JP2019093095A - Non-invasion type pain monitoring system and method - Google Patents

Abstract

To provide a non-invasion type pain monitoring system and non-invasion type pain monitoring method.SOLUTION: A non-invasion type pain monitoring system comprises: at least one physical signal sensing device; and a control host. The physical signal sensing device senses a physical state of a user, for generating at least one physical signal. The control host is electrically connected to the physical signal sensing device, and comprises a storage module, an analysis module and a warning unit. The storage module stores a plurality of pain generation determination rules. The analysis module analyzes the physical signal for generating a plurality of physical parameter change ratios, and when the physical parameter change ratio matches the pain generation determination rules, determines that, the user is in a pain generation state. The warning unit generates a pain generation alarm, when a pain prediction determination unit determines that, the user is in the pain generation state.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a non-invasive pain monitoring system and a non-invasive pain monitoring method, and more particularly to a non-invasive pain monitoring system and a non-invasive pain which make judgments by physiological signals obtained by sensing with a physiological signal sensing device. It relates to the monitoring method.

  In places such as hospitals, it is common for the physiological signal tracing facility to display the patient's physiological signal immediately. For example, with an electrocardiogram tracing facility, the medical staff assesses whether the patient's body condition is normal or if symptoms appear. However, since the physiological signal tracing facility can only faithfully display the patient's physiological signal and can not know if the patient is in pain, the medical staff immediately relieves the patient's pain I can not do it. In particular, when the patient can not express pain independently, the medical staff can not know clearly whether the patient feels uncomfortable. At this time, when the medical staff gives the patient a course of treatment that increases pain, the patient may be shocked or comatose due to such pain.

  Conventionally, in a hospital or the like, the physiological signal tracing facility can display the patient's physiological signal immediately, but medical staff can directly know whether the patient feels discomfort such as pain by the physiological signal. Can not. For this reason, if the patient can not express the feeling of pain proactively, the medical staff can not immediately relieve the patient's pain discomfort.

  The main objective of the present invention is to analyze whether the patient is in pain by analyzing the information collected by the physiological signal tracing facility, and the medical staff immediately grasps whether the patient feels pain To provide a non-invasive pain monitoring system and a non-invasive pain monitoring method capable of effectively solving the problems that can not be solved.

According to the non-invasive pain monitoring system of the present invention, at least one physiological signal sensing device for sensing a physiological condition of a user to generate at least one physiological signal;
A control host electrically connected to the at least one physiological signal sensing device;
Equipped with
Control host is
A storage module in which a pain occurrence determination rule is stored by a plurality of physiological parameters;
An analysis module electrically connected to the at least one physiological signal sensing device and the storage module;
A warning unit electrically connected to the analysis module and generating a pain occurrence alarm when the pain prediction determination unit determines that the user is in a pain occurrence state;
Equipped with
Analysis module
An analysis unit for receiving at least one physiological signal and analyzing the at least one physiological signal to generate a plurality of physiological parameter change rates corresponding to these physiological parameters;
A pain prediction judgment unit which is electrically connected to the analysis unit and which judges that the user is in a pain generation state when the physiological parameter change rate meets the pain generation judgment rule;
And the like.

  According to the non-intrusive pain monitoring system of the present invention, at least one physiological signal sensing device comprises an electrocardiogram generator, and the physiological parameter change rates are: heart rate (HR) change rate; Judgment of pain prediction including interval (R-R interval, RRI) change rate, heart rate low frequency range power (low frequency, LF) change rate, and heart rate high frequency range power (high frequency, HF) change rate If the user is in a state of pain when the unit is in a state of pain when the heart rate change rate, RR interval change rate, heart rate low frequency range power change rate and heart rate high frequency range power change rate meet the pain occurrence determination rule It is characterized by judging.

  According to the non-intrusive pain monitoring system of the present invention, the at least one physiological signal sensing device comprises a photoplethysmography (PPG) device, and the physiological parameter change rate is PPG amplitude, PPGA The pain prediction judgment unit includes a change rate, a base line (BL) change rate, and an autonomic nervous system state (ANSS) change rate, and the pain prediction judgment unit is an optical volume tracing rate change rate, When the baseline change rate and the autonomic nervous system state change rate meet the pain occurrence determination rule, the user is determined to be in a pain occurrence state.

