JP2019103750A - Information processing device, information processing program, server, and information processing method - Google Patents

Abstract

To provide an information processing device capable of grasping a mental state or a physical state of a creature more accurately or more efficiently than in a conventional practice, a program, a server, or an information processing method.SOLUTION: An information processing device 300 is provided, which includes: a display 330; and a processor 310 for causing the display 330 to sequentially display first graphs about beat intervals or pulse intervals in each creature individual or each timing. The processor 310 causes the display 330 to display the first graphs and second graphs being a different type from the first graphs at the same time in each creature individual or each timing when a first condition is satisfied, and causes the display 330 to display the first graphs when the first condition is not satisfied.SELECTED DRAWING: Figure 24

Description

  The following disclosure relates to techniques for obtaining the mental or physical condition of an organism.

  BACKGROUND ART Conventionally, techniques for acquiring the mental or physical state of an organism are known. For example, Japanese Patent Application Laid-Open No. 2010-155166 (Patent Document 1) discloses a pulse wave diagnostic apparatus and a pulse wave diagnostic apparatus control method. According to Patent Document 1, the pulse wave diagnostic apparatus and the pulse wave diagnostic apparatus control method are characterized in that a pulse wave is detected using a photoelectric sensor and fluctuation of the pulse wave is calculated from the detected pulse wave. Specifically, in the pulse wave diagnostic apparatus control method according to the present invention, there is provided a photoplethysmogram detection unit for detecting pulse waves by receiving transmitted light transmitted through an artery or scattered light scattered by an artery; The pulse wave amplitude of each pulse of the pulse wave detected by the wave detection unit is calculated, and the point of the pulse wave amplitude on the orthogonal coordinate plane formed by the two continuously calculated pulse wave amplitudes And a pulse wave amplitude Poincare calculating unit which is calculated for each beat as Poincare coordinates.

JP, 2010-155166, A

  An object of the present disclosure is to provide an information processing device, a state acquisition program, a server, and the like that can grasp the mental state or physical state of an organism more accurately than before or more efficiently than before. It is to provide an information processing method.

  According to an aspect of the present invention, an information processing apparatus is provided that includes a display, and a processor for causing a display to sequentially display a first graph regarding a pulse interval or pulse interval for each living organism or for each timing. The processor causes the display to simultaneously display the first graph and the second graph different from the first graph when the first condition is satisfied, for each living organism or each timing, and the first condition is If not satisfied, the first graph is displayed on the display.

  As described above, according to the present disclosure, an information processing apparatus capable of grasping the mental state or physical state of an organism more accurately than before or more efficiently than before, state acquisition A program, a server, and an information processing method are provided.

FIG. 1 is a diagram showing an entire configuration of an information processing system 1 according to a first embodiment. It is a figure showing functional composition of information processing system 1 concerning a 1st embodiment. It is a flowchart which shows the process sequence for calculating the 1st autonomic-nerves balance of the information processing system 1 concerning 1st Embodiment. It is an example of the electrocardiogram data and the pulsation interval according to the first embodiment. It is a figure which shows the correspondence relationship table of pulsation interval RR (n) and the following pulsation interval RR (n + 1) concerning 1st Embodiment. Correspondence table 321A between the pulsation interval RR (n) and the next pulsation interval RR (n + 1) according to the first embodiment to the axis in the Y = X direction and the direction perpendicular thereto It is an image figure which shows conversion. As a first autonomic nervous balance for each mental state or physical state of a dog according to the first embodiment, a standard deviation on the Y = X axis and a standard deviation on the axis perpendicular to the Y = X It is a table showing a standard. It is a Poincare plot figure in the excited state of the dog concerning a 1st embodiment. It is a Poincare plot in the state where breathing is stable in the normal state of the dog according to the first embodiment. It is a Poincare plot figure in the normal state of the dog concerning a 1st embodiment. It is a Poincare plot in the state of rest of the dog concerning a 1st embodiment. It is a flowchart which shows the process sequence for calculating the 2nd autonomic-nerves balance of the information processing system 1 concerning 1st Embodiment. The standard deviation on the Y = X axis, the standard deviation on the axis perpendicular to Y = X, and the standard deviation as the second autonomic balance for each mental or physical condition of the dog according to the first embodiment It is a table showing a standard of the product of and the ratio of the standard deviation. It is a flowchart which shows the 1st process sequence for calculating the respiration rate of the information processing system 1 concerning 1st Embodiment. It is an example of the relationship between the pulsation detection timing concerning 1st Embodiment and a pulsation interval. It is an example of power spectrum distribution concerning a 1st embodiment. It is an example of RRI fluctuation and power spectrum distribution after spline interpolation at the time of resting of a dog concerning a 1st embodiment. It is an example of RRI fluctuation and power spectrum distribution after spline interpolation at the time of the excitement of the dog concerning a 1st embodiment. It is an example of the effect of the acquisition method of the respiration rate by 1st Embodiment. It is a flow chart which shows the 2nd processing procedure of the respiration rate of information processing system 1 concerning a 1st embodiment. It is an image figure which shows the voltage value of the electrocardiogram concerning 1st Embodiment. It is an image figure showing a display screen of a diagnostic graph which shows a normal state concerning a 1st embodiment. It is an image figure which shows the display screen of the diagnostic graph which shows the abnormal condition concerning 1st Embodiment. It is an image figure showing an example of a display screen of diagnostic terminal 300 concerning a 1st embodiment. It is a flowchart which shows the process procedure of the determination processing regarding the respiration concerning 1st Embodiment. It is a flow chart which shows a processing procedure of flat judging concerning a 1st embodiment. It is an image figure showing a diagnostic graph concerning a 3rd embodiment. It is an image figure showing an example of the 1st display screen of diagnostic terminal 300 concerning a 3rd embodiment. It is an image figure showing an example of the 2nd display screen of diagnostic terminal 300 concerning a 3rd embodiment. It is an image figure showing an example of transition of a display screen of diagnostic terminal 300 concerning a 3rd embodiment. It is an image figure showing the 1st diagnostic graph concerning a 4th embodiment. It is an image figure showing the 2nd diagnostic graph concerning a 4th embodiment. It is an image figure showing the 3rd diagnosis graph concerning a 4th embodiment. It is a figure showing functional composition of the 1st information processing system 1 concerning a 6th embodiment. It is a figure showing functional composition of the 2nd information processing system 1 concerning a 6th embodiment. It is a figure which shows the function structure of the 3rd information processing system 1 concerning 6th Embodiment. It is a figure which shows the function structure of the 4th information processing system 1 concerning 6th Embodiment.

Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In the following description, the same components are denoted by the same reference numerals. Their names and functions are also the same. Therefore, detailed description about them will not be repeated.
First Embodiment
<Overall configuration of information processing system>

  First, the entire configuration of an information processing system 1 according to the present embodiment will be described with reference to FIG. FIG. 1 is a diagram showing an entire configuration of an information processing system 1 according to the present embodiment. In addition, below, the case where the state of the dog which has a respiratory arrhythmia is judged on behalf of a biological thing is demonstrated.

  The information processing system 1 according to the present embodiment mainly includes an electrode for acquiring electrocardiograms 401, 402, and 403 attached to the chest of a dog, a signal processing device 500 for processing an electrocardiogram signal, and signal processing. A diagnostic terminal 300 capable of communicating with the device 500 is included.

The electrodes 401, 402, 403 for acquiring the electrocardiogram are preferably attached at positions sandwiching the heart in the chest etc. For example, hairs such as the bulbar portions of both forefoot (or forefoot and hindfoot) are grown There may be no place. In addition, it is desirable that the hair is in a cut state, or an electrode with a gel or the like attached, or has a protrusion-like structure so as to be in contact with the skin even with hair. Alternatively, it is desirable to use a form that induces the electrocardiogram in a non-contact manner via the capacitive material in the presence of hair. As a result, it is possible to obtain an electrocardiogram even if the skin of a dog or the like is covered with hair. In the present embodiment, three electrodes 401, 402, and 403 are used. However, two or more electrodes may be used, and more electrodes may be used.
<Functional Configuration and Processing Procedure of Information Processing System>

  Next, the functional configuration and the processing procedure of the information processing system 1 according to the present embodiment will be described with reference to FIGS. 2 and 3. FIG. 2 is a diagram showing a functional configuration of the information processing system 1 according to the present embodiment. FIG. 3 is a flowchart showing the processing procedure of the information processing system 1 according to the present embodiment.

  First, the configuration of the signal processing device 500 of the information processing system 1 will be described. The signal processing device 500 includes an electrocardiogram pre-processing unit 511, a pulsation interval calculation unit 512, and a transmission unit 560.

  The electrocardiogram pre-processing unit 511 includes a filter and an amplifier. The electrocardiogram pre-processing unit 511 converts the electrocardiogram signal sent from the electrodes 401, 402, and 403 into pulsation data, and delivers the pulsation data to the pulsation interval calculation unit 512.

  More specifically, the electrocardiogram pre-processing unit 511 includes a filter device such as a high pass filter or a low pass filter, an amplification device including an operational amplifier, an A / D conversion device that converts an electrocardiogram analog signal into a digital signal Is included. The filter device, the amplification device, and the like may be implemented by software. In addition, in the A / D conversion device, it is desirable to perform sampling with a period and accuracy by which the difference in fluctuation amount of the pulsation interval can be determined. That is, it is desirable to acquire an electrocardiogram signal at a frequency of at least 25 Hz or more. For example, in the present embodiment, sampling of an electrocardiogram signal at 100 Hz is performed. By increasing the sampling frequency, it becomes possible to accurately grasp the fluctuation amount of the pulsation interval.

  The pulsation interval calculation unit 512 is realized, for example, by the CPU (Central Processing Unit) 510 executing a program of the memory. The pulsation interval calculation unit 512 sequentially calculates the pulsation interval based on the pulsation data. More specifically, the heartbeat interval calculation unit 512 detects the peak signal (R wave) of the electrocardiogram by a method such as threshold detection, and calculates the interval (time) of the peak of each electrocardiogram. As a method of calculating the pulsation interval, in addition to the above, a method of deriving a period using an autocorrelation function, a method using a rectangular wave correlation trigger, or the like may be used.

  In the present embodiment, as shown in FIG. 4, the pulsation interval calculation unit 512 continuously calculates the pulsation interval on the electrocardiogram signal input continuously. The pulsation interval calculation unit 512 transmits the calculated pulsation interval and the pulsation data itself to the diagnostic terminal 300 via the transmission unit 560. The transmission unit 560 is realized by, for example, a communication interface including an antenna, a connector, and the like.

  Next, the configuration of the diagnostic terminal 300 will be described. The diagnosis terminal 300 includes a reception unit 361, a pulsation interval storage unit 321, a statistical processing unit 311, a graph creation unit 312, a result output unit 313, a display 330, a data storage unit 322, and a transmission unit 362. .

  First, the reception unit 361 and the transmission unit 362 are realized by, for example, the communication interface 360 including an antenna, a connector, and the like. The receiving unit 361 receives data indicating the pulsation interval from the signal processing device 500 (step S102).

  The pulsation interval storage unit 321 is configured by various memories 320 and the like, and stores data received from the signal processing device 500. In the present embodiment, CPU 310 successively stores the pulsation intervals received via communication interface 360 in memory 320 as a pulsation interval table (step S104). However, these data may be stored in the memory 320 of the diagnostic terminal 300 or may be stored in another device accessible from the diagnostic terminal 300.

