JP6149450B2 - Respiratory information estimation apparatus and method, and program - Google Patents

Description

  The present invention relates to a respiratory information estimation apparatus and method, and a program.

  For example, physiological indices used to estimate the arousal state of a driver of a vehicle, an operator, etc. (hereinafter also referred to as “subject”) include heart rate, respiration, blood pressure, eye movement, and skin potential.

  In order to accurately estimate the arousal state, it is desirable to sense the physiological index from various angles. However, if a dedicated sensor is used for each physiological index, the load on the subject wearing various sensors increases, and the cost increases as the number of sensors increases. Therefore, as an example of a technique for estimating a large number of physiological indices with a small number of sensors in order to reduce the load on the subject and reduce the cost, heart rate and respiration 2 from a heart rate signal output from a heart rate sensor or a pulse sensor or the like. A method for estimating one physiological index has been proposed.

  In the first method for estimating the respiratory rate from the heart rate, a proportional relationship between the heart rate and the respiratory rate is obtained in advance, and the respiratory rate is estimated from the heart rate. Specifically, the subject's heart rate is acquired in advance using, for example, a heart rate sensor or a pulse sensor, and at the same time, the respiratory cycle of the subject is acquired using, for example, a respiration sensor using a thermistor or a pressure sensor wound around the chest. Keep it. By using a typical proportionality coefficient obtained from both heart rate and respiratory cycle data acquired in advance, the respiratory rate can be estimated from the heart rate detected by the heart rate sensor attached to the subject (for example, patent document) 2). However, since the relationship between the heart rate and the respiratory rate varies dynamically and varies from subject to subject, it is difficult to improve the estimation accuracy of the respiratory rate.

  A second method for extracting a respiratory component by performing frequency analysis on time-series data of heartbeat intervals extracts a respiratory cycle by performing so-called heartbeat fluctuation analysis. In the heartbeat signal output from the heartbeat sensor or the like, a peak signal called R wave appears characteristically, and R-wave R-wave interval data is called RRI (R-wave R-wave Interval). It is known that autonomic nerve activity can be estimated by frequency analysis of time-series data obtained by collecting RRIs in time series (for example, Non-Patent Documents 1 and 2).

  For example, a relatively high frequency band from 0.15 Hz to 0.4 Hz is called an HF (High-Frequency) region. For example, a relatively low frequency band from 0.05 Hz to less than 0.15 Hz is LF (Low-Frequency). ) Sometimes called a region. Autonomic nerve activity is a sympathetic nerve that works to activate the body, such as speeding up the heartbeat and increasing the respiratory rate, and a parasympathetic nerve that works in a direction to rest the body, such as slowing the heartbeat and reducing the respiratory rate. It is known that the HF region is affected by parasympathetic nerves, and the LF region is affected by parasympathetic nerves and sympathetic nerves. Moreover, the peak seen in the HF region is called respiratory sinus arrhythmia RSA (Respiratory Sinus Arrhythmia), and the peak seen in the LF region is called blood pressure arrhythmia MWSA (Mayer Wave related Sinus Arrhythmia). The respiratory cycle can be estimated with relatively high accuracy from the frequency component of the RSA variation described above, but it is difficult to easily estimate the respiratory information from the heartbeat information because of the large amount of estimation processing based on the frequency component. For this reason, it is difficult to implement such estimation processing in, for example, an inexpensive embedded device.

JP 2009-232990 A JP 2012-152458 A

Mitsuyuki Nakao et al., “Heartbeat Rhythm and Fluctuation”, Corona, pp. 66-76, November 2004 Junichiro Hayano, “Heartbeat Rhythm and Age Changes”, CLINICIAN, '94 No.429, pp.336-340

  In the conventional method, it is difficult to easily estimate respiratory information from heartbeat information with high accuracy.

  Accordingly, an object of the present invention is to provide a respiratory information estimation apparatus, method, and program that can easily estimate respiratory information from heartbeat information with high accuracy.

