JP5107519B2 - State analysis device and software program - Google Patents

Description

  The present invention relates to a state analysis apparatus and a software program, and more particularly to a state analysis apparatus and a software program that can accurately analyze the state of an object, particularly the presence or absence of apnea / hypopnea or the type thereof.

2. Description of the Related Art Conventionally, a motion detection sensor has been proposed as a motion detection device that detects a motion of an object in a space, for example, a bathroom or a toilet, for example, a person. Some of them can detect slight movements due to breathing. As a typical example, a pattern is projected onto a sleeping person on a bed, and the amount of pattern movement from an image obtained by continuously capturing the projected pattern. There is a monitoring device for monitoring the sleep of a sleeping person by calculating (For example, refer to Patent Document 1).
JP 2002-175582 A (page 5-9, FIG. 1-13)

  As a conventional apparatus as described above, for example, data indicating a person's state measured from an object having a periodic motion, for example, an area corresponding to substantially the whole person, and a predetermined reference value are compared. However, there has been a state analysis device that discriminates one or both of hypopnea and apnea of a person at the time of interest based on the comparison result.

  However, in the conventional apparatus as described above, for example, in order to perform a simple threshold evaluation with respect to the average movement as a whole with respect to a complex respiratory movement, for example, apnea and hypopnea in sleep apnea syndrome. In some cases, detection failure occurs or it is difficult to identify the type of apnea or hypopnea, and more accurate state analysis such as detailed analysis and improvement of analysis accuracy has been desired.

  Therefore, an object of the present invention is to provide a state analysis apparatus and a software program that can accurately analyze the state of an object, in particular, the presence or absence of apnea / hypopnea or the type thereof.

In order to achieve the above object, the state analysis apparatus according to the first aspect is configured such that the state of the object 2 is based on measurement data indicating the state of the object 2 having a periodic motion, for example, as shown in FIG. The first state determination unit 24 includes: first measurement data corresponding to the first region 2a of the object 2; and the first region 2a of the object 2. The first evaluation according to the complementarity with the second measurement data corresponding to the second region 2b different from the above and the symmetry of the first measurement data or the time axis direction of the second measurement data Based on the value, the state of the object 2 is determined.

  If comprised in this way, a 1st state discrimination | determination means will be based on the measurement data which shows the state of the periodic motion of the target object 2 according to a 1st area | region and a 2nd area | region, 1st measurement data and 1st measurement data Since the state of the object is discriminated using the first evaluation value corresponding to the complementarity with the measurement data of 2 and the symmetry in the direction of the time axis, the state of the object, particularly apnea / hypopnea It is possible to provide a state analysis apparatus that can accurately analyze the presence or absence or the type thereof.

  For example, when the object is a breathing person, the first region is the chest region 2a and the second region is the abdominal region 2b. Further, in this case, when the state of the person 2 is, for example, a so-called obstructive apnea state, the first measurement data and the second measurement data are typically substantially in reverse phase. Become. The first measurement data and the second measurement data tend not to be sin waveforms symmetrical with respect to the direction of the time axis, but have a shape close to a complementary sawtooth waveform (see, for example, FIG. 8A). Therefore, in this case, by determining the state of the object using the first evaluation value, the obstructive apnea state can be accurately detected and determined.

The second mode is a cross-correlation function calculation means 22 for calculating a cross-correlation function between the first measurement data and the second measurement data as shown in FIG. 1, for example , in the state analysis apparatus of the first mode . The first evaluation value may be a value based on an average value of the peak and bottom of the cross-correlation function calculated by the cross-correlation function calculating means 22.

  If comprised in this way, a cross-correlation function calculating means will calculate the cross-correlation function of 1st measurement data and 2nd measurement data, and a 1st state discrimination means will use a cross-correlation function as a 1st evaluation value. Since the value based on the average value of the peak and bottom of the cross-correlation function calculated by the calculation means 22 is used, the complementarity between the first measurement data and the second measurement data and the symmetry in the direction of the time axis Can be evaluated, and the state of the object can be accurately analyzed.

  For example, the first evaluation value is a cross correlation function bias PB calculated as an average value of the correlation value CP of the cross correlation peak of the cross correlation function C (τ) and the correlation value CB of the cross correlation bottom. When the first measurement data and the second measurement data are asymmetrical and complementary sawtooth waves in the direction of the time axis (for example, see FIG. 8A), the cross-correlation function C (τ) is τ When = 0, the bottom is reached, and C (0) ≈−1. On the other hand, since the waveforms do not overlap exactly at the peak, the value is not close to +1. That is, in the case of the obstructive apnea state, the cross-correlation function C (τ) is shifted to the negative side (side closer to −1) as a whole. In other words, the deviation PB of the cross correlation function C (τ) indicates the symmetry of the first measurement data and the second measurement data in the direction of the time axis, and the cross correlation function C (τ) is biased to the negative side. Sometimes, it is possible to reliably detect and discriminate an obstructive apnea state in which the first measurement data and the second measurement data represent complementary sawtooth waves.

In order to achieve the above object, the state analysis apparatus according to the third aspect is configured so that the state of the object 2 is based on measurement data indicating the state of the object 2 having a periodic movement, as shown in FIG. A first state discriminating means 24 for discriminating between the first measurement data corresponding to the first region 2a of the object 2 and the first region 2a of the object. Is configured to determine the state of the object 2 based on the second evaluation value relating to the similarity to the second measurement data corresponding to the different second region 2b.

  If comprised in this way, a 1st state discrimination | determination means will be based on the measurement data which shows the state of the periodic motion of the target object 2 according to a 1st area | region and a 2nd area | region, 1st measurement data and 1st measurement data Since the state of the object is discriminated using the second evaluation value related to the similarity to the measurement data of 2, the state analysis that can accurately analyze the state of the object, particularly the presence or absence of apnea / hypopnea An apparatus can be provided.

The fourth mode is a cross-correlation function calculation means 22 for calculating a cross-correlation function between the first measurement data and the second measurement data, for example, as shown in FIG. 1, in the state analysis apparatus of the third mode . The second evaluation value may be a value based on the cross-correlation function calculated by the cross-correlation function calculating means 22.

  If comprised in this way, a cross-correlation function calculating means will calculate the cross-correlation function of 1st measurement data and 2nd measurement data, and a 1st state discrimination means will be a cross-correlation function as a 2nd evaluation value. Since the value based on the cross-correlation function calculated by the calculation means is used, the similarity between the first measurement data and the second measurement data can be evaluated, and the state of the object can be analyzed accurately. it can.

  For example, if the second evaluation value is a value related to the value C (0) when τ = 0 of the cross-correlation function C (τ) between the first measurement data and the second measurement data, the first measurement data If the similarity between the data and the second measurement data is high and in phase, the value is close to +1, and if the phase is opposite, the value is close to -1. Therefore, the phase shift between the first measurement data and the second measurement data can be evaluated. Here, when the first measurement data and the second measurement data are data such that the average value becomes “0” when the sign is changed between expiration and inspiration, such as an air flow rate due to respiration, C (0) can be regarded as substantially a correlation coefficient CC. Further, for example, the second evaluation value may be C (0) as a phase shift index PI using as a parameter the quantitative relationship between C (0), the correlation value CP of the cross-correlation peak, and the correlation value CB of the cross-correlation bottom. Quantitatively represents whether it is close to CP or CB, the phase shift between the first measurement data and the second measurement data can be evaluated, and the obstructive apnea state is detected and discriminated. be able to.

Further, the fifth aspect is the state analysis apparatus according to the fourth aspect , in which , for example, as shown in FIG. 1, the object breathes, and the first state determination means 24 has a second evaluation value. It is good to discriminate | determine that the state of the target object 2 is an obstructive apnea state when it is below the 1st threshold value set with respect to this 2nd evaluation value.

  If comprised in this way, a 1st state discrimination | determination means will discriminate | determine that the state of a target object is an obstructive apnea state, when a 2nd evaluation value is below a 1st threshold value. Thereby, since it can be detected that the first measurement data and the second measurement data are clearly in reverse phase, for example, regardless of the magnitude of the periodic movement, the symmetry of the object It can be determined that the state is an obstructive apnea state.

In order to achieve the above object, the state analysis apparatus according to the sixth aspect , for example, as shown in FIG. 1, based on measurement data indicating the state of the object 2 having a periodic movement, the state of the object 2. The first state determination unit 24 includes: first measurement data corresponding to the first region 2a of the object 2; and the first region 2a of the object 2. The state of the object 2 is determined based on the third evaluation value corresponding to the phase difference from the second measurement data corresponding to the second region 2b different from the second region 2b.

  If comprised in this way, a 1st state discrimination | determination means will be based on the measurement data which shows the state of the periodic motion of the target object 2 according to a 1st area | region and a 2nd area | region, 1st measurement data and 1st measurement data Since the state of the object is discriminated using the third evaluation value corresponding to the phase difference with the measurement data of 2, the state of the object, particularly the presence or absence of apnea / hypopnea or the type thereof can be accurately analyzed. A state analysis apparatus can be provided.

The seventh mode is a cross-correlation function calculation means 22 for calculating a cross-correlation function between the first measurement data and the second measurement data, for example, as shown in FIG. 1, in the state analysis apparatus of the sixth mode . An autocorrelation function calculating means 23 for calculating the autocorrelation function of the first measurement data or the second measurement data; a peak position of the cross correlation function calculated by the cross correlation function calculating means 22 and an autocorrelation function calculation; The comparison result of the first measurement data or the second measurement data calculated by the means 23 with the peak position of the autocorrelation function, or the bottom position of the cross correlation function calculated by the cross correlation function calculation means 22, and the self The third evaluation corresponding to the phase difference based on the comparison result of the first measurement data or the second measurement data calculated by the correlation function calculation means 23 with the bottom position of the autocorrelation function. May be provided with a phase difference calculating means 21 for calculating a.

  If comprised in this way, a cross-correlation function calculating means will calculate the cross-correlation function of 1st measurement data and 2nd measurement data, and an auto-correlation function calculating means will be 1st measurement data or 2nd measurement. An autocorrelation function of the data is calculated, and the phase difference calculating means calculates the comparison result between the peak position of the cross correlation function and the peak position of the autocorrelation function of the first measurement data or the second measurement data, or the cross correlation. Based on the comparison result between the bottom position of the function and the bottom position of the autocorrelation function of the first measurement data or the second measurement data, a third evaluation value corresponding to the phase difference is calculated. Since the first state determination unit uses the third evaluation value corresponding to the phase difference calculated based on the cross-correlation function and the autocorrelation function by the phase difference calculation unit, the first measurement data and the second measurement Similarity with the data can be evaluated, and the state of the object can be accurately analyzed.

  For example, the third evaluation value corresponding to the phase difference calculated by the phase difference calculating means is the phase difference P = (cross-correlation peak position / autocorrelation peak position of the first measurement data) as the third evaluation value × It can be calculated by 2π. When the first measurement data and the second measurement data are in phase, the autocorrelation peak of the first measurement data or the second measurement data and the cross-correlation peak of the first measurement data and the second measurement data And appear at approximately the same position. On the other hand, in the case of reverse phase, the cross-correlation peak appears near the bottom of the autocorrelation. Therefore, for example, (cross-correlation peak position / autocorrelation peak position of the first measurement data) × 2π is the third evaluation value corresponding to the phase difference between the first measurement data and the second measurement data. Thereby, the phase shift between the first measurement data and the second measurement data can be evaluated, and the obstructive apnea state can be detected and determined.

The eighth aspect is the state analysis apparatus of the sixth aspect or the seventh aspect , for example, as shown in FIG. 1, the object 2 breathes, and the first state determination means 24 is When the absolute value of the third evaluation value corresponding to the phase difference is equal to or greater than the second threshold value set for the third evaluation value corresponding to the phase difference, the state of the object is obstructive apnea It is good to determine that it is in a state.

  If comprised in this way, when the absolute value of the 3rd evaluation value corresponding to a phase difference is more than a 2nd threshold value, a 1st state discrimination | determination means will be a closed apnea state of a target object. Is determined. Thereby, since it can be detected that the first measurement data and the second measurement data are clearly in reverse phase, for example, regardless of the magnitude of the periodic movement, the symmetry of the object It can be determined that the state is an obstructive apnea state.

In order to achieve the above object, the state analysis apparatus according to the ninth aspect performs the state of the object 2 based on the measurement data indicating the state of the object 2 having a periodic motion, for example, as shown in FIG. The first state determination unit 24 is configured to determine the first measurement data corresponding to the first region 2a of the object 2 and the first region 2a of the object. 4th evaluation value regarding the similarity with respect to the time gap of the at least 1 measurement data chosen from the group which consists of the 2nd measurement data corresponding to different 2nd field 2b, and the whole measurement data corresponding to the substantially whole object 2 Based on the above, the state of the object 2 is determined.

  If comprised in this way, a 1st state discrimination | determination means will be based on the measurement data which shows the state of the periodic motion of the target object 2 according to the 1st area | region and the 2nd area | region, 1st measurement data, 1st measurement data, The state of the object is discriminated using the fourth evaluation value relating to the similarity to the time lag of at least one measurement data selected from the group consisting of the two measurement data and the entire measurement data. It is possible to provide a state analysis apparatus capable of discriminating sex and accurately analyzing the state of an object, in particular, the presence or absence of apnea / hypopnea or the type thereof.

The tenth aspect, in the state analyzer of the ninth aspect, for example, as shown in FIG. 1, the first measurement data, second measurement data, at least one measurement selected from the group consisting of whole measurement data An autocorrelation function calculating means 23 for calculating an autocorrelation function of the data; the fourth evaluation value is based on the peak or bottom of the autocorrelation function calculated by the autocorrelation function calculating means 23 or the difference between the peak and the bottom When the autocorrelation function calculating means 23 determines the state based on at least the fourth evaluation value of the first measurement data by the first state determining means 24, the first measurement data When the state is determined based on the autocorrelation function of the second measurement data and the fourth evaluation value of the second measurement data, the autocorrelation function of the second measurement data and the fourth evaluation value of the entire measurement data are used. State It may be configured to calculate the autocorrelation function of the entire measurement data when performing.

  If comprised in this way, an autocorrelation function calculating means will calculate the autocorrelation function of at least 1 measurement data selected from the group which consists of 1st measurement data, 2nd measurement data, and whole measurement data. Since the first state determination means uses a value based on the peak or bottom of the autocorrelation function calculated by the autocorrelation function calculation means or the difference between the peak and the bottom as the fourth evaluation value, the first measurement data The similarity with respect to the time lag of at least one measurement data selected from the group consisting of the second measurement data and the whole measurement data can be evaluated, and the state of the object can be analyzed accurately.

  For example, if the waveform of the measurement data is a perfect sine wave, the correlation value AP = 1 of the autocorrelation peak and the correlation value AB = −1 of the autocorrelation bottom are obtained. As the fourth evaluation value, when the difference AG between the peak and bottom of the autocorrelation function A (τ) is used as a value based on the peak or bottom of the autocorrelation function or the difference between the peak and the bottom, the larger this value is, the larger the value is. It indicates that there is a stable movement with a constant period, and that it indicates no movement when approaching 0. That is, it is possible to evaluate the presence or absence of periodic motion regardless of the amplitude of the measurement data.

In order to achieve the above object, the state analysis apparatus according to the eleventh aspect , for example, as shown in FIG. 1, is based on the measurement data indicating the state of the object 2 having a periodic motion. A first state discriminating means 24 for discriminating between the first measurement data corresponding to the first region 2a of the object 2 and the first region 2a of the object 2. Is a fifth evaluation indicating symmetry with respect to time of at least one measurement data selected from the group consisting of second measurement data corresponding to different second regions 2b and overall measurement data corresponding to substantially the entire object 2 Based on the value, the state of the object 2 is determined.

  If comprised in this way, a 1st state discrimination | determination means will be selected from the group which consists of 1st measurement data according to 1st area | region, 2nd area | region, and whole area | region, 2nd measurement data, and whole measurement data. The state of the object is discriminated using the fifth evaluation value indicating the symmetry with respect to the time of at least one measurement data, so that the state of the object, particularly the presence or absence of apnea / hypopnea, or its type can be accurately determined. A state analysis device that can be analyzed can be provided.

The twelfth aspect, in the state analyzer of the eleventh aspect, for example, as shown in FIG. 1, the first measurement data, second measurement data, at least one measurement selected from the group consisting of whole measurement data A cross-correlation function calculating means 22 for calculating a cross-correlation function between the measurement data obtained by inverting the data in the direction of the time axis and the measurement data before being inverted; the fifth evaluation value is a cross-correlation function calculating means The cross-correlation function calculation means 22 is based on at least the fifth evaluation value corresponding to the first measurement data by the first state determination means 24. When the state is discriminated, the cross-correlation function between the measurement data obtained by inverting the first measurement data in the direction of the time axis and the first measurement data before the inverting, the first state discriminating means 24 When determining the state based on the fifth evaluation value according to the second measurement data, the measurement data obtained by inverting the second measurement data in the direction of the time axis and the second measurement before the inverting is performed. When the state is determined based on the cross-correlation function with the data and the fifth evaluation value corresponding to the entire measurement data by the first state determination unit 24, the measurement is performed by inverting the entire measurement data in the direction of the time axis. You may comprise so that the cross correlation function of data and the whole measurement data before inversion may be calculated.

  If comprised in this way, a cross correlation function calculating means will be the measurement data which reversed at least 1 measurement data chosen from the group which consists of 1st measurement data, 2nd measurement data, and whole measurement data in the direction of a time-axis. And a cross-correlation function between the measurement data before inversion. Since the first state determination unit uses a value based on the cross-correlation function calculated by the cross-correlation function calculation unit as the fifth evaluation value, from the first measurement data, the second measurement data, and the entire measurement data The symmetry with respect to time of at least one measurement data selected from the group can be evaluated, and the state of the object can be analyzed accurately.

In order to achieve the above object, the state analysis apparatus according to the thirteenth aspect , for example, as shown in FIG. 1, based on the measurement data indicating the state of the object 2 with periodic motion, A first state determining unit 24 for determining the first measurement data corresponding to the first region 2a of the object and the first region 2a of the object. A first evaluation value corresponding to complementarity with second measurement data corresponding to different second regions 2b and symmetry of the first measurement data or the time axis direction of the second measurement data; A second evaluation value relating to the similarity between the first measurement data and the second measurement data; a third evaluation value corresponding to the phase difference between the first measurement data and the second measurement data; Group of measurement data, second measurement data, and overall measurement data corresponding to substantially the entire object. A fourth evaluation value relating to the similarity of at least one selected measurement data to a time shift, and a time of at least one measurement data selected from the group consisting of the first measurement data, the second measurement data, and the entire measurement data The state of the object is determined based on a plurality of evaluation values selected from the group consisting of fifth evaluation values indicating symmetry.

  If comprised in this way, a 1st state discrimination | determination means will be based on the measurement data which shows the state of the periodic motion of the target object 2 according to a 1st area | region and a 2nd area | region, 1st measurement data and 1st measurement data A first evaluation value corresponding to the complementarity with the two measurement data and the symmetry in the direction of the time axis, a second evaluation value relating to the similarity between the first measurement data and the second measurement data, the first Time shift of at least one measurement data selected from the group consisting of the third evaluation value corresponding to the phase difference between the measurement data and the second measurement data, the first measurement data, the second measurement data, and the entire measurement data 4th evaluation value regarding the similarity with respect to 1st measurement data, 2nd measurement data, It consists of 5th evaluation value which shows the symmetry regarding the time of at least 1 measurement data chosen from the group which consists of whole measurement data Multiple evaluation values selected from a group Because have discriminating states of the object, it is possible to provide a condition analysis apparatus state of an object, which in particular whether or types of apnea-hypopnea can be accurately analyzed. Further, the state of the person 2 can be determined more accurately by performing weighted determination using different types of evaluation values.