  The statistical processing unit 311, the graph creation unit 312, and the result output unit 313 are realized, for example, by the CPU 310 executing a program of the memory 320. The statistical processing unit 311 reads out the pulsation interval data from the pulsation interval storage unit 321 in a fixed time unit, for example, a time unit necessary to determine the state, such as 1 minute, 10 minutes, 1 hour, etc. A correspondence relationship table 321A between the pulsation interval RR (n) and the next pulsation interval RR (n + 1) as shown in 5 is created (step S106). The pulsation interval is calculated, for example, in units of msec (millisec) as shown in the figure.

  From the correspondence relationship table between the pulsation interval RR (n) and the next pulsation interval RR (n + 1) as shown in FIG. Conversion to the axis of (step S108).

  The statistical processing unit 311 calculates, as a numerical value indicating the autonomic nervous balance, a standard deviation regarding a numerical value sequence constituting each axis after axis conversion (step S110). The statistical processing unit 311 may calculate only the standard deviation on the Y = X axis, or may calculate only the standard deviation on the axis perpendicular to Y = X, or may calculate both of them. . FIG. 7 is a table showing the standard deviation of the Y = X axis and the standard deviation of the vertical axis of Y = X, for each mental or physical condition of the dog.

  The statistical processing unit 311 may specify an axis with the largest variance by a method such as principal component analysis, and calculate the standard deviation of the axis and an axis perpendicular to the axis. Furthermore, the statistical processing unit 311 may calculate the standard deviation regarding the X axis and the Y axis without performing axis conversion. When the direction of large dispersion is the X-axis direction and the Y-axis direction, the dispersion state of the beat interval plotted by Poincare plot is evaluated by calculating the standard deviation of the X-axis and Y-axis without performing axis conversion. it can. In this case, the amount of calculation can be reduced because there is no need to perform axis conversion.

  The result output unit 313 displays the standard deviation or outputs a voice message on an output device such as the display 330 or a speaker outside the diagnostic terminal 300, for example (step S114). More specifically, the result output unit 313 may output only the standard deviation on the Y = X axis, or may output only the standard deviation on the vertical axis with Y = X, or both of them. Only the larger one may be output, or only the smaller one may be output.

  By calculating the standard deviation, it is possible to evaluate the dispersion state of the pulsation interval plotted on the Poincare plot with the pulsation interval RR (n) and the subsequent pulsation interval RR (n + 1) as axes. Here, the degree of variation in pulsation interval is regarded as the degree of autonomic nervous balance. As described later, the numerical value indicating the autonomic nervous balance is not limited to the standard deviation after axis conversion.

  In the present embodiment, the CPU 310 performs the calculation shown in FIG. 3 every predetermined period, for example, several minutes, and accumulates the calculation result in the database of the memory 320 for creating a diagnostic graph described later.

  Although details will be described later, the information processing system 1 according to the present embodiment may have a form including the server 100 with which the diagnostic terminal 300 can communicate, as shown in FIG. In that case, the CPU 310 as the result output unit 313 stores the standard deviation, the relationship table, and the like in the data storage unit 322, and transmits the data to the server 100 via the Internet or the like by using the transmission unit 362. By this, the output result of this time can be used for grasping the short-term or long-term stress condition to be observed.

  In the present embodiment, apart from step S108, the graph creating unit 312 determines from the correspondence relationship table of FIG. 5 the beat interval RR (n) of the range used for the calculation of the standard deviation and the beat following it. The data with the movement interval RR (n + 1) is acquired, and a Poincare plot as shown in FIGS. 8 to 11 is created.

  Then, the result output unit 313 displays the created Poincare plot on an output device such as the display of the diagnostic terminal 300 or an external display. The graph creating unit 312 may create and output a Poincare plot after axis conversion using the result of step S108.

  Here, the Poincare plot will be described. FIG. 8 is a Poincare plot of the dog in the excited state according to the present embodiment. FIG. 9 is a Poincare plot of the dog according to the present embodiment in a normal state in which breathing is stable. FIG. 10 is a Poincare plot in the normal state of the dog according to the present embodiment. FIG. 11 is a Poincare plot in the resting state of the dog according to the present embodiment.

  First, in the case of an organism having a respiratory arrhythmia such as a dog, for example, in an excited state as shown in FIG. 8, the heart rate increases (beat interval becomes short), and the fluctuation of beat interval becomes small, and the plot It will be in the state where the points of are gathered in a certain place.

  And in the normal state where breathing is stable as shown in FIG. 9, the heart rate is not as small as at rest (the spread of the points of the plot is not as large as at rest), but at the center of the distribution of plot points. There is an area where there are few plots (hole blanks). Such a shape is considered to be caused by periodic change in pulsation fluctuation (respiratory arrhythmia) because the heart rate of the dog is greatly affected by respiration. Therefore, although it is not a relaxed and gentle beat, it is considered that a blank state exists because breathing is performed stably.

  Then, in the normal state as shown in FIG. 10, although the pulsation is fluctuated and the variation is increased (the plot point is spread), the plot point is in a scattered state.

  Then, in the resting state shown in FIG. 11, since the dog is relaxed, the beat interval is increased, and the influence of the respiratory arrhythmia is greatly affected, so that the spread of the plot point is increased, and It has a shape close to the shape of a circle or a shape close to a triangle. In any of these shapes, in the resting state, a blank portion can be seen at the center of the distribution of the plot points of the Poincare plot.