According to one aspect of the present invention, a first calculation means for calculating a heartbeat cycle based on information dependent on a heartbeat of a subject, and a variance value of a heartbeat transition pattern based on a plurality of successive cycles of the heartbeat. Second calculation means for calculating; estimation means for estimating respiration information based on a variance value of the transition pattern; storage means for storing a plurality of heartbeat patterns; and information dependent on the heartbeat of the subject, Compensating means for correcting based on the heart rate pattern of the subject read out from the storage means , wherein the first calculating means calculates the heart rate calculated based on the information dependent on the heart rate of the subject corrected by the correcting means. A breathing information estimation device is provided that supplies the period to the second calculation means .

  According to the disclosed respiratory information estimation apparatus, method, and program, it is possible to easily estimate respiratory information from heartbeat information with high accuracy.

It is a block diagram which shows an example of the hardware constitutions of the respiration information estimation apparatus in 1st Example. It is a block diagram which shows an example of a function structure of the respiration information estimation apparatus in 1st Example. It is a flowchart explaining an example of the respiration information estimation process in 1st Example. It is a figure which shows an example of the heart rate signal which a heart rate sensor outputs. It is a figure which shows an example of heart rate data. It is a figure which shows an example of RRI time series data. It is a figure which shows an example of the RRI time series data formed by the continuous group of the fixed period P. It is a figure which shows an example of RRI time series data. It is a figure explaining an example of the dispersion value of the transition pattern of a heartbeat. It is a figure which shows an example of a respiration time series. It is a figure which shows an example of the relationship between respiration and heartbeat about RRI * 1-RRI * 8. It is a block diagram which shows an example of a function structure of the respiration information estimation apparatus in 2nd Example. It is a flowchart explaining an example of the registration process of the heart rate pattern in 2nd Example. It is a flowchart explaining an example of the respiration information estimation process in 2nd Example. It is a figure explaining the dynamic correction of a proportionality constant.

  The present inventor has found that, among physiological indices, respiration, in particular, is more suitable as a physiological index because it is less susceptible to noise caused by the movement of the subject than the heartbeat. Heartbeats are sensitive to dynamic changes, but breathing is relatively slow to heartbeats. Therefore, according to the disclosed respiratory information estimation apparatus, method, and program, the respiratory information estimation process estimates respiratory information such as a respiratory cycle and a respiratory rate from the heartbeat information or pulse information of the subject. The respiration information estimation process enables dynamic analysis without using frequency analysis.

  The human heart and lung are both under the control of autonomic nerves, and in a state close to the steady rest of the subject, the respiratory cycle often takes a value of, for example, about 3 to 4 times the heartbeat cycle. However, the constant of proportionality between the respiratory cycle and the heartbeat cycle varies dynamically with changes in the subject's condition. Therefore, in the respiratory information estimation process, the respiratory cycle is dynamically estimated with high accuracy by sequentially dynamically correcting the dynamic fluctuation of the proportionality constant.

  Embodiments of the disclosed respiratory information estimation apparatus and method and program will be described below with reference to the drawings.

  First, the first embodiment will be described. FIG. 1 is a block diagram illustrating an example of a hardware configuration of the respiratory information estimation apparatus according to the first embodiment. A respiratory information estimation apparatus 1 illustrated in FIG. 1 includes a CPU (Central Processing Unit) 11 that is an example of a processor (or a computer), a storage device 12, and an interface (I / F) 13. In this example, the CPU 11, the storage device 12, and the I / F 13 are connected by the bus 15, but the connection is not limited to the bus 15.

  The heart rate sensor (or an ear clip type pulse sensor) 14 is connected to the bus 15 in this example, but may be connected to the I / F 13. The heart rate sensor 14 may be externally connected to the respiratory information estimation device 1 or may form a part of the respiratory information estimation device 1. The heart rate sensor 14 is an example of a known sensor that detects information dependent on a heart rate such as a heart rate of a subject (not shown) and a pulse rate by a known method.

  The CPU 11 controls the entire breathing information estimation apparatus 1 and executes a breathing information estimation process, which will be described later, by executing a program. The storage device 12 stores a program executed by the CPU 11, an intermediate result of an operation executed by the CPU 11, a program executed by the CPU 11, data used for the operation, and the like. The storage device 12 can be formed of a computer-readable storage medium. The computer-readable storage medium may be a semiconductor storage device. When the computer-readable storage medium is a magnetic recording medium, an optical recording medium, a magneto-optical recording medium, or the like, the storage device 12 can be formed by a reader / writer that reads and writes information from and on the loaded recording medium. . The I / F 13 can communicate with an external device (not shown) in a wired or wireless manner. When the heart rate sensor 14 is provided with a wireless communication function, the heart rate sensor 14 may perform wireless communication with the respiratory information estimation apparatus 1 via the wireless communication function of the I / F 13.

