Biological information processing device, biological information processing method, and biological information processing program

Abstract

To reduce a load on evaluating persons performing pain evaluation and reduce variations in the evaluation among the evaluating persons.SOLUTION: A biological information processing device includes an acquisition unit for acquiring blood vessel kinetic information indicating a blood circulation state of a subject, and a processing unit for acquiring an index value of pain evaluation for the subject by subjecting the blood vessel kinetic information to normalization processing. The processing unit calculates a reference value on the basis of the blood vessel kinetic information acquired in a reference period before starting a treatment event on the treatment for the subject, calculates an object value on the basis of the blood vessel kinetic information acquired after starting the treatment event, and calculates the ratio of the object value to the reference value as the index value as the normalization processing.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present disclosure relates to a biological information processing apparatus, a biological information processing method, and a biological information processing program.

Background Art

[0002] Most premature infants and immature infants (hereinafter referred to as premature infants, etc.) undergo treatment and examinations in a neonatal intensive care unit (NICU), and treatments and examinations for premature infants, etc. may require painful procedures. Also, it is said that experiencing pain during the neonatal period may have an adverse effect on pain sensitivity and the stress system of the autonomic nervous system. On the other hand, since newborns cannot sufficiently communicate their intentions, they cannot express pain on their own. Thus, in a treatment event involving pain, there is a need for means for medical staff to accurately grasp the pain of newborns.

[0003] In this regard, Non-Patent Document 1 discloses verification results regarding the pain assessment scale PIPP (Premature Infant Pain Profile) that uses a total of seven indicators, namely two background indicators, two physiological indicators, and three expression indicators.

Prior Art Documents

Non-Patent Documents

[0004]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] Incidentally, when using the PIPP (Premature Infant Pain Profile Revised) pain assessment scale for premature infants, or its improved version, PIPP-R (Premature Infant Pain Profile Revised), it is necessary to observe and evaluate three facial features—brow ridges, tightly closed eyes, and nasolabial folds—for 30 seconds. This places a heavy burden on the evaluator. Furthermore, facial expression assessments tend to vary from evaluator to evaluator.

[0006] The purpose of this disclosure is to provide a bio-information processing device, a bio-information processing method, and a bio-information processing program that can reduce the burden on evaluators performing pain assessments and reduce variability in evaluations among evaluators. [Means for solving the problem]

[0007] A biological information processing device relating to one aspect of this disclosure is An acquisition unit that acquires vascular dynamic information indicating the blood circulation status of the subject, The system includes a processing unit that obtains an index value for the subject's pain assessment by applying normalization processing to the vascular dynamics information, The aforementioned processing unit, Based on the vascular dynamics information obtained during the reference period prior to the start of the treatment event for the subject, a reference value is calculated. Based on the vascular dynamics information obtained after the start of the aforementioned treatment event, the target value is calculated. As part of the normalization process, the ratio of the target value to the reference value is calculated as the index value.

[0008] A biometric information processing method relating to one aspect of this disclosure is: A method for processing biological information in a biological information processing device, A step to obtain vasodynamic information indicating the blood circulation status of the subject, The step includes obtaining an index value for the subject's pain assessment by applying a normalization process to the vascular dynamics information, In the step of obtaining the aforementioned index value, Based on the vascular dynamics information obtained during the reference period prior to the start of the treatment event for the subject, a reference value is calculated. Based on the vascular dynamics information obtained after the start of the aforementioned treatment event, the target value is calculated. As part of the normalization process, the ratio of the target value to the reference value is calculated as the index value.

[0009] A biometric information processing program relating to one aspect of this disclosure is: A bio-information processing program used in a bio-information processing device, In the computer of the aforementioned biological information processing device, A step to obtain vasodynamic information indicating the blood circulation status of the subject, The process involves performing a normalization process on the vascular dynamics information to obtain an index value for the subject's pain assessment, and then executing the following steps: In the step of obtaining the aforementioned index value, Based on the vascular dynamics information obtained during the reference period prior to the start of the treatment event for the subject, a reference value is calculated. Based on the vascular dynamics information obtained after the start of the aforementioned treatment event, the target value is calculated. As part of the normalization process, the ratio of the target value to the reference value is calculated as the index value. [Effects of the Invention]

[0010] According to this disclosure, it is possible to reduce the workload of evaluators who perform pain assessments and to reduce the variability in evaluations among evaluators. [Brief explanation of the drawing]