As described above, in the present embodiment, the size and shape of the spread of the distribution of the plot points of the Poincare plot indirectly, and whether the number of plots appears in the center or not are indirectly predicted based on the calculation results. As a result, it is possible to predict the mental or physical condition of the organism. Then, as described above, the statistical processing unit 311 calculates the degree of variation of the Poincare plot, that is, the standard deviation of the pulsation interval, as a numerical value indicating the autonomic nervous balance.
<Another form of autonomic balance>

  In the above embodiment, the diagnostic terminal 300 outputs the standard deviation along the Y = X axis of the Poincare plot or the standard deviation along the axis perpendicular to Y = X. However, the product of the two standard deviations may be calculated as a value indicating autonomic balance. Hereinafter, the processing procedure of the information processing system 1 according to the present embodiment will be described with reference to FIG.

  FIG. 12 is a flowchart showing the processing procedure of the information processing system 1 according to the present embodiment. Steps S102 to S108 are the same as those in FIG. 3 and thus the description will not be repeated here.

  The CPU 310 as the statistical processing unit 311 calculates the standard deviation of each axis after the conversion of the axis (step S110). The statistical processing unit 311 may specify an axis at which the variance is maximized, and calculate the standard deviation with respect to the axis and an axis perpendicular to the axis.

  Then, the statistical processing unit 311 calculates a product of the two standard deviations, a square root of the product, and the like as a numerical value indicating the autonomic nervous balance (step S112).

  The result output unit 313 displays the product of the standard deviation, the square root of the product, or the like on an output device such as a display or a speaker outside the diagnostic terminal 300, for example, or outputs a voice message (step S114). ). More specifically, the result output unit 313 may output the standard deviation about the Y = X axis, the standard deviation about the Y = −X axis, the product of both, the square root of the product, and the like.

  FIG. 13 shows the product or product of the standard deviation on the Y = X axis, the standard deviation on the axis perpendicular to Y = X, and the standard deviation as a value showing autonomic balance for each mental or physical state of the dog It is a table | surface which shows the standard of the square root etc., and the ratio of a standard deviation.

  By calculating the product of the standard deviations, the size and extent of the spread of the distribution of pulsation intervals plotted on the Poincare plot with the pulsation interval R-R (n) and the subsequent pulsation interval R-R (n + 1) as axes. It is possible to evaluate the dispersion state such as the shape, uniformly dispersed, and a space at the center. Further, the variation state can be effectively evaluated, for example, when the aspect ratio is the same and only the size changes, or when the spread area of the distribution is the same and the variation state of the central portion is different.

  Also in this case, the result output unit 313 stores the standard deviation, the product of the standard deviation, the square root of the product, the correspondence relationship table, etc. in the data storage unit 322 or by using the transmission unit 362 via the Internet etc. Send to the server 100 or the like. By this, the output result of this time can be used for grasping the short-term or long-term stress condition to be observed.

  The statistical processing unit 311 calculates the product of the standard deviations of the two axes and the square root of the product, etc. However, even if it calculates the product of the standard deviations of three or more axes and the power root thereof Good.

The CPU 310 performs the calculation shown in FIG. 12 every predetermined period, for example, several minutes, and accumulates the calculation result in the database of the memory 320 for creating a diagnostic graph to be described later.
<Method of calculating respiratory rate>

  The CPU 310 of the diagnosis terminal 300 according to the present embodiment may calculate the respiration rate of the target organism in addition to the information indicating the autonomic nervous balance of the target organism. Referring to FIG. 14, CPU 310 of diagnostic terminal 300 executes the program of memory 320 to execute, for example, the following processing.

  The CPU 310 acquires a pulsation interval as shown in FIG. 4 (step S204). As shown in FIG. 15, the CPU 310 mathematically interpolates (for example, spline interpolation) the relationship between the pulsation detection time for one minute and the pulsation interval (step S206). More specifically, the CPU 310 detects a peak signal (R wave) of the electrocardiogram by a method such as threshold detection, and calculates an interval (time) of the peak of each electrocardiogram. As a method of calculating the pulsation interval, in addition to the above, a method of deriving a period using an autocorrelation function, a method using a rectangular wave correlation trigger, or the like may be used.

  Then, the CPU 310 analyzes the frequency of the function obtained as shown in FIG. 16 (step S208).

  The CPU 310 identifies the largest peak of the power spectrum in an arbitrary frequency range (for example, between 0.05 and 0.5 Hz) in the power spectrum distribution as shown in FIG. 16 obtained by the frequency analysis (step S210). ). Here, as an example, when the ratio of the largest peak compared to the second largest peak has a magnitude (for example, three times) greater than or equal to an arbitrary threshold, it is determined to be the “measurable state”.

  More specifically, for example, RRI variation after spline interpolation of a dog in a state of relaxing in a quiet room indoors is as shown in FIG. 17 (a). The power spectrum distribution in this case is as shown in FIG. 17 (b), and the ratio of the largest peak to the second largest peak is greater than an arbitrary threshold (for example, three times). The CPU 310 determines that the measurement is possible.

  Conversely, for example, the RRI fluctuation after spline interpolation in a restless dog in an outdoor noisy environment is as shown in FIG. 18 (a). The power spectrum distribution in this case is as shown in FIG. 18 (b), and the ratio of the largest peak to the second largest peak has a magnitude (for example, three times) greater than an arbitrary threshold Because there is not, the CPU 310 determines that the measurement is not possible.

  When the CPU 310 determines that it is in the “non-measurable state”, the processing from step S106 is repeated based on the pulsation interval already acquired by the signal processing device 500 with respect to another timing.

  The CPU 310 detects various types of vital data when it is determined that the “measurable state” is set. For example, the CPU 310 calculates the respiration rate by calculating the reciprocal with the maximum peak in an arbitrary frequency range (for example, a range of 0.05 to 0.5 Hz) in frequency analysis as the respiration frequency.

