Information processing device, monitoring device, and computer program

Abstract

This method suppresses the decrease in accuracy of respiratory rate estimation caused by the subject's condition without requiring the addition of sensors. [Solution] The input interface 111 receives a waveform signal WF corresponding to the waveform of biological information with periodic changes from a sensor 30 attached to the subject 20. The processor 112 visualizes information corresponding to the estimated respiratory rate of the subject 20 on the visualization device 12 based on the waveform signal WF. The processor 112 extracts a plurality of feature quantities corresponding to the respiratory rate of the subject 20 from the waveform signal WF. The processor 112 obtains a plurality of preliminary estimates for the respiratory rate by applying frequency analysis processing to each of the plurality of feature quantities. The processor 112 excludes one of the plurality of preliminary estimates obtained from one of the plurality of feature quantities based on state information corresponding to the state of the subject 20, and determines the estimate based on at least one of the remaining plurality of preliminary estimates.

Description

Technical Field

[0001] The present disclosure relates to an information processing apparatus that processes biometric information of a subject. The present disclosure also relates to a monitoring apparatus that monitors biometric information of a subject. The present disclosure also relates to a computer program executable by a processor mounted on the information processing apparatus.

Background Art

[0002] There is known a technique for acquiring a pulse wave waveform, which is an example of a biometric information waveform with a periodic change, from a subject and estimating the respiration rate of the subject based on the pulse wave waveform. Patent Document 1 discloses a technique for improving the estimation accuracy of the respiration rate by detecting the body movement of a subject in combination with an acceleration sensor. Patent Document 2 discloses a technique for improving the estimation accuracy of the respiration rate by increasing the number of sensors for acquiring the pulse wave waveform.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a demand for suppressing a decrease in the estimation accuracy of the respiration rate due to the state of the subject without adding sensors.

Means for Solving the Problems

[0005] One example of an aspect that can be provided by the present disclosure is a biometric information processing apparatus that processes biometric information of a subject, an interface that receives a signal corresponding to a waveform of biometric information with a periodic change from a sensor attached to the subject, A processor that causes an output device to output information corresponding to an estimated value of the subject's respiratory rate based on the aforementioned signal, It is equipped with, The aforementioned processor, From the aforementioned signal, multiple feature quantities corresponding to the subject's respiration are extracted, By applying frequency analysis processing to each of the aforementioned multiple features, multiple preliminary estimates of the respiratory rate are obtained. Based on the state information corresponding to the state of the subject, one of the multiple preliminary estimates obtained from one of the multiple features is excluded. The estimated value is determined based on at least one of the remaining multiple preliminary estimates.

[0006] One example of the embodiments that may be provided by this disclosure is a monitoring device for monitoring the biological information of a subject, An information processing device according to the above embodiment example, An output device that outputs information corresponding to the estimated respiratory rate of the subject obtained by the information processing device, It is equipped with.

[0007] One example of the embodiments that may be provided by this disclosure is a computer program that can be executed by a processor installed in an information processing device that processes the biological information of a subject, By being executed, the information processing device will The system receives signals corresponding to the waveform of biological information that undergoes periodic changes from sensors attached to the subject. From the aforementioned signal, multiple feature quantities corresponding to the subject's respiration are extracted, By applying frequency analysis processing to each of the aforementioned multiple features, multiple preliminary estimates of the subject's respiratory rate are obtained. Based on the state information corresponding to the state of the subject, one of the multiple preliminary estimates obtained from one of the multiple features is excluded. The estimate is determined based on at least one of the remaining multiple preliminary estimates, The output device outputs information corresponding to the aforementioned estimated value.

[0008] According to the configuration according to each of the above-described embodiment examples, since the process of excluding the preliminary estimation value that reduces the estimation accuracy of the respiration rate based on the state of the subject is executed by the processor mounted on the information processing device, it is possible to suppress the decrease in the estimation accuracy of the respiration rate caused by the state of the subject without adding a sensor.