[0011] [Figure 1] Figure 1 shows an example of the usage of the processing apparatus according to this embodiment. [Figure 2] Figure 2 shows the configuration of the processing unit shown in Figure 1. [Figure 3]FIG. 3 is a graph showing an electrocardiogram waveform indicated by the electrocardiogram data output from the acquisition unit shown in FIG. 2. [Figure 4] FIG. 4 is a graph showing the time-series change of the RR interval measured from the electrocardiogram waveform shown in FIG. 3. [Figure 5] FIG. 5 is a graph showing the time-series change of a predetermined frequency component among the RR intervals shown in FIG. 4. [Figure 6] FIG. 6 is a part of a graph showing the time-series change of the RRI fluctuation component shown in FIG. 5, and is a graph with the vertical axis enlarged. [Figure 7] FIG. 7 is a graph showing the time-series change of the degree of variation of the RRI fluctuation component calculated based on the RRI fluctuation component shown in FIG. 6. [Figure 8] FIG. 8 is a graph showing the index value R calculated by performing normalization processing on the SDNN shown in FIG. 7. [Figure 9] FIG. 9 is a flowchart for explaining the operation flow when the processing device 2 according to one aspect of the present disclosure performs a pain evaluation of a subject.

Mode for Carrying Out the Invention

[0012] [Configuration of Biological Information Processing Device] An example of an embodiment of a biological information processing device, a biological information processing method, and a biological information processing program according to the present invention will be described based on the accompanying drawings. Hereinafter, the biological information processing device 2 according to the present embodiment will be referred to as the "processing device 2". FIG. 1 is a diagram showing an example of the usage situation of the processing device 2 according to the present embodiment. FIG. 2 is a diagram showing the configuration of the processing device 2 shown in FIG. 1.

[0013] Referring to Figure 1, the processing device 2 is a device for evaluating the pain experienced by subject X when a procedure event such as blood sampling is performed on subject X. The procedure event is not necessarily limited to events that cause pain to subject X, but may be any event that has the potential to cause pain to subject X. For example, a procedure event that causes pain is not limited to blood sampling, but may also be an event involving the administration of drugs, an examination involving puncture such as a bone marrow biopsy, or an event involving a procedure involving incision, etc. Healthcare professionals M1 and M2 can determine whether or not subject X is experiencing pain by checking the evaluation results from the processing device 2.

[0014] Figure 1 illustrates a newborn as subject X. Because newborns have difficulty expressing the degree of pain they experience through communication, it is especially important for healthcare professionals M1 and M2 to understand the degree of pain experienced by the newborn (subject X) through the device 2. However, subject X is not limited to newborns; it could also be a child or an adult.

[0015] Subject X is fitted with a biosensor 32 for acquiring vascular dynamics information indicating the state of blood circulation. The biosensor 32 is, for example, an electrocardiogram sensor that measures the electrocardiogram signal (ECG) of subject X, and transmits the ECG data showing the ECG signal to the processing unit 2 as vascular dynamics information.

[0016] Referring to Figure 2, the processing unit 2 comprises a control unit 20, a storage device 21, an input operation unit 24, an output unit 25, and an acquisition unit 27. The control unit 20, storage device 21, input operation unit 24, output unit 25, and acquisition unit 27 are connected to each other via a bus 26 so as to be able to communicate with each other.

[0017] The acquisition unit 27 is an interface for connecting the biological information sensor 32 to the processing unit 2 in a communicative manner. The acquisition unit 27 receives vascular dynamics information transmitted from the biological information sensor 32 and outputs the vascular dynamics information to the control unit 20.

[0018] The control unit 20 includes a processing unit 41 and an evaluation unit 42. The processing unit 41 obtains an index value R used for pain assessment of subject X by normalizing the vascular dynamics information received from the acquisition unit 27. The evaluation unit 42 performs pain assessment of subject X based on the index value R obtained by the processing unit 41. Details of the acquisition of the index value R by the processing unit 41 and the pain assessment by the evaluation unit 42 will be described later.

[0019] The storage device 21 is, for example, a flash memory and is configured to store programs and various data. The storage device 21 may incorporate a bio-information processing program used by the control unit 20 during pain assessment. The storage device 21 may also store the vascular dynamics information of the subject X.