The fourteenth aspect, in the state analysis apparatus of the thirteenth aspect, for example, as shown in FIG. 1, based on the measurement data of the first predetermined time period before and after the determination time point among the measurement data, the object 2 is a threshold value calculation means for calculating a threshold value for determining a decrease in the magnitude of the periodic motion of 2, a third threshold value corresponding to the first region 2a and a second threshold value corresponding to the second region 2b. A threshold value calculating means 29 for calculating a threshold value of 4 and a fifth threshold value corresponding to substantially the entire object; and the magnitude of the periodic movement at the determination target time point of the first measurement data and the third threshold value The comparison result or the comparison result between the periodic movement magnitude at the determination target time point of the second measurement data and the fourth threshold value or the periodic movement magnitude at the determination target time point of the entire measurement data and the fifth Based on the comparison result with the threshold value of A first period (for example, a hypopnea event period) or a second period (for example, an apneic event period) in which the magnitude of the periodic movement is further lower than the first period. A second state determining unit 25; the first state determining unit 24 may be configured to determine the state of the object 2 for the period determined by the second state determining unit 25;

  When configured in this way, the threshold value calculation means includes a third threshold value corresponding to the first area, a fourth threshold value corresponding to the second area, and a fifth threshold value corresponding to substantially the entire object. Is calculated. The second state discriminating means is based on a comparison result between the magnitude of the periodic movement at the determination target time point of the first measurement data, the second measurement data, and the entire measurement data and the corresponding threshold value. Period (for example, hypopnea event period) and a second period (for example, apnea event period) are determined. Since the first state determination unit determines the state of the object for the period determined by the second state determination unit, the first period (for example, low breathing) is determined based on the magnitude of the periodic movement. After determining the event period) and the second period (for example, apnea event period), the state of the object can be determined using various evaluation values different from the magnitude of the periodic movement. In other words, it is possible to comprehensively and reliably detect and discriminate a state that cannot be determined only by the magnitude of the periodic movement.

Further, the fifteenth aspect is the state analysis apparatus according to the fourteenth aspect , in which , for example, as shown in FIG. 1, the object 2 breathes, and the first state discriminating means 24 has the first period or The third evaluation corresponding to the phase difference when the second evaluation value is equal to or lower than the first threshold set with respect to the second evaluation value in a period that is greater than or equal to the second period. When the absolute value of the value is equal to or greater than a second threshold set for the third evaluation value corresponding to the phase difference, the state of the object 2 is determined to be an obstructive apnea state. It may be configured.

  If comprised in this way, when the 2nd evaluation value is below a 1st threshold value or when the absolute value of a phase difference is above a 2nd threshold value, the 1st state discrimination | determination means will be the target object 2. Is determined to be an obstructive apnea state. Thereby, since it can be detected that the first measurement data and the second measurement data are clearly in reverse phase, for example, regardless of the magnitude of the periodic movement, the symmetry of the object It can be determined that the state is an obstructive apnea state.

The sixteenth aspect, in the state analysis device according to any one of the embodiments of the first aspect to the fifteenth aspect, for example, FIG. 11 (suitably Figure 10 also refer) as shown in, the measuring device for acquiring measurement data The measuring apparatus 10 includes a projection device 11 that projects a plurality of bright spots 11b on the target region 3, an imaging device 12 that images the target region 3 on which the plurality of bright spots 11b are projected, and an imaging unit 12. A movement amount calculating means 141 for calculating a movement amount between two frames of a plurality of bright spots 11b from two frames of images acquired at different time points, and a movement amount for generating movement amount waveform data in which the movement amounts are arranged in time series. You may comprise so that it may have the waveform generation means 142. FIG.

  If comprised in this way, since a measuring apparatus is provided and measurement data are acquired from the measuring apparatus, the state of a target object can be measured correctly. Further, since the measuring device is configured to include the projection device, the imaging device, the movement amount calculating means, and the movement amount waveform generating means, it is possible to accurately measure the state of the object without contact, for example.

In order to achieve the above object, the software program of the seventeenth aspect is a software program that is installed in a computer and operates the computer as a state analysis device, for example, as shown in FIG. The computer is controlled to execute the process (S13) for determining the state of the object based on the measurement data indicating the state of the object having a smooth movement; the process (S13) for determining the first state of the object Complementarity between the first measurement data corresponding to the second area and the second measurement data corresponding to the second area different from the first area of the object, and the first measurement data or the second A first evaluation value corresponding to the symmetry of the measurement data in the direction of the time axis, a second evaluation value relating to the similarity between the first measurement data and the second measurement data, and the first measurement data Second measurement At least one measurement data selected from the group consisting of a third evaluation value corresponding to a phase difference from the data, first measurement data, second measurement data, and overall measurement data corresponding to substantially the entire object. A fourth evaluation value relating to similarity to time lag, and a fifth evaluation indicating symmetry with respect to time of at least one measurement data selected from the group consisting of first measurement data, second measurement data, and overall measurement data Based on a plurality of evaluation values selected from the group consisting of values, it is configured to include processing for determining the state of the object.

  If comprised in this way, the complementarity of 1st measurement data and 2nd measurement data based on the measurement data which shows the state of the periodic motion of the target object 2 according to the 1st field and the 2nd field And the first evaluation value corresponding to the symmetry with respect to the time axis, the second evaluation value relating to the similarity between the first measurement data and the second measurement data, the first measurement data and the second measurement data A third evaluation value corresponding to the phase difference, a first evaluation data, a second evaluation data, a fourth evaluation value relating to the similarity to the time shift of at least one measurement data selected from the group consisting of the whole measurement data, A plurality of evaluation values selected from the group consisting of fifth evaluation values indicating symmetry with respect to time of at least one measurement data selected from the group consisting of the first measurement data, the second measurement data, and the entire measurement data are used. The state of the object Since the, it is possible to provide a software program state of the object, which in particular whether or types of apnea-hypopnea can be accurately analyzed. Further, the state of the person 2 can be determined more accurately by performing weighted determination using different types of evaluation values.

  As described above, according to the present invention, the first state determination unit includes the first state determination unit that determines the state of the object based on the measurement data indicating the state of the object having a periodic motion. Is complementary to the first measurement data corresponding to the first region of the object and the second measurement data corresponding to the second region different from the first region of the object, and the first The state of the object 2 is discriminated based on the first evaluation value corresponding to the symmetry of the measurement data of the second measurement data or the time axis direction of the second measurement data. It is possible to provide a state analysis apparatus that can accurately analyze the presence or absence of hypopnea or the type thereof.

  Embodiments of the present invention will be described below with reference to the drawings. In addition, in each figure, the same code | symbol is attached | subjected to the mutually same or equivalent member, and the overlapping description is abbreviate | omitted.

  FIG. 1 is a block diagram showing a configuration example of a respiration monitor 1 as a state analysis apparatus according to the first embodiment of the present invention. The respiration monitor 1 is configured to include an arithmetic device 20 and an FG sensor 10 as a measuring device. The arithmetic unit 20 is, for example, a computer such as a personal computer or a microcomputer. The computing device 20 also has an input device 35 for inputting information for operating the respiration monitor 1 and a display 40 for displaying the processing state of the respiration monitor 1. The input device 35 is, for example, a touch panel, a keyboard, or a mouse. The display 40 is typically an LCD (Liquid Crystal Display, liquid crystal display device). The processing state of the respiration monitor 1 displayed on the display 40 includes, for example, measurement data acquired by the FG sensor 10 described later, the state of the object 2 by the first state determination unit 24 and the second state determination unit 25 described later. And the like. Although the input device 35 and the display 40 are illustrated as being externally attached to the main body of the arithmetic device 20 in this figure, they may be built in. The FG sensor 10 will be described in detail with reference to FIG.

  The arithmetic unit 20 includes a first state determination unit 24 as a first state determination unit that determines the state of the target, based on the measurement data indicating the state of the target 2 with periodic movement, and the target First measurement data corresponding to the chest region 2a as the second first region and second measurement data corresponding to the abdominal region 2b as the second region different from the chest region 2a of the object 2 A group consisting of a cross-correlation function calculation unit 22 as a cross-correlation function calculation means for calculating a cross-correlation function, and the first measurement data, the second measurement data, and the entire measurement data corresponding to substantially the entire object 2 And an autocorrelation function calculator 23 as an autocorrelation function calculator that calculates an autocorrelation function of at least one measurement data selected from the above. In the present embodiment, the cross-correlation function calculation unit 22 further inverts at least one measurement data selected from the group consisting of the first measurement data, the second measurement data, and the entire measurement data in the direction of the time axis. The cross-correlation function between the measured data and the measurement data before being inverted is calculated. Details will be described later.

  Furthermore, the arithmetic unit 20 periodically analyzes the object 2 based on the measurement data of the first predetermined period before and after the determination target time point among the measurement data indicating the state of the object 2 with periodic movement. A threshold value calculation unit 29 as a threshold value calculation unit for calculating a threshold value for determining a decrease in movement, and the first period in which the magnitude of the periodic movement is reduced based on the threshold value, or the period The second state determination unit 25 is provided as a second state determination unit that determines a second period in which the magnitude of the actual movement is further lower than the first period.

  The arithmetic unit 20 also includes a representative coordinate calculation unit 26 that calculates the representative coordinates of the position coordinate group of the measurement points at which the phases of the plurality of movements are substantially the same for the measurement points where the movement of the object 2 is measured, When there are two or more position coordinate groups having different phases, a dividing line forming unit 27 that forms an area dividing line for dividing the target area between two or more representative coordinates is provided. The dividing line forming unit 27 typically includes a chest area 2a as a first area and an abdominal area 2b as a second area for a person 2 as an object existing in the bed 3 that is the target area. An area dividing line 2c is formed so as to be divided into two. In addition, when there is substantially one representative coordinate calculated by the representative coordinate calculation unit 26, the dividing line forming unit 27 divides the target region in an appropriate direction through the calculated representative coordinates. It may be configured to form a region dividing line. The chest region 2a (first region) and the abdominal region 2b (second region) will be described later.

  Furthermore, the arithmetic unit 20 determines and detects the non-periodic movement that occurs together with the periodic movement based on the measurement data indicating the state of the object 2 having the periodic movement. The threshold calculation unit 29 typically excludes a period in which a non-periodic motion is detected by the body motion detection unit 28 from periods before and after the determination target time point. A threshold value is calculated based on the measurement data for the first predetermined period.

  Here, although the target object is described as being the person 2 in the present embodiment, it may be a domestic animal such as a cow or a pig. Livestock includes pets such as cats and dogs. Hereinafter, the object is described as a person 2. Here, the case where the person 2 is sleeping on the bed 3 will be described. Here, the periodic motion is, for example, the respiratory motion of the person 2, and the non-periodic motion is, for example, a body motion other than the respiratory motion of the person 2 (hereinafter referred to as “body motion unless otherwise specified”). "). It should be noted that the respiratory motion of the person 2 is actually unstable in later-described waveform data indicating the respiratory motion, such as its period and amplitude. In extreme cases, the amplitude may be lost, but there is some instability. If so, it can be ignored and analyzed. The body movement of the person 2 is a movement of the person 2 that is less periodic than breathing, such as turning over, and is larger than the breathing. Further, the movement of the person 2 larger than the respiration is characterized by respiration, for example, by the magnitude of the movement or the magnitude of the change in movement.

  The respiratory monitor 1 according to the present embodiment is a respiratory motion detection device that analyzes the respiratory state of a person during sleep and is used for diagnosis of sleep apnea syndrome (SAS). In the present embodiment, typically, during the first period in which the magnitude of the respiration movement determined by the second state determination unit 25 is decreasing, the air flow caused by the respiration of the object 2 is typically clear. This is a hypopnea event period in which the hypopnea state is reduced. Further, in the second period in which the magnitude of the movement of breathing is further lower than the hypopnea event period as the first period, typically, the apnea in which the air flow caused by the object 2 almost stops It is an apnea event period which is a state (apnea). In other words, the first period and the second period are periods of abnormal breathing that are problematic in the diagnosis of sleep apnea syndrome (SAS).

  In the present embodiment, the state of the person 2 as the object to be determined by the first state determination unit 24 is typically that the passage of the air behind the throat (upper airway) is blocked during sleep. So-called obstructive apnea, where breathing commands are not received from the respiratory center to the respiratory muscles during sleep. Is a subtype of so-called central apnea, obstructive apnea, in which thoracoabdominal movement (that is, respiratory movement itself) stops, and is a mixed type in which obstructive apnea and central apnea are combined It is an apnea state (Mixed apnea). In the state of obstructive apnea, so-called bizarre movement is often observed.

  In the present embodiment, the measurement data is data measured by the FG sensor 10 as a three-dimensional sensor. The measurement data indicating the state of the person 2 is the amount related to the movement of the person 2 within a certain time within the measurement range (for example, the range including the chest and abdomen of the person 2), more specifically, the vertical movement of the body surface. It is a quantity related to speed. The measurement data indicating the state of the person 2 is data based on measurement results obtained by measuring the movement of the person 2 at a plurality of points. Respiration data based on the sum of the measurement values at the plurality of points, It includes both and / or one of body motion data which is data of the sum of absolute values of each measured value at a point. The sum of absolute values is a concept including the sum of the squares of the measured values. Moreover, you may employ | adopt only the point whose absolute value of a measured value is more than fixed among several measuring points as a measuring point. Thereby, noise can be removed. Here, the measurement data includes both respiration data and body movement data. Further, the respiration data and the body movement data may be data obtained by further dividing the number of measurement points or the number of measurement points at which the absolute value of the measurement value is a certain value or more.

  FIG. 2 is a schematic diagram showing an example of a waveform pattern formed by respiration data and body motion data used in the respiration monitor according to the embodiment of the present invention. For example, the measurement data forms a waveform pattern as shown in FIG. In addition, (a) is an example which simplified the respiration data at the time of measuring the normal respiration of the person 2. FIG. Further, (b) is a typical example of respiratory data including data indicating body movement, and (c) is a typical example of body movement data including data indicating body movement. As shown in (b) and (c), when there is a body motion other than breathing (body motion), the body motion data has a large value distribution, and at the same time, the fluctuation width of the breathing data becomes large and periodicity is increased. Lose. Here, the sampling interval of measurement data (acquisition interval of one data) is, for example, 0.1 to 0.25 seconds. Further, here, each measurement value obtained by measuring the movement of the person 2 at a plurality of points is a value related to the movement of the person 2 within a predetermined time, in other words, the movement speed of the person 2. Therefore, by adding the measurement values at each measurement point, it is possible to obtain data relating to the overall average position change in a certain direction within a certain period (breathing data). Further, if the absolute values of the respective measured values are added together, data relating to the total amount of the entire movement can be obtained (body movement data). Here, the case where both respiration data and body motion data are used will be described. However, for example, all processes described below can be performed using only respiration data.

  Note that the analysis of the measurement data acquired from the FG sensor 10 by the present apparatus may be performed after acquiring the measurement data for a certain period, or the acquisition of the data with the state transition of the person 2 It may be done in real time. In this embodiment, a case where measurement data for a certain period, for example, overnight (about 7 to 10 hours) is collectively processed (analyzed) will be described.

Here, the determination target time point is a time point at which the state of the person 2 is determined. The determination target time points are set at approximately equal intervals with respect to the acquired measurement data. The interval between the determination target points may be appropriately determined according to, for example, the sampling interval of the measurement data. That is, here, the interval of the determination target time points (1 data acquisition interval) is, for example, about 0.1 to 0.25 seconds.
Hereinafter, each of the above-described configurations will be described in detail with reference to FIG. 1 again.

  As described above, the body motion detection unit 28 is based on the measurement data indicating the state of the object having a periodic motion, that is, the person 2 having the respiratory motion, and the non-periodic motion that occurs with the respiratory motion, that is, the body motion. And detecting a body movement period which is a period in which data indicating the body movement of the object exists from the measurement data.

  In the present embodiment, the body motion detection unit 28 is configured to calculate the body motion reference value based on the distribution state of measurement data (for example, body motion data) within the second predetermined period immediately before the determination target time point. . The second predetermined period is a period for calculating the body movement reference value, and may be, for example, about 1 to 2 minutes immediately before the determination target time point.

  Here, the distribution state of the body motion data is calculated by, for example, the average value of the body motion data (measured values) in the second predetermined period + standard deviation × a, and this is used as the body motion reference value. Here, a is a constant. If this a is 3, for example, and the data has a Gaussian distribution, 99.85% of the body movement data (measured value) falls within the range of values calculated by this equation. It can be determined that the body motion is not a normal distribution. That is, when the body motion data (measured value) at the determination target time exceeds the body motion reference value calculated as described above, the body motion detection unit 28 determines that the motion at the determination target time is a body motion. judge.

  When the body motion detection unit 28 determines that the motion of the person 2 at the determination target time point is a body motion, the body motion detection unit 28 further determines a body motion period that is a period in which data indicating the body motion of the person 2 exists from the measurement data. Configured to detect. The body movement period may be a period in which it is determined that there is actually body movement, but it can be predicted in advance that measurement data (for example, respiratory data) becomes somewhat unstable for several seconds before and after the period in which body movement is detected. Therefore, it is preferable that a period obtained by adding several seconds before and after the period to a period in which it is determined that there is actually body movement is a body movement period. Note that, as described above, the body motion detection unit 28 detects whether or not the movement at the determination target time point is a body motion, in other words, determines whether or not the determination target time point is a body motion period. It is also a thing.

  In addition, if this work is performed sequentially from the past (front) data in time, it is known at the next determination target time whether the past determination target time is a body movement, That is, it is preferable to exclude the period indicating the body movement from the second predetermined period, similarly to the first predetermined period described below. That is, it is preferable that measurement data indicating body movement is not included in the subsequent calculation of the body movement reference value. By doing so, body movement can be detected more accurately. The body motion detection unit 28 determines that the body motion is detected when the body motion data (measured value) continuously exceeds the body motion reference value calculated as described above, for example, for about 1 to 5 seconds. You may comprise.

  Further, the body motion detection unit 28 determines the body motion reference value based on the measurement data in the second predetermined period before the determination target time point and the measurement data in the second predetermined period after Calculated every second predetermined period and the subsequent second predetermined period, the determination of the body movement of the person 2 is evaluated by evaluating the effectiveness of each calculated body movement reference value, Furthermore, the determination may be performed by comparing the data at the determination target time with the adopted body movement reference value. The evaluation of effectiveness may be performed, for example, by evaluating an effective amount of information. The effective amount of information can be evaluated by, for example, calculating and comparing information entropies of body motion data in the previous second predetermined period and the subsequent second predetermined period. Information entropy will be described later.