Brief Description of Drawings

[0009] [Figure 1] The functional configuration of the monitoring device according to one embodiment example is illustrated. [Figure 2] The flow of the process executed by the processor in FIG. 1 is illustrated. [Figure 3] The baseline variation of the waveform signal in FIG. 1 is illustrated. [Figure 4] The amplitude variation of the waveform signal in FIG. 1 is illustrated. [Figure 5] The frequency variation of the waveform signal in FIG. 1 is illustrated. [Figure 6] An example of the flow of the selection process in FIG. 2 is shown. [Figure 7] Another example of the flow of the selection process in FIG. 2 is shown. [Figure 8] The waveform of the pulse wave acquired from a subject with a relatively low vascular age is illustrated. [Figure 9] The waveform of the pulse wave acquired from a subject with a relatively high vascular age is illustrated.

Modes for Carrying Out the Invention

[0010] The example of the embodiment will be described in detail while referring to the accompanying drawings.

[0011] FIG. 1 illustrates the functional configuration of a monitoring device 10 according to one embodiment example. The monitoring device 10 is a device that monitors the biological information of a subject 20. The monitoring device 10 includes an information processing device 11 and a visualization device 12.

[0012] The information processing device 11 is a device that processes the biological information of the subject 20. Specifically, the information processing device 11 is configured to acquire information corresponding to an estimated value of the respiration rate of the subject 20 based on a signal corresponding to the waveform of the pulse wave of the subject 20. The pulse wave is an example of biological information accompanied by periodic changes. The term "waveform" used in the present disclosure means the change over time of the value of specific biological information.

[0013] The visualization device 12 is configured to visualize information corresponding to the estimated value of the respiration rate of the subject 20. As an example, the visualization device 12 can be a display device that displays the information. As another example, the visualization device 12 can be a printing device that prints the information on a medium. The visualization device 12 is an example of an output device.

[0014] The information processing device 11 includes an input interface 111. The input interface 111 is configured as a hardware interface that receives a waveform signal WF corresponding to the waveform of the pulse wave from the sensor 30 attached to the subject 20. The waveform signal WF may be an analog signal or a digital signal according to the specifications of the sensor 30.

[0015] Examples of the sensor 30 for acquiring the pulse wave include a photoelectric pulse wave sensor including a light emitting element and a light receiving element. The light emitted from the light emitting element passes through the biological tissue of the subject 20 and is incident on the light receiving element. As the subject 20 pulsates, the intensity of the light incident on the light receiving element changes. Therefore, the change over time of the incident light intensity can correspond to the waveform of the pulse wave.

[0016] When the waveform signal WF is an analog signal, the input interface 111 includes an appropriate conversion circuit including an A / D converter. This description is similarly applicable to other signals to be received by the input interface 111 described later.

[0017] The information processing device 11 includes a processor 112. The processor 112 is configured to obtain an estimated respiratory rate of the subject 20 based on the waveform signal WF received by the input interface 111.

[0018] The information processing device 11 is equipped with an output interface 113. The processor 112 is configured to output a control signal CT from the output interface 113, which causes the visualization device 12 to visualize information corresponding to the estimated respiratory rate. The control signal CT may be an analog signal or a digital signal, depending on the specifications of the visualization device 12.

[0019] In other words, the output interface 113 is configured as a hardware interface capable of outputting a control signal CT. When the control signal CT is an analog signal, the output interface 113 includes an appropriate conversion circuit, including a D / A converter. This description also applies to other signals that the output interface 113 can output, as described later.

[0020] Referring to Figure 2, we will now explain the specific processing flow performed by the processor 112 to obtain an estimated respiratory rate for subject 20.

[0021] The pulse wave waveform corresponding to the waveform signal WF received by the input interface 111 has superimposed frequency components corresponding to pulse (0.5~2Hz), frequency components corresponding to respiration (0.1~1Hz), and frequency components of the Mayer wave (0.04~0.4Hz). The frequency components corresponding to respiration overlap with some of the other frequency components in terms of bandwidth, making it difficult to extract them by filtering alone.