[0020] The input operation unit 24 receives input operations from medical personnel M1 and M2. The input operation unit 24 is, for example, a touch panel, mouse, or keyboard superimposed on a monitor (not shown). For example, medical personnel M1 starts up the processing unit 2 and performs an input operation to the processing unit 2 immediately before performing a treatment event. The input operation unit 24 identifies the time when the input operation was performed, or a predetermined time after the time when the input operation was performed, as the start time tx of the treatment event, and outputs start time information indicating the start time tx to the control unit 20 via the bus 26.

[0021] [Acquisition of indicator values ​​by processing unit] (Preliminary processing) This section describes the procedure by which the processing unit 41 obtains the pain assessment index value R based on electrocardiogram data, which is an example of vascular dynamics information.

[0022] Figure 3 is a graph showing the electrocardiogram waveform represented by the electrocardiogram data output from the acquisition unit 27 shown in Figure 2. Referring to Figure 3, when the processing unit 41 receives electrocardiogram data from the acquisition unit 27, it detects the timing of the R wave generation for each heartbeat of the subject X (hereinafter referred to as "R timing") based on the electrocardiogram waveform represented by the electrocardiogram data. The processing unit 41 then measures the RR interval, which is the interval between adjacent R timings.

[0023] Figure 4 is a graph showing the time-series change of the RR interval measured from the electrocardiogram waveform shown in Figure 3. In Figure 4, the horizontal axis represents time, and the vertical axis represents the value of the RR interval. Figure 5 is a graph showing the time-series change of a predetermined frequency component (hereinafter referred to as the "RRI fluctuation component") of the RR interval shown in Figure 4. In Figure 5, the horizontal axis represents time, and the vertical axis represents the value of the RRI fluctuation component.

[0024] As shown in Figures 4 and 5, the processing unit 41 extracts the RRI fluctuation component from the RR interval measured based on the electrocardiogram waveform. Specifically, the processing unit 41 extracts the frequency components included in the range from 0.2 Hz to 1.0 Hz from the RR interval as the RRI fluctuation component.

[0025] Here, it is known that the frequency components within the RR interval, specifically those in the range of 0.2 Hz to 1.0 Hz, reflect the parasympathetic nervous system activity of subject X. Specifically, when subject X is not experiencing pain and the parasympathetic nervous system is active, the amplitude of the RRI fluctuation component graph increases. On the other hand, when subject X is experiencing pain and the parasympathetic nervous system is not active, the amplitude of the RRI fluctuation component graph decreases.

[0026] In other words, the pain of subject X can be evaluated by quantifying the magnitude of the amplitude of the RRI fluctuation component graph. For this reason, the processing unit 41 quantifies the magnitude of the amplitude of the RRI fluctuation component graph by calculating the degree of variability of the RRI fluctuation component shown in Figure 5. The degree of variability can be the standard deviation (SDNN: Standard Deviation of Normal to Normal intervals), variance, or entropy.

[0027] Figure 6 is a portion of the graph showing the time-series changes of the RRI fluctuation component shown in Figure 5, with the vertical axis magnified. Referring to Figure 6, more specifically, the processing unit 41 sets multiple sub-calculation periods ST for the period including the start time tx of the treatment event, and calculates the degree of variability of the RRI fluctuation component for each sub-calculation period ST. The length of the sub-calculation period ST is, for example, 10 seconds. Also, consecutive sub-calculation periods ST include periods that overlap with each other.

[0028] Specifically, the processing unit 41 sets the startup timing of the processing unit 2 as time t0, and the period from time t0 to time t10 (10 seconds later) as sub-calculation period ST1. The processing unit 41 also sets the period from time t1 (1 second after time t0) to time t11 (10 seconds later) as sub-calculation period ST2. The processing unit 41 also sets the period from time t2 (1 second after time t1) to time t12 (10 seconds later) as sub-calculation period ST3. In this way, the processing unit 41 calculates the degree of variation of the RRI fluctuation component for each set sub-calculation period ST.

[0029] Furthermore, consecutive sub-calculation periods ST do not need to have overlapping periods. Also, the length of a sub-calculation period ST is not limited to 10 seconds.

[0030] (Obtaining index values ​​through normalization) Figure 7 is a graph showing the time-series change in the degree of variability of the RRI variation component, calculated based on the RRI variation component shown in Figure 6. The graph in Figure 7 shows the time-series change of the SDNN of the RRI variation component as an example of the degree of variability of the RRI variation component.