The effectiveness may be evaluated using a histogram as shown in FIG. 3 described later. In the case of using such a histogram, for example, from each of the body motion data histograms of the previous second predetermined period and the subsequent second predetermined period, an output distribution block is obtained, and the lower output is obtained. You may evaluate by making the one with much frequency contained in the lump (left lump in the figure) distributed. In this way, the sensitivity changes due to the posture change of the person 2 due to a large body movement or the like, and it is possible to respond immediately when the level to be used as a reference (for example, the body movement reference value) changes. Here, the range of this lump is assumed to be a Gaussian distribution, and the frequency of the frequency up to a position where the frequency becomes a constant ratio, for example, 60% (that is, e ^−1/2 ) with respect to the peak (mode) of the histogram. A certain multiple (for example, 3 times) of the distance from the value is added to the mode value. If a Gaussian distribution is assumed, the right side of the histogram peak in the figure (the side where the body motion data is large) is affected by body motion, so the left side (the side where the body motion data is small) corresponds to the peak. It is recommended to evaluate the frequency ratio. Or, it is determined as simple as 4 times the mode value.

  The body motion detection unit 28 evaluates the validity of the measurement data in the second predetermined period before the determination target time point and the measurement data in the second predetermined period after the second target period. Any one of the measurement data may be adopted, and the body movement reference value may be calculated based on the adopted measurement data. In this case, the body movement of the person 2 is determined by comparing the data of the second predetermined period with the calculated body movement reference value.

  Further, the body motion detection unit 28 calculates a body motion determination value for determining the body motion of the person 2 at the determination target time point based on measurement data (for example, body motion data) within the third predetermined period, The calculated body movement determination value may be compared with a body movement reference value that is a reference for body movement determination, and the body movement of the person 2 may be determined based on the comparison result.

  In this case, the third predetermined period is within a time period having a width sufficient to determine whether the movement of the person 2 is periodic or aperiodic. For example, calculation of a body movement determination value In the case where information entropy or CV value is used as will be described later, it may be set to about 5 to 10 seconds. The body movement determination value is a secondary parameter at each time point obtained from measurement data (for example, body movement data).

  Furthermore, in this case, the body movement determination value may be an amount related to variation in measurement data (for example, body movement data) within the third predetermined period. The amount related to variation is, for example, information entropy or variation coefficient (CV value). First, the CV value can be calculated by “CV value = standard deviation / average value”, and has the meaning of normalizing the magnitude of the fluctuation with the bias level. Therefore, it does not react greatly to fluctuations in the bias level of the measurement data or slow changes compared to the calculation period, but reacts to sudden fluctuations that are large compared to the bias state.

Information entropy is the relative value of uncertainty before and after receiving information, assuming that the entropy used in physics is the uncertainty of the event, and the decrease in uncertainty due to certain information is the amount of information in the information. It is. For example, when rolling a dice, the uncertainty or information entropy is maximal since it is not known at all what the eye came out before looking at the result. Upon receiving the information that an odd number of eyes have appeared, the information entropy decreases. Knowing that a 1 is seen, the result is uniquely determined and the information entropy is minimized. The amount of information is a monotonically decreasing function with respect to the probability. Knowing that an unusual event has occurred, the amount of information is large, but of course an unusual event rarely occurs. Each time you know that something unusual has happened, but it is not unusual, the total amount of information observed over time can be large. From this point of view, the average amount of information for a long time is information entropy. Here, p (x) indicates the probability that the event x will occur in the set X of a plurality of events.

  Information entropy can be obtained from a histogram of measurement data. The histogram is, for example, as shown in FIG. In FIG. 3, the vertical axis represents frequency and the horizontal axis represents class (class). The class corresponds to, for example, the magnitude of the output of measurement data, and is set at equal intervals based on the distribution of measurement data in the drawing. Specifically, the class width may be divided into N from 0 to the average value + standard deviation × 2 based on the average value and standard deviation of all measurement data. Here, N may be determined based on the total number of data. For example, it is usually 10 to several tens. Alternatively, it may be possible to divide 50 times by 4 times the frequent value obtained by the provisionally set class width. Here, the occurrence probability p (x) of x of a certain class = frequency of class / total number of data. Note that the rightmost class (N class) in the figure has a high frequency because all the data of the N class or higher is counted.

  In this case, as shown in FIG. 3, when obtaining a histogram with the vertical axis representing the frequency and the horizontal axis representing the class (class), the class width (horizontal axis) is preferably a log scale histogram. As a result, the class width changes in proportion to the center value of the class, and information entropy that is invariant to fluctuations in the sensitivity of the signal (for example, measurement data) can be obtained. The width of the class of the histogram can be obtained from the number of data in the third predetermined period, the distribution range of body motion data in the overnight (about 7 to 10 hours), variation, and the like. it can.

  The body movement determination value may be calculated from the frequency distribution around the determination target time point. Here, the periphery of the determination target time point is, for example, a third predetermined period including or adjacent to the determination target time point. The frequency distribution is reflected in, for example, an output through a high-pass filter, a distribution of frequency components by Fourier transform, and an output distribution with respect to a scale by wavelet transform. The body motion determination value may be calculated based on the distribution of the magnitude (output value or the like) of the measurement data (for example, body motion data) thus extracted.

  In addition, the body motion reference value used as a reference when calculating and comparing the body motion determination value as described above is a single value as a reference value regardless of individual measurement data (for example, body motion data). Or may be determined from a distribution of a plurality of body movement determination values for each determination target time point or a distribution of body movement determination values for one night (about 7 to 10 hours) measured in advance. . In addition, the body motion detection unit 28 calculates a body motion reference value that is a reference for determining the body motion of the person 2 based on measurement data (for example, body motion data) within a second predetermined period before the determination target time point. Then, the body motion determination value at the determination target time point may be compared with the body motion reference value, and the body motion of the person 2 may be determined based on the comparison result.

The determination of body movement by the body movement detection unit 28 is performed by setting a body movement threshold value from the distribution of measurement data (for example, body movement data) in the second predetermined period, for example, and setting the body movement reference value. And may be determined by comparison with body motion data at the determination target time point. Here, the body motion threshold Thm (shown in FIG. 3) is, for example, the mode up to a position where the frequency of the mode of the histogram in FIG. 3 becomes a certain ratio (for example, 60% (ie, e ^−1/2 )). It is possible to determine a position obtained by adding a certain multiple (for example, three times) of the distance from the value to the mode value. In addition, since the right side of the peak of the histogram in the figure is affected by body movement, the ratio of the frequency to the peak may be evaluated on the left side.

  Next, the threshold value calculation unit 29 is based on the measurement data of the first predetermined period excluding the body motion period that is the period in which the body motion is detected by the body motion detection unit 28 among the periods before and after the determination target time point. A threshold value used for determination by the second state determination unit 25 is calculated.

  The first predetermined period may be, for example, 1 minute or 1 hour, and may be a time sufficient to calculate the threshold, and typically includes a plurality of determination target time points. Preferably, since the first predetermined period represents the most recent state of the person 2, approximately 1 minute to several minutes is appropriate, and here, for example, 2 minutes.

  Here, as shown in FIG. 4A, when the body motion period detected by the body motion detector 28 overlaps with the first predetermined period, the remaining data excluding the measurement data of the body motion period are included. Measurement data for a period corresponding to the excluded period is added to the measurement data to obtain measurement data for the first predetermined period. Specifically, for example, when there is a body motion period detected by the body motion detection unit 28 on the rear side (future side) of the first predetermined period, the front side of the first predetermined period (2 minutes) ( It is preferable to add measurement data for a period corresponding to the excluded period from the measurement data on the past side (see FIG. 4B). For example, when there is a large body motion slightly before the determination target time point and a body motion period exists on the front side (past side) of the first predetermined period, the first predetermined period (2 minutes) It is preferable to add measurement data in a period corresponding to the excluded period from the measurement data on the rear side (future side) following the determination target time (FIG. 4C). That is, measurement data for two consecutive minutes (first predetermined period) not including the body movement period can be secured. In addition, it is good to take the continuous period without a body movement period as much as possible for the 1st predetermined period including a determination object time point. By not including the body movement period in the first predetermined period, the threshold calculation unit 29 can calculate a respiration reference value described later with high accuracy, and accurately calculate a threshold calculated based on the respiration reference value. Therefore, the state determination by the second state determination unit 25 can be performed more accurately.

  Referring back to FIG. 1 again, the threshold value calculated by the threshold value calculation unit 29 based on the measurement data for the first predetermined period and used for determining the state of the person 2 is determined based on a respiration reference value described later. In other words, the threshold calculation unit 29 calculates a respiration reference value based on measurement data, here, respiration data, and calculates a threshold based on the respiration reference value.

  The second state determination unit 25 includes first measurement data corresponding to the chest region 2a as the first region of the person 2, or an abdominal region as a second region different from the chest region 2a of the person 2 The second measurement data corresponding to 2b is compared with the threshold value to determine the hypopnea event period (first period) and the apnea event period (second period). Further, as will be described later, the second state determination unit 25 is also configured to determine the state of the person 2 by comparing the entire measurement data corresponding to substantially the entire person 2 with a threshold value.

  Here, in the present embodiment, the chest region 2a as the first region of the person 2 is the upper part (chest region) of the upper body of the person 2, and the abdominal region 2b as the second region of the person 2 is the person. 2 is the lower part (abdomen) of the upper body. Further, substantially the entire person 2 is typically an area obtained by combining the chest area 2 a and the abdominal area 2 b of the person 2. That is, the term “substantially whole of the person 2” here means not the meaning from the head to the toe of the person 2 as the object, but the meaning of the substantially whole portion of the portion to be measured by the FG sensor described later with reference to FIG. Yes, it is a region where the chest and abdomen of the person 2 are combined.

  The first measurement data corresponding to the first region is measurement data corresponding to the upper part (chest) of the upper body of the person 2, and the second measurement data corresponding to the second region is It is measurement data corresponding to the lower part (abdomen) of the upper body. The overall measurement data is measurement data corresponding to substantially the entire person 2, and is typically data obtained by combining the first measurement data and the second measurement data.

  Furthermore, the threshold value calculation unit 29 uses the first measurement data as a third threshold value corresponding to the chest region 2a (the first threshold value and the second threshold value will be described later), and the second measurement data as the second measurement data. The fourth threshold value corresponding to the abdominal region 2b and the fifth threshold value corresponding to substantially the entire person 2 are calculated in the entire measurement data. The respiration reference value or threshold is set for an amount indicating the magnitude of the periodic movement of the object, that is, the magnitude of the respiration movement of the person 2. Typically, the second state determination unit 25 compares the amount indicating the magnitude of the respiratory movement of the person 2 at the determination target time point of the first measurement data with the third threshold value, or the second The comparison result between the amount indicating the magnitude of the respiratory movement of the person 2 at the determination target time of the measurement data and the fourth threshold value, or the magnitude of the respiratory movement of the person 2 at the determination target time of the entire measurement data Based on the comparison result between the indicated amount and the fifth threshold value, the hypopnea event period (first period) and the apnea event period (second period) are determined.

  Unless otherwise specified, the first region of the person 2 is referred to as a “chest region 2a”, the second region of the person 2 is referred to as an “abdomen region 2b”, and the substantially entire region of the person 2 is referred to as an “overall region”. . Further, the first measurement data is referred to as “chest measurement data”, and the second measurement data is referred to as “abdominal measurement data”. Further, the third threshold is referred to as “chest threshold”, the fourth threshold is referred to as “abdominal threshold”, and the fifth threshold is referred to as “overall threshold”. Note that the chest threshold value, the abdominal threshold value, and the overall threshold value are simply referred to as threshold values when there is no need to explain them separately.

  Here, the magnitude of the breathing motion of the person 2 at the determination target time is, for example, respiratory data within a certain period (for example, 5 to 15 seconds) including a period of at least one cycle (FIG. 2A). Amplitude), peak, bottom, amount corresponding to tidal volume, etc. can be used. Here, the peak is the peak portion of the waveform pattern formed by the measurement data, and the bottom is the valley portion (see FIG. 2A). Here, the amplitude is the difference between the peak and the bottom.

  The threshold calculation unit 29 calculates the respiration reference value based on the magnitude of the respiration movement of the person 2. Here, the threshold value calculation unit 29 is configured to calculate the respiration reference value based on the average value of both or one of the peak and the bottom of the measurement data. The respiration reference value calculated by the threshold value calculation unit 29 may be an average value of either the peak or the bottom of the measurement data within the first predetermined period before the determination target time, or both the peak and the bottom It may be an average value. Furthermore, it is good also as an average value of the amplitude of the respiration data which can be calculated from the difference of the average value of a peak and a bottom.

  Furthermore, it is more preferable that the threshold value calculation unit 29 evaluates the stability of the measurement data within the first predetermined period and calculates a respiration reference value based on the evaluation. The stability evaluation may be performed based on variations in the peak (or bottom) and peak intervals (or bottom intervals and zero-cross intervals) of the respiratory data in the target period, here the first predetermined period. The threshold value calculation unit 29 may calculate the respiration reference value by a different method (for example, a different calculation formula) based on the evaluation result. Here, the zero cross means that the value of the periodic motion is zero, for example, as shown in FIG. 2A, when the horizontal axis is time and the vertical axis is speed, the speed intersects the time axis. Or the crossing point. That is, the point where the speed becomes zero or zero.

  Specifically, when it is evaluated as stable, as described above, the average value of either or both of the peak and the bottom of the respiratory data within the first predetermined period, or the difference between the peak and the bottom The average value of (respiration data amplitude) or the difference between the average value of the peak and the average value of the bottom is used as the respiration reference value. In addition, if it is evaluated as unstable, the respiratory data peak and bottom within the first predetermined period and / or either one of the larger ones from the average value of the three values or the peak from the larger one The difference between the average value of the three values and the average value of the three values from the larger one at the bottom is taken as the respiratory reference value. Or let the average value of three values from the one with the larger amplitude of respiration data be a respiration standard value.

  Moreover, the threshold value calculation unit 29 evaluates the stability of the measurement data for a certain period after the determination target time when it is evaluated as unstable in the above-described stability evaluation. The respiration reference value calculated in the subsequent fixed period is set as the respiration reference value at the determination target time point. Here, the subsequent fixed period is the same period as the first predetermined period described above, but here, the first predetermined period is shorter, and may be, for example, about one minute. For example, when the front side (past side) of the determination target time point of person 2 is unstable breathing and the rear side (future side) is stable breathing with a small amplitude, If the evaluation is performed in a predetermined period of 1, the respiration reference value is calculated in an unstable respiration state. For this reason, there is a possibility that all future breaths with a small amplitude will be determined to be apnea and hypopnea as determined by the second state determination unit 25 described later. Also, for example, when the past (first predetermined period before the determination target time) is stable breathing and the future (first predetermined period after the determination target time) is unstable breathing, the determination target time point is unstable breathing. If it enters the region, it is evaluated as unstable relatively quickly, so that the determination by the second state determination unit 25 during the period evaluated as unstable is correctly determined.

  The threshold calculation unit 29 always evaluates the stability of the measurement data in the first predetermined period after the determination target time in the above-described stability evaluation, and the first predetermined period before the determination target time. If it is evaluated as unstable, the respiration reference value calculated in the first predetermined period after the determination target time may be used as the respiration reference value at the determination target time. That is, the stability of the measurement data in the first predetermined period after the determination target time is always evaluated, not limited to the case where the evaluation is unstable.

  Further, the threshold value calculation unit 29 calculates a respiration reference value in both the case where it is evaluated as stable and the case where it is evaluated as unstable in the evaluation of the stability. If there is a divergence, it should be evaluated as unstable. In addition, it is good to use the respiration reference value calculated by the method different in the case evaluated as stable, and the case evaluated as unstable. In other words, it is preferable to calculate the respiration reference value by both the calculation method for the case where the evaluation is stable and the case where the evaluation is unstable. For example, the respiration reference value l (el) when stable is the average value of the peaks in the period for which stability is evaluated, and the respiration reference value m when unstable is the upper (from the larger) peak in the period 3 Assuming that two average values are used, m is assumed to be unstable when | l−m | / (l + m)> threshold th1, and l is adopted as a respiration reference value when it is not. Alternatively, when the average value excluding the top three is n, m is adopted when | mn − / (m + n)> threshold th2, and l is adopted otherwise.

  Note that a pseudo tidal volume can also be used as an amount indicating the magnitude of the movement of respiration at the determination target time point of the entire measurement data corresponding to the entire region. In this case as well, for example, the average value of the pseudo tidal volume within the first predetermined period may be taken as the respiration reference value.

  Here, the pseudo tidal volume at the determination target time point is a value obtained by integrating measurement data from the zero cross to the zero cross before the determination target time point, in this case, respiration data (see FIG. 2A). Because it is not a strict tidal volume but a value corresponding to tidal volume, it is described as “pseudo”. “From zero cross to zero cross” means a period in which measurement data has a predetermined positive or negative sign between two consecutive zero crosses. In addition, here, “from the zero cross before the determination target time to the zero cross” is typically “from the zero cross immediately before the determination target time to the zero cross”, and more specifically, the determination target time is the reference. As described above, it means from the zero cross just before the zero cross just before the determination target time to the zero cross just before the determination target time. It should be noted that, during three consecutive zero crossings, the measurement data is integrated between the first two zero crosses and the second half zero crosses, which have different signs of the measurement data, and the average of the absolute values is taken. It is good also as pseudo tidal volume. In addition, since a plurality of determination target time points are typically included in the first predetermined period, a plurality of pseudo tidal volumes are determined based on the respiration data in the first predetermined period. Obtainable.

  By using the pseudo tidal volume as the magnitude of the breathing movement of the person 2 at the determination target time point of the whole measurement data corresponding to the whole area, changes in both the amplitude and the interval of the breathing data can be reflected. That is, if the amplitude becomes smaller and the interval becomes shorter, the pseudo tidal volume becomes smaller and it can be said that the change is reflected more accurately than the amplitude alone.

  As described above, when the peak value and the bottom value of the positive and negative respiratory signals are used as the magnitude of the respiratory movement of the person 2, for example, in the central apnea state, the respiratory signal is negative. In some rare cases, a loose change on the side, that is, a state in which a slight exhalation appears may occur, but even in this case, erroneous determination can be prevented. In the following embodiment, unless otherwise specified, the description will be made assuming that the peak value and the bottom value of the respiratory signal on the positive side and the negative side are used as values indicating the magnitude of respiration.

  As described above, the threshold value calculation unit 29 performs the chest measurement data corresponding to the chest region 2a within the first predetermined period before and after the determination target time point, the abdominal measurement data corresponding to the abdominal region 2b, and the entire region corresponding to the entire region. Based on the measurement data, a respiration reference value for each region is calculated. In addition, each respiration reference value forms the baseline within the period (for example, overnight, about 7 to 10 hours) of the measurement data acquired collectively by collecting the values calculated for each determination target time point.

  FIG. 5 shows an example of the waveform pattern of respiratory data and body motion data corresponding to the entire region acquired by actual measurement for a certain period (about 6 minutes). An outline of the baseline will be described with reference to FIG. Further, in this figure, the baseline formed by the calculated respiration reference value is also shown corresponding to the respiration data. As described above, the respiration reference value forming the baseline is calculated based on the respiration data in the present embodiment. As shown in the body motion data shown in the drawing, the portion where the data indicating the body motion (see FIG. 2C) is present is excluded from the calculation of the respiration reference value because the respiration data also changes greatly. That is, the signal level may change at the boundary of the body movement period, and the first predetermined period is set as a series of periods that do not include the body movement period as much as possible. (Respiration standard value) changes. In this figure, the average value of the body movement data is increased on the right side. This is because the measurement point reflecting the movement of the person 2 is changed due to, for example, a change in posture accompanying the body movement. This is because the body motion data is biased with the change in sensitivity.