  The CPU 310 displays the respiration rate per unit time or outputs an audio through the display 330, the speaker 370, the communication interface 360 for transmitting data to the outside, and the like. Further, the CPU 310 performs the calculation shown in FIG. 14 every predetermined period, for example, several minutes, and accumulates the calculation result in the database of the memory 320 for creating a diagnostic graph to be described later.

  In the present embodiment, CPU 310 calculates the respiration rate by calculating the reciprocal of the frequency with the frequency of the maximum peak in frequency analysis as the frequency of respiration. FIG. 19 shows the results of 60-minute respiration rate measurement. When the status is not determined, the measurement result can be output every minute as shown in FIG. 19 (a), but the measurement result in various states is included, and it is difficult to secure the accuracy. . On the other hand, it is possible to calculate the respiration rate as shown in FIG. 19 (b) by not calculating the data of the time determined to be the “immeasurable state”, and only the respiration rate under appropriate conditions is obtained. be able to.

  More specifically, accumulating vital data has medical significance, but it is necessary to compare and analyze data measured in a certain environment (for example, at rest). In particular, in the case of comparing data over a long period of time, or when the subject (for example, a dog) can not maintain a constant state by itself, in order to reliably record vital data, It is necessary to determine the condition of the measurer. In particular, it is difficult for a person to be measured to create a consciously measurable state because the respiratory rate changes arbitrarily, and at present, no means has been established to determine whether it can be measured automatically.

  However, the condition of the subject is determined by analyzing measurement data (for example, an electrocardiogram signal), and vital data (for example, the respiration rate derived from the electrocardiogram signal) is calculated based on the determination result of the condition. , Can be recorded. In particular, as a means for state determination, it is determined whether or not the appropriate state has been maintained for a certain period of time (for example, one minute) during measurement. Then, the determination criterion of "whether the appropriate state is maintained" is defined from a fluctuation cycle due to respiration, for example, using a heart rate fluctuation analysis. Animals such as dogs also have changes in heart rate and respiration rate even when motion is not seen, and this discrimination criterion can determine the appropriate state with higher accuracy than analyzing motion using an acceleration sensor or the like. Further, by performing both the state determination and the vital data detection from a single measurement data such as an electrocardiogram signal, the measurement device can be made compact and simple. And, by miniaturizing the apparatus or system, it is possible to reduce stress and load on the measurer side and to perform measurement in a more natural state.

  In step S110 of FIG. 14, the CPU 310 searches for the maximum peak of the power spectrum in an arbitrary frequency range (for example, between 0.05 Hz and 0.5 Hz) in the power spectrum distribution obtained by frequency analysis. If the ratio of the integral value of the power spectrum from the peak to the half value width is equal to or more than the set threshold value, it may be determined that the respiratory rate can be measured. More specifically, in the power spectrum distribution, it can be determined whether or not the largest peak in any frequency range (for example, between 0.05 and 0.5 Hz) is prominent compared to other power spectra. Alternatively, the CPU 310 may determine that it is in the “measurable state” by another method.

Alternatively, as shown in FIG. 20, the CPU 310 determines that the target organism is in a resting state when the product of the standard deviation or the product of the standard deviation or the square root thereof is larger than a predetermined value based on the Poincare plot of the pulsation interval. You may judge (step S302-312). Then, when it is determined that the “measurable state” is set, the CPU 310 may calculate the number of local maxima (or local minima) in the time-series change of the pulsation interval as the respiration rate as shown in FIG. The CPU 310 performs the calculation shown in FIG. 20 every predetermined period, for example, several minutes, and accumulates the calculation result in the database of the memory 320 for creating a diagnostic graph described later.
<Output method of result>

  As described above, in the present embodiment, the CPU 310 of the diagnostic terminal 300 causes the display 330 to display various graphs based on the signal as shown in FIG. 21 acquired by the signal processing device 500. For example, as shown in FIG. 22, the CPU 310 shows, for each individual, a first graph obtained by spline interpolation of the pulsation interval, a second graph indicating the electrocardiogram peak signal itself, and a Poincare plot of the pulsation interval. The display 330 displays a third graph for interpolation, a fourth graph obtained by frequency analysis of the first graph, and the like. Note that FIG. 22 is a graph showing a first graph obtained by spline interpolation of the heartbeat interval, a second graph showing the electrocardiogram peak signal itself, and an interpolation showing a Poincare plot of the heartbeat interval for an individual in a normally relaxed state. The 3rd graph and the 4th graph which frequency-analyzed the 1st graph are shown. On the other hand, FIG. 23 shows a first graph showing a signal itself when an electrocardiogram peak signal can not be correctly acquired, a second graph showing a Poincare plot of a pulsation interval, and a second graph in which the pulsation interval is spline-interpolated. The graph of 3 and the 4th graph which frequency-analyzed the 3rd graph are shown. Note that, in FIG. 23, in the second graph showing the lower right electrocardiogram peak signal itself, “not adopted” is attached to the time zone in which it could not be acquired correctly. The “non-adoption” period refers to a period determined to have a high probability that R waves can not be detected correctly or can not be detected correctly. An example of the determination method will be described later. The second graph of this embodiment shows the transition of environmental data of the environment in which the beating or pulse of the living individual is measured for each living individual or every timing instead of the graph showing the electrocardiographic peak signal itself. good. With environmental data, for example, the transition of temperature, humidity, noise, etc. can be considered.

  In the present embodiment, as shown in FIG. 24, CPU 310 can select a mode in which any type of graph relating to each of a plurality of individuals is arranged and displayed on display 330. In particular, in the present embodiment, CPU 310 causes the display mode of the graph related to the measurement determined to be likely to be not normal to be different from the display mode related to the measurement determined to be normal. For example, the CPU 310 causes the display 330 to display the second graphs of a plurality of individuals side by side, and simultaneously displays the first graph of the original together with the second graph regarding the measurement determined to be not normal. It is. Here, the second graph and the first graph may be simultaneously displayed for a predetermined period. For example, after the second graph is displayed, the first graph may be displayed after a while, and if the first graph and the second graph are simultaneously displayed for a predetermined period, the first graph may be displayed. And either of the second graph may not be displayed first on the display.