[0031] Referring to Figure 7, the processing unit 41 performs normalization on the calculated SDNN. Specifically, as part of the normalization process, the processing unit 41 calculates the ratio of the SDNN in the period after the start time tx of the treatment event to the SDNN in the period before the start time tx. Through this, the processing unit 41 obtains the index value R used for pain assessment.

[0032] (i) Calculation of the reference value BS More specifically, when the processing unit 41 receives start time information from the input operation unit 24, for example, it sets a portion of the period prior to the start time tx indicated by the start time information as the reference period. The length of the reference period is, for example, 15 seconds. In the example shown in Figure 7, 15 seconds of the period prior to the start time tx is set as the reference period.

[0033] The processing unit 41 then uses the SDNN based on the vascular dynamics information acquired during the reference period to calculate the reference value BS used for evaluating the pain of subject X. Specifically, the processing unit 41 calculates the mean or median of the SDNN during the reference period as the reference value BS.

[0034] (ii) Calculation of the target value V Furthermore, the processing unit 41 calculates a target value V used for pain assessment of subject X using an SDNN based on vascular dynamics information acquired after the start time tx. More specifically, the processing unit 41 divides the period after the start time tx into several target value calculation periods AT. The length of the target value calculation period AT is, for example, 15 seconds.

[0035] Specifically, the processing unit 41 sets the time t21, 15 seconds after the start time tx, as the target value calculation period AT1. The processing unit 41 also sets the time t22, 15 seconds after time t21, as the target value calculation period AT2. Then, for each of the target value calculation periods AT set in this way, the processing unit 41 calculates the mean or median of the SDNN as the target value V.

[0036] Furthermore, consecutive target value calculation periods AT may have overlapping periods, as shown in the sub-calculation period ST in Figure 6. Also, the length of the target value calculation period AT is not limited to 15 seconds.

[0037] (iii) Calculation of the index value R Furthermore, for each target value calculation period AT, the processing unit 41 calculates the ratio of the target value V to the reference value BS as an index value R as a normalization process. Specifically, the processing unit 41 calculates the index value R1 for the target value calculation period AT1 as the value obtained by dividing the target value V1 by the reference value BS (R1 = V1 / BS). The processing unit 41 also calculates the index value R2 for the target value calculation period AT2 as the value obtained by dividing the target value V2 by the reference value BS (R2 = V2 / BS). Then, the processing unit 41 outputs each of the index values ​​R calculated in this way to the evaluation unit 42 shown in Figure 2.

[0038] In the example described above, the processing unit 41 obtains the SDNN of the RRI fluctuation component by performing preliminary processing on electrocardiogram data, which is an example of vascular dynamics information, and calculates the index value R by applying normalization processing to the obtained SDNN. However, the processing unit 41 is not limited to such a configuration of performing preliminary processing, and may, for example, be configured to apply normalization processing to the value indicated by the vascular dynamics information without performing preliminary processing.

[0039] [Pain assessment by the evaluation department] Figure 8 is a graph showing the index value R calculated by applying normalization to the SDNN shown in Figure 7. In Figure 8, the horizontal axis represents time, and the vertical axis represents the index value R. Figure 8 also shows, as an example, a graph obtained by plotting the index value R every second.

[0040] Referring to Figures 2 and 8, the evaluation unit 42 uses the index value R output from the processing unit 41 to evaluate the pain of the subject X. For example, each time the evaluation unit 42 receives a newly calculated index value R from the processing unit 41, it determines whether the index value R is less than or equal to the threshold Th. Specifically, the threshold Th is assumed to be a value less than 1. In the example shown in Figure 8, the index value R is less than or equal to the threshold Th from the start time tx of the treatment event onward. In this case, when the index value R is less than or equal to the threshold Th, the evaluation unit 42 sends an instruction signal to the output unit 25.

[0041] When the output unit 25 receives an instruction signal from the evaluation unit 42, it performs a warning process to inform medical personnel M1 and M2 that the subject X is experiencing pain. For example, the output unit 25 controls the display so that a display indicating that the subject X is experiencing pain is shown on a monitor (not shown). This allows medical personnel M1 and M2 to understand that the subject X is experiencing pain. In addition to display control, the output unit 25 may also perform output control to output a warning sound, for example.