  Referring back to FIG. 1 again, the threshold value calculation unit 29 calculates each threshold value by multiplying each obtained respiration reference value by a certain coefficient. As for the overall threshold, two thresholds, a first overall threshold and a second overall threshold, are calculated. For example, the threshold value calculation unit 29 calculates a value of about 25% to 15% of each obtained respiration reference value, here a value of 20%, as the chest threshold value, the abdominal threshold value, and the first overall threshold value at the determination target time point. . That is, the threshold calculation unit 29 calculates the chest threshold, the abdominal threshold, and the first overall threshold by multiplying the respiration reference value for each of the chest region 2a, the abdominal region 2b, and the entire region by a coefficient of 0.2. Furthermore, the threshold value calculation unit 29 calculates a value of about 55% to 45% of the respiration reference value obtained according to the entire region, here, a value of 50% as the second overall threshold value. That is, the threshold calculation unit 29 calculates the second overall threshold by multiplying the respiration reference value for the entire region by the coefficient 0.5.

  The second state determination unit 25 compares the measurement data of each region at the determination target time point with each threshold value calculated by the threshold value calculation unit 29, and based on the comparison result, the hypopnea event period (first period) ) To determine the apnea event period (second period). The second state determination unit 25 typically compares the respiratory data at the determination target time point with each threshold value.

  For example, when the respiration reference value calculated by the threshold value calculation unit 29 is an average value of either the peak or the bottom, the determination by the second state determination unit 25 is positive (peak side, inspiration side). ) Or negative side (bottom side, exhalation side) only, or if the average value of both the peak and the bottom, is the judgment on both the positive side and the negative side matched? Alternatively, the determination may be made based on whether the determination is made only by one (AND or OR).

  The second state determination unit 25 uses the amount indicating the magnitude of the respiratory movement of the person 2 at the determination target time of the chest measurement data and the chest threshold value to determine the respiratory movement of the person 2 at the determination target time of the abdominal measurement data. The amount indicating the magnitude and the abdominal threshold are compared with the amount indicating the magnitude of respiratory movement of the person 2 at the determination target time point of the total measurement data and the first total threshold or the second total threshold, respectively. Based on the comparison result, a hypopnea event period (first period) and an apnea event period (second period) are determined. Here, the second state determination unit 25 may simply be configured to compare the measurement value itself at the determination target time point of each measurement data with each threshold value. In this case, for example, even when the peak and bottom of the measurement data are not clear, the comparison can be made without any problem. In the following description, unless otherwise noted, as described above, the amount of breathing movement of the person 2 at the determination target time point of each measurement data is compared with each threshold value.

  More specifically, the second state determination unit 25 indicates that the amount indicating the magnitude of the respiratory movement of the person 2 at the determination target point of the chest measurement data continuously falls below the chest threshold for the fourth predetermined period. Or when the amount indicating the magnitude of the respiratory movement of the person 2 at the determination target time of the abdominal measurement data falls below the abdominal threshold continuously for a fourth predetermined period, or the determination target time of the entire measurement data When the amount indicating the magnitude of the breathing motion of the person 2 falls below the first overall threshold continuously for the fourth predetermined period, the period of time during which the amount has fallen below the threshold is defined as the apnea event period (second Period). That is, when the threshold value set for any one of the chest measurement data, the abdominal measurement data, and the whole measurement data is continuously lowered for the fourth predetermined period, the apnea event period (second Period).

  Here, the fourth predetermined period is typically a period considered to be longer than the normal breathing interval of the person 2, and may be, for example, about 6 seconds to 15 seconds. Note that, in the sleep apnea determination criterion that is most commonly employed at present, typically, respiratory stop or respiratory depression continues for 10 seconds or longer, so here it is also set to about 10 seconds. However, as will be described later, the interval to the peak before and after falling below the threshold may be set to about 10 seconds. In the present embodiment, the fourth predetermined period is continuous. For example, the determination target time point when the magnitude of the respiratory motion of the chest measurement data falls below the chest threshold corresponds to the fourth predetermined period. It is continuous at a plurality of determination target time points. That is, for example, if the interval between determination target points is set to about 0.25 seconds and the second predetermined period is set to about 10 seconds, the above condition continues for 40 determination target points. It can be determined that two predetermined periods have been continued. Note that continuously lowering for the fourth predetermined period does not necessarily mean that the condition must not be deviated, but it is only necessary that it is substantially continuous. If there is no. The same applies to the following description unless otherwise specified.

  As described above, the second state determination unit 25 continuously lowers the threshold set for any one of the chest measurement data, the abdomen measurement data, and the whole measurement data for the fourth predetermined period. When it is determined that the apnea event period (second period), it is not possible to determine the apnea event period (second period) simply by reducing the magnitude of the respiratory movement in the entire measurement data. That is, the period in which the magnitude of the respiratory movement of only the chest measurement data or the abdominal measurement data is reduced can be accurately determined as the apnea event period (second period).

  FIG. 6 is a diagram illustrating a first specific example of a respiratory waveform (respiratory data) of obstructive apnea. In the figure, the upper part shows the whole measurement data, the middle part shows the chest measurement data, and the lower part shows the abdominal measurement data. In FIG. 6, the magnitude of the breathing movement in the chest measurement data during the apnea period is almost the same as the movement during the normal breathing period, but the abdominal measurement data clearly breathes during the apnea period. The magnitude of the movement has decreased. In other words, the magnitude of the respiratory movement of only one of the chest measurement data and the abdominal measurement data falls below the threshold value, but the other may not be significantly reduced. That is, in an extreme case, it can be determined that an apnea cannot be determined only by evaluation of the entire measurement data, and an apnea can be detected by discriminating the measurement data separately from the chest measurement data and the abdomen measurement data. If the determination of apnea is made only by the magnitude of the respiratory movement of the whole measurement data, as shown in this figure, when the movement magnitude of only one of the chest measurement data or the abdominal measurement data is significantly reduced, However, as described above, it can be determined that only one of them is significantly reduced by detecting that there is a significant decrease. Respiration can be detected.

  Note that when any measurement data falls below the threshold, the second state determination unit 25 sets the interval between the breath data peaks that are equal to or greater than the threshold immediately before and after that as the event period, and the event period is a fixed period. For example, if it is about 10 seconds (fourth predetermined period) or more, this event period may be determined to be the apnea event period (second period). In this case, strictly speaking, there are periods where the respiratory data exceeds the threshold at the beginning and end of the apnea event period (second period). If it is within a certain percentage of the event period, within a certain percentage of the fourth predetermined period, or within a predetermined period, it is sufficient to satisfy the determination conditions such as apnea and hypopnea. In other words, when the start end of the event period to be compared with the fourth predetermined period is the immediately preceding peak and the rear end is the immediately following peak, the event period is longer than the fourth predetermined period, If the period exceeding the threshold is within a certain percentage of the event period, or within a certain percentage of the fourth predetermined period, or within a predetermined period, the period actually falling below the threshold is greater than the fourth predetermined period. Although it may be shortened, it can be regarded as substantially falling below the threshold and included in the concept of continuously falling for the fourth predetermined period. By doing in this way, it can be seen that the period during which the respiratory data falls below the threshold is substantially continuous for the fourth predetermined period. The same applies to the state determination by the second state determination unit 25 described below unless otherwise specified.

  In addition, the second state determination unit 25 picks up the peak and the bottom of the respiratory data as the determination target time point, and the difference, that is, the amplitude of the respiratory data is less than the threshold value and continues for the fourth predetermined period or longer. Alternatively, the duration may be determined to be an apnea event period (second period). As another determination method, the peak (or bottom) of the respiratory data is picked up as the determination target time point, and when the interval of the peak of the respiratory data that is equal to or higher than the threshold value becomes a certain period, for example, about 10 seconds or more, further You may comprise so that it may discriminate | determine that it is an apnea event period (2nd period) when the period when breathing data is less than a threshold value between both peaks is more than a 4th predetermined period. The same applies to the state determination by the second state determination unit 25 described below unless otherwise specified.

  In addition, when a plurality of measurement data among the chest measurement data, the abdominal measurement data, and the whole measurement data continuously falls below the respective threshold values for the fourth predetermined period, the measurement data lasted the longest. The period of the measurement data may be an apnea event period (second period). In other words, as described above, the event period that is not detected as the apnea event period (second period) can be detected without exception from the entire measurement data alone.

  Furthermore, the second state determination unit 25 detects that the amount indicating the magnitude of the breathing motion of the person 2 at the determination target time point of the total measurement data continuously falls below the second total threshold for the fifth predetermined period. In addition, the period that has fallen below the threshold is determined as the hypopnea event period (first period). Here, the fifth predetermined period is typically the same period as the fourth predetermined period, which is considered to be longer than the normal breathing interval of the person 2, for example, 6 to 15 seconds. It should be about. In this case, too, the sleep apnea criterion that is most commonly adopted at present is typically that respiratory stop or hypopnea lasts for 10 seconds or more, so here again, it is about 10 seconds. To do.

  Note that, as described above, the overall measurement data typically corresponds to measurement data obtained by adding the chest measurement data and the abdomen measurement data, so that the second state determination unit 25 includes, for example, chest measurement data and A threshold value obtained by multiplying a respiration reference value for the chest region 2a and the abdomen region 2b by a coefficient of 0.5 for each of the breathing movements of the person 2 at the determination target time point of the abdominal measurement data for a fifth predetermined period. When the value falls below the threshold, the period that falls below the threshold may be determined as the hypopnea event period (first period).

  The first state determination unit 24 complements the chest measurement data corresponding to the chest region 2a of the person 2 and the abdominal measurement data corresponding to the abdomen region 2b different from the chest region 2a, and the chest measurement data or the abdomen measurement. The state of the object is determined based on a first evaluation value corresponding to the symmetry of the time axis direction of the data. As the first evaluation value, for example, a value based on the cross-correlation function calculated by the cross-correlation function calculator 22 described above can be used. In the present embodiment, as the first evaluation value, a value based on the average value of the peak and bottom of the cross-correlation function calculated by the cross-correlation function calculator 22 is used.

  Here, as described above, the computing device 20 of the respiratory monitor 1 according to the embodiment of the present invention includes the cross-correlation function computing unit 22 that computes the cross-correlation function and the auto-correlation function computing unit that computes the auto-correlation function. 23. The correlation function typically represents the similarity between both waveforms as a function of the time shift τ while shifting one of the two waveforms little by little on the time axis. Typically, the closer the numerical value is to “1”, the higher the similarity is. Cross-correlation performs correlation processing on two different signal waveforms and examines the similarity between the two waveforms, whereas autocorrelation performs correlation processing on one signal waveform. It is examined whether or not there is periodicity in one signal waveform.

For example, if the breathing rate is 10 to 30 times / minute, the cycle is 2 seconds to 6 seconds, so the bottom is approximately τ = 1 to 3 seconds and the peak is approximately τ = 2 to 6 seconds. It will appear there. Here, the cross-correlation function and the autocorrelation function are both correlated with data of 2 to 18 seconds, typically 6 seconds, centered on the peak and bottom positions of the entire measurement data, and lag. The calculation is performed using data obtained by adding the time lag τ between data taking The time series data and the abdominal measurement data of the chest measurement data are respectively S ^ch = (S ₁ ^ch , S ₂ ^ch ,..., S _n ^ch ) and S ^ab = (S ₁ ^ab , S ₂ ^ab ,..., S _n ^ab ). Then, the cross-correlation function C (τ) can be expressed by the following equation (2), for example.
Similarly, when the time series data of the measurement data is S = (S ₁ , S ₂ ,..., S _n ), the autocorrelation function A (τ) can be expressed by the following equation (3), for example.

  As described above, the first state determination unit 24 includes the complementarity between the chest measurement data corresponding to the chest region 2a of the person 2 and the abdomen measurement data corresponding to the abdomen region 2b different from the chest region 2a, and the chest The state of the person 2 is determined based on the first evaluation value corresponding to the symmetry of the time axis direction of the measurement data or the abdomen measurement data. The first evaluation value is a value based on the bias PB of the cross-correlation function C (τ) calculated by the cross-correlation function calculation unit 22, and in this embodiment, the cross-correlation of the cross-correlation function C (τ). This is an average value of the correlation value CP of the peak and the correlation value CB of the cross correlation bottom. That is, the first state determination unit 24 calculates the cross correlation function bias PB = (CP + CB) / 2, and when the cross correlation function bias PB is equal to or less than the sixth threshold, Determine that there is. In other words, when the cross correlation function bias PB is equal to or greater than the sixth threshold, the central apnea state is determined. The sixth threshold here may be a value of 0.2 or less, preferably about 0, for example.

  FIG. 7 is a diagram showing an example of a correlation function of the respiratory monitor 1 according to the first embodiment of the present invention, where (a) is an example of an autocorrelation function and (b) is an example of a cross-correlation function. is there. Here, the cross-correlation function C (τ) is calculated using equation (2) and lag (time shift τ) is −6 to +6 seconds, and the autocorrelation function A (τ) is expressed using equation (3). (Time deviation τ) is calculated as 0 to 6 seconds, and the position of the correlation peak and the bottom, the correlation value at that time, etc. are determined within the set lag range. This figure shows the correlation between the correlation peak and bottom, the correlation coefficient, and the like. In the case of the autocorrelation function A (τ), when the time lag τ = 0, A (0) = 1 is the maximum, so the next bottom and subsequent peaks may be obtained (see FIG. 7A). In the case of the cross-correlation function C (τ), the peak and bottom may be obtained during the calculation period. However, since it is unknown whether the peak position is in the positive or negative direction of τ, the period for searching for the peak and the bottom with respect to lag is set to ± 6 seconds. Here, the value of τ = 0 and C (0) are used as correlation coefficients (see FIG. 7B).

  In addition, since the value calculated | required here may have dispersion | variation by the noise of a signal, when determining a state, you may average and use the data for about 3 to 5 breaths before and after. Moreover, since these correlation peak values and bottom values are for evaluating the respiratory motion of the person 2, the calculation period (lag range) is a normal number of breaths (for example, about 10 to 30 times per minute). ), That is, as described above, it may be detected within about 6 seconds which is the longest respiratory cycle. However, for autocorrelation, a range of 0 to 1 second may be excluded as a noise region.

  Here, the correlation coefficient between the chest measurement data and the abdomen measurement data necessarily takes a value between the cross-correlation peak and the bottom. If the correlation coefficient matches the cross-correlation peak, it can be considered that the chest region 2a and the abdominal region 2b move almost completely in phase, while if the correlation coefficient matches the cross-correlation bottom. It can be considered that the movement is out of phase. In the above-mentioned obstructive apnea state, so-called strange heterosexual movement is often observed. In odd heterosexual exercise, airflow stops despite respiratory effort, so the chest region 2a and abdomen region 2b move in opposite directions, cancel each other, and the overall measurement data almost loses the amplitude of motion. It becomes like. Therefore, by comparing the correlation coefficient with the cross-correlation peak or bottom as described above, it is possible to determine the obstructive apnea state.

  FIG. 8 is a diagram for explaining an obstructive apnea state, where (a) is a diagram showing a typical respiratory motion waveform in the obstructive apnea state, and (b) is a diagram when (a). It is a diagram which shows the cross correlation function of the chest measurement data and abdominal measurement data. In the obstructive apnea state, typically, the respiratory motion of the chest region 2a and the respiratory motion of the abdominal region 2b are complementary, in other words, almost “0”. The measurement data does not have a sin waveform symmetric in the direction of the time axis, but tends to have a shape close to a sawtooth waveform as shown in FIG. This is thought to be because, in the obstructive apnea state, when the airway remains obstructed despite the respiratory effort, the respiratory muscle tension is released at the same time as the respiratory effort is stopped.

  When the chest measurement data and the abdomen measurement data are complementary sawtooth waves, as shown in FIG. 8B, the cross-correlation function C (τ) becomes the bottom when τ = 0, and C (0) ≈ -1. On the other hand, since the waveforms do not overlap exactly at the peak, the value is not close to +1. In other words, the chest measurement data and the abdominal measurement data are asymmetric in the direction of the time axis, that is, in the case of an obstructive apnea, the cross-correlation function C (τ) is generally negative (−1 It seems to have shifted to the side closer to. That is, the bias of the cross-correlation function C (τ) indicates the symmetry of the time axis direction of the chest measurement data and the abdominal measurement data, and the cross-correlation function C (τ) is on the negative side regardless of the phase shift. When it is biased, it can be determined that the patient is in an obstructive apnea state. If the cross correlation function bias PB is a value indicating the bias, a value other than the average value of the correlation value CP of the cross correlation peak and the correlation value CB of the cross correlation bottom described above, for example, simply the cross correlation peak. The correlation value CP may be used. Here, the fact that the chest measurement data and the abdominal measurement data are symmetrical in the direction of the time axis typically means that when one waveform is shifted by a half cycle, it is almost symmetrical with the other waveform. Including that. In other words, it includes that the waveform from one zero cross to the zero cross is substantially symmetrical between the positive side and the negative side.

  FIG. 9 is a diagram showing a second specific example of the respiratory waveform (respiration data) of the obstructive apnea. Like FIG. 6, in the figure, the upper part shows the whole measurement data, the middle part shows the chest measurement data, and the lower part shows Abdominal measurement data is shown. The motion amplitude remains in both the chest measurement data and the abdominal measurement data, but each cancels out, and the motion amplitude is almost lost in the whole measurement data. This is a typical example of the bizarre movement generally called. As shown in FIG. 8, the movements in the whole region are almost equal because the chest region and the abdominal region move in opposite directions. Looks like it disappeared.

  The first state determination unit 24 is further based on a second evaluation value relating to the similarity between the chest measurement data corresponding to the chest region 2a of the person 2 and the abdomen measurement data corresponding to the abdomen region 2b of the person 2. The state of the person 2 is determined. Here, the second evaluation value is typically a value based on the cross-correlation function C (τ) calculated by the cross-correlation function calculation unit 22. More specifically, the second evaluation value is the correlation coefficient between the chest measurement data and the abdominal measurement data, or the cross-correlation function value when the time difference τ = 0 between the chest measurement data and the abdominal measurement data. Alternatively, the value is based on the cross-correlation coefficient value when the time lag τ = 0 between the chest measurement data and the abdominal measurement data.

  For example, when data obtained by removing the average value from the measured value is used as data, the value of τ = 0 matches the Pearson product moment correlation coefficient in the normalized cross-correlation function (that is, the cross-correlation coefficient). . In the case of a respiratory motion signal, if the constant position of the cycle is the start point and end point, the average value is approximately 0. Therefore, even if the average value is not subtracted, the value of τ = 0 of the data cross-correlation coefficient is used as the correlation coefficient. There is no problem. That is, the cross-correlation function value and the cross-correlation coefficient value when τ = 0 correspond to the correlation coefficient as a result when the average value of the data is almost zero as in the case of respiratory motion. Hereinafter, unless otherwise specified, the second evaluation value is described as a value related to the correlation coefficient CC of the cross-correlation function C (τ) between the chest measurement data and the abdominal measurement data, and the correlation coefficient CC is broadly defined. In the following description, the cross-correlation function value and the cross-correlation coefficient value when τ = 0 are assumed.