  FIG. 2 is a block diagram illustrating an example of a functional configuration of the respiratory information estimation apparatus according to the first embodiment. FIG. 2 shows a functional configuration of the CPU 11, which includes a control unit 111, a heart rate information calculation unit 112, and a respiration information estimation unit 113. A heart rate signal output from the heart rate sensor 14 is input to the control unit 111. The control unit 111 controls various calculations related to the heart rate information by the heart rate information calculation unit 112 and the estimation of respiration information by the respiration information estimation unit 113. As will be described later, the heart rate information calculation unit 112 performs calculations related to the heart rate period, the heart rate transition pattern, the variance value of the heart rate transition pattern, and the like. The respiration information estimation unit 113 estimates respiration information such as a respiration cycle and a respiration rate by comparing dispersion values. The control unit 111 outputs the respiration information estimated by the respiration information estimation unit 113.

  FIG. 3 is a flowchart for explaining an example of the respiratory information estimation process in the first embodiment. In FIG. 3, when the respiration information estimation process of the CPU 11 is started in step S1, in step S2, it is determined whether or not the fixed period P has elapsed by the control unit 111, and if the determination result is YES, the process is step. Proceed to S3. The fixed period P is a parameter for setting how much calculation is performed. The fixed period P can be set to, for example, several tens of seconds to several minutes, depending on whether the subject's breathing cycle is observed relatively finely or with relatively large transitions.

  In step S <b> 3, under the control of the control unit 111, a heartbeat cycle is obtained by calculation based on the heartbeat signal output from the heartbeat sensor 14 by the heartbeat information calculation unit 112. FIG. 4 is a diagram illustrating an example of a heartbeat signal output from the heartbeat sensor 14, where the vertical axis indicates electrocardiogram strength in arbitrary units (au), and the horizontal axis indicates time in arbitrary units (au). u.) The heart rate information calculation unit 112 acquires feature points such as peaks from the heart rate data as shown in FIG. 4, calculates the time between successive feature points, and obtains a heart rate cycle. In the example shown in FIG. 4, the heart rate information calculation unit 112 calculates R-wave R-wave interval data RRI (R-wave R-wave Interval), which is an example of a peak, by a well-known method to obtain a heartbeat cycle. .

  In step S <b> 4, a heart rate transition pattern is obtained by calculation by the heart rate information calculation unit 112 under the control of the control unit 111. For example, in the case of heart rate data as shown in FIG. 5, the RRI time-series data is as shown in FIG. 6, for example. In FIG. 6, the vertical axis represents RRI (seconds), and the horizontal axis represents the number of heartbeat samples. In the case of heart rate data as shown in FIG. 5, for example, two consecutive RRIs (RRI * 2), three consecutive RRIs (RRI * 3), four consecutive RRIs (RRI * 4), 5 RRIs (RRI * 5), ..., n consecutive RRIs (RRI * n) as a set, RRI time series data formed by a continuous set of a fixed period P is calculated, and the heart rate The transition pattern may be obtained. In FIG. 5, for convenience of explanation, three consecutive RRI (1), RRI (2), and RRI (3) are shown.