  According to the non-invasive pain monitoring system of the present invention, the analysis unit analyzes at least one physiological signal and generates data of a plurality of physiological parameters corresponding to these physiological parameters, and generates data of these physiological parameters. Furthermore, these physiological parameter change rates are calculated, and the storage module further stores a plurality of pain degree determination rules, and the analysis module further includes a pain degree determination unit, and the pain degree determination unit Is characterized in that the degree of pain of the user is judged by comparing these pain degree judgment rules with the data of these physiological parameters.

  According to the non-intrusive pain monitoring system of the present invention, the control host further includes an operation module, and the operation module is electrically connected to the analysis module and operated to operate the pain prediction determination unit and the pain degree determination unit. It is characterized in that at least one of them is controlled and operated.

According to the non-intrusive pain monitoring method of the present invention, the step (a) of establishing a pain occurrence judgment rule;
Sensing at least one physiological signal sensing device to generate at least one physiological signal by sensing the physiological condition of the user;
The analysis unit analyzes at least one physiological signal to generate data of a plurality of physiological parameters corresponding to the physiological parameter, and the data of the physiological parameter further calculates a plurality of physiological parameter change rates Step (c) to
Step (d) of determining that the user is in a state of pain occurrence when the rate of change in physiological parameters meets the pain occurrence determination rule by the pain prediction determination unit;
It is characterized by including.

  According to the non-intrusive pain monitoring method of the present invention, the step (a) can be performed by sensing a plurality of physiological conditions of the person receiving the experiment when simulating that the person receiving the experiment is in a state of pain occurrence. It is characterized by establishing a pain occurrence judgment rule by the actual pain physiological parameter change rate.

  According to the non-intrusive pain monitoring method of the present invention, in step (d), the tendency of the change in the heart rate change rate of the user is increased according to the pain occurrence determination rule, and the change in the user's RR interval change rate is The tendency is a drop, the change in the user's heart rate low frequency range power change rate is a drop, and the change in the user's heart rate high frequency range power change rate is a drop, the light volume tracing amplitude of the user If the change rate change trend is a drop, the user's baseline change rate trend is a fall, and the user's voluntary nervous system state change rate trend is a drop, then the user experiences pain It is characterized by judging that it is in a state.

  According to the non-intrusive pain monitoring system and the non-intrusive pain monitoring method of the present invention, it is possible to determine whether a patient is in pain or not by analyzing the information collected by the physiological signal tracing facility. Staff members have the effect of being able to effectively solve problems that can not be immediately determined whether the patient is feeling pain.

FIG. 1 shows a non-invasive pain monitoring system according to an embodiment of the present invention. It is a figure which shows the experimental data which contrast the temperature control and pain index figure which concern on this invention with a heart rate graph. FIG. 6 shows experimental data for comparing the temperature control and pain index diagram according to the present invention with an RR interval (RRI) diagram. FIG. 5 shows experimental data comparing temperature control and pain index diagram according to the present invention with heart rate low frequency range power (LF) and heart rate high frequency range power (HF) diagram. FIG. 6 shows experimental data contrasting the temperature control and pain index diagram according to the invention with a light volume tracing amplitude (PPGA) diagram. FIG. 6 shows experimental data contrasting the temperature control and pain index diagram according to the invention with a baseline (BL) diagram. It is a figure which shows the experimental data which contrasts temperature control and a pain index figure which concern on this invention with an autonomic-nerves system state (ANSS) figure.

  Hereinafter, a specific application method of the present invention will be described in detail with reference to the drawings. The advantages and features of the present invention can be understood by the following description and claims. In particular, the proportions of the respective members in the drawings of the embodiment of the present invention are shown for easy understanding of the description, and are not actual proportions.

  Please refer to FIG. FIG. 1 is a schematic view showing a non-invasive pain monitoring system according to an embodiment of the present invention. As shown in FIG. 1, the non-invasive pain monitoring system 100 includes a physiological signal sensing device 1 and a control host 2.

  The physiological signal sensing device 1 is for sensing a physiological condition of a user to generate a physiological signal S1. The physiological signal sensing device 1 includes at least one of an electrocardiogram generator and a photoplethysmography (PPG) device. The physiological signal S1 includes at least one of an electrocardiogram signal corresponding to the electrocardiogram generator and a light volume tracing signal corresponding to the light volume tracing device.