  The correlation coefficient CC is −1 if the chest measurement data and the abdominal measurement data are complementary, in other words, in reverse phase (that is, completely cancel each other and the total measurement data is zero). Become. Conversely, if it is in phase (same wave type), it becomes +1. Further, in the present embodiment, the second evaluation value is a value related to the correlation coefficient CC of the cross-correlation function C (τ) between the chest measurement data and the abdominal measurement data, and the correlation between the correlation coefficient CC and the cross-correlation peak. The phase shift index PI is a parameter that has a numerical relationship with the value CP or the correlation value CB of the cross-correlation bottom. Here, the first state determination unit 24 calculates the phase shift index PI = (CC−CB) / (CP−CB) as the second evaluation value, and the phase shift index PI becomes the second evaluation value. On the other hand, it is determined that the state of the person 2 is an obstructive apnea state when it is equal to or less than the first threshold set for the person. In other words, when the phase shift index PI is equal to or greater than the first threshold value, the central apnea state is determined. Here, the first threshold value may be set to a value of 0.8 or less, preferably about 0.6, for example. In general, the correlation coefficient CC and the correlation value CB of the cross-correlation bottom are different depending on individual correlations in the degree of correlation coefficient CC when a normal respiratory state or obstructive apnea occurs. Is divided by the difference between the correlation value CP of the cross-correlation peak and the correlation value CB of the cross-correlation bottom, and normalized to obtain an evaluation value, thereby enabling more effective discrimination without being influenced by individual differences. Can do.

  Although the phase shift index PI = (CC−CB) / (CP−CB) is used as the second evaluation value here, PI = CC−CB may be simply used, or PI = (CP−CC) / (CP-CB) may be used. Also, PI = CC may be simply set, or it may be standardized to be PI = CC / (CP-CB). Furthermore, PI = (CC− (CP + CB) / 2) may be set, or this may be normalized and set to PI = (CC− (CP + CB) / 2) / (CP−CB). However, depending on how the PI is obtained at that time, the magnitude relationship with the threshold value at the time of determination may be reversed from the above description.

  The first state determination unit 24 is further based on a third evaluation value corresponding to the phase difference between the chest measurement data corresponding to the chest region 2a of the person 2 and the abdomen measurement data corresponding to the abdomen region of the person 2. Thus, the state of the person 2 is determined. Here, the respiration monitor 1 of the present embodiment is configured so that the peak position of the cross-correlation function C (τ) calculated by the cross-correlation function calculation unit 22 and the chest measurement data calculated by the auto-correlation function calculation unit 23 are as follows. Or the comparison result with the peak position of the autocorrelation function A (τ) of the abdomen measurement data, or the bottom position of the cross correlation function C (τ) calculated by the cross correlation function calculation unit 22, and the auto correlation function calculation unit 23 As a phase difference calculation means for calculating a third evaluation value corresponding to the phase difference based on the comparison result of the autocorrelation function A (τ) of the chest measurement data or the abdominal measurement data calculated by A phase difference calculation unit 21 is provided. The first state determination unit 24 determines the state of the person 2 based on the third evaluation value corresponding to the phase difference calculated by the phase difference calculation unit 21.

  When the chest measurement data and the abdomen measurement data are in phase, the autocorrelation peak of the chest measurement data or the abdomen measurement data and the cross-correlation peak between the chest measurement data and the abdomen measurement data come at substantially the same position. On the other hand, in the case of reverse phase, the cross-correlation peak appears near the bottom of the autocorrelation. That is, for example, (cross-correlation peak position / autocorrelation peak position of chest measurement data) × 2π is the phase difference between chest measurement data and abdominal measurement data. Here, the phase difference calculation unit 21 calculates the phase difference P = (cross-correlation peak position / autocorrelation peak position of chest measurement data) × 2π as the third evaluation value, and the first state determination unit 24 When the calculated absolute value of the phase difference P is equal to or greater than a second threshold set for the phase difference, it is determined that the state of the person 2 is an obstructive apnea state. In other words, when the calculated absolute value of the phase difference P is equal to or smaller than the second threshold value, the central apnea state is determined. The second threshold value here may be a value of π / 3 or less, preferably about π / 4, for example.

  Here, phase difference P = (cross-correlation peak position / autocorrelation peak position of chest measurement data) × 2π, but phase difference P = (cross-correlation bottom position / autocorrelation bottom position of chest measurement data) × π, phase difference P = (cross correlation peak position / autocorrelation peak position of abdominal measurement data) × 2π, or phase difference P = (cross correlation bottom position / autocorrelation bottom position of abdominal measurement data) × π may be used.

  The first state determination unit 24 further includes chest measurement data corresponding to the chest region 2 a of the person 2, abdominal measurement data corresponding to the abdomen region 2 b of the person 2, and overall measurement data corresponding to substantially the entire person 2. The state of the person 2 is determined based on the fourth evaluation value regarding the similarity to the time lag τ of at least one measurement data selected from the group. Here, the fourth evaluation value is typically a value based on the peak or bottom of the autocorrelation function A (τ) calculated by the autocorrelation function calculation unit 23 or the difference between the peak and the bottom. The autocorrelation function calculation unit 23, when determining the state based on at least the fourth evaluation value of the chest measurement data by the first state determination unit 24, the autocorrelation function of the chest measurement data, the abdominal measurement data When determining the state based on the fourth evaluation value, the autocorrelation function of the abdominal measurement data, and when determining the state based on the fourth evaluation value of the entire measurement data, the self of the entire measurement data Calculate the correlation function.

  In the present embodiment, the first state determination unit 24 uses the peak and bottom of the autocorrelation function A (τ) of at least one of chest measurement data, abdominal measurement data, and overall measurement data as the fourth evaluation value. Difference AG = correlation value AP of the autocorrelation peak−correlation value AB of the autocorrelation bottom, and when the difference AG between the peak and the bottom of the autocorrelation function A (τ) is greater than or equal to the seventh threshold, It is determined that the state 2 is an obstructive apnea state. In other words, when the difference AG between the peak and the bottom of the autocorrelation function A (τ) is equal to or smaller than the seventh threshold value, the central apnea state is determined. Here, the third threshold value may be a value of 1.4 or less, preferably about 1.2, for example.

  Thus, by taking the difference between the correlation value AP of the autocorrelation peak and the correlation value AB of the autocorrelation bottom of any measurement data, the presence or absence of periodic motion can be evaluated. If the waveform of the measurement data is a perfect sine wave, the correlation value AP = 1 of the autocorrelation peak and the correlation value AB = −1 of the autocorrelation bottom are obtained. At this time, a bottom and a peak appear at the position of lag (time shift τ) corresponding to the frequency of respiratory motion. The difference AG between the peak and the bottom of the autocorrelation function A (τ) indicates the presence or absence of periodic motion, that is, respiratory motion at that time. A larger value indicates that there is a stable respiratory motion with a constant cycle, and a value closer to 0 indicates that there is no respiratory motion. Even if there is no respiratory motion, the measurement data may change temporarily. It can be clearly determined that there is not.

  Here, the state is determined based on the difference AG between the peak and bottom of the autocorrelation function A (τ), but only the correlation value AP of the autocorrelation peak or only the correlation value AB of the autocorrelation bottom. It is also possible to determine by. The presence or absence of periodic motion can also be determined by frequency analysis (such as obtaining a power spectrum). If the power of the spectrum of the frequency corresponding to respiration is clearly larger than the other frequency bands, it can be determined that there is motion in the frequency band corresponding to the respiration.

  The first state determination unit 24 further includes chest measurement data corresponding to the chest region 2 a of the person 2, abdominal measurement data corresponding to the abdomen region 2 b of the person 2, and overall measurement data corresponding to substantially the entire person 2. The state of the person 2 is determined based on a fifth evaluation value indicating symmetry with respect to time of at least one measurement data selected from the group. As the fifth evaluation value, for example, a value based on the cross-correlation function calculated by the cross-correlation function calculator 22 described above can be used. The cross-correlation function may be calculated in substantially the same manner as when calculating the evaluation value described above. However, here, the fifth evaluation value is the measurement data obtained by inverting at least one measurement data selected from the group consisting of chest measurement data, abdominal measurement data, and overall measurement data in the direction of the time axis, and the above-described inversion. It differs from the above-described evaluation value based on the cross-correlation function between the chest measurement data and the abdominal measurement data in that the value is based on the cross-correlation function with the previous measurement data.

  That is, here, the cross-correlation function calculation unit 22 uses the chest measurement data at least when the first state determination unit 24 determines the state based on the fifth evaluation value corresponding to the chest measurement data. Based on the cross-correlation function between the measurement data inverted in the direction of the time axis and the chest measurement data before the inversion, the first evaluation unit according to the fifth evaluation value corresponding to the abdominal measurement data by the first state determination unit 24. When performing the discrimination, the cross-correlation function between the measurement data obtained by inverting the abdomen measurement data in the direction of the time axis and the abdomen measurement data before being inverted, the first state determination unit 24 according to the total measurement data. When determining the state based on the fifth evaluation value, a cross-correlation function between the measurement data obtained by inverting the whole measurement data in the direction of the time axis and the whole measurement data before being inverted is calculated. It is made.

  The measurement data obtained by inverting at least one measurement data selected from the group consisting of chest measurement data, abdominal measurement data, and whole measurement data in the direction of the time axis will be described with reference to, for example, chest measurement data as shown in FIG. This is measurement data obtained by inverting the chest measurement data for a certain time shown in a) in the left-right direction toward the paper surface of FIG. Here, the fixed time may be, for example, about 2 to 18 seconds, typically about 6 seconds. In the present embodiment, the cross-correlation function calculation unit 22 uses the cross-correlation function C ′ (τ) between the measurement data obtained by inverting the abdominal measurement data in the direction of the time axis for a certain time and the chest measurement data before the inverting. ), That is, when the first state determination unit 24 determines the state based on the fifth evaluation value corresponding to the chest measurement data, as described above, the abdomen measurement data The corresponding fifth evaluation value, the fifth evaluation value corresponding to the entire measurement data, or all three may be used. Here, the fifth evaluation value is the cross-correlation of the cross-correlation function C ′ (τ) between the measurement data obtained by inverting the abdomen measurement data in the direction of the time axis for a predetermined time and the chest measurement data before the inverting. The peak correlation value CP ′ is assumed.

  When the correlation value CP ′ of the cross-correlation peak of the cross-correlation function C ′ (τ) as the fifth evaluation value is equal to or less than the eighth threshold value, the first state determination unit 24 is in the obstructive apnea state. Determine that there is. In other words, when the correlation value CP ′ of the cross-correlation peak is equal to or greater than the eighth threshold value, the central apnea state is determined. The eighth threshold here may be a value of 0.5 to 0.7 or less, preferably about 0.7. In the obstructive apnea state, as described above with reference to FIG. 8A, the chest measurement data and the abdominal measurement data tend not to have a sine waveform symmetric in the direction of the time axis, but to have an asymmetric shape close to a sawtooth waveform. There is. In the case of a sawtooth wave, for example, the waveform of the measurement data obtained by inverting the chest measurement data in the direction of the time axis for a certain time and the waveform of the chest measurement data before the inversion do not exactly overlap, so that the cross-correlation function C ′ (Τ) is not close to +1 even at the peak. Therefore, by determining whether or not the correlation value CP ′ of the cross-correlation peak is equal to or less than the eighth threshold, a state in which an asymmetric waveform close to a sawtooth waveform is generated, that is, an obstructive apnea state is determined. be able to.

  Note that the first state determination unit 24 is typically configured to determine the state of the person 2 for the period determined by the second state determination unit 25. That is, the first state determination unit 24 determines the state of the person 2 during the period determined by the second state determination unit 25 as the hypopnea event period (first period) or the apnea event period (second period). Is determined. Further, in the present embodiment, the first state determination unit 24 described above is configured such that the first state determination unit includes the complementarity between the chest measurement data and the abdomen measurement data, and the chest measurement data or the abdomen measurement data. The phase difference between the first evaluation value according to the symmetry of the time axis direction, the second measurement value regarding the similarity between the chest measurement data and the chest measurement data, the chest measurement data, and the chest measurement data 4th regarding the similarity with respect to the time gap of at least 1 measurement data chosen from the group which consists of the corresponding 3rd evaluation value, chest measurement data, abdominal measurement data, and the whole measurement data corresponding to the substantially whole of the above-mentioned object. Selected from the group consisting of an evaluation value and a fifth evaluation value indicating symmetry with respect to time of at least one measurement data selected from the group consisting of chest measurement data, abdominal measurement data, and overall measurement data Based on the evaluation value of the number, it makes a decision as to the state of the person 2.

  Specifically, the first state determination unit 24 first determines the state of the person 2 for the period determined by the second state determination unit 25 as the apnea event period (second period). Since each evaluation value is not always constant during the apnea event period (second period) and changes, the minimum value or maximum value of the period may be used as appropriate. In this case, both the minimum value and the maximum value can be used for the determination. Naturally, the threshold values in this case need to be provided individually corresponding to each. The same applies to a hypopnea event period (first period) described later. In mixed apnea, which is a variant of obstructive apnea, typically starts with central apnea and transitions to obstructive apnea within the same event, depending on the evaluation value, It is recommended to use values corresponding to the first half and the second half. In that case, if both the first half and the second half meet the central type condition, it is determined to be the central type. it can.

Therefore, in the present embodiment, the first state determination unit 24 uses the cross correlation function bias PB _Min that is the minimum value in the event period as the first evaluation value, and 6 seconds after the start of the event period as the second evaluation value. within (i.e., the first half) the minimum value at which the phase shift index _{PI MinFront} and 6 seconds later (i.e. the second half) the minimum value at which the phase shift index _{PI MinBack} of event period after the start as a third evaluation value corresponding to the phase difference 6 The phase difference P _MaxFront that is the maximum value within 2 seconds (that is, the first half) and the phase shift phase difference P _MaxBack that is the maximum value after 6 seconds (that is, the second half), and the autocorrelation function A ( a difference AG peak and bottom tau), the difference between the peak and the bottom is the minimum value within 6 seconds after the start of the event period (i.e. half) AG _{C EstMinFront,} 6 seconds later (i.e. the second half) the minimum value in the form of the peak and bottom difference _{AG ChestMinBack,} and, a difference AG peak and bottom of the abdomen measurement data autocorrelation function A (tau), after the start event period minimum value in the form of the peak and bottom difference _{AG AdMinFront,} 6 seconds later (i.e. the second half) the minimum value in the form of the peak and bottom difference _{AG AdMinBack} of within 6 seconds (i.e. half), the event period as an evaluation value of the fifth The correlation value CP ′ _Min of the cross-correlation peak of the cross-correlation function C ′ (τ), which is the minimum value of. Each threshold mentioned above uses the value corresponding to these evaluation values.

In the first state determination unit 24, the cross correlation function bias PB _Min as the first evaluation value is equal to or greater than the sixth threshold, and the phase shift indices PI _MinFront and PI _MinBack as the second evaluation values are the first threshold. As described above, the absolute values of the phase differences P _MaxFront and P _MaxBack as the third evaluation values are equal to or smaller than the second threshold, and the difference between the peak and bottom of the autocorrelation function A (τ) as the fourth evaluation value AG _{ChestMinFront} and AG _ChestMinBack , AG _AdMinFront and AG _AdMinBack are equal to or less than the seventh threshold value, and the correlation value CP ′ _Min of the cross-correlation peak of the cross-correlation function C ′ (τ) as the fifth evaluation value is equal to or greater than the eighth threshold value. The state of 2 is determined as a central apnea state. As described above, the cross correlation function bias PB _Min is equal to or larger than the sixth threshold value, and the correlation value CP ′ _Min of the cross correlation peak is equal to or larger than the eighth threshold value. This means that it is not asymmetric in the direction of the axis. Similarly, the phase shift indexes PI _MinFront and PI _MinBack are _equal to or greater than the first threshold, and the absolute values of the phase difference P _MaxFront and the phase difference P _MaxBack calculated by the phase difference calculation unit 21 are equal to or smaller than the second threshold. This means that there is little phase shift between the chest data and the abdomen data in both the first half and the second half of the event period. Difference between peak and bottom of autocorrelation function A (τ) AG _{ChestMinFront} , AG _ChestMinBack , AG _AdMinFront , AG _AdMinBack is less than or equal to the seventh threshold value. It means not.

In the first half of the event period, the first state determination unit 24 has the same condition as that of central apnea, that is, the cross-correlation function bias PB _Min as the first evaluation value is greater than or equal to the sixth threshold value, and the second evaluation value Of the phase difference index PI _MinFront as the first threshold value, the absolute value of the phase difference P _MaxFront as the third evaluation value is equal to or less than the second threshold value, and the autocorrelation function A (τ) as the fourth evaluation value Difference between peak and bottom AG _{ChestMinFront} , AG _AdMinFront is equal to or smaller than the seventh threshold, and the correlation value CP ′ _Min of the cross-correlation peak of the cross-correlation function C ′ (τ) as the fifth evaluation value is equal to or _larger than the eighth threshold. , in the second half, the phase shift index _{PI MinBack} as a second evaluation value is less than the first threshold value, or the absolute value of the phase difference _{P MaxBack} as a third evaluation value is a second threshold On, or when either or both of the peak and bottom difference _{AG ChestMinBack} and _{AG AdMinBack} of the autocorrelation function A of the fourth evaluation value (tau) is the seventh threshold value or more, the state of the person 2 Is determined to be in a mixed apnea state. In the apnea event period (second period), the first state determination unit 24 determines that the state of the person 2 is obstructive apnea when neither the central apnea state nor the mixed apnea state is applied. It is determined that the state is present.

Further, the first state determination unit 24 also determines the period determined as the hypopnea event period (first period) by the second state determination unit 25 in the same manner as the apnea event period (second period). The state of the person 2 is determined. However, in the determination of the hypopnea event period (first period), if it does not apply to either the central apnea state or the mixed apnea state, the hypopnea event period (first period) The phase difference index PI _MinFront and PI _MinBack as the second evaluation values are equal to or less than the first threshold value or the phase difference P calculated by the phase difference calculation unit 21 as the third evaluation value in a period of a certain period of _Only when the absolute values of _MaxFront and _PMaxBack are equal to or greater than the second threshold value, the state of the person 2 is determined to be an obstructive apnea state, and otherwise, it is determined to be a mere hypopnea state. Here, the period of a certain period or more is, for example, a period of 50% to 100%, preferably 75% or more of the hypopnea event period.

It should be _noted that the phase shift index PI _MinFront and PI _MinBack as the second evaluation values are equal to or lower than the first threshold value or the third evaluation value in a period of a certain period or more in the period for determining the state of the person 2 When the absolute values of the phase differences P _MaxFront and P _MaxBack calculated by the phase difference calculation unit 21 are equal to or _larger than the second threshold, the chest measurement data and the abdominal measurement data are clearly _{displayed in} the target period. This means that the phase is in reverse phase, that is, the odd-heterogeneous movement is occurring, so that the magnitude of the respiratory movement and the above-mentioned are also observed in the above-mentioned apnea event period (second period) and other periods. What is necessary is just to discriminate | determine that the state of the person 2 is an obstructive apnea state irrespective of the other conditions.