  This makes it easy for a user such as a veterinarian to determine whether or not a graph that is not normal should be adopted or considered in diagnosis or the like.

  As shown in FIG. 24, the horizontal axes of the first graph and the second graph both indicate measurement times. The measurement intervals, lengths, and scales of the horizontal axes of the first graph and the second graph are preferably aligned. Furthermore, the horizontal axis of the first graph and the horizontal axis of the second graph are preferably arranged parallel to each other. By aligning the horizontal axes of the first graph and the second graph, when visually comparing the two graphs, it becomes easy to confirm the correspondence between data included in the two graphs.

  The original first graph is displayed together with the second graph smaller than the graph relating to the measurement determined as normal so that the position at which the graph relating to the measurement determined as normal does not change.

  Below, the process for judging whether the acquired various data in the diagnostic terminal 300 are normal is demonstrated. As an example, FIG. 25 is a flowchart for determining whether the obtained electrocardiogram data is data sufficient to detect an R wave, that is, whether there is a high possibility that R waves can not be detected correctly or correctly. is there.

  Referring to FIG. 25, CPU 310 first obtains electrocardiographic voltage value Et for the most recent t seconds (for example, t = 1). Assuming that the sampling frequency for voltage acquisition is 100 Hz, Et is composed of 100 numerical values (step S402). The CPU 110 determines the maximum value Em of Et (step S404).

  In the present embodiment, the determination is divided into two steps. The first step is a step of determining according to whether the amplitude of the R wave is sufficient, and the second step is a step of determining according to whether or not the noise is equal to or more than the R wave. Hereinafter, the first step is referred to as flat determination (step S410), and the second step is referred to as multi-noise determination.

  As shown in FIG. 26, the CPU 310 acquires the average Et_ave of Et as the flat determination (step S412), and obtains a difference from the maximum value Em of Et (step S414). If the difference exceeds a predetermined threshold value Jflat (for example, 30), the CPU 310 sets the flat determination result as FALSE (step S416), and if it does not exceed the threshold value Jflat (step S416). The CPU 310 determines that this section Et can not be adopted when it becomes TRUE in the flat determination (step S452).

  Referring back to FIG. 25, in addition to Et, the CPU 310 acquires a voltage value Eref of the nearest ref second (for example, ref = 15) (step S 426). The CPU 310 obtains variance values Dt and Dref of Et and Eref (step S434). When Dt exceeds Dref, the CPU 310 does not adopt this section Et because of too much noise (step S446). Conversely, if Dt falls below Dref, the CPU 310 assumes that Et can be adopted (step S 442).

  In the present embodiment, in consideration of the magnitude of noise in this multi-noise determination, each maximum value is determined according to whether or not a predetermined threshold EMAX (for example, 150) is exceeded at the time of Et and Eref acquisition. And may be normalized (steps S422, S424, S430, and S432).

  In addition, with regard to numerical values indicating autonomic balance, not only the standard deviation and the product of the standard deviation of the Poincare plot, but also using the average of the distance between two consecutive plots of the Poincare plot or other Poincare blot variability The numerical values shown may be used, or other calculation methods other than the Poincare plot may be used.

Further, in the above embodiment, the pulsation interval is calculated using the electrodes 401, 402, and 403 for acquiring an electrocardiogram, but the present invention is not limited to such a form. For example, a pulse wave signal may be acquired by a pulse wave meter or pulse oximeter of a photoelectric pulse wave system, and the pulsation interval may be calculated from the pulse wave signal. In this case, it is preferable that the measurement site of the pulse wave is a site where the skin is exposed, such as the tongue and the ear. Alternatively, a heart sound signal may be acquired by an electronic stethoscope or the like, and the heart sound signal or the pulsation interval may be calculated. In these cases, measurement can be performed by a method that does not use an electrode. A pulse wave acquisition sensor such as a microwave Doppler sensor may be used to acquire a pulse wave signal, and the pulsation interval may be calculated from the pulse wave signal. For example, a microwave transmission device is installed on a ceiling etc., and the form which acquires the pulse wave from living things, such as a dog, without contact can be considered. In this case, non-contact measurement is possible, which has the effect of further reducing the load on the subject.
Second Embodiment

  The method of outputting the result is not limited to that of the above embodiment. For example, the CPU 310 of the diagnosis terminal 300 acquires the average Et_ave of Et (step S412) as the flat determination shown in FIG. 26 and obtains the difference from the maximum value Em of Et (step S414). Then, when the difference does not exceed the second predetermined threshold Jflat (for example, 15), the CPU 310 may not arrange the graph itself on the display 330.

  Further, in step S446 in FIG. 25, when Dt exceeds twice Dref, the graph itself may not be arranged on the display 330 because there is too much noise.

  That is, in the present embodiment, CPU 310 is different from the other graphs as having a non-normal range in the graph when part or all of the graphs included in the graph satisfy the first predetermined condition. Display the graph 330 on display. Then, when a part or all of the graph further satisfies the second predetermined condition, the CPU 310 does not display the graph itself, and instead displays the next graph on the display 330.

As a result, the user such as a veterinarian may not be normal, eg, 30% or more, while the graph is automatically excluded beforehand, for example, 80% or more, which is likely to be abnormal. It makes it easier to decide whether to adopt or consider. It is preferable that the CPU 310 receives settings such as the numerical value itself of the possibility itself and other threshold values serving as an index of the above determination via an operation unit such as a button or a touch panel.
Third Embodiment

  The method of outputting the result is not limited to that of the above embodiment. Also in the present embodiment, the CPU 310 makes the display mode of the graph related to the measurement determined to be highly unlikely to be normal different from the display mode related to the measurement determined to be normal. Then, for example, while displaying the second graphs of a plurality of individuals side by side on the display 330, the CPU 310 is determined not to be normal on the screen as shown in FIG. The original first graph may be superimposed on the second graph for measurement.