  In other words, when the physiological signal sensing device 1 is an electrocardiogram generator, the physiological signal S1 generated by sensing the physiological condition of the user is an electrocardiogram signal. When the physiological signal sensing device 1 is an optical volume tracing device, a physiological signal S1 generated by sensing the physiological state of the user is an optical volume tracing signal. On the other hand, although the present embodiment has been described by way of example where there is only one physiological signal sensing device 1, actually, there are two physiological signal sensing devices 1 and two physiological signal sensing devices 1 It may be an electrocardiogram generator and an optical volume tracing device, respectively.

  The control host 2 includes a storage module 21, an analysis module 22, a warning unit 23, a display unit 24 and an operation module 25.

  The storage module 21 includes a plurality of pain occurrence determination rules 211 (only one is displayed in the drawing) and a plurality of pain degree determination rules 212 (only one is displayed in the drawing). It is stored. The pain occurrence determination rule 211 and the pain degree determination rule 212 correspond to a plurality of physiological parameters, respectively. Physiological parameters include heart rate (heart rate, HR), RR interval (RR interval, RRI), heart rate low frequency range power (low frequency, LF), and heart rate high frequency range power (high frequency) , HF), light volume tracing amplitude (PPG amplitude, PPGA), baseline (BL), and autonomic nervous system state (ANSS).

  As described above, in the pain occurrence determination rule 211 stored in the storage module 21, the physiological state corresponding to each physiological parameter change rate is recorded. When the physiological parameter change rate meets the pain occurrence determination rule 211, it indicates that the user's physiological condition is in a pain occurrence state. And in this embodiment, in each physiological parameter, the proportion that heart rate and RR interval occupy more. That is, when determining whether the user is in a pain state, it is mainly necessary to fit the heart rate and the RR interval, and fitting the other physiological parameters increases the probability of the decision. effective. Further, the pain occurrence determination rule corresponding to each of the above physiological parameter change rates is shown in Table 1.

  On the other hand, in this embodiment, the pain level determination rule 212 stored in the storage module 21 is shown in Table 2. In Table 2, according to the pain evaluation method, the patient directly determines the degree of pain by himself, and according to the record of the data of the physiological parameter of the patient, the physiological parameter and the degree of pain judgment rule obtained statistically. It is a control table. Physiological parameters to which each pain level in Table 2 is controlled are average values calculated by statistically measuring data of a large number of patients.

  The analysis module 22 is electrically connected to the physiological signal sensing device 1 and the storage module 21. The analysis module 22 includes an analysis unit 221, a pain prediction determination unit 222, and a pain degree determination unit 223.

  The analysis unit 221 is for receiving and analyzing the physiological signal S1 to generate data of a plurality of physiological parameters and a tendency of changes in the plurality of physiological parameters corresponding to the above-mentioned physiological parameters.

  The pain prediction determination unit 222 is electrically connected to the analysis unit 221, and when at least two of the physiological parameter change rates match the corresponding one of the pain occurrence determination rules 211, the user is in a pain occurrence state Judge that it is. For example, when the heart rate change rate of the physiological parameter change rate increases and the RR interval change rate decreases, the pain prediction determination unit 222 generates pain in the heart rate change rate and the RR interval change rate. By meeting the determination rule 211, it is determined that the user is in a pain generation state. In fact, the pain prediction judgment unit 222 can set the quantity in which the physiological parameter change rate meets the pain occurrence judgment rule 211, and there are at least two. In addition, the precision probability of judgment is high, so that the said number is large.

The pain degree determination unit 223 is electrically connected to the analysis unit 221, and determines the degree of pain felt by the user by comparing the pain degree determination rule 212 and the data of the physiological parameter. And, in fact, it is preferable to compare the data of many physiological parameters with each other. For example, if the heart rate is 93 beats / min and the heart rate high frequency range power is 483 ms ² , the pain level of the user is determined by the physiological parameters of the pain level determination rule 212 and the pain level determination rule comparison table. Is considered severe pain. However, when the heart rate is 80 beats / min, in the present embodiment, the pain level determination unit 223 determines that the 80 beats / min simultaneously corresponds to the sections such as mild pain, moderate pain and severe pain. As the number of types of physiological parameters to be compared by simultaneously comparing and determining two or more types of physiological parameters increases, the precision probability of the determination is higher. On the other hand, in fact, a pain degree determination unit 223 incorporates a classifier. The classifier sets the proportionate proportion according to the pain degree determination rule 212 to establish a pain degree model. Accordingly, the analysis module 22 compares the various types of physiological parameters with each other according to the pain level model to determine the degree of pain corresponding to the physiological parameter.