  According to the respiratory monitor 1 according to the present embodiment, based on the measurement data indicating the state of the periodic movement of the object 2 according to the chest region 2a, the abdominal region 2b, and the entire region as described above, the object By discriminating the two states, for example, it is possible to perform accurate state analysis with respect to complex respiratory motions as a whole, such as correct evaluation, detailed state analysis, and improvement in analysis accuracy. For example, in the determination based only on the entire measurement data corresponding to the entire area of the person 2, a more detailed determination of the apnea event period (second period) that is difficult to determine, that is, the obstructive apnea state, the central It is possible to distinguish between a type apnea state and a mixed type apnea state.

  The more detailed type of the apnea state determined by the respiration monitor 1 is, for example, the number of times that an obstructive apnea state, a central apnea state, a mixed apnea state occurred in the overnight measurement test, and the average duration By recording the time, the longest duration, etc., it can be used as an item necessary for diagnosis of sleep apnea syndrome (SAS) by a doctor or the like.

  In addition, as described above, the second state determination unit that determines the hypopnea event period (first period) and the apnea event period (second period) based on the magnitude of the periodic movement. 25, the first state discriminating unit 24 uses the chest measurement data, the abdomen measurement data, the cross-correlation functions C (τ) and C ′ (τ) of the whole measurement data, the autocorrelation function A (τ), and the chest measurement data. The state of the person 2 during the event period is determined by using various evaluation values based on the phase difference between the mouse and the abdomen measurement data, etc., so that a state that cannot be determined only by the magnitude of the periodic movement is comprehensive Can be reliably detected and discriminated.

The first state determination unit 24 can determine the state of the person 2 more accurately by performing weighted determination using different types of evaluation values. That is, based on the bias PB of the cross-correlation function C (τ) as the first evaluation value, the complementarity between the chest measurement data and the abdomen measurement data and the symmetry in the direction of the time axis can be evaluated. Therefore, the obstructive apnea state can be detected and determined. Based on the phase shift index PI as the second evaluation value and the phase difference P calculated by the phase difference calculation unit 21 as the third evaluation value, the phase shift between the chest data and the abdomen data can be evaluated, Therefore, the obstructive apnea state can be detected and determined. Based on the difference AG between the peak and bottom of the autocorrelation function A (τ) as the fourth evaluation value, the presence or absence of a clear respiratory motion can be evaluated. Correlation value CP ′ of the cross-correlation peak of the cross-correlation function C ′ (τ) as the fifth evaluation value as the fifth evaluation value Based on this, it is possible to evaluate the symmetry of the measurement data with respect to time, and therefore it is possible to detect and discriminate an obstructive apnea state.

  In the above description, the first state determination unit 24 performs weighting based on the first evaluation value, the second evaluation value, the third evaluation value, the fourth evaluation value, and the fifth evaluation value. However, it is not always necessary to use all these parameters. For example, the determination may be made using only the first evaluation value. In this case, the amount of calculation by the first state determination unit 24 can be reduced, which is preferable when it is desired to obtain a result simply and quickly. Further, for example, since both the first evaluation value and the fifth evaluation value include a property relating to symmetry in the direction of the time axis of the measurement data, either one may be selected and used. . In addition, for example, since both the second evaluation value and the third evaluation value include a property relating to the phase shift between the chest measurement data and the abdomen measurement data, either one may be selected and used. Good.

  In the above description, the first state determination unit 24 and the second state determination unit 25 have been described as being configured separately, but may be configured integrally. A state determining unit for determining a state based on the first evaluation value; a state determining unit for determining a state based on the second evaluation value; a state determining unit for determining a state based on the third evaluation value; The state determination unit that determines the state based on the fourth evaluation value and the state determination unit that determines the state based on the fifth evaluation value may be configured separately.

  Further, in the above embodiment, the case has been described where measurement data for a certain period of each region is acquired and analyzed together. However, even if measurement data is acquired and analyzed in real time along with the state transition of the person 2 good. Also in this case, it is possible to perform more accurate analysis of the state, such as a detailed analysis of the state and an improvement in the accuracy of the analysis as compared with the conventional case. When acquiring and analyzing measurement data in real time, for example, the first predetermined period for calculating the threshold is typically a predetermined period before the determination target time, but after the determination target time. There is no problem even if the predetermined period is used. For example, the state analysis may be performed after a lapse of several seconds from the determination target time, that is, there is no problem even if there is a slight time lag between the acquisition of measurement data and the state analysis as long as there is no problem in the state analysis.

  FIG. 10 is a schematic perspective view of the FG sensor 10 of the respiratory monitor 1 according to the embodiment of the present invention. Here, with reference to this figure, the FG sensor 10 which is a measuring apparatus suitable for the respiration monitor 1 is demonstrated. The FG sensor 10 includes a projection device 11 that projects a predetermined illumination pattern 11a, in other words, a plurality of bright spots 11b, on the bed 3 as a target area, and light projected by the projection apparatus 11, that is, a plurality of bright spots 11b. Is configured to include an imaging device 12 that images the bed 3 on which the image is projected, and a measurement unit 14 that generates measurement data indicating the state of the person 2 based on an image captured by the imaging device 12. In the present embodiment, the measurement unit 14 is described as being configured integrally with the arithmetic device 20.

  FIG. 11 is a block diagram showing a schematic configuration of the FG sensor 10 of the respiratory monitor 1 according to the embodiment of the present invention. The measurement unit 14 includes a movement amount calculation unit 141 serving as a movement amount calculation unit that calculates a movement amount between the two frames of a plurality of bright spots from two frames of images acquired at two different times by the imaging device 12; A movement amount waveform generation unit 142 is provided as movement amount waveform generation means for generating movement amount waveform data in which movement amounts calculated by the movement amount calculation unit 141 are arranged in time series.

  Here, the measurement of the movement of the bright spot based on the images at two different time points will be described. The images at two different time points may be an arbitrary time point and a slightly previous time point. “Slightly before” only needs to be a time interval sufficient to detect the movement of the person 2. In this case, when a slight movement of the person 2 is desired to be detected, the time is short, for example, the movement of the person 2 does not become too large, and the time can be regarded as substantially no movement, for example, about 0.1 seconds. . Or it is good to set it as the 1-10 period (1 / 30-1 / 3) of a television period. Further, when it is desired to detect a rough movement of the person 2, it may be long, for example, about 10 seconds. However, in the case of detecting the respiration of the person 2 as in the present embodiment, if it is too long, accurate detection of respiration cannot be performed. Hereinafter, an image acquired at an arbitrary time (current) is described as an acquired image, and an image acquired slightly before (past) the acquired image is described as a reference image. The reference image is stored in a storage unit (not shown).

  Further, in the present embodiment, the images at two different time points are an acquired image (N frame) and an image acquired immediately before the acquired image (N-1 frame). That is, the reference image is an image acquired immediately before the acquired image. The image acquisition interval may be appropriately determined depending on, for example, the processing speed of the apparatus and the content of the motion to be detected as described above. For example, it is 0.1 to 3 seconds, preferably about 0.1 to 0.5 seconds. It is good to do. Here, it is set to 0.1 to 0.25 seconds. In addition, it is effective to acquire images at shorter time intervals and perform averaging or filtering to reduce the influence of random noise, for example. The measuring unit 14 will be described in detail later.

  Returning to FIG. 10, an installation example of the FG sensor 10 will be described. On the bed 3 in the figure, the person 2 lies in a sleeping state. Here, the bedding 4 is further hung on the person 2 and covers a part of the person 2 and a part of the bed 3. In this case, the FG sensor 10 measures the amount related to the movement of the upper surface of the bedding 4 in the height direction within a certain time. When the bedding 4 is not used, the FG sensor 10 measures an amount related to the movement of the person 2 in the height direction within a certain time. Note that the movement of the person 2 in the height direction is, for example, movement accompanying the breathing or body movement of the person 2.

  The projection device 11 and the imaging device 12 constituting the FG sensor 10 are arranged above the bed 3 that is the target region in the vertical direction. The measurement range is set to a range including the chest and abdomen of the person 2. In the drawing, the projection device 11 is disposed approximately above the head of the person 2, and the imaging device 12 is disposed approximately at the center of the bed 3 or slightly above the head from the center. The projection device 11 projects a pattern 11 a as an illumination pattern on the bed 3. The pattern 11a is a plurality of bright spot lights. In addition, the angle of view of the imaging device 12 is set so that an approximately central portion of the bed 3 including a portion corresponding to the upper half of the person 2 can be captured. The imaging device 12 is installed so as to look down on the bed 3 substantially vertically, but may be installed with a certain degree of inclination. Here, each position of the plurality of bright spots 11b projected on the person 2 that can be imaged by the imaging device 12 corresponds to each measurement point.

  The projection device 11 and the imaging device 12 may be installed with a certain distance. By doing so, the distance d (baseline length d, see FIG. 13) becomes longer, so that the change can be detected sensitively (detection sensitivity is improved). The base line length is preferably long, but may be short. However, in this case, it is difficult to detect small movements such as respiration, but small movements (breathing) can be detected by detecting the barycentric position of the bright spot as described later.

  Here, the baseline length d (see FIG. 13) will be described. Here, the FG sensor 10 measures the movement of a bright spot forming a pattern, as will be described later with reference to FIG. At this time, for example, the greater the movement of the object (here, the person 2) in the height or height direction, the greater the movement amount of the bright spot. For this reason, according to the concept described later with reference to FIG. 13, if the amount of movement of the bright spot is large, a phenomenon may occur in which the bright spot adjacent to the bright spot to be compared is skipped. In this case, it is determined that the light has moved from the adjacent bright spot, and the amount of movement of the bright spot to be measured may be small. That is, the movement amount of the bright spot cannot be measured accurately. When the base line length is short, the amount of movement of the bright spot is small and the above jump is unlikely to occur, but it is difficult to distinguish it from noise for minute movements. Further, when the base line length d (see FIG. 13) is long, for example, even a slight movement of the object is greatly reflected in the movement amount of the bright spot. Can be measured, but jumping may occur, for example, when there is a large movement.

  FIG. 12 is a schematic perspective view for explaining the projection device 11 of the respiratory monitor 1 according to the embodiment of the present invention. With reference to this figure, the projection apparatus 11 suitable for the respiration monitor 1 is demonstrated. Here, for the sake of explanation, a case will be described where the target region is the plane 102 and a laser beam L1 described later is projected perpendicularly to the plane 102. The projection apparatus 11 includes a light beam generation unit 105 serving as a light beam generation unit that generates a coherent light beam, and a fiber grating 120 (hereinafter simply referred to as a grating 120). The coherent light beam projected by the light beam generation unit 105 is typically an infrared laser. The light beam generation unit 105 is configured to generate a parallel light beam. The light flux generation unit 105 is typically a semiconductor laser device including a collimator lens (not shown), and the generated parallel light flux is a laser light flux L1. The laser light beam L1 is a light beam having a substantially circular cross section. Here, the parallel light flux only needs to be substantially parallel, and includes a nearly parallel light flux. The substantially circular shape includes a substantially elliptical shape.

  Further, here, the grating 120 is disposed in parallel to the plane 102 (perpendicular to the Z axis). Laser light L1 is incident on the grating 120 in the Z-axis direction. Then, the laser light L1 is collected in a plane having the lens effect by each optical fiber 121, then spreads as a diverging wave, interferes, and a plurality of bright spots on the plane 102 which is a light projecting surface. The pattern 11a which is an array is projected. Note that the arrangement of the grating 120 in parallel with the plane 102 means, for example, that the plane including the axis of each optical fiber 121 of the FG element 122 constituting the grating 120 and the plane 102 are parallel.

  The grating 120 includes two FG elements 122. In the present embodiment, the planes of the FG elements 122 are parallel to each other. Hereinafter, the plane of each FG element 122 is referred to as an element plane. In the present embodiment, the axes of the optical fibers 121 of the two FG elements 122 are substantially orthogonal to each other.

  The FG element 122 is configured by arranging, for example, several tens to several hundreds of optical fibers 121 having a diameter of several tens of microns and a length of about 10 mm in parallel in a sheet shape. Further, the two FG elements 122 may be arranged in contact with each other, or may be arranged at a distance from each other in the normal direction of the element plane. In this case, the distance between the two FG elements 122 is set so as not to interfere with the projection of the pattern 11a. The laser beam L1 is typically incident perpendicular to the element plane of the grating 122.

  Thus, since the grating 120 configured to include the two FG elements 122 serves as an optical system, the optical housing can be downsized without requiring a complicated optical system. Furthermore, by using the grating 120, the projection device 11 can project a plurality of bright spots 11b as patterns 11a onto the target area with a simple configuration. The pattern 11a is typically a plurality of bright spots 11b arranged in a square lattice pattern. The bright spot has a substantially circular shape including an ellipse.

  FIG. 13 is a conceptual perspective view for explaining the concept of the bright spot movement of the respiratory monitor 1 according to the embodiment of the present invention. The imaging device 12 will be described with reference to this figure. The imaging device 12 has an imaging optical system 12 a and an imaging element 15. The image sensor 15 is typically a CCD image sensor. In addition to the CCD, an element having a CMOS structure has recently been actively announced as the image pickup element 15, and these can naturally be used. In particular, some of the elements themselves have inter-frame difference calculation and binarization functions, and it is preferable to use these elements.

  In addition, the imaging device 12 may include a filter 12b that attenuates light having a wavelength other than the peripheral portion of the wavelength of the laser light beam L1 generated by the light beam generation unit 105 (see FIG. 12). The filter 12b is typically an optical filter such as an interference filter, and may be disposed on the optical axis of the imaging optical system 12a. In this way, the imaging device 12 can reduce the influence of disturbance light because the light intensity of the pattern 11a projected from the projection device 11 out of the light received by the imaging device 15 is relatively increased. Further, the laser beam L1 (see FIG. 12) generated by the beam generation unit 105 (see FIG. 12) is typically an infrared laser beam. Further, the laser beam L1 (see FIG. 12) may be irradiated continuously or may be irradiated intermittently. When irradiating intermittently, imaging by the imaging device 12 is performed in synchronization with the timing of irradiation.

  Here, the concept of bright spot movement will be described. Here, it is easy to understand, and the target area will be described as the plane 102 and the target object as the object 103. Further, here, for description, the reference image is an image of the pattern 11a when the object 103 is not present on the plane 102, and the acquired image is described as the pattern 11a when the object 103 is present on the plane 102. To do.

  In the figure, an object 103 is placed on the plane 102. Further, the orthogonal coordinate system XYZ is taken so that the XY axis is placed in the plane 102, and the object 103 is placed in the first quadrant of the XY coordinate system. On the other hand, a projection device 11 and an imaging device 12 are arranged above the plane 102 on the Z axis in the drawing. The imaging device 12 images the plane 102 on which the pattern 11 a is projected by the projection device 11. In other words, the object 103 placed on the plane 102 is imaged.

  Here, the imaging lens 12a as the imaging optical system of the imaging device 12 is disposed so that its optical axis coincides with the Z-axis. The imaging lens 12 a forms an image of the pattern 11 a on the plane 102 or the object 103 on the imaging surface 15 ′ (image plane) of the imaging device 15 of the imaging device 12. The image plane 15 'is typically a plane orthogonal to the Z axis. Further, an xy orthogonal coordinate system is taken in the image plane 15 'so that the Z axis passes through the origin of the xy coordinate system. The projection device 11 is arranged at a distance equal to the imaging lens 12a from the plane 102 and a distance d (baseline length d) from the imaging lens 12a in the negative direction of the Y axis. A pattern 11 a formed by a plurality of bright spots 11 b is projected onto the object 103 and the plane 102 by the projection device 11.

  The pattern 11 a projected onto the plane 102 by the projection device 11 is blocked by the object 103 and does not reach the plane 102 in a portion where the object 103 exists. Here, if the object 103 exists, the bright spot 11 b to be projected onto the point 102 a on the plane 102 is projected onto the point 103 a on the object 103. Since the bright spot 11b has moved from the point 102a to the point 103a and the imaging lens 12a and the projection device 11 are separated by a distance d (baseline length d), the point 102a is formed on the imaging plane 15 ′. An image to be imaged at '(x, y) is imaged at a point 103a' (x, y + δ). That is, when the object 103 does not exist and when the object 103 exists, the image of the bright spot 11b moves by a distance δ in the y-axis direction.

  For example, as shown in FIG. 14, the bright spot imaged on the imaging surface 15 ′ of the image sensor 15 is moved in the y-axis direction by δ by the object 103 having a height.

  Thus, by calculating the moving amount δ of the bright spot, the position of the point 103a on the object 103 can be specified three-dimensionally. That is, for example, the height of the point 103a is known. Thus, by calculating the difference between a point that should be imaged on the imaging plane 15 ′ if the object 103 is not present and the actual imaging position on the imaging plane 15 ′, The height distribution of the object 103, in other words, the three-dimensional shape can be measured. Alternatively, the three-dimensional coordinates of the object 103 can be measured. Further, if the pitch of the pattern 11a, that is, the pitch of the bright spot 11b is made fine enough that the correspondence relationship of the bright spot 11b is not unknown, the height distribution of the object 103 can be measured in detail.

  Based on the above concept, the height of the object can be measured by calculating the movement amount of the bright spot. However, here, since the movement in the height direction is measured based on the acquired image and the image acquired immediately before the acquired image, that is, the reference image, the movement amount of the bright spot is observed. For this reason, for example, the absolute height of the person 2 cannot be measured, but there is no problem because the purpose is to detect the movement of the person 2 in the height direction.

  Returning to FIG. 11 again, the measurement unit 14 will be described in detail. The movement amount calculation unit 141 calculates the movement amount of the bright spot as described with reference to FIG. The movement amount calculation unit 141 is configured to calculate the movement amount of the bright spot as described above for each of the plurality of bright spots forming the pattern 11a. That is, the positions of a plurality of bright spots are measurement points. The movement amount calculation unit 141 outputs the movement amount of the bright spot calculated for each of the plurality of bright spots forming the pattern 11 a to the movement amount waveform generation unit 142. That is, the calculated movement amount of each bright spot becomes a measurement value at each measurement point. In other words, here, each measured value obtained by measuring the movement of the person 2 at a plurality of points corresponds to the amount of movement of each bright spot.

  It should be noted that a waveform (for example, a sum of bright spot movement amounts) obtained by measuring bright spot movement based on an image at an arbitrary time point and two time points slightly different from the previous time point is a differential waveform of distance, The waveform represents the speed change. For example, when it is desired to obtain a waveform representing a change in height, if the waveform is integrated, a waveform of distance, that is, a waveform indicating a change in height is obtained.

  Here, the acquired image and the reference image are images picked up by the image pickup device 12, for example, and are concepts including the position information of the bright spot on each image. In other words, the acquired image and the reference image are images of the pattern 11a formed by the projection of the projection device 11 at each time point. In the present embodiment, the reference image is not stored as a so-called image, for example, but is stored in a storage unit (not shown) in the form of positional information such as coordinates regarding the position of each bright spot. In this way, when calculating the amount of movement of the bright spot, which will be described later, for example, it is only necessary to compare the coordinates and direction of the bright spot, so the processing becomes simple. Further, here, the position of the bright spot is the barycentric position of the bright spot. By doing so, a slight movement of the bright spot can be measured.

  Further, the movement amount of the bright spot can be calculated by comparing the position information of each bright spot on the reference image with the position information of each bright spot on the acquired image. Each amount of movement can be obtained, for example, by counting the number of pixels to which the position of the bright spot has moved (how many pixels have moved). However, if the position of the bright spot is obtained as the position of the center of gravity, the movement amount can be calculated in units smaller than one pixel. The calculated moving amount of the bright spot is a concept including the moving direction of the bright spot. In other words, the measured moving amount of the bright spot includes information on the moving direction. In this way, as will be described later, it is not necessary to generate a difference image, so that the processing can be simplified.