[0042] The evaluation unit 42 may be configured to perform pain assessment according to the PIPP or PIPP-R described above. In this case, the evaluation unit 42 can, for example, use the index value R calculated by the processing unit 41 as one of the indices in PIPP or PIPP-R. Alternatively, the evaluation unit 42 may make a comprehensive judgment based on the evaluation results of the pain assessment based on the index value R and the evaluation results of the pain assessment according to PIPP or PIPP-R to make a final pain assessment.

[0043] Furthermore, the processing unit 41 and the evaluation unit 42 may be provided in separate devices. For example, the processing unit 2 may not include the evaluation unit 42 and output unit 25 shown in Figure 2, and may be configured to transmit the index value R calculated by the processing unit 41 to another device that includes the evaluation unit 42 and output unit 25.

[0044] [Operation Flow] Figure 9 is a flowchart illustrating the flow of operations when a processing device 2 according to one aspect of this disclosure performs pain assessment of a subject X using a biometric information processing method. Here, the flow of operations when the processing device 2 performs pain assessment based on electrocardiogram data will be explained.

[0045] Referring to Figure 9, first, when the processing unit 2 is activated, for example by a medical professional M1, it acquires electrocardiogram data transmitted from an electrocardiogram sensor, which is an example of a bio-information sensor 32 (step S11).

[0046] Next, the processing unit 2 detects the R timing of each heartbeat based on the electrocardiogram waveform shown in the acquired electrocardiogram data (step S12), and measures the RR interval, which is the interval between adjacent R timings (step S13). The processing unit 2 may also perform linear interpolation on the measured RR intervals.

[0047] Next, the processing unit 2 extracts predetermined frequency components from the measured RR interval. Specifically, the processing unit 2 extracts frequency components included in the range from 0.2 Hz to 1.0 Hz from the RR interval as RRI fluctuation components (step S14).

[0048] Next, the processing unit 2 sets multiple sub-calculation periods ST for the period including before and after the start time tx of the treatment event for subject X, and calculates the degree of variability of the RRI variation component for each sub-calculation period ST (step S15).

[0049] Next, the processing unit 2 sets a portion of the period prior to the start time tx of the treatment event as the reference period, and calculates the mean or median of the degree of variation of the RRI fluctuation component during that reference period as the reference value BS (step S16).

[0050] Next, the processing unit 2 divides the period after the start time tx of the treatment event into multiple target value calculation periods AT, and calculates the average or median value of the degree of variability of the RRI variation component as the target value V for each target value calculation period AT (step S17).

[0051] Next, for each target value calculation period AT, the processing unit 2 calculates the ratio of the target value V to the reference value BS as an index value R as a normalization process (step S18). Next, the processing unit 2 determines whether the calculated index value R is less than or equal to the threshold Th (step S19).

[0052] Then, if the processing unit 2 determines that the index value R is below the threshold Th (YES in step S19), it performs a warning process to inform healthcare workers M1 and M2 that the subject X is experiencing pain (step S20). On the other hand, if the processing unit 2 determines that the index value R is greater than the threshold Th (NO in step S19), it does not perform a warning process.

[0053] Furthermore, the vasodynamic information is not limited to the electrocardiogram waveform of subject X, but may also be the pulse wave waveform of subject X. In this case, the processing unit 41 in the processing unit 2 acquires the pulse wave waveform instead of electrocardiogram data in step S11, for example, and detects the peak of the ejection wave in the pulse wave waveform in step S12. Then, in step S13, the processing unit 41 measures the interval between adjacent peaks as the RR interval.

[0054] [Differentiation] The evaluation unit 42 may be configured to perform pain assessment of the subject X based on one or more types of biological information other than vasodynamic information, in addition to the index value R calculated by the processing unit 41. The biological information may include, for example, information indicating arterial blood oxygen saturation, information indicating heart rate, information indicating blood pressure, information indicating pulse wave propagation time, information indicating QTc (corrected QT time), information indicating body temperature, or information indicating perfusion index.

[0055] In this case, the acquisition unit 27 of the processing device 2 shown in Figure 2 is connected to one or more bio-information sensors 32, such as a pulse wave sensor, blood pressure sensor, or body temperature sensor, in addition to the electrocardiogram sensor. The evaluation unit 42 performs a predetermined weighting on the index value R and the values ​​indicated by each bio-information, for example, and evaluates the pain of the subject X through an overall evaluation.