  Further, when the physiological signal S1 received by the analysis unit 221 is an electrocardiogram signal, the physiological parameter corresponding to the data of the physiological parameter obtained by analyzing the physiological signal S1 by the analysis unit 221 corresponds to the heart rate, RR Interval, heart rate low frequency range power, or heart rate high frequency range power. In practice, the above-mentioned physiological parameters can be obtained by analyzing the physiological signal S1 obtained by sensing by the electrocardiogram generator.

  On the other hand, when the physiological signal S1 received by the analysis unit 221 is a light volume tracing signal, the physiological parameter corresponding to the data of the physiological parameter obtained by analyzing the physiological signal S1 by the analysis unit 221 is a light volume signal. Racing amplitude, baseline, or autonomic nervous system status. In fact, the above-mentioned physiological parameter can be obtained by analyzing the physiological signal S1 obtained by sensing by the light volume tracing device.

  When the analysis unit 221 analyzes the physiological signal S1 continuously, the data of the physiological parameter may be gradually increased according to the sensing frequency of the physiological signal S1 to obtain data of a large number of physiological parameters. The analysis unit 221 calculates the rate of change of a large number of physiological parameters by using data of a large number of physiological parameters. The rate of change of the above physiological parameters is the rate of change in heart rate derived from heart rate, rate of change in RR interval derived from RR interval, rate of heart rate low frequency range power change rate derived from heart rate low frequency range power , Heart rate high frequency range power derived heart rate high frequency range power change rate, light volume tracing amplitude derived light volume tracing amplitude change rate, baseline derived baseline change rate derived from autonomic nervous system state Includes rate of change of voluntary nervous system status.

  On the other hand, it is possible to know the tendency of change in physiological parameter calculated from the data of each type of physiological parameter from the rate of change in numerical value in a specific time length. For example, if the rate of change in heart rate increases at 8 times per minute, the tendency of change in heart rate shows an increase, and at this time, it is another matter about the tendency of increase in heart rate in the pain occurrence determination rule 211. If the material of the physiological parameter meets the corresponding pain occurrence determination rule 211, it can be determined that the user is in a pain occurrence state.

  The warning unit 23 is electrically connected to the analysis module 22 and generates a pain occurrence alarm when it is determined by the pain prediction determination unit 222 that the user is in a pain occurrence state. Also, the pain occurrence alarm is, for example, a voice alarm such as a buzzer, a light alarm in which a light blinks, a vibration alarm, or a combination of these.

  The display unit 24 is electrically connected to the analysis module 22 to display data of each type of physiological parameter and the degree of pain. The display unit 24 is, for example, a display screen.

  The operation module 25 is electrically connected to the analysis module 22, and the user switches to the pain prediction determination unit 222 or the pain degree determination unit 223. The operation module 25 is, for example, a button set.

  Thus, in the non-intrusive pain monitoring method according to the present invention, first, when the examinee is in the simulated pain generation state, the physiological signal sensing device 1 senses the physiological states of the examinee and the plurality of actual cases are obtained. The pain occurrence judgment rule 211 is established according to the pain physiological parameter change rate. Thereafter, the physiological signal sensing device 1 senses the physiological condition of the user to generate at least one physiological signal, and the analysis unit 221 analyzes the physiological signal to generate data of a plurality of physiological parameters corresponding to the physiological parameter. To generate a plurality of physiological parameter change rates according to the physiological parameter data. Next, when the physiological parameter change rate meets the pain occurrence determination rule 211, the pain prediction determination unit 222 determines that the user is in a pain occurrence state.

  Also, according to the pain occurrence judgment rule, the change tendency of the user's heart rate change rate is rising, the change tendency of the user's RR interval change rate is fall, and the user's heart rate low frequency range power change rate The change trend of is the descent, the change trend of the user's heart rate high frequency range power change rate is the fall, the change trend of the light volume tracing amplitude change rate of the user is the fall, the baseline change of the user When the change tendency of the rate is a descent or the change tendency of the user's voluntary nervous system state change rate is a descent, it is judged that the user is in a pain generation state. However, in fact, when all of the above-mentioned various kinds of physiological parameter change rates meet the pain occurrence judgment rule, the precision probability of the judgment is the highest.