  In the above description, the position information of the bright spots is compared. However, a difference image between the reference image and the acquired image may be created. In this case, the movement amount of the bright spot is calculated from the difference image based on the position of the corresponding bright spot. In this way, since only the moved bright spot remains on the difference image, the processing amount can be reduced.

  The movement amount waveform generation unit 142 generates movement amount waveform data in which the movement amounts of the bright spots calculated by the movement amount calculation unit 141 are arranged in time series. Here, as described above, the movement amount of the bright spot calculated by the movement amount calculation unit 141 includes the acquired image (N frame) and the image acquired immediately before the acquired image (N-1 frame). Are calculated on the basis of images of two frames acquired at different points in time. In other words, the calculation is made based on images of two time points, which are different from an arbitrary time point and a slightly previous time point. For this reason, the generated movement amount waveform data (for example, when the total movement amount of each bright spot is taken) is a volume fluctuation waveform per unit time or a rough fluctuation waveform of average height per unit time. That is, the waveform represents a change in volume fluctuation or a change in average height. For example, when it is desired to obtain a waveform representing the transition of the height, the waveform of the distance, that is, the waveform indicating the transition of the height is obtained by integrating the waveform.

  Here, when the total amount of movement is taken, the volume fluctuation amount per unit time is generally shown. Since the movement of each bright spot indicates individual height fluctuations, taking the summation results in volume fluctuations. The amount of movement of each bright spot is a change in the bright spot position per unit time at each bright spot position (ie, bright spot moving speed), and roughly corresponds to a height change in unit time.

  The movement amount waveform generation unit 142 generates movement amount waveform data for each of the chest region 2a (first region) and the abdominal region 2b (second region), and uses the generated movement amount waveform data as measurement data. 20 first state determination unit 24 (see FIG. 1), second state determination unit 25 (see FIG. 1), body motion detection unit 28 (see FIG. 1), threshold value calculation unit 29 (see FIG. 1), etc. Output. That is, the movement amount waveform generation unit 142 is a body that is movement data waveform data that is at least the total movement amount waveform data of the bright spot and the absolute value of the absolute movement amount of the bright spot, for example. Measurement data for each of the chest region 2a (first region) and abdominal region 2b (second region) including both waveform data and motion data is output.

  Note that the detection sensitivity and noise level of the measurement data acquired by the FG sensor may be affected by environmental conditions such as ambient light conditions and the posture and position of the person 2 on the bed 3. For example, in order to increase the baseline length d (see FIG. 13), the projection device 11 (see FIG. 10) is placed on the bed 3 (see FIG. 10). The projection of the pattern 11a (see FIG. 10) is performed obliquely when installed off the center of the screen, and the density of bright spots on the bed 3, in other words, the bright spot interval is As the distance increases, the detection sensitivity and noise level may differ between a position close to and far from the projection device 11.

  Returning to FIG. 1 again, the description will be continued. The representative coordinate calculation unit 26 is configured to calculate the representative coordinates of the position coordinate group of the measurement points at which the phases of the plurality of movements are substantially the same for the measurement points where the movement of the person 2 is measured. Here, as described above, the positions of a plurality of bright spots projected by the projection device 11 (see FIG. 10) are measurement points. The dividing line forming unit 27 is configured to form an area dividing line 2c that divides the target area between two or more representative coordinates when there are two or more position coordinate groups having different motion phases. The dividing line forming unit 27 typically divides the measurement range of the person 2 as an object existing in the target region into a chest region 2a (first region) and an abdominal region 2b (second region). A region dividing line 2c is formed so as to be divided. The target region is a region on the bed 3 and imaged by the above-described imaging device 12 (see FIG. 10).

  The movement waveform generation unit 142 (see FIG. 11) described above is an area divided by the area dividing line 2c formed by the dividing line forming part 27. In the present embodiment, the chest area 2a and the first area are defined as the first area. Measurement data (breathing data) of person 2 by integrating the data of the measurement point group (movement amount of each bright spot) calculated by the movement amount calculation unit 141 (see FIG. 11) for each abdominal region 2b as the second region. And body movement data). That is, chest measurement data and abdominal measurement data are generated and output. The total measurement data may be generated and output by integrating the measurement point group data (movement amount of each bright spot) in the entire area, but is simply the sum of the chest measurement data and the abdominal measurement data But you can.

  The movement amount waveform generation unit 142 (see FIG. 11) can generate and output measurement data for each chest region 2a (first region) and abdominal region 2b (second region) divided by the region dividing line 2c. Therefore, for example, even when the movement phases are different between the chest and the abdomen, each measurement data is output, so that the state of the person 2 can be accurately grasped. In the present embodiment described above, the measurement data generated and output by the movement amount waveform generation unit 142 (see FIG. 11) is collected for a certain period, for example, overnight (about 7 to 10 hours). Although described in the case of acquisition, it may be acquired in real time with the state transition of the person 2. That is, the movement amount waveform generation unit 142 (see FIG. 11) may be configured to output measurement data in real time. To output in real time means to immediately output, for example, the sum of the movement amount of the bright spot corresponding to the representative coordinates calculated for each image captured by the imaging device 12 and the region dividing line 2c. Furthermore, in this case, by outputting the sum in real time, the movement amount waveform generation unit 142 (see FIG. 11) outputs a respiration waveform pattern of the person 2 formed side by side in the time direction. Become.

  Here, among the plurality of measurement points measured by the FG sensor 10, the measurement points that do not move, that is, the measurement points that do not move the bright spot are ignored (excluded) and the above calculation (for example, representative) Coordinate calculation). That is, here, processing is performed using all measurement points that are in motion (measured).

  Here, the phase is a concept including the direction of motion, and the phase is substantially the same is a concept including simply matching the directions of motion. In other words, it is a concept including, for example, the direction of movement is approximately upward (upward) or downward (downward). Further, here, the identification of whether the phases are substantially the same is performed by identifying whether the movement measured at each measurement point by the measurement unit 14 is an upward movement or a downward movement. . That is, in the present embodiment, the phase of motion is two directions, upward and downward. Thus, since the phase of the movement is in two directions, up and down, in this case, hereinafter, the phase being substantially the same is simply referred to as the same phase. The representative coordinates are set in the captured image.

  Further, dividing the target area means, for example, dividing an existing area of a plurality of measurement points, and here, dividing an area captured by the imaging device 12 with the person 2 existing on the bed 3. Here, since the imaging device 12 mainly captures the bright spots projected on the chest and abdomen of the person 2 as the measurement range, the region dividing line 2c that divides the target region is the chest region 2a of the person 2 (the first region). ) And an abdominal region 2b (second region). The area dividing line 2c is typically a straight bisector of a straight line connecting representative coordinates of different phases or a straight line perpendicular to the longitudinal direction of the bed.

  Furthermore, integrating the data of the measurement point group means, for example, measurement points existing in the region divided by the region dividing line 2c, that is, the chest region 2a (first region) and the abdominal region 2b (second region). The sum of the bright spot movement amounts and the sum of the absolute values of the bright spot movement amounts are calculated, and the output motion waveform is a waveform pattern formed by arranging the sums in the time direction.

The representative coordinate calculation unit 26 uses the average value of the coordinates of each measurement point where the representative coordinates form a position coordinate group. Hereinafter, this average value is referred to as center coordinates. The center coordinates are expressed by the following formula (4).
Here, n is the number of measurement points forming the position coordinate group. yi is the coordinate value of each measurement point. When calculating in two dimensions, for example, the central coordinates are calculated with Xi as the coordinate value in the x-axis direction and Yi as the coordinate value in the y-axis direction.

Alternatively, the representative coordinate calculation unit 26 may use an average value of values obtained by weighting the coordinates of each measurement point forming the position coordinate group by the amount related to the amount of movement. Hereinafter, this average value is referred to as a barycentric coordinate. Note that the amount related to the amount of movement is typically the amount of movement of the bright spot at each measurement point. Furthermore, the weight is unsigned. That is, the barycentric coordinate is an average value of values obtained by weighting the coordinates of each measurement point with the moving amount of the bright spot. The barycentric coordinates are expressed by the following equation (5).
Here, ΔZi is a weight at each measurement point, that is, a moving amount of the bright spot. | ΔZi | is an unsigned weight at each measurement point, that is, an absolute value of the amount of movement of the bright spot.

  Here, with reference to FIG. 15, the calculation of the representative coordinates by the representative coordinate calculation unit 26 and the formation of the region dividing line 2 c by the dividing line forming unit 27 will be specifically described. In the figure, for the purpose of explanation, the captured image, the angle of view, the position of the measurement point within the angle of view (only the measurement point that has moved), and the person 2 (shown by a broken line in the figure) are shown. However, it is better to actually calculate only with the numerical value of coordinates. As shown in (a), the representative coordinate calculation unit 26 first calculates representative coordinates of a position coordinate group that is a set of measurement points having the same movement phase. The position coordinate group is, for example, a set of measurement points having the same movement phase, that is, the same moving direction of the bright spots. By doing so, the amount of calculation can be reduced. In the figure, the representative coordinates indicate the case where the average value of the values obtained by weighting the coordinates of each measurement point with the bright spot movement amount, that is, the barycentric coordinates is calculated.

  Here, the case where there are two or more position coordinate groups having different motion phases has been described, but there is no position coordinate group having different motion phases, that is, when a position coordinate group having the same phase is dominant. For example, the representative coordinate calculation unit 26 calculates the representative coordinates of the position coordinate group of all of the plurality of measurement points, and the dividing line forming unit 27 divides the target region in an appropriate direction through the calculated representative coordinates. A straight line to be formed may be formed and used as the region dividing line 2c. The appropriate direction is typically a direction perpendicular to the baseline direction of the FG sensor 10 (see FIG. 9). This is based on the assumption that the spine direction of the person 2 on the bed 3 is substantially parallel to the center line of the bed 3 because the base line direction is parallel to the center line of the bed 3. That is, the spine direction of the person 2 can be viewed as being substantially parallel to the baseline direction. In other words, the appropriate direction is a direction perpendicular to the spine direction of the person 2. That is, in this case, since the region dividing line 2c is perpendicular to the spine direction, for example, the chest and abdomen of the person 2 can be accurately divided, which is preferable. Here, the region dividing line 2c is typically a straight line that is perpendicular to the base line direction and passes through the representative coordinates. For example, when the center line of the bed 3 (the longitudinal direction of the bed), that is, the spine direction of the person 2 is arranged substantially orthogonal to the base line direction, the region dividing line 2c is a direction parallel to the base line direction.

  As shown in the example (a), when there are two position coordinate groups having different movement phases, the dividing line forming unit 27 generates a vertical bisector of a straight line connecting the two representative coordinates. The vertical bisector is formed as a region dividing line 2c. Since the figure shows a case where representative coordinates having different movement phases are calculated between the chest and abdomen of the person 2, the region dividing line 2 c is formed between the abdomen and the chest of the person 2. It can also be said that the straight line connecting the representative coordinates of different phases is substantially parallel to the spine direction.

  As shown in (b), the formed region dividing line 2c is a straight bisector of a straight line connecting representative coordinates of different phases, and thus is not necessarily parallel to the base line direction (even if it is oblique). Good). That is, there is no problem even if the spine direction of the person 2 sleeping on the bed 3 is oblique to the baseline direction.

  Further, the dividing line forming unit 27 calculates a value obtained by weighting the representative coordinates calculated by the representative coordinate calculating unit 26 with an amount related to the representative coordinates, and there are at least two calculated representative coordinates, and there are two representative coordinates. The area dividing line 2c may be formed so as to intersect the straight line connecting the two representative coordinates at a point where the straight line connecting the coordinates is internally divided by a value weighted by the amount related to the representative coordinates. The amount related to the representative coordinate is typically the total amount of movement at each measurement point that forms the position coordinate group used for calculating the representative coordinate, that is, the total amount of movement of the bright spot, but the measurement that forms the position coordinate group. There may be a number of points or an average value. Further, it may be a center value (median) or a peak value (maximum value of the amount of movement in the position coordinate group) at each measurement point forming the position coordinate group. Hereinafter, the amount relating to the representative coordinates will be described in the case where the amount of movement of the bright spot at each measurement point forming the position coordinate group is the sum. The center value and the peak value can also be employed as quantities related to the representative coordinates and the amount of movement other than the above.

  For example, as shown in FIG. 16, a point that is internally divided by the value weighted by the total amount of movement of the bright spots is a ratio between the weights with signs corresponding to the representative coordinates. (Point A in the figure). In other words, the point to be internally divided is a weighted coordinate corresponding to each representative coordinate which is a coordinate when the amount of weight is indicated in the vertical direction with respect to a straight line connecting the two representative coordinates at the position of each representative coordinate. The point A is an intersection of a straight line connecting G1 and G2 and a straight line connecting two representative coordinates. That is, the dividing line forming unit 27 sets a straight line passing through the point A and perpendicular to the straight line connecting the two representative coordinates as the area dividing line 2c. The region dividing line 2c may be a straight line that passes through the point A and is perpendicular to the base line direction. In this way, the area dividing line 2c reflecting the amount of movement of the person 2 can be formed.

  When the position of the area dividing line 2c is extremely different from the center coordinates of all the measurement points (with movement) ignoring, for example, ascending and descending, the center coordinates of all the measuring points are set to the area dividing line 2c. You may employ | adopt as a passing position. In addition, when the respective weights in the regions having different phases are extremely different, the position through which the region dividing line 2c passes the representative coordinate (center coordinate or barycentric coordinate) with the larger weight or the representative coordinates of all the measurement points. It is good to do. This is because it can be seen that all measurement points are moving in phase. When one representative coordinate is close to the end of the target region, the representative coordinate that is not the end of the target region, or the representative coordinates of all the measurement points may be set as the position where the region dividing line 2c passes.

  Further, the representative coordinate calculation unit 26 calculates the representative coordinates using only the coordinates in the one-dimensional direction of the position coordinates of the plurality of measurement points, and the dividing line forming unit 27 calculates the area dividing line 2c perpendicular to the one-dimensional direction. You may make it form. The one-dimensional direction is, for example, the baseline direction of the FG sensor 10 or the spine direction of the person 2, in other words, the direction perpendicular to the straight line connecting the center of the chest and the center of the abdomen, or the line connecting the centers of the left and right lungs. For example, when the spine direction and the base line direction of the person 2 are approximately the same as in the present embodiment (see FIG. 10), the one-dimensional direction is the base line direction. The representative coordinate calculation unit 26 calculates the representative coordinates using only the base line direction, that is, the y-axis coordinates (see FIG. 14). In this case, the dividing line forming unit 27 forms the area dividing line 2c perpendicular to the base line direction. In this way, the amount of computation by the computing device 20 can be reduced, so that the processing speed can be increased.

  The dividing line forming unit 27 may perform the formation of the area dividing line 2c based on the position of the area dividing line 2c formed in the past. Specifically, when the region dividing line 2c is formed, an average value of the positions of the current and the past several times of the region dividing line 2c may be adopted. If the area dividing line 2c is determined for each movement measurement by the FG sensor 10, the area dividing line 2c may fluctuate due to, for example, noise (not necessarily hand movement or the like). Therefore, by determining the position of the area dividing line 2c based on the average value of the positions of the past area dividing lines 2c, the influence of noise and the like can be removed. That is, in this case, the position of the area dividing line 2c is stabilized, and abrupt movement of the area dividing line 2c due to, for example, noise or movement of the hand of the person 2 can be prevented. For this reason, for example, the output of measurement data for each region by the movement amount waveform generation unit 142 (see FIG. 10), in other words, the chest region 2a (first region) and the abdominal region 2b (second region) of the person 2 is output. It can be done stably.

  FIG. 17 is a flowchart showing an outline of processing steps by the respiratory monitor 1 according to the embodiment of the present invention. With reference to this figure, the analysis method of the state of the person 2 by the respiration monitor 1 is demonstrated. For the configuration of the respiratory monitor 1, refer to FIG. 1 or FIG. 11 as appropriate.

  First, as a measurement process, for each of a plurality of bright spots 11b (see FIG. 10) projected on the bed 3 by the movement amount calculation unit 141, a plurality of images are obtained from two frames of images acquired at different times by the imaging device 12. The amount of bright spot movement between the two frames of the bright spot 11b is calculated. The movement amount waveform generation unit 142 generates measurement data as movement amount waveform data in which the bright spot movement amounts calculated by the movement amount calculation unit 141 are arranged in time series (S1). Specifically, the movement amount waveform generation unit 142 generates the respiration data that is the movement amount waveform data of the sum of the bright spot movement amounts for each chest region 2a (first region) and abdominal region 2b (second region). Generate. The body movement data, which is the movement amount waveform data of the sum of the absolute values of the movement amounts of the bright spots, only needs to be detected. Needless to say. Data generation is performed for a certain period, for example, overnight (about 7 to 10 hours).

  Next, as the body movement period detection step, measurement data (for example, body movement data) within a second predetermined period (here, about 1 to 2 minutes) before and after the determination target time point, typically measurement data (for example, A body motion reference value is calculated based on the distribution state of the body motion data), the body motion of the person 2 is determined, and a body motion period in which data indicating the body motion of the person 2 exists from the measurement data, Determination and detection are performed in the entire processing range of the acquired measurement data (all determination target points in the overnight measurement data) (S3).

  Next, as a threshold value calculation step, a first predetermined period (here, about 2 minutes) excluding a body movement period that is a period in which body movement is detected by the body movement detection unit 28 among periods before and after the determination target time point. Based on the measurement data, the respiratory reference value is calculated for each of the chest region 2a (first region), the abdominal region 2b (second region), and the entire region, and the person 2 at the determination target time point is further calculated. The chest threshold value, the abdominal threshold value, the first overall threshold value, and the second overall threshold value that are used to determine the state are calculated (S5). Here, the threshold value calculation unit 29 calculates each threshold value based on the measurement data of the first predetermined period excluding the body movement period.

Next, as the event period determination step, as described above, the second state determination unit 25 performs the comparison between the magnitude of the respiratory movement at the determination target time point of the chest measurement data and the chest threshold value, or the abdominal measurement data. Based on the comparison result between the magnitude of the respiratory movement at the determination target time point and the abdominal threshold, or the comparison result between the magnitude of the respiratory movement at the determination target time point in the overall measurement data and the total threshold value, the magnitude of the respiratory movement is determined. Decreased hypopnea event period (first period), or apnea event period (second period) in which the magnitude of respiratory movement is further reduced than the hypopnea event period (first period) ) Is determined (S7).

  When the determination is completed, the determination target time point is incremented, for example, the determination target time point is moved to a time point adjacent to the rear side (S9). Here, if the new attention time point is within the processing range by this movement, for example, within a certain period (here, overnight, about 7 to 10 hours) (S11; No), the process returns to the threshold value calculation step (S5) again. Repeat the above. In addition, when the new determination target time point is outside a certain period (here, about 7 to 10 hours, overnight) (S11; Yes), the process proceeds to the next state determination step (S13).