  FIG. 7 shows a series of three consecutive RRIs (RRI * 3), four consecutive RRIs (RRI * 4), and five consecutive RRIs (RRI * 5). It is a figure which shows an example of the RRI time series data formed by the group. In FIG. 7, RRI time-series data formed by a continuous set having a fixed period P with a set of three consecutive RRIs (RRI * 3) is the first three consecutive RRIs (1), RRI (2 ), RRI (3) is the first set, the next three consecutive RRI (4), RRI (5), RRI (6) are the next set,... Is included. RRI time-series data formed by a continuous set having a constant period P with a set of four consecutive RRIs (RRI * 4) is the first four consecutive RRIs (1), RRI (2), RRI ( 3), RRI (4) is the first set, the next four consecutive RRI (5), RRI (6), RRI (7), RRI (8) are the next set, and so on. A plurality of sets are included in the period P. Similarly, RRI time-series data formed by a continuous set having a fixed period P with a set of five consecutive RRIs (RRI * 5) is the first five consecutive RRIs (1) and RRI (2). , RRI (3), RRI (4), RRI (5) are the first set, the next five consecutive RRI (6), RRI (7), RRI (8), RRI (9), RRI (10 ) Includes the next set,..., And a plurality of sets are included in the fixed period P. Although illustration is omitted, RRI time-series data formed by a continuous group having a constant period P with two consecutive RRIs (RRI * 2) as a group is the first two consecutive RRIs (1). , RRI (2) includes the first set, the next two consecutive RRI (3), RRI (4) include the next set,... Note that the number of sets included in the fixed period P may be one or more, and is not limited to an integer. Also, the method of creating a set is not limited to one. For example, the first two consecutive RRIs (2) and RRI (3) are the first group, and the next two consecutive RRIs (4) and RRI (5). May be registered in a different pattern than the next group.

  FIG. 8 is a diagram illustrating an example of RRI time-series data obtained by calculating a heartbeat transition pattern in step S4. In FIG. 8 and FIGS. 9 and 10 to be described later, the vertical axis represents RRI time (seconds), and the horizontal axis represents elapsed time (minutes).

  In step S5, under the control of the control unit 111, the heart rate information calculation unit 112 obtains a variance value of the heart rate transition pattern by calculation. For example, the variance value of the heartbeat transition pattern may be calculated based on the following equation.

上記の式中、ｎは連続するＲＲＩの個数、ＲＲＩ_ａｖは連続するｎ個のＲＲＩの区間でのＲＲＩの平均値、ＲＲＩ_ｉは連続するｎ個のＲＲＩ中のｉ番目のＲＲＩの値を示す。

In the above formula, n is the number of consecutive RRIs, RRI _av is the average value of RRIs in the interval of n consecutive RRIs, and RRI _i is the value of the i-th RRI in the consecutive n RRIs. .

  In step S6, under the control of the control unit 111, the variance values of the heartbeat transition patterns (that is, RRI time-series data) calculated in step 5 by the respiration information estimation unit 113 are compared to obtain the minimum value by calculation. . It is determined that the RRI time series data having the minimum variance value has a cycle corresponding to the respiratory cycle. FIG. 9 is a diagram for explaining an example of a variance value of a heartbeat transition pattern. In FIG. 9, (A) is RRI time series data with RRI * 3 as a set, (B) is RRI time series data with RRI * 4 as a set, and (C) is RRI time series with RRI * 5 as a set. Data is shown. In this example, the variance of RRI time-series data with RRI * 3 as a set is D1, and the variance of RRI time-series data with RRI * 4 as a set is the variance of RRI time-series data with D2 and RRI * 5 as a set. The variance value is D3, and D1 <D2 <D3 (here, for the sake of explanation in the figure, the variance is expressed as the maximum-minimum value. Is used). Accordingly, in step S6, the variance values D1, D2, and D3 calculated in step S5 are compared, and the variance value D1 that is the minimum value in this example is obtained by calculation. In FIG. 9, D1 to D3 schematically show dispersion values, and each of the dispersion values D1 to D3 is calculated based on the above formula in this example.

  In step S7, under the control of the control unit 111, the respiration information is estimated based on the minimum value of the variance values obtained in step S6 by the respiration information estimation unit 113. For example, the RRI time-series data cycle having the minimum variance value may be specified as the respiratory cycle in the current respiratory estimation section. FIG. 10 is a diagram showing an example of a respiratory time series when the period of the RRI time series data shown in FIG. 9A is specified as the respiratory period in the current respiratory estimation section in FIG. is there. Note that the respiration information estimated in step S7 may be a respiration rate.

  As described above, in this embodiment, the RRI time-series data obtained by the RRI moving average process is synchronized with the respiratory component, that is, the respiratory component variation element is deleted when the cycle is matched. The same idea as a kind of notch filter that removes frequency periodicity is adopted.