  This makes it easy for a user such as a veterinarian to determine whether or not a graph that is not normal should be adopted or considered in diagnosis or the like.

  In this case, the CPU 310 preferably aligns the main range in the vertical direction of the first graph and the main range in the vertical direction of the second graph to the same extent.

  Alternatively, as shown in FIG. 28, the CPU 310 may change the display mode of the graph related to the measurement determined to be highly likely to be not normal to the display mode related to the measurement determined to be normal. That is, the CPU 310 causes the display 330 to display the second graphs of a plurality of individuals side by side, and displays a frame around the second graphs regarding the measurement results that are determined not to be normal. Alternatively, the CPU 310 changes the color of the line of the second graph, changes the back color, and adds exclamation.

  Then, when the graph is selected via the operation unit such as a button or a touch panel, the CPU 310 displays the original on the screen together with the second graph regarding the measurement determined to be not normal as shown in FIG. The first graph is also displayed side by side.

More preferably, at this time, as shown in FIG. 30, the CPU 310 displays, on the screen, an image or text for accepting an instruction to leave a graph related to the measurement on the screen or an instruction to exclude the graph. Then, the CPU 310 returns the display mode of the target measurement graph to the same as the normal graph, or is input via the operation unit, in accordance with the “remaining instruction” input via the operation unit. In accordance with the exclusion instruction, the graph of the target measurement is removed from the screen, and the graph of the next measurement is padded and displayed.
Fourth Embodiment

  Alternatively, with regard to a graph related to measurement which is determined to be highly likely to be not normal, as shown in FIG. 31, the CPU 310 displays the first graph in parallel with the second graph while displaying the first and second The portion of the graph in which it is determined that the possibility of not being normal is high may be displayed in a display mode different from that of the other portions. For example, the CPU 310 displays a frame that encloses a portion of the first and second graphs that is determined to be highly unlikely to be normal.

  Alternatively, as shown in FIG. 32, the CPU 310 makes the back surface color of the region of the first and second graphs determined to be not likely to be normal different from the other portions.

Alternatively, when the graph related to the measurement that is determined to be likely to be not normal is selected in FIG. 27, as shown in FIG. 33, the CPU 310 displays three or more related to the measurement determined to be not normal on the screen. It may transition to a screen displaying a graph. Also in this case, the CPU 310 may display a portion of the first and second graphs that is determined to be unlikely to be normal, in a display mode different from the other portions.
Fifth Embodiment

The output method of the result is not limited to the method of arranging and displaying graphs of a plurality of different individuals as in the above embodiment. For example, as shown in FIG. 24, the CPU 310 of the diagnostic terminal 300 may arrange and display on the display 330 graphs regarding each of a plurality of different timings regarding a designated individual.
Sixth Embodiment

  In the information processing system 1 according to the above-described embodiment, the signal processing device 500 acquires the pulsation interval based on the electrocardiogram signal from the electrodes 401, 402, 403, and the diagnosis terminal 300 determines the state of the living body from the pulsation interval. Information for determining the condition of the living body or the result of the determination of the condition of the living body. However, the role of all or part of one device may be played by another device or may be shared by a plurality of devices. Conversely, one device may play a role of all or part of the plurality of devices, and another device may play a role.

  For example, as shown in FIG. 34, the diagnostic terminal 300 may be equipped with all or part of the functions of the signal processing device 500. In this case, the diagnostic terminal 300 acquires an electrocardiogram signal from the electrodes 401, 402, and 403 from the simplified signal processing device 501 by wireless communication. The electrocardiogram signal from the electrode is converted into a digital signal by the simplified electrocardiogram pre-processing unit 570 including the minimum filter device, amplification device and A / D conversion device, and is transmitted from the transmission unit 560. The diagnostic terminal 300 calculates, from the electrocardiogram signal, information for determining the pulsation interval and the state of the living body or information of the determination result of the state of the living body. Then, the diagnostic terminal 300 outputs information of the final result to a display or a speaker.

  Alternatively, as shown in FIG. 35, the signal processing device 500 may be equipped with all or part of the functions of the diagnostic terminal 300. In this case, based on the electrocardiogram signal from the electrodes 401, 402, 403, the signal processing device 500 calculates information for determining the pulsation interval or the state of the living thing or the information of the determination result of the living thing state. Then, the signal processing device 500 outputs information of the final result to a display or a speaker.

  Alternatively, as shown in FIG. 36, the server 100 may play the role of the diagnosis terminal 300. In this case, the server 100 has the function of the diagnostic terminal 300 of the above embodiment. For example, the communication terminal as the diagnostic terminal 300 transmits necessary information such as the pulsation interval from the signal processing device 500 to the server 100 via a router, a carrier network, the Internet, or the like. The server 100 calculates information for determining the state of the living thing or information indicating the determination result of the state of the living thing, and transmits the information to the diagnostic terminal 300. It is conceivable that the diagnostic terminal 300 outputs information of the final result to a display or a speaker.

  In this case, of course, the receiving unit 161 and the transmitting unit 162 of the server 100 are realized by the communication interface 160 of the server 100. The pulsation interval storage unit 121 and the data storage unit 122 are realized by the memory 120 of the server 100 or another device accessible from the server 100 or the like. The statistical processing unit 111, the graph creation unit 112, and the result output unit 113 are realized by the CPU 110 executing a program of the memory 120.