  Please refer to FIG. 2 to FIG. FIG. 2 is a diagram showing experimental data comparing the temperature control and pain index diagram according to the present invention to the heart rate graph. FIG. 3 is a diagram showing experimental data comparing the temperature control and pain index diagram according to the present invention to the RR interval (RRI) diagram. FIG. 4 is a diagram showing experimental data comparing the temperature control and pain index chart according to the present invention to the heart rate low frequency range power (LF) and heart rate high frequency range power (HF). FIG. 5 shows experimental data contrasting the temperature control and pain index diagram according to the present invention with a light volume tracing amplitude (PPGA) diagram. FIG. 6 shows experimental data comparing the temperature control and pain index diagram according to the present invention to a baseline (BL) diagram. FIG. 7 is a diagram showing experimental data in which the temperature control and pain index diagram according to the present invention are contrasted with the autonomic nervous system condition (ANSS) diagram.

  As described above, when establishing the steps of the above-described pain occurrence determination rule, in the present invention, pain is sensed by sensing physiological signals by a method of simulating pain mainly by heating water, and pain at different water temperatures Compare the trend of change of index and each type of physiological parameter. In actual operation, the present invention divides the experiment into five test stages A, B, C, D and E, and in the five test stages A, B, C, D and E, the hand of the receiver is different. Maintain at temperature. First, in the test stage A, the receiver is kept calm. In the test stage B, the hand of the receiver enters the tank where water is circulating, and at this time, the temperature indicated by the temperature curve TC is maintained at 37 ° C. In the test stage C, heating is performed at a temperature rising rate of 1 ° C./min, and the heating is stopped when the temperature curve TC reaches 47 ° C. After this, in the test stage D, the temperature of the temperature curve TC is maintained at 47.degree. Finally, in test phase E, water at 25 ° C. is injected into the above tank until the temperature curve TC drops to 32 ° C. On the other hand, the time for all five test stages A, B, C, D and E is 10 minutes, and the delay time is 1 minute. Also, in the test phase A, the data of the previous five minutes are ignored since it only holds the receiver calm.

  Thus, test stage C is a pain generating staircase, test stage D is a pain maintenance staircase, and test stage E is a pain relief staircase. The pain index (NRS score) NS felt by the receiver at the end of test phase C is approximately 5.03 ± 1.99. It should be noted that the pain index NS of the receiver starts to increase in the test stage C or occurs when the water temperature approaches around 47 ° C. because the difference in pain index when the test stages A and B are finished is not large Indicates to do.

  As shown in FIG. 2, in the test stage C of pain generation, the tendency of the change in heart rate is an increase, so when it is known that the change tendency of the heart rate is an increase, Indicates that it is For example, the calculation method of the heart rate change rate (beats / min / s) in the test stage C is (84.5-81.5) / ((31-21) X60) = 0.005 (beats / min / s). That is, the heart rate increases by 0.005 (beats / min) every second. Since 0.005 (beats / min) is positive, it indicates that the tendency for change in heart rate is an increase. And in the test stage E, the heart rate change rate from 41 minutes to 46 minutes is -0.01 (beats / min / s) calculated from (84-87.2) / ((46-41) X 60) . That is, this indicates that the tendency of the change in heart rate is a drop. Although the above explanation is intended to briefly explain the calculation method of the heart rate change rate, in fact, the calculation of the heart rate change rate analyzes the parameters approximately every 30 to 60 seconds, and the calculation interval is There is no such thing as 10 minutes.

  As shown in FIG. 3, in the test phase C in which pain occurs, the tendency of the change of the RR interval is a drop, so when it is known that the tendency of the change of the RR interval is a drop, It indicates that the measurer is on the stairs where the pain occurs.

  As shown in FIG. 4, in the test stage C where pain occurs, the tendency of the change in heart rate low frequency range power and the tendency of the change in heart rate high frequency range power is a descent and the test in which the pain is relieved In stage E, the tendency of change of heart rate low frequency range power and the tendency of change of heart rate high frequency range power are rising, so the change tendency of heart rate low frequency range power or change of heart rate high frequency range power It can be seen that when the trend of is a descent, it indicates that the receiver is on the pain-causing stairs.