[0022] On the other hand, the pulse wave waveform contains several features that reflect the effects of respiration. Figure 3 illustrates baseline fluctuations of the waveform, which are one example of features. Baseline fluctuations can be caused by changes in intrathoracic pressure, vasoconstriction of arteries during inspiration, etc. Figure 4 illustrates amplitude fluctuations of the waveform, which are another example of features. Amplitude fluctuations can be caused by changes in intrathoracic pressure, etc. Figure 5 illustrates frequency fluctuations of the waveform, which are yet another example of features. Frequency fluctuations can be caused by respiratory sinus arrhythmias (RSA), etc.

[0023] Therefore, as illustrated in Figure 2, the processor 112 performs processing to extract baseline fluctuations, amplitude fluctuations, and frequency fluctuations from the waveform signal WF.

[0024] Next, processor 112 performs a process to obtain a preliminary estimate of the respiratory rate based on each of the extracted features. Specifically, frequency analysis is applied to each feature to identify the frequency with the largest power spectral value. Examples of frequency analysis processes include fast Fourier transform and wavelet transform. Processor 112 then associates the identified frequency with a preliminary estimate of the respiratory rate (number of breaths per minute).

[0025] In this example, the respiratory rate estimate obtained based on baseline fluctuations is referred to as the first preliminary estimate PE1. Similarly, the respiratory rate estimate obtained based on amplitude fluctuations is referred to as the second preliminary estimate PE2, and the respiratory rate estimate obtained based on frequency fluctuations is referred to as the third preliminary estimate PE3.

[0026] Next, the processor 112 performs a selection process to select at least one of the multiple preliminary estimates obtained as described above, based on the state information corresponding to the state of the subject 20. Figure 6 shows an example of the flow of the selection process.

[0027] The processor 112 determines whether the second preliminary estimate PE2 or the third preliminary estimate PE3 is below the threshold (STEP 11). If it is determined that the second preliminary estimate PE2 or the third preliminary estimate PE3 is below the threshold (YES in STEP 11), the processor 112 excludes the first preliminary estimate PE1 (STEP 12). In other words, the preliminary estimate of respiratory rate obtained based on baseline fluctuations is excluded.

[0028] Next, processor 112 calculates representative values ​​for the second preliminary estimate PE2 and the third preliminary estimate PE3 (STEP 13). In this example, the representative value is the mean value. Processor 112 uses the calculated mean value as the estimated respiratory rate ES of subject 20 in Figure 2. That is, the respiratory rate of the subject is estimated based on the preliminary estimate obtained based on amplitude fluctuations and the preliminary estimate obtained based on frequency fluctuations.

[0029] If both the second preliminary estimate PE2 and the third preliminary estimate PE3 are determined to be above the threshold (NO in STEP 11), the processor 112 calculates a representative value for all preliminary estimates (STEP 13). In this example, the representative value is the mean. However, the median or mode may also be used as the representative value.

[0030] The processor 112 outputs a control signal CT from the output interface 113 to the visualization device 12, which causes the visualization device 12 to visualize information corresponding to the estimated respiratory rate ES of the calculated subject 20. This information may be the estimated value itself, or it may be a color, symbol, or graphic corresponding to the estimated value.

[0031] The inventors of this application focused on the fact that the accuracy of estimating the respiratory rate based on baseline fluctuations decreases when the respiratory rate of subject 20 is low (for example, 10 breaths per minute or less). According to the configuration of the processing example described above, if it is determined that the respiratory rate estimated based on the amplitude fluctuation or frequency fluctuation of the waveform signal WF corresponding to the pulse wave waveform is below a threshold, the first preliminary estimate PE1 obtained based on baseline fluctuations is excluded and the estimated respiratory rate ES of subject 20 is determined. Therefore, the second preliminary estimate PE2 and the third preliminary estimate PE3 can be examples of state information corresponding to the state of subject 20.

[0032] Since the processor 112 mounted on the information processing device 11 performs a process to exclude preliminary estimates that reduce the accuracy of respiratory rate estimation based on the respiratory frequency of the subject 20, it is possible to suppress the reduction in respiratory rate estimation accuracy caused by the respiratory frequency of the subject 20 without adding any sensors.