  In step S8, the control unit 111 determines whether or not the target section of the respiration estimation process has ended. If the determination result is NO, the process returns to step S2, and if the determination result is YES, the process proceeds to step S9. End with. In step S9, when the termination processing is performed by the control unit 111, the estimated respiration information is stored in the storage device 12, or the estimated respiration information is output to the outside via the I / F 13, or the heart rate sensor 14 You may end communication with.

  The heartbeat information such as the heartbeat cycle obtained in step S3 may be stored in the storage device 12 or output to the outside via the I / F 13.

  Thus, in this embodiment, two types of physiological indices, that is, heart rate information and respiration information, can be obtained based on the heart rate signal output from the single heart rate sensor 14. In addition, it is possible to estimate respiratory information with high accuracy by simple calculation in the time domain from heartbeat information while suppressing the load on the subject.

  FIG. 11 is a diagram illustrating an example of the relationship between respiration and heartbeat for RRI * 1 to RRI * 8. 11, (A) to (D) show the measurement results for four different subjects H1 to H4, the vertical axis shows the frequency in arbitrary units (au), and the horizontal axis shows RRI * n ( n = 1 to 8). FIG. 11 shows the result of examining the relationship between the respiratory cycle and the heartbeat cycle measured every 10 seconds during driving operation by each of the subjects H1 to H4 simulating the driving operation of the vehicle for 90 minutes with the simulation device. In FIG. 11, from the measurement results for subjects H1 to H4 shown in (A) to (D), the majority of cases are RRI * 2 to RRI * 5 where the respiratory cycle is about 2 to 5 times the heartbeat cycle. It was. Therefore, in step S4, two consecutive RRIs (RRI * 2), three consecutive RRIs (RRI * 3), four consecutive RRIs (RRI * 4), and five consecutive RRIs. (RRI * 5) is a set, and RRI time series data formed by a set having a constant period P (that is, RRI * n (n = 2 to 5)) is calculated to obtain a heartbeat transition pattern. It was confirmed that it was preferable. That is, it was confirmed that n of RRI * n is preferably 2 to 5, and more preferably 2 to 4.

  Note that, in step S4, n consecutive RRIs (RRI * n) are used as a set, and the set is formed by a set having a constant period P for any of n = 2, n = 3, n = 4, and n = 5. When calculating RRI time-series data, step S6 can be omitted.

  By the way, instead of calculating RRI time series data formed by a continuous set having a constant period P, for example, by setting three consecutive RRIs (RRI * 3) as a set, the first set includes three consecutive RRIs, In such a group, the number of consecutive RRIs in each group within a certain period P may be variable instead of being constant, such as two or four consecutive RRIs. For example, the first set is RRI (1), RRI (2), RRI (3), the next set is RRI (4), RRI (5), RRI (6), RRI (7), and the next set is RRI. (8), RRI (9), RRI (10), etc., the number of consecutive RRIs in the consecutive set may be 3, 4, 3. Similarly, the first set is RRI (1), RRI (2), the next set is RRI (3), RRI (4), RRI (5), and the next set is RRI (6), RRI (7), For example, the number of continuous RRIs in a continuous set may be 2, 3, 4 such as RRI (8), RRI (9).

  Furthermore, for example, when calculating RRI time-series data formed by a continuous set having a fixed period P using three consecutive RRIs as a set, the first set is RRI (1), RRI (2), RRI (3) In the next set, RRI (2), RRI (3), RRI (4), and so on, RRI may partially overlap between successive sets.

  Next, a second embodiment will be described. Since the hardware configuration of the respiratory information estimation apparatus in the second embodiment may be the same as that shown in FIG. 1, its illustration and description are omitted. FIG. 12 is a block diagram illustrating an example of a functional configuration of the respiratory information estimation apparatus according to the second embodiment. In FIG. 12, the same parts as those in FIG.

