intended to be within the scope of this application unless expressly excluded herein. Further, while various alternative embodiments of the aspects, concepts, and features of this disclosure may be described herein - such as alternative structures, configurations, methods, devices, and components, etc. - such descriptions are not intended to be a complete or exhaustive list of available alternative embodiments, whether currently known or later developed. One or more of the inventive aspects, concepts, and features may be readily adapted by those skilled in the art for additional embodiments and uses within the scope of this application even if such embodiments are not expressly disclosed herein. For example, in the above exemplary embodiments within the detailed description section of this specification, elements may be described as separate units and shown as being independent of one another for ease of description. In alternative embodiments, such elements may be configured as combined elements.

[0125] In addition, although some features, concepts, or aspects of this disclosure may be described herein as preferred arrangements or methods, such descriptions are not intended to indicate that such features are required or necessary unless expressly so stated. Further, exemplary or representative values and ranges may be included to assist in understanding this application; however, such values and ranges are not to be construed in a limiting sense and are only intended to be critical values or ranges if so expressly stated.

[0126] Therapeutic techniques, methods, steps, etc. described or indicated herein or in the references incorporated herein may be performed on live animals or non-live simulations (such as on cadavers, cadaver hearts, anthropomorphic ghosts, or mock-ups (e.g., having simulated body parts, tissues, etc.)).

[0127] Any one of the various systems, devices, equipment, etc. in this disclosure can be sterilized (e.g., using heat, radiation, ethylene oxide, hydrogen peroxide) to ensure their safety for patient use, and the methods herein can include sterilizing the associated systems, devices, equipment, etc.; for example, using heat, radiation, ethylene oxide, hydrogen peroxide, etc.

Claims

1. A method for non-invasively determining continuous total hemoglobin data, the method comprising: continuously sensing tissue of a subject using a near-infrared spectroscopy (NIRS) sensing device, wherein an NIRS signal is generated from the sensing; and using the generated NIRS signal to determine continuous total hemoglobin (THb) data.

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

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

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

5. The method according to claim 4, wherein the reference absolute THb value is obtained non-invasively from the subject.

6. The method according to claim 4, wherein the reference absolute THb value is obtained from a blood sample invasively collected from the subject.

7. The method according to claim 4, further comprising providing an indication to perform calibration of the NIRS sensing device based on the NIRS signal.

8. The method according to claim 7, wherein the step of providing the indication to perform the calibration of the NIRS sensing device is based on a determination of the acceptability of the NIRS signal in order to perform the determination of the continuous absolute THb data.

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

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

11. The method according to claim 8, wherein the determination of the acceptability of the NIRS signal comprises evaluating the NIRS signal over a predetermined period to assess the hemodynamic changes of the sensed tissue.

12. The method according to claim 1, wherein the step of determining the continuous THb data comprises using an oximetric feature based on the NIRS signal.

13. The method according to claim 12, wherein the oximetric feature based on the NIRS signal comprises continuous relative tissue hemoglobin (ΔctHb) data.

14. The method according to claim 12, wherein the oximetric feature based on the NIRS signal comprises at least one of the following: skin temperature, the path length traveled by photons between the NIRS transducer light source and the NIRS transducer light detector, deoxygenated tissue hemoglobin, oxygenated tissue hemoglobin, or tissue oxygen saturation.

15. The method according to claim 1, further comprising evaluating the NIRS signal to determine the acceptability of the NIRS signal in order to perform the determination of the continuous THb data.

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

17. The method according to claim 15, wherein the evaluating step of determining the acceptability of the NIRS signal comprises evaluating the NIRS signal within a predetermined period to assess the hemodynamic stability of the sensed tissue.

18. The method according to claim 15, wherein the evaluating step of determining the acceptability of the NIRS signal comprises evaluating the NIRS signal within a predetermined period to assess the hemodynamic changes of the sensed tissue.

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

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

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

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

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

24. The method according to claim 22, wherein the NIRS signal is in the form of an original signal.

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

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

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

28. 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: continuously control the NIRS sensing device to sense the tissue of the subject and generate a NIRS signal from the sensing; and determine continuous total hemoglobin (THb) data using the generated NIRS signal.

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

30. The system according to claim 28, wherein the continuous THb data is continuous absolute THb.

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

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

33. The system according to claim 31, wherein evaluating the NIRS signal to determine the acceptability of the NIRS signal includes evaluating the NIRS signal over a predetermined period to assess the hemodynamic stability of the sensed tissue or the hemodynamic changes of the sensed tissue or both.

34. The system according to claim 28, wherein the instructions, when executed, cause the controller to provide an indication to perform calibration of the NIRS sensing device based on the NIRS signal.

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

36. The system according to claim 35, wherein the determination of the acceptability of the NIRS signal includes evaluating the NIRS signal over a predetermined period to determine the stability of the NIRS signal.

37. The system according to claim 35, wherein evaluating the NIRS signal to determine the acceptability of the NIRS signal includes evaluating the NIRS signal over a predetermined period to assess the hemodynamic stability of the sensed tissue or the hemodynamic changes of the sensed tissue or both.

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

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

41. The system according to claim 28, wherein the NIRS sensing device and the controller are integrated.

42. A non-transitory computer-readable medium comprising software code segments adapted to execute a method for non-invasively determining continuous relative total hemoglobin data, the method comprising the steps of: continuously controlling a near-infrared spectroscopy (NIRS) sensing device to sense tissue of a subject, the sensing generating a NIRS signal; and using the generated NIRS signal to determine continuous relative total hemoglobin (ΔTHb).

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

Images