  Alternatively, as shown in FIG. 37, the signal processing apparatus 500 transmits necessary information such as a pulsation interval to the server 100 via a router, a carrier network, the Internet, or the like. The server 100 calculates information for determining the state of a living thing or information of a determination result of the state of the living thing, and transmits the information to the communication terminal as the diagnostic terminal 300 via the Internet, a carrier network, a router, or the like. The diagnostic terminal 300 outputs information of the final result to a display or a speaker. In this case, the signal processing apparatus 500 and the diagnostic terminal 300 may not be connected by a wireless LAN or a wired LAN.

  Also in this case, naturally, the reception unit 161 and the transmission unit 162 of the server 100 are realized by the communication interface 160 of the server 100. The pulsation interval storage unit 121 and the data storage unit 122 are realized by the memory 120 of the server 100 or another device accessible from the server 100 or the like. The statistical processing unit 111, the graph creation unit 112, and the result output unit 113 are realized by the CPU 110 executing a program of the memory 120.

In the description of the above embodiment, although processing of performing “Poincare plot” and processing of performing “axis conversion after Poincare plot processing” are described, the processing is performed by the diagnostic terminal 300, the server 100, and the signal processing. It should not be limited to the CPU of the device 500 actually printing or displaying the Poincare plot image on a paper medium or display. The process is, for example, a concept including a process in which the CPU substantially stores, in a memory, data representing a Poincare plot or develops.
<Other application examples>

  It is needless to say that the present disclosure can be applied to the case where it is achieved by supplying a program to a system or apparatus. Then, a storage medium (or memory) storing a program represented by software for achieving the present disclosure is supplied to a system or apparatus, and a computer (or CPU or MPU) of the system or apparatus stores the storage medium in the storage medium. The effects of the present disclosure can also be enjoyed by reading out and executing the program code.

  In this case, the program code itself read from the storage medium implements the functions of the above-described embodiments, and the storage medium storing the program code constitutes the present disclosure.

  Further, by executing the program code read out by the computer, not only the functions of the above-described embodiments are realized, but also an operating system (OS) or the like operating on the computer based on the instructions of the program code. It is needless to say that the present invention also includes the case where the processing of part or all of the actual processing is performed and the processing of the above-described embodiment is realized by the processing.

  Furthermore, after the program code read out from the storage medium is written to a function expansion board inserted into the computer or another storage medium provided in a function expansion unit connected to the computer, based on the instruction of the program code, It goes without saying that the case is also included where the CPU provided in the function expansion board or the function expansion unit performs part or all of the actual processing and the functions of the above-described embodiment are realized by the processing.

1: Information processing system 100: Server 110: CPU
111: statistical processing unit 112: graph creation unit 113: result output unit 120: memory 121: pulsation interval storage unit 122: data storage unit 160: communication interface 161: reception unit 162: transmission unit 300: diagnostic terminal (information processing apparatus )
310: CPU
311: statistical processing unit 312: graph creation unit 313: result output unit 320: memory 321: pulsation interval storage unit 321A: correspondence relationship table 322: data storage unit 330: display 360: communication interface 361: reception unit 362: transmission unit 370: speaker 401: electrode 402: electrode 403: electrode 500: signal processing device 501: simplified signal processing device 511: electrocardiogram pre-processing unit 512: pulsation interval calculating unit 560: transmitting unit 570: simplified electrocardiogram pre-processing unit

Claims (8)

A display,
A processor for sequentially displaying on the display a first graph relating to a pulse interval, pulse interval or pulse time for each living organism or each timing;
When the first condition is satisfied, the processor is different from the first graph and the first graph when the first condition is satisfied for each living organism or for each timing, and the living organism for each living organism or for each timing. Simultaneously displaying on the display vital data of the individual or a second graph showing environmental data in the environment measuring the pulse or pulse of the individual, and the first condition is not satisfied; An information processing apparatus for displaying a graph of the above on the display.   The processor skips the display of the first and second graphs when the first condition is satisfied and the second condition is satisfied for each living individual or each timing. The information processing apparatus according to claim 1.   The processor is different from the display mode of the portion satisfying the first condition when the first condition is satisfied for each living individual or each timing, from the display mode of the portion not satisfying the first condition The information processing apparatus according to claim 1, wherein the first graph or the second graph is displayed on the display. A display,
A processor for causing the display to sequentially display a first graph relating to a pulse interval or pulse interval for each living organism or each timing;
The processor displays the first graph on the display so that the display mode of the portion satisfying the first condition is different from the display mode of the portion not satisfying the first condition for each living individual or each timing. Information processing device to be displayed. The first graph is a graph in which a beat interval or pulse interval for each living organism or for each timing is spline-interpolated,
The information processing apparatus according to any one of claims 1 to 4, wherein the second graph is a graph showing a peak signal of electrocardiogram for each living individual or each timing. Determining whether the first condition is satisfied for each living organism or each timing;
Simultaneously displaying on the display a second graph different from the first graph if the first condition is satisfied;
An information processing program causing the processor to execute the displaying of the first graph on the display when the first condition is not satisfied. A communication interface for communicating with the output device;
The pulsation interval or pulse interval for each living organism or for each timing is acquired via the communication interface, and the first condition is satisfied for each living organism or for each timing, and the first communication interface is used for the first communication. And a second graph different from the first graph are simultaneously displayed on the output device, and the first graph is output to the output device via the communication interface when the first condition is not satisfied. And a processor for displaying. It is an information processing method in a server, and
The processor acquiring a pulse interval or pulse interval for each living organism or for each timing through the communication interface;
Determining whether the processor satisfies a first condition for each living individual or every timing;
Causing the output device to simultaneously display a first graph and a second graph different from the first graph via the communication interface when the first condition is satisfied;
An information processing method comprising: displaying the first graph on the output device via the communication interface when the processor does not satisfy the first condition.