  FIG. 12 shows a functional configuration of the CPU 11, which includes a heart rate pattern reading unit 116 in addition to the control unit 111, the heart rate information calculation unit 112, and the respiration information estimation unit 113. A heartbeat pattern ROM (Read Only Memory) 115 stores heartbeat data for heartbeat signals obtained in advance for each subject, that is, heartbeat patterns of a plurality of subjects. The heartbeat pattern ROM 115 can be formed by the storage device 12. The heart rate pattern reading unit 116 reads the heart rate pattern of the subject to be tested from the heart rate pattern ROM 115 and outputs it to the control unit 111 under the control of the control unit 111. The control unit 111 obtains a heart rate pattern of the subject based on the heart rate signal output from the heart rate sensor 14 and an average heart rate pattern of the heart rate pattern of the same subject read from the heart rate pattern ROM 115. The calculation by the heart rate information calculation unit 112 and the estimation by the respiration information estimation unit 113 are executed for the average heart rate pattern. The averaging process for obtaining the average heartbeat pattern is an example of a correction process for correcting the heartbeat pattern of the subject based on the heartbeat signal output from the heartbeat sensor 14 based on the heartbeat pattern of the same subject read from the heartbeat pattern ROM 115. The correction process is not limited to the averaging process.

  FIG. 13 is a flowchart illustrating an example of a heartbeat pattern registration process according to the second embodiment. In FIG. 13, when the registration process of the CPU 11 is started in step S21, in step S22, the CPU 11 determines whether or not a predetermined time has elapsed. If the decision result in the step S22 becomes YES, in a step S23, the heartbeat data for the heartbeat signal obtained by the CPU 11 for one subject whose heartbeat pattern should be registered, that is, the heartbeat pattern is stored in the heartbeat pattern ROM 115. In step S24, the CPU 11 determines whether or not the registration process for all the subjects to be processed has been completed. If the determination result is NO, the process returns to step S22 to start the registration process for the next subject. On the other hand, if the decision result in the step S24 is YES, the process ends in a step S25.

  FIG. 14 is a flowchart for explaining an example of the respiratory information estimation process in the second embodiment. 14, the same steps as those shown in FIG. 3 are denoted by the same reference numerals, and the description thereof is omitted. In FIG. 14, in step S31, it is determined whether or not the heart rate pattern of the subject subject is registered in the heart rate pattern ROM 115 by the control unit 111. If the determination result is YES, the process proceeds to step S32, and the determination result If NO is NO, the process proceeds to step S33. In step S32, under the control of the control unit 111, the heart rate pattern reading unit 116 reads out the heart rate pattern of the subject from the heart rate pattern ROM 115 and outputs it to the control unit 111, and the process proceeds to step S33. In step S33, the control unit 111 determines the heart rate pattern of the subject. If the determination result in step S31 is NO, in step S33, the heart rate pattern of the subject is determined based on the heart rate signal output from the heart rate sensor 14. On the other hand, when step S33 is executed after step S32, in step S33, the heart rate pattern of the subject based on the heart rate signal output from the heart rate sensor 14 is corrected based on the heart rate pattern of the same subject read from the heart rate pattern ROM 115. The correction process is performed, and the result of the correction process is determined as a heartbeat pattern. In this example, for convenience of explanation, it is assumed that the correction process is an averaging process, and when step 33 is executed after step S32, the heart rate pattern of the subject based on the heart rate signal output from the heart rate sensor 14, and the heart rate pattern The average heart rate pattern of the same subject's heart rate pattern read from the ROM 115 is obtained.

  Thereafter, the calculation by the heart rate information calculation unit 112 and the estimation by the respiration information estimation unit 113 after step S2 are executed for the heart rate pattern determined in step S33. According to the present embodiment, it is possible to improve the accuracy of the calculation of the heartbeat period by correcting the heartbeat pattern measured using the heartbeat pattern stored in advance.

  The respiratory cycle can be estimated by acquiring the average heart rate or the correlation between the constant set and the respiratory cycle in advance for each subject, and estimating the respiratory cycle based on the current average heart rate or the length of the constant set. good.

  The constant of proportionality between the respiratory cycle and the heartbeat cycle varies dynamically with changes in the subject's condition. Therefore, in the disclosed respiration information estimation process, the dynamic change of the proportionality constant is dynamically corrected sequentially as described above to dynamically estimate the respiration cycle with high accuracy. FIG. 15 is a diagram for explaining the dynamic correction of the proportionality constant. In FIG. 15, the vertical axis represents the respiratory rate and the horizontal axis represents the heart rate. In addition, I, II, and III indicate cases where the heart rate is a proportional constant of 3 times, 4 times, and 5 times the respiration rate, and the respiration information is estimated from the heart rate information as in each of the above embodiments. Thus, the proportionality constant can be dynamically corrected to any one of I, II, III, for example, according to the estimated respiratory information.