[0033] Figure 7 shows another example of the selection process flow executed by processor 112. In this example, the age information AG of subject 20 is first obtained (STEP 21). In this example, the age information AG corresponds to the vascular age of subject 20. The age information AG is an example of state information.

[0034] Figure 8 illustrates the waveform of an acceleration pulse wave obtained by taking the second derivative of the pulse wave waveform acquired from a subject with a vascular age in their 30s. Figure 9 similarly illustrates the waveform of an acceleration pulse wave obtained from a subject with a vascular age in their 70s. The symbol 'a' represents the wave (a wave) when the heart pumps blood. The symbol 'b' represents the wave (b wave) that appears when the a wave bounces back, and the magnitude of its amplitude corresponds to the flexibility of the blood vessels. The symbol 'c' represents the wave (c wave) whose amplitude decreases with age. The symbol 'd' represents the wave (d wave) that reflects the state of blood vessels throughout the body.

[0035] When vascular age is relatively low, the slope of the line connecting the peaks of wave b and wave d tends to be positive. On the other hand, when vascular age is relatively high, the slope of the same line tends to be negative. Therefore, the vascular age of a subject can be estimated from the value of the slope of this line.

[0036] For example, the processor 112 may be configured to identify the b-wave and d-wave from the waveform signal WF received by the input interface 111 and to obtain the gradient value of the straight line described above. Preferably, the gradient value is calculated as a representative value of multiple gradients obtained from multiple pulse waves corresponding to multiple pulsations of multiple periods. Examples of representative values ​​include the mean, median, mode, minimum, and maximum.

[0037] Next, processor 112 determines whether the subject 20's age is above a threshold (STEP 22). For example, processor 112 is configured to be able to access a function that takes the gradient value as input and outputs an estimated vascular age. That is, the estimated vascular age can be accessed as age information AG. In this case, processor 112 determines whether the subject 20's age is above a threshold based on whether the estimated vascular age is above a threshold.

[0038] Alternatively, the gradient value itself may be referenced as age information (AG). For example, if the gradient takes a negative value, it may be determined that the age of subject 20 is above a threshold.

[0039] If the age of subject 20 is determined to be above the threshold (YES in STEP 22), the processor 112 excludes the third preliminary estimate PE3 (STEP 23). In other words, the preliminary estimate of respiratory rate obtained based on frequency fluctuations is excluded.

[0040] Next, processor 112 calculates representative values ​​for the first preliminary estimate PE1 and the second preliminary estimate PE2 (STEP 24). In this example, the representative value is the mean value. Processor 112 uses the calculated mean value as the estimated respiratory rate ES of subject 20 in Figure 2. That is, the respiratory rate of the subject is estimated based on the preliminary estimate obtained based on baseline fluctuations and the preliminary estimate obtained based on amplitude fluctuations.

[0041] If the age of subject 20 is determined to be below the threshold (NO in STEP 22), the processor 112 calculates a representative value for all preliminary estimates (STEP 24). In this example, the representative value is the mean. However, the median or mode may also be used as the representative value.

[0042] The inventors of this application focused on the fact that the accuracy of estimating the respiratory rate based on frequency fluctuations decreases when the age of subject 20 is high. According to the configuration of the processing example described above, if the age of subject 20 estimated based on the waveform signal WF corresponding to the pulse wave waveform is determined to be above a threshold, the respiratory rate estimated based on frequency fluctuations is excluded and the final respiratory rate is estimated. Therefore, the estimated age of subject 20 can be an example of state information corresponding to the state of subject 20.

[0043] Since the processor 112 mounted on the information processing device 11 performs a process to exclude preliminary estimates that reduce the accuracy of respiratory rate estimation based on the vascular age of the subject 20, the decrease in respiratory rate estimation accuracy caused by the vascular age of the subject 20 can be suppressed without the need for additional sensors.

[0044] As illustrated in Figure 1, the monitoring device 10 may include a user interface 13. The user interface 13 is configured to accept user input of age information AG, which indicates the actual age of the subject 20. The age information AG, which indicates the actual age, may be input from a database that stores electronic medical record data.

[0045] In other words, the age information AG obtained in STEP 21 of the processing example explained with reference to Figure 7 may represent the actual age of subject 20. The age information AG representing the actual age of subject 20 is state information corresponding to the state of subject 20.

[0046] With this configuration, the processor 112 mounted on the information processing device 11 performs a process to exclude preliminary estimates that reduce the accuracy of respiratory rate estimation based on the actual age of the subject 20. Therefore, the reduction in respiratory rate estimation accuracy due to the age of the subject 20 can be suppressed without the need for additional sensors.

[0047] The processor 112 of the information processing device 11, which has various functions as exemplified above, can be realized by at least one general-purpose microprocessor operating in cooperation with at least one general-purpose memory. Examples of general-purpose microprocessors include CPUs, MPUs, and GPUs. Examples of general-purpose memory include ROMs and RAMs. In this case, the ROM may store computer programs that implement the various functions described above. ROM is an example of a non-temporary computer-readable medium that stores computer programs. The general-purpose microprocessor selects at least a portion of the program stored in the ROM and loads it onto the RAM, and then executes the above-described processes in cooperation with the RAM. The computer program may be pre-installed in the general-purpose memory, or it may be downloaded from an external server device via a communication network and then installed in the general-purpose memory. In this case, the external server device is an example of a non-temporary computer-readable medium that stores computer programs.

[0048] The processor 112 may be implemented by at least one dedicated integrated circuit capable of executing the above-described computer program. Examples of dedicated integrated circuits include microcontrollers, ASICs, FPGAs, etc. In this case, the above-described computer program is pre-installed in a memory element included in the dedicated integrated circuit. This memory element is an example of a computer-readable medium that stores the computer program. The processor 112 can also be implemented by a combination of a general-purpose microprocessor and a dedicated integrated circuit.

[0049] The various configurations described herein are merely examples to facilitate understanding of this disclosure. Each example configuration may be modified or combined with others as appropriate within the scope of the intent of this disclosure.

[0050] The processes illustrated in Figure 6 and Figure 7 can be combined. If at least one of the first preliminary estimate PE1 and the third preliminary estimate PE3 is excluded based on the waveform signal WF, the respiratory rate estimate of subject 20 is determined based on the remaining preliminary estimates, including at least the second preliminary estimate PE2.

[0051] In the above embodiment, baseline fluctuation, amplitude fluctuation, and frequency fluctuation are extracted as multiple feature quantities related to the waveform signal WF. However, a configuration in which only two of these feature quantities are extracted can also be adopted. In this case, it is preferable that one of the two feature quantities is amplitude fluctuation.

[0052] With this configuration, the processor 112 mounted on the information processing device 11 performs a process to exclude preliminary estimates that reduce the accuracy of respiratory rate estimation based on the subject's condition 20. Therefore, a decrease in respiratory rate estimation accuracy due to the subject's condition 20 can be suppressed without the need for additional sensors.

[0053] In the above embodiment, the estimated respiratory rate ES of the subject 20 is visualized by the visualization device 12. However, the estimated value ES may also be output as sound by a speaker, or transmitted as data to another device by a data transmission device. In this case, the speaker or data transmission device can be an example of an output device.

[0054] In the above embodiment, a waveform signal WF corresponding to the pulse wave waveform of subject 20 is acquired. However, a waveform signal WF corresponding to the electrocardiogram waveform of subject 20 may also be acquired. That is, an electrocardiogram can be an example of biological information that involves periodic changes.