[0056] Furthermore, the processing unit 41 may be configured to perform the normalization process described above on some or all of the biological information other than vascular dynamic information. For example, the processing unit 41 may calculate an index value by normalizing the perfusion index from the values ​​indicated by the biological information received from the acquisition unit 27. In this case, the evaluation unit 42 uses the index value calculated based on the perfusion index, the index value R calculated based on the electrocardiogram data, and the values ​​indicated by each piece of biological information other than the information indicating the perfusion index to evaluate the pain of the subject X.

[0057] As described above, in the biomedical information processing device 2 according to one aspect of this disclosure, the acquisition unit 27 acquires vascular dynamic information indicating the blood circulation state of the subject X. The processing unit 41 acquires an index value R for the pain assessment of the subject X by applying normalization processing to the vascular dynamic information. Furthermore, the processing unit 41 calculates a reference value BS based on the vascular dynamic information acquired during a reference period before the start of a treatment event related to treatment for the subject X, calculates a target value V based on the vascular dynamic information acquired after the start of the treatment event, and further calculates the index value R as the ratio of the target value V to the reference value BS as a normalization process.

[0058] Thus, the configuration that automatically calculates the pain assessment index value R reduces the burden on evaluators, i.e., healthcare professionals, who perform pain assessments, and also reduces variability in evaluations among evaluators. Furthermore, by performing normalization processing on the vasodynamic information of subject X and obtaining the index value R, individual differences among subject X are eliminated, enabling more accurate pain assessments.

[0059] As described above, a procedure event in which the biomedical information processing device 2 according to one aspect of this disclosure is used is an event in which subject X experiences pain, and an event in which pain occurs includes at least one of the following: an event in which a drug is administered to subject X, an event in which an examination is performed on subject X, and an event in which a procedure is performed on subject X.

[0060] This configuration allows healthcare professionals to determine whether or not subject X is experiencing pain during multiple types of treatment events.

[0061] As described above, the vascular dynamics information acquired by the biomedical information processing device 2 according to one aspect of this disclosure is information showing the electrocardiogram waveform or pulse wave waveform of subject X.

[0062] Thus, by using information influenced by the activity state of the parasympathetic nervous system, the pain assessment of subject X can be performed more accurately. In addition, since electrocardiogram sensors for acquiring electrocardiogram waveforms are often attached to subject X during treatment, pain assessment can be performed using existing sensors.

[0063] As described above, in the biological information processing device 2 according to one aspect of the present disclosure, the processing unit 41 calculates the degree of variation of the values ​​indicated by the vascular dynamics information based on the vascular dynamics information, and further divides the period after the start of the treatment event into a plurality of target value calculation periods AT, and calculates a target value V for each target value calculation period AT based on the degree of variation of each target value calculation period AT.

[0064] This configuration allows for the quantification of parasympathetic nervous system activity.

[0065] As described above, in the biometric information processing device 2 according to one aspect of this disclosure, each target value calculation period AT includes a plurality of sub-calculation periods ST. The processing unit 41 calculates the degree of variation for each of the sub-calculation periods ST. The processing unit 41 also calculates the average or median of the degree of variation of each of the plurality of sub-calculation periods ST as the target value V for each target value calculation period AT.

[0066] This configuration allows for more accurate evaluation by avoiding the effects of noise and other factors that may temporarily alter the degree of variation.

[0067] As described above, in the biometric information processing device 2 according to one aspect of this disclosure, consecutive sub-calculation periods ST include periods that overlap with each other.

[0068] With this configuration, each target value calculation period AT includes many sub-calculation periods ST, allowing for the calculation of more reliable target values ​​using a larger range of variability.

[0069] As described above, in the biological information processing device 2 according to one aspect of this disclosure, the evaluation unit 42 performs a pain assessment of the subject X based on the index value R acquired by the processing unit 41. The acquisition unit 27 further acquires at least one of the following as biological information: information indicating the arterial blood oxygen saturation of the subject X, information indicating the heart rate of the subject X, information indicating the blood pressure of the subject X, information indicating the pulse wave propagation time of the subject X, information indicating the QTc of the subject X, information indicating the body temperature of the subject X, and information indicating the perfusion index of the subject X. The evaluation unit 42 then performs a pain assessment of the subject X based on one or more types of the above biological information in addition to the index value R.