  As shown in FIG. 5, in the test phase C in which pain occurs, the tendency of the change in the light volume tracing amplitude is a drop, so when the change trend of the light volume tracing amplitude is a drop, the receiver It can be seen to indicate that the pain is on the stairs.

  As shown in FIG. 6, in the test phase C where pain occurs, the tendency of the change in the baseline is an increase, and therefore, when the change tendency of the baseline is an increase, the receiver is in the pain steps It can be seen to indicate that.

  As shown in FIG. 7, in the test phase C where pain occurs, the tendency of the change of the voluntary nervous system state is a drop, so when the tendency of the change of the voluntary nervous system state is a drop, the receiver pains It turns out that it shows that it is in the development stairs.

  Thus, as shown in FIG. 1, the non-invasive pain monitoring system 100 according to the present invention is established by the results of the above experiment, and in fact, the judgment unit 23 of the non-invasive pain monitoring system 100 Although it is set to judge that the user is in a state of pain occurrence when the tendency of change of two or more physiological parameters matches the tendency of pain occurrence, the present invention is not limited thereto. That is, as described in the above embodiment, it may be determined that the user is in a pain generation state if the tendency of change of one physiological parameter matches the tendency of pain generation. Of course, the more the tendency of the change in physiological parameter is matched to the corresponding tendency of pain occurrence, the higher the precision with which the user judges that he / she is in a pain state.

  On the other hand, in the control host 2 described above, when the physiological signal sensing apparatus 1 measures the physiological signal S1 and the analysis module 22 analyzes the physiological signal S1 to generate a physiological parameter, the user operates the operation module 25. In addition, by inputting the pain index NS, the classifier of the pain degree determination unit 223 can establish the pain degree model by the pain index NS and the physiological parameter, and thus the precision probability of the classification of the pain degree model can be increased.

  Accordingly, an example in which the plurality of analysis modules 22 make the determination based on the physiological parameter will be exemplified below.

  As described above, in comparison with Table 1 described above in Example 1, since the physiological parameters other than the baseline match the tendency of the onset pain state in the pain occurrence determination rule 211, the pain prediction determination unit 222 determines It can be seen that the user determines that he is in pain. In Example 2, since only three kinds of physiological parameters such as heart rate high frequency range power, light volume tracing amplitude and autonomic nervous system state among the seven physiological parameters meet the pain occurrence judgment rule 211, the pain prediction judgment is made. Unit 222 determines that the user of Example 2 is not in pain. In Example 3, among the seven physiological parameters, only three physiological parameters such as heart rate, RR interval and baseline do not meet the pain occurrence determination rule 211, but in the present embodiment, the heart rate and R- The pain prediction judgment unit 222 judges that the user of the example 3 is not in a state of pain occurrence because the R interval is higher in proportion to the judgment.

  What I would like to supplement with is that the average results obtained by testing a large number of recipients during the course of the experiment of the present invention are main, and the explanation of this embodiment is mainly The average test results are taken as an example, and this is not the result of experimental data of only one individual.

  Thus, since the conventional physiological signal tracing equipment can only display the patient's physiological signal, the medical staff can not immediately grasp the discomfort caused by the patient's pain. The present invention analyzes the physiological parameter and the tendency of the change of the physiological parameter by the physiological signal sensed by the physiological signal sensing device, compares whether the tendency of the change of the pain is satisfied, and the user generates the pain Determine if it is in state. On the other hand, in the present invention, since the pain level determination unit is further provided, a pain level model is established by the physiological parameter and the pain level index, and the pain level index corresponding to the physiological parameter can be determined by the pain level model. is there.

  Thus, although described with reference to specific examples of the present invention, those examples are for illustration only, and are not intended to limit the present invention, and those of ordinary skill in the art. It will be appreciated that modifications, additions or omissions may be made to the embodiments disclosed herein without departing from the spirit and scope of the present invention and claims.