  The use of the estimated respiratory information is not particularly limited. The estimated respiratory information may be used, for example, to estimate the subject's arousal state.

The following additional notes are further disclosed with respect to the embodiment including the above examples.
(Appendix 1)
A first calculating means for calculating a heartbeat period based on the heartbeat-dependent information;
Second calculation means for calculating a variance value of a heartbeat transition pattern based on a plurality of successive cycles of the heartbeat;
An apparatus for estimating respiratory information, comprising: estimating means for estimating respiratory information based on a variance value of the transition pattern.
(Appendix 2)
The second calculation means calculates a variance value of a minimum value by calculating a plurality of transition patterns and a variance value of the plurality of transition patterns,
The respiratory information estimation apparatus according to appendix 1, wherein the estimation means estimates a respiratory cycle from a cycle of a transition pattern having a variance value that is the minimum value.
(Appendix 3)
The first calculation means calculates R-wave R-wave interval data RRI (R-wave R-wave Interval) based on the information dependent on the heartbeat,
The respiratory information estimation apparatus according to appendix 1 or 2, wherein the transition pattern calculated by the second calculation means is RRI time-series data.
(Appendix 4)
The respiratory information estimation apparatus according to supplementary note 3, wherein the RRI time-series data includes one or more sets of n consecutive RRIs within a fixed period, and n is an integer of 2 or more.
(Appendix 5)
The RRI time series data includes two or more sets of n consecutive RRIs within a fixed period, and n is an integer of 2 or more and is constant or variable within the fixed period. 3. The respiratory information estimation device according to 3.
(Appendix 6)
Storage means for storing a plurality of heartbeat patterns;
The information processing apparatus further comprises correction means for correcting the information dependent on the heartbeat of the subject based on the heartbeat pattern of the subject read from the storage means and supplying the information to the first calculation means. The respiratory information estimation device according to any one of appendices 1 to 5.
(Appendix 7)
A first step in which a processor calculates a heartbeat period based on heartbeat-dependent information;
A second step by which the processor calculates a variance value of a heartbeat transition pattern based on a plurality of successive cycles of the heartbeat;
A respiration information estimation method, comprising: an estimation step in which the processor estimates respiration information based on a variance value of the transition pattern.
(Appendix 8)
The second step calculates a variance value that is a minimum value by calculating a plurality of transition patterns and a variance value of the plurality of transition patterns,
The respiratory information estimation method according to appendix 7, wherein the estimating step estimates a respiratory cycle from a cycle of a transition pattern having a variance value that is the minimum value.
(Appendix 9)
The first step calculates R-wave R-wave interval data RRI (R-wave R-wave Interval) based on the information depending on the heartbeat,
9. The respiratory information estimation method according to appendix 7 or 8 , wherein the transition pattern calculated by the second step is RRI time-series data.
(Appendix 10)
The respiratory information estimation method according to appendix 9, wherein the RRI time-series data includes one or more sets of n consecutive RRIs within a fixed period, and n is an integer of 2 or more.
(Appendix 11)
The RRI time series data includes two or more sets of n consecutive RRIs within a fixed period, and n is an integer of 2 or more and is constant or variable within the fixed period. 9. The respiratory information estimation method according to 9.
(Appendix 12)
And a correction step in which the processor corrects the information dependent on the heartbeat of the subject based on the heartbeat pattern of the subject read out from the storage means storing a plurality of heartbeat patterns, and supplies the information to the first step. The respiratory information estimation method according to any one of appendices 7 to 11, characterized in that:
(Appendix 13)
A program for causing a computer to execute processing for estimating respiration information,
A first procedure for calculating a heartbeat period based on heartbeat-dependent information;
A second procedure for calculating a variance value of a heartbeat transition pattern based on a plurality of successive cycles of the heartbeat;
A program for causing the computer to execute an estimation procedure for estimating respiration information based on a variance value of the transition pattern.
(Appendix 14)
In the second procedure, a plurality of transition patterns and a variance value of the plurality of transition patterns are calculated to obtain a variance value that is a minimum value,
14. The program according to appendix 13, wherein the estimation procedure estimates a respiratory cycle from a cycle of a transition pattern having a variance value that is the minimum value.
(Appendix 15)
The first procedure calculates R-wave R-wave interval data RRI (R-wave R-wave Interval) based on the information dependent on the heartbeat,
15. The program according to appendix 13 or 14 , wherein the transition pattern calculated by the second procedure is RRI time series data.
(Appendix 16)
The program according to claim 15, wherein the RRI time-series data includes one or more sets of n consecutive RRIs within a fixed period, and n is an integer of 2 or more.
(Appendix 17)
The RRI time series data includes two or more sets of n consecutive RRIs within a fixed period, and n is an integer of 2 or more and is constant or variable within the fixed period. 15. The program according to 15.
(Appendix 18)
Causing the computer to further execute a correction procedure for correcting the information dependent on the heartbeat of the subject based on the heartbeat pattern of the subject read from the storage means storing a plurality of heartbeat patterns and supplying the information to the first procedure Any one of Supplementary notes 13 to 17, characterized in that
Program described in the section.