[0070] In this way, by using multiple types of indicators to assess pain, it is possible to perform a more accurate pain assessment.

[0071] Although embodiments of the present invention have been described above, the technical scope of the present invention should not be interpreted as being limited by the description of these embodiments. These embodiments are examples, and it will be understood by those skilled in the art that various modifications to the embodiments are possible within the scope of the invention as described in the claims. The technical scope of the present invention should be determined based on the scope of the invention as described in the claims and the scope of its equivalents. [Explanation of Symbols]

[0072] 2: Biological information processing unit (processing unit), 20: Control unit, 21: Storage unit, 24: Input operation unit, 25: Output unit, 26: Bus, 27: Acquisition unit, 32: Biological information sensor, 41: Processing unit, 42: Evaluation unit

Claims

1. An acquisition unit that acquires vascular dynamic information indicating the blood circulation status of the subject, The system includes a processing unit that obtains an index value for the subject's pain assessment by applying normalization processing to the vascular dynamics information, The aforementioned processing unit, Based on the vascular dynamics information obtained during the reference period prior to the start of the treatment event for the subject, a reference value is calculated. Based on the vascular dynamics information obtained after the start of the aforementioned treatment event, the target value is calculated. A bio-information processing device that, as part of the normalization process, calculates the ratio of the target value to the reference value as the index value.

2. The aforementioned treatment event is an event that causes pain to the subject, The biological information processing device according to claim 1, wherein the event causing pain includes at least one of the following: an event in which a drug is administered to the subject, an event in which an examination is performed on the subject, and an event in which a procedure is performed on the subject.

3. The biomedical information processing device according to claim 1, wherein the vascular dynamics information is information showing the electrocardiogram waveform or pulse wave waveform of the subject.

4. The aforementioned processing unit, Based on the vascular dynamics information, the degree of variation of the values ​​indicated by the vascular dynamics information is calculated. The biological information processing device according to claim 3, wherein the period after the start of the treatment event is divided into a plurality of target value calculation periods, and the target value is calculated for each target value calculation period based on the degree of variation of each target value calculation period.

5. Each of the aforementioned target value calculation periods includes multiple sub-calculation periods. The aforementioned processing unit, The degree of variation is calculated for each of the aforementioned sub-calculation periods. The biological information processing apparatus according to claim 4, wherein for each of the target value calculation periods, the average or median value of the degree of variation of each of the multiple sub-calculation periods is calculated as the target value.

6. The biological information processing apparatus according to claim 5, wherein the consecutive sub-calculation periods include periods that overlap with each other.

7. The biological information processing device further includes an evaluation unit that performs pain assessment of the subject based on the index value acquired by the processing unit, The acquisition unit further acquires at least one of the following as biological information: information indicating the subject's arterial blood oxygen saturation, information indicating the subject's heart rate, information indicating the subject's blood pressure, information indicating the subject's pulse wave propagation time, information indicating the subject's QTc (corrected QT time), information indicating the subject's body temperature, and information indicating the subject's perfusion index. The biological information processing device according to claim 1, wherein the evaluation unit performs a pain assessment of the subject based on one or more types of biological information in addition to the index value.

8. A method for processing biological information in a biological information processing device, A step to obtain vasodynamic information indicating the blood circulation status of the subject, The step includes obtaining an index value for the subject's pain assessment by applying a normalization process to the vascular dynamics information, In the step of obtaining the aforementioned index value, Based on the vascular dynamics information obtained during the reference period prior to the start of the treatment event for the subject, a reference value is calculated. Based on the vascular dynamics information obtained after the start of the aforementioned treatment event, the target value is calculated. A biometric information processing method comprising, as the normalization process, calculating the ratio of the target value to the reference value as the index value.

9. A bio-information processing program used in a bio-information processing device, In the computer of the aforementioned biological information processing device, A step to obtain vasodynamic information indicating the blood circulation status of the subject, The process involves performing a normalization process on the vascular dynamics information to obtain an index value for the subject's pain assessment, and then executing the following steps: In the step of obtaining the aforementioned index value, Based on the vascular dynamics information obtained during the reference period prior to the start of the treatment event for the subject, a reference value is calculated. Based on the vascular dynamics information obtained after the start of the aforementioned treatment event, the target value is calculated. A biometric information processing program that, as part of the normalization process, calculates the ratio of the target value to the reference value as the index value.

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