Reference Signs List 1 physiological signal sensing device 2 control host 21 storage module 22 analysis module 23 warning unit 24 display unit 25 operation module 100 non-intrusive pain monitoring system 211 pain occurrence judgment rule 212 pain degree judgment rule 221 analysis unit 222 pain prediction judgment unit 223 pain Degree judgment unit NS Pain index S1 Physiological signal TC Temperature curve

Claims (8)

At least one physiological signal sensing device for sensing a physiological condition of a user and generating at least one physiological signal;
A control host electrically connected to the at least one physiological signal sensing device;
Equipped with
The control host is
A storage module in which a pain occurrence determination rule is stored by a plurality of physiological parameters;
An analysis module electrically connected to the at least one physiological signal sensing device and the storage module;
A warning unit electrically connected to the analysis module and generating a pain occurrence alarm when the pain prediction determination unit determines that the user is in the pain occurrence state;
Equipped with
The analysis module
An analysis unit for receiving the at least one physiological signal and analyzing the at least one physiological signal to generate a plurality of physiological parameter change rates corresponding to the physiological parameters;
A pain prediction / judging unit which is electrically connected to the analysis unit and which judges that the user is in a state of pain occurrence when the rate of change in physiological parameter meets the pain occurrence determination rule;
A non-invasive pain monitoring system comprising:   The at least one physiological signal sensing device comprises an electrocardiogram generator, and the physiological parameter change rates of these are a heart rate (HR) change rate and an RR interval (R-R interval) change. The pain prediction / judging unit comprises: a rate, a heart rate low frequency range power (LF) rate of change, and a heart rate high frequency range power (HF) rate of change; When the RR interval change rate, the heart rate low frequency range power change rate, and the heart rate high frequency range power change rate meet the pain occurrence determination rule, it is determined that the user is in a pain occurrence state The non-invasive pain monitoring system according to claim 1, characterized in that   The at least one physiological signal sensing device may include a photoplethysmography (PPG) device, and the physiological parameter change rate may be a light volume tracing amplitude (PPG amplitude) change rate and a base line (base line). , BL) change rate and autonomic nervous system state (ANSS) change rate, and the pain prediction / judging unit comprises the light volume tracing amplitude change rate, the baseline change rate, and the voluntary change rate. The non-intrusive pain monitoring system according to claim 1, wherein it is determined that the user is in a state of pain occurrence when a rate of change in neural system status meets the pain occurrence determination rule.   The analysis unit analyzes the at least one physiological signal to generate data of a plurality of physiological parameters corresponding to the physiological parameters, and further, according to the data of the physiological parameters, The parameter change rate is calculated, and the storage module further stores a plurality of pain degree determination rules, the analysis module further includes a pain degree determination unit, and the pain degree determination unit The non-intrusive pain monitoring system according to claim 1, wherein the degree of pain of the user is determined by comparing the degree-of-pain determination rule with the data of the physiological parameter.   The control host further includes an operation module, and the operation module is electrically connected to the analysis module, and is operated to control at least one of the pain prediction determination unit and the pain degree determination unit. 5. The non-intrusive pain monitoring system according to claim 4, characterized in that it is for operating. Establishing a pain occurrence determination rule (a);
Sensing at least one physiological signal sensing device to generate at least one physiological signal by sensing the physiological condition of the user;
The analysis unit analyzes the at least one physiological signal to generate data of a plurality of physiological parameters corresponding to the physiological parameter, and the data of the physiological parameter further changes the physiological parameter. Calculating the rate (c),
(D) determining that the user is in a state of pain occurrence when the rate of change in the physiological parameter meets the pain occurrence determination rule by the pain prediction determination unit;
A non-invasive pain monitoring method comprising:   In step (a), when simulating that the person receiving the experiment is in a state of pain generation, the pain is changed according to a plurality of actual pain physiological parameter change rates obtained by sensing the physiological state of the person receiving the experiment The non-intrusive pain monitoring method according to claim 6, characterized in that an outbreak judgment rule is established.   In step (d), according to the pain occurrence determination rule, the change tendency of the heart rate change rate of the user is rising, and the change tendency of the RR interval change rate of the user is fall, and the user's The trend of change of heart rate low frequency range power change rate is a drop, the trend of change of heart rate high frequency range power change rate of the user is a fall, trend of change of light volume tracing amplitude change rate of the user Is a drop, and the tendency of the change in baseline change rate of the user is a drop, and the change in the rate of change of the voluntary nervous system state of the user is a drop. The non-invasive pain monitoring method according to claim 7, characterized in that it is determined to be.

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