  In the state determination step (S13), as described above, the first state determination unit 24 is a step of determining the state of the person 2 based on the measurement data indicating the state of the person 2 having a breathing movement. Then, the state of the person 2 in the hypopnea event period (first period) and the apnea event period (second period) determined in the event period determination step (S7) is determined. As described above, the state determination step (S13) includes complementarity between the chest measurement data corresponding to the chest region 2a of the person 2 and the abdominal measurement data corresponding to the abdomen region 2b different from the chest region 2a of the person 2. A second correlation PB of the cross-correlation function as a first evaluation value corresponding to the symmetry of the direction of the time axis of the chest measurement data or the abdomen measurement data, and the second related to the similarity between the chest measurement data and the abdomen measurement data The phase shift index PI as the evaluation value, the phase difference P as the third evaluation value corresponding to the phase difference between the chest measurement data and the abdominal measurement data, and the chest measurement data, the abdominal measurement data, and almost the entire person 2 The difference AG between the peak and bottom of the autocorrelation function A (τ) as the fourth evaluation value regarding the similarity to the time lag of at least one measurement data selected from the group consisting of the corresponding total measurement data Correlation of cross-correlation peak of cross-correlation function C ′ (τ) as a fifth evaluation value indicating symmetry with respect to time of at least one measurement data selected from the group consisting of chest measurement data, abdominal measurement data, and whole measurement data It includes a step of determining the state of the person 2, that is, an obstructive apnea state, a central apnea state, and a mixed apnea state based on a plurality of evaluation values selected from the group consisting of the values CP ′.

  After the determination of the state of the person 2 for each of the hypopnea event period (first period) and the apnea event period (second period), the obstructive apnea state, the central apnea state, and the mixed apnea state are performed. Statistical data such as the name of the detected state, such as the state, the time at the start of the state, the time at the end, and the number of detections are calculated and stored in a storage unit (not shown) (S15). The process of analyzing the state 2 is terminated. In addition, if it is the structure which discriminate | determines a hypopnea state, what is necessary is just to calculate and preserve | save including the statistical data of a hypopnea state.

  Here, each process in each unit of the arithmetic unit 20 and the above-described method can be realized as a software program that is installed and executed in a computer, and can also be used as a recording medium for recording the software program. It is feasible. The software program may be recorded and used in a program unit (not shown) built in the computer, recorded in an external storage device or CD-ROM, and read and used by the program unit (not shown). Alternatively, it may be downloaded from the Internet to a program unit (not shown) and used.

  According to the respiratory monitor 1 or the software program according to the embodiment of the present invention described above, the state of the periodic movement of the person 2 corresponding to the chest region 2a, the abdominal region 2b, and the entire region as described above. By discriminating the state of the person 2 based on the measurement data shown, for example, accurate assessment of complex breathing motion as a whole, accurate assessment of detailed status, analysis accuracy improvement, etc. It can be performed. For example, in the conventional determination of only the entire signal corresponding to the entire area of the person 2, a more detailed determination of an apnea event period (second period) that is difficult to determine, that is, an obstructive apnea state, a central It is possible to distinguish between a type apnea state and a mixed type apnea state.

  In addition, since the respiratory monitor 1 uses the FG sensor 10 as a measuring device, measurement data indicating the state of the person 2 can be acquired without touching the person 2, so sleep apnea without imposing a burden on the person 2 The type can be identified and automatically detected, and the breathing state of the person 2 can be determined more simply. Further, for example, compared with a PSG device that determines the state based on the amplitude of the flow sensor and the chest or abdominal movement detected by the chest-abdominal motion sensor, the measurement data indicating the state of the person 2 is contacted with the person 2 Therefore, there is no possibility that data in a natural state during sleep cannot be obtained or data is lost due to a sensor being detached during sleep. In addition, low breathing cannot be accurately determined compared to conventional PSG devices that use a flow sensor that can detect the presence or absence of airflow but has poor quantitativeness, and a thoracoabdominal motion sensor that can detect only part of the upper body. Without the need for manual operation to attach a sensor such as a flow sensor or thoracoabdominal motion sensor to a person's body. Moreover, the load can be reduced psychologically. Furthermore, even small movements of the person 2 such as breathing can be accurately measured.

  In addition, as described above, the second state determination unit that determines the hypopnea event period (first period) and the apnea event period (second period) based on the magnitude of the periodic movement. 25. In addition, the first state discriminating unit 24 performs chest measurement data, abdominal measurement data, cross-correlation functions C (τ), C ′ (τ), autocorrelation function A (τ), chest measurement data By using various evaluation values based on the phase difference from the abdominal measurement data to determine the person 2 state during the event period, it is possible to comprehensively ensure a state that cannot be determined only by the magnitude of the periodic movement. Can be detected and discriminated.

  In the above description, the respiratory monitor 1 includes the representative coordinate calculation unit 26 (see FIG. 1) and the dividing line forming unit 27 (see FIG. 1), and includes a chest region 2a (first region) and an abdominal region 2b ( The second region is described as being separated, but a breathing monitor that does not include these configurations may be used. For example, when it is known that the person 2 does not move much, the chest region 2a (first region) and the abdominal region 2b (second region) are manually manually in advance via the input device 35 (see FIG. 1). May be set in advance.

It is a block diagram which shows the structural example of the respiration monitor which concerns on the 1st Embodiment of this invention. It is the schematic shown about the example of the waveform pattern which the respiration data and body movement data which are used for the respiration monitor which concerns on the 1st Embodiment of this invention form. It is a diagram which shows the example of the histogram of measurement data used for the respiration monitor which concerns on the 1st Embodiment of this invention. It is a figure explaining the case where a body movement period overlaps in the 1st predetermined period with the respiration monitor concerning a 1st embodiment of the present invention. It is a diagram explaining the respiration data of the whole area | region used for the respiration monitor which concerns on the 1st Embodiment of this invention, body movement data, and a baseline. It is a figure which shows the 1st specific example of the respiration waveform (respiration data) of the respiration monitor which concerns on the 1st Embodiment of this invention, an upper stage shows whole measurement data, a middle stage shows chest measurement data, and a lower stage shows abdominal measurement data. Show. It is a diagram which shows an example of the correlation function of the respiration monitor which concerns on the 1st Embodiment of this invention, (a) is an example of an autocorrelation function, (b) is an example of a cross correlation function. It is a figure for demonstrating an obstruction type apnea state. It is a figure which shows the 2nd specific example of the respiration waveform (respiration data) of the respiration monitor which concerns on the 1st Embodiment of this invention, an upper stage is whole measurement data, a middle stage is chest measurement data, and a lower stage is abdominal measurement data. Show. It is a typical perspective view of the FG sensor of the respiration monitor which concerns on the 1st Embodiment of this invention. It is a block diagram which shows schematic structure of the FG sensor of the respiration monitor which concerns on the 1st Embodiment of this invention. It is a typical perspective view explaining the projection apparatus of the respiration monitor which concerns on the 1st Embodiment of this invention. It is a conceptual perspective view explaining the concept of the movement of the bright spot of the respiratory monitor according to the first embodiment of the present invention. It is a schematic diagram explaining the bright spot imaged on the image receiving surface in the case of FIG. It is a typical top view explaining the example of calculation of the representative coordinate by the representative coordinate calculating part of the respiration monitor which concerns on the 1st Embodiment of this invention, and formation of the area dividing line by a dividing line formation part. It is a diagram explaining the point internally divided by the value weighted with the quantity regarding the representative coordinate which concerns on the 1st Embodiment of this invention. It is a flowchart which shows the outline of the process process by the respiration monitor which concerns on the 1st Embodiment of this invention.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Respiration monitor 2 Person 2a Thoracic area | region 2b Abdominal area | region 2c Area | region dividing line 2d Area | region boundary line 3 Bed 10 FG sensor 11 Projector 11a Pattern 11b Bright spot 12 Imaging device 14 Measuring part 20 Computing unit 21 Phase difference calculation part 22 Cross correlation function Calculation unit 23 Autocorrelation function calculation unit 24 First state determination unit 25 Second state determination unit 26 Representative coordinate calculation unit 27 Dividing line formation unit 28 Body motion detection unit 29 Threshold acid calculation unit 141 Movement amount calculation unit 142 Movement amount Waveform generator

Claims (17)

First state determining means for determining the state of the object based on measurement data indicating the state of the object with respiratory motion;
The first state determination means includes first measurement data corresponding to a first area of the object and second measurement data corresponding to a second area different from the first area of the object. And determining the state of the object based on the first evaluation value corresponding to the symmetry in the direction of the time axis of the first measurement data or the second measurement data,
Cross-correlation function calculating means for calculating a cross-correlation function between the first measurement data and the second measurement data;
The first evaluation value is a value based on a peak-to-bottom bias of a cross-correlation function value calculated by the cross-correlation function calculating unit.
State analysis device. First state determining means for determining the state of the object based on measurement data indicating the state of the object with respiratory motion;
The first state determination means includes first measurement data corresponding to a first area of the object and second measurement data corresponding to a second area different from the first area of the object. And determining the state of the object based on the first evaluation value corresponding to the symmetry in the direction of the time axis of the first measurement data or the second measurement data,
Cross-correlation function calculating means for calculating a cross-correlation function between the first measurement data and the second measurement data;
The first evaluation value is a value based on an average value of a peak and a bottom of a cross-correlation function value calculated by the cross-correlation function calculating unit.
State analysis device. First state determining means for determining the state of the object based on measurement data indicating the state of the object with respiratory motion;
The first state determination means includes first measurement data corresponding to a first area of the object and second measurement data corresponding to a second area different from the first area of the object. Based on the second evaluation value regarding the similarity to the object, the state of the object is determined,
Cross-correlation function calculating means for calculating a cross-correlation function between the first measurement data and the second measurement data;
The second evaluation value is a value based on a cross-correlation function calculated by the cross-correlation function calculating means,
The second evaluation value is a relationship between a value when the time shift τ = 0 of the cross-correlation function and a peak and bottom value, a relationship between a value when the τ = 0 and a peak value, From one of the relations between the value when τ = 0 and the bottom value,
State analysis device. When the second evaluation value is equal to or lower than a first threshold set for the second evaluation value, the first state determination unit is configured to determine whether the state of the object is an obstructive apnea state. Determine that there is,
The state analysis apparatus according to claim 3. First state determining means for determining the state of the object based on measurement data indicating the state of the object with respiratory motion;
The first state determination means includes first measurement data corresponding to a first area of the object and second measurement data corresponding to a second area different from the first area of the object. And determining the state of the object based on the third evaluation value corresponding to the phase difference between
Cross-correlation function calculating means for calculating a cross-correlation function between the first measurement data and the second measurement data;
Autocorrelation function calculating means for calculating an autocorrelation function of the first measurement data or the second measurement data;
The peak position of the cross-correlation function calculated by the cross-correlation function calculation means and the peak position of the auto-correlation function of the first measurement data or the second measurement data calculated by the auto-correlation function calculation means Comparison result, or bottom position of the cross-correlation function calculated by the cross-correlation function calculating means, and autocorrelation function of the first measurement data or the second measurement data calculated by the auto-correlation function calculating means Phase difference calculation means for calculating a third evaluation value corresponding to the phase difference based on the comparison result with the bottom position of
State analysis device. When the absolute value of the third evaluation value corresponding to the phase difference is equal to or greater than a second threshold value set for the third evaluation value corresponding to the phase difference, the first state determination unit Determining that the state of the object is an obstructive apnea state,
The state analysis apparatus according to claim 5. The first state discriminating means is for a time lag of at least one measurement data selected from the group consisting of the first measurement data, the second measurement data, and overall measurement data corresponding to substantially the entire object. Based on the fourth evaluation value related to similarity, further determining the state of the object,
Autocorrelation function calculating means for calculating an autocorrelation function of at least one measurement data selected from the group consisting of the first measurement data, the second measurement data, and the total measurement data;
The fourth evaluation value is a value based on a peak or bottom of the autocorrelation function value calculated by the autocorrelation function calculating means or a difference between the peak and the bottom,
The autocorrelation function calculating means determines the state of the first measurement data when determining the state based on the fourth evaluation value of the first measurement data by at least the first state determination means. When determining the state based on the autocorrelation function and the fourth evaluation value of the second measurement data, the autocorrelation function of the second measurement data, the fourth of the overall measurement data When determining the state based on the evaluation value, the autocorrelation function of the entire measurement data is calculated.
The state analysis apparatus of any one of Claim 1 thru | or 4. The first state discriminating means is for a time lag of at least one measurement data selected from the group consisting of the first measurement data, the second measurement data, and overall measurement data corresponding to substantially the entire object. Based on the fourth evaluation value related to similarity, further determining the state of the object,
The autocorrelation function calculating means calculates an autocorrelation function of the whole measurement data when the whole measurement data is selected,
The fourth evaluation value is a value based on a peak or bottom of the autocorrelation function value calculated by the autocorrelation function calculating means or a difference between the peak and the bottom,
The autocorrelation function calculating means determines the state of the first measurement data when determining the state based on the fourth evaluation value of the first measurement data by at least the first state determination means. When determining the state based on the autocorrelation function and the fourth evaluation value of the second measurement data, the autocorrelation function of the second measurement data, the fourth of the overall measurement data When determining the state based on the evaluation value, the autocorrelation function of the entire measurement data is calculated.
The state analysis apparatus according to claim 5 or 6. The first state discriminating means is symmetrical with respect to time of at least one measurement data selected from the group consisting of the first measurement data, the second measurement data, and overall measurement data corresponding to substantially the entire object. Further determining the state of the object based on a fifth evaluation value indicating the property,
The state analysis apparatus according to any one of claims 1 to 8. Measurement data obtained by inverting at least one measurement data selected from the group consisting of the first measurement data, the second measurement data, and the entire measurement data in the direction of the time axis, and the measurement data before the inversion A cross-correlation function calculating means for calculating a cross-correlation function;
The fifth evaluation value is a value based on the cross-correlation function calculated by the cross-correlation function calculating unit.
The cross-correlation function calculating means performs the first measurement when determining the state based on the fifth evaluation value according to the first measurement data by the first state determining means. A cross-correlation function between the measurement data obtained by inverting the data in the direction of the time axis and the first measurement data before the inverting, and the fifth state according to the second measurement data by the first state determination means. A cross-correlation function between the measurement data obtained by reversing the second measurement data in the direction of the time axis and the second measurement data before the reversal when determining the state based on the evaluation value of When determining the state based on the fifth evaluation value corresponding to the overall measurement data by the first state determination means, the measurement data obtained by inverting the overall measurement data in the direction of the time axis, Before flip Calculating a cross-correlation function between the whole measurement data,
The state analysis apparatus according to claim 9. First state determining means for determining the state of the object based on measurement data indicating the state of the object with respiratory motion;
The first state determination means includes first measurement data corresponding to a first area of the object and second measurement data corresponding to a second area different from the first area of the object. And a first evaluation value corresponding to the symmetry in the direction of the time axis of the first measurement data or the second measurement data,
A second evaluation value relating to the similarity between the first measurement data and the second measurement data;
Based on a plurality of evaluation values selected from the group consisting of third evaluation values corresponding to the phase difference between the first measurement data and the second measurement data, the state of the object is determined,
Cross-correlation function calculating means for calculating a cross-correlation function between the first measurement data and the second measurement data;
The first evaluation value is a value based on a peak-to-bottom bias of a cross-correlation function value calculated by the cross-correlation function calculating unit,
The second evaluation value is a value based on a cross-correlation function calculated by the cross-correlation function calculating means,
An autocorrelation function calculating means for calculating an autocorrelation function of the first measurement data or the second measurement data;
The peak position of the cross-correlation function calculated by the cross-correlation function calculation means and the peak position of the auto-correlation function of the first measurement data or the second measurement data calculated by the auto-correlation function calculation means Comparison result, or bottom position of the cross-correlation function calculated by the cross-correlation function calculating means, and autocorrelation function of the first measurement data or the second measurement data calculated by the auto-correlation function calculating means A phase difference calculating means for calculating a third evaluation value corresponding to the phase difference based on a comparison result with the bottom position of
State analysis device. The first state discriminating means is for a time lag of at least one measurement data selected from the group consisting of the first measurement data, the second measurement data, and overall measurement data corresponding to substantially the entire object. A fourth evaluation value for similarity;
At least one selected from the group consisting of fifth evaluation values indicating symmetry with respect to time of at least one measurement data selected from the group consisting of the first measurement data, the second measurement data, and the total measurement data. Further determining the state of the object based on the evaluation value,
The state analysis apparatus according to claim 11. A threshold value calculation means for calculating a threshold value for determining a decrease in the magnitude of the respiratory motion of the object based on the measurement data of the first predetermined period before and after the determination target time point in the measurement data. Te, wherein a third threshold value corresponding to the first region, said fourth threshold value corresponding to the second region, the threshold value calculating means for calculating a fifth threshold value corresponding to substantially the entire said object When;
The comparison result between the magnitude of the respiratory motion at the determination target time point of the first measurement data and the third threshold value, or the magnitude of the respiratory motion at the determination target time point of the second measurement data and the first The magnitude of the respiratory motion is reduced based on a comparison result with the threshold value of 4 or a comparison result between the magnitude of the respiratory motion at the determination target time point of the overall measurement data and the fifth threshold value. Second state determining means for determining a first period or a second period in which the magnitude of the respiratory motion is further lower than the first period;
The first state determining means determines the state of the object for the period determined by the second state determining means;
Status analyzing apparatus according to 請 Motomeko 12. In the first state determination means, the first evaluation value is set with respect to the second evaluation value in a certain period or more of the first period or the second period. When the absolute value of the third evaluation value corresponding to the phase difference is equal to or greater than a second threshold set for the third evaluation value corresponding to the phase difference, Determining that the state of the object is an obstructive apnea state;
The state analysis apparatus according to claim 13. A measuring device for acquiring the measurement data;
The measurement apparatus includes: a projection apparatus that projects a plurality of bright spots on a target area; an imaging apparatus that captures a target area on which the plurality of bright spots are projected; and two frames acquired at different times by the imaging apparatus . A movement amount calculating means for calculating a movement amount between the two frames of the plurality of bright spots from an image; and a movement amount waveform generating means for generating movement amount waveform data in which the movement amounts are arranged in time series.
The state analysis apparatus of any one of Claim 1 thru | or 14. A software program installed on a computer and operating the computer as a state analysis device;
Controlling the computer to execute a process for determining the state of the object based on measurement data indicating the state of the object in respiratory motion;
The determination process is performed by complementing first measurement data corresponding to a first region of the object and second measurement data corresponding to a second region different from the first region of the object. And a first evaluation value corresponding to the symmetry of the first measurement data or the second measurement data in the direction of the time axis,
A second evaluation value relating to the similarity between the first measurement data and the second measurement data;
Processing for determining the state of the object based on a plurality of evaluation values selected from the group consisting of third evaluation values corresponding to the phase difference between the first measurement data and the second measurement data Including
Including a process of calculating a cross-correlation function between the first measurement data and the second measurement data;
The first evaluation value is a value based on a peak-to-bottom bias of a cross-correlation function value calculated by a process of calculating the cross-correlation function,
The second evaluation value is a value based on a cross-correlation function calculated by a process of calculating the cross-correlation function,
Processing for calculating an autocorrelation function of the first measurement data or the second measurement data;
The peak position of the cross-correlation function calculated by the process of calculating the cross-correlation function and the auto-correlation function of the first measurement data or the second measurement data calculated by the process of calculating the auto-correlation function The comparison result with the peak position, the bottom position of the cross-correlation function calculated by the process of