[0055] The configurations listed below also constitute part of this disclosure. Item 1: An information processing device for processing the biological information of a subject, An interface that receives signals corresponding to the waveform of biological information with periodic changes from a sensor attached to the subject, A processor that causes an output device to output information corresponding to an estimated value of the subject's respiratory rate based on the aforementioned signal, It is equipped with, The aforementioned processor, From the aforementioned signal, multiple feature quantities corresponding to the subject's respiration are extracted, By applying frequency analysis processing to each of the aforementioned multiple features, multiple preliminary estimates of the respiratory rate are obtained. Based on the state information corresponding to the state of the subject, one of the multiple preliminary estimates obtained from one of the multiple features is excluded. Based on at least one of the remaining multiple preliminary estimates, the estimate is determined. Information processing device. Item 2: The aforementioned multiple feature quantities include baseline variation, amplitude variation, and frequency variation. The state information is an estimated value of the respiratory rate obtained based on the amplitude fluctuation or the frequency fluctuation, One of the multiple preliminary estimates obtained based on the baseline fluctuations is excluded. The information processing device described in item 1. Item 3: The aforementioned multiple feature quantities include frequency variations, The aforementioned status information corresponds to the age of the subject, One of the multiple preliminary estimates obtained based on the frequency fluctuations is excluded. The information processing device described in item 1. Item 4: The status information corresponds to the vascular age of the subject obtained based on the signal, The information processing device described in item 3. Item 5: The aforementioned biological information is a pulse wave. An information processing device as described in any one of items 1 through 4. [Explanation of Symbols]

[0056] 10: Monitoring device, 11: Information processing device, 111: Input interface, 112: Processor, 12: Visualization device, 20: Subject, 30: Sensor, AG: Age information, ES: Respiratory rate estimate, PE1: First preliminary estimate, PE2: Second preliminary estimate, PE3: Third preliminary estimate, WF: Waveform signal

Claims

1. An information processing device for processing the biological information of a subject, An interface that receives signals corresponding to the waveform of biological information with periodic changes from a sensor attached to the subject, A processor that causes an output device to output information corresponding to an estimated value of the subject's respiratory rate based on the aforementioned signal, It is equipped with, The aforementioned processor, From the aforementioned signal, multiple feature quantities corresponding to the subject's respiration are extracted, By applying frequency analysis processing to each of the aforementioned multiple features, multiple preliminary estimates of the respiratory rate are obtained. Based on the state information corresponding to the state of the subject, one of the multiple preliminary estimates obtained from one of the multiple features is excluded. Based on at least one of the remaining multiple preliminary estimates, the estimate is determined. Information processing device.

2. The aforementioned multiple feature quantities include baseline variation, amplitude variation, and frequency variation. The state information is an estimated value of the respiratory rate obtained based on the amplitude fluctuation or the frequency fluctuation, One of the multiple preliminary estimates obtained based on the baseline fluctuations is excluded. The information processing apparatus according to claim 1.

3. The aforementioned multiple feature quantities include frequency variations, The aforementioned status information corresponds to the age of the subject, One of the multiple preliminary estimates obtained based on the frequency fluctuations is excluded. The information processing apparatus according to claim 1.

4. The status information corresponds to the vascular age of the subject obtained based on the signal, The information processing apparatus according to claim 3.

5. The aforementioned biological information is a pulse wave. The information processing apparatus according to claim 1.

6. A monitoring device that monitors the biological information of a subject, An information processing device according to any one of claims 1 to 5, An output device that outputs information corresponding to the estimated respiratory rate of the subject obtained by the information processing device, It is equipped with Monitoring device.

7. A computer program that can be executed by a processor installed in an information processing device that processes the biological information of a subject, By being executed, the information processing device will The system receives signals corresponding to the waveform of biological information that undergoes periodic changes from sensors attached to the subject. From the aforementioned signal, multiple feature quantities corresponding to the subject's respiration are extracted, By applying frequency analysis processing to each of the aforementioned multiple features, multiple preliminary estimates of the subject's respiratory rate are obtained. Based on the state information corresponding to the state of the subject, one of the multiple preliminary estimates obtained from one of the multiple features is excluded. Based on at least one of the remaining preliminary estimates, the estimated respiratory rate is determined. The output device outputs information corresponding to the aforementioned estimated value. Computer program.

Images