  As described above, the disclosed respiratory information estimation apparatus, method, and program have been described with reference to the embodiments. However, the present invention is not limited to the above embodiments, and various modifications and improvements can be made within the scope of the present invention. Needless to say.

1 Respiratory Information Estimator 11 CPU
12 storage device 14 heart rate sensor 111 control unit 112 heart rate information calculation unit 113 respiration information estimation unit

Claims (7)

First calculating means for calculating a heartbeat period based on information dependent on the heartbeat of the subject ;
Second calculation means for calculating a variance value of a heartbeat transition pattern based on a plurality of successive cycles of the heartbeat;
An estimation means for estimating respiratory information based on a variance value of the transition pattern ;
Storage means for storing a plurality of heartbeat patterns;
Correction means for correcting the information dependent on the heartbeat of the subject based on the heartbeat pattern of the subject read from the storage means ;
The first calculation means supplies the second calculation means with the cycle of the heartbeat calculated based on the information dependent on the heartbeat of the subject corrected by the correction means. Estimating device. The second calculation means calculates a variance value of a minimum value by calculating a plurality of transition patterns and a variance value of the plurality of transition patterns,
The respiration information estimation apparatus according to claim 1, wherein the estimation means estimates a respiration cycle from a cycle of a transition pattern having a variance value that is the minimum value. The first calculation means calculates R-wave R-wave interval data RRI (R-wave R-wave Interval) based on the information dependent on the heartbeat,
3. The respiratory information estimation apparatus according to claim 1, wherein the transition pattern calculated by the second calculation means is RRI time series data. The respiratory information estimation apparatus according to claim 3, wherein the RRI time-series data includes one or more sets of n consecutive RRIs within a fixed period, and n is an integer of 2 or more. The RRI time-series data includes two or more sets of n consecutive RRIs within a fixed period, and n is an integer of 2 or more, and is constant or variable within the fixed period. Item 4. The respiratory information estimation device according to item 3. A first step in which a processor calculates a heartbeat period based on information dependent on the subject's heartbeat;
A second step by which the processor calculates a variance value of a heartbeat transition pattern based on a plurality of successive cycles of the heartbeat;
An estimation step in which the processor estimates respiratory information based on a variance value of the transition pattern ;
Dependent information to the heartbeat of the subject, wherein the processor is seen containing a correction step of correcting, based on the subject's heart rate pattern read from the storage means for storing a plurality of heartbeat patterns,
The first step includes supplying the second step with the cycle of the heartbeat calculated based on the information dependent on the heartbeat of the subject corrected in the correction step. . A program for causing a computer to execute processing for estimating respiration information of a subject ,
A first procedure for calculating a heartbeat period based on heartbeat-dependent information;
A second procedure for calculating a variance value of a heartbeat transition pattern based on a plurality of successive cycles of the heartbeat;
An estimation procedure for estimating respiration information based on the variance value of the transition pattern ;
Causing the computer to execute a correction procedure for correcting the information dependent on the heartbeat of the subject based on the heartbeat pattern of the subject read out from a storage unit storing a plurality of heartbeat patterns ;
The first procedure supplies the second procedure with the cycle of the heartbeat calculated based on the information dependent on the heartbeat of the subject corrected by the correction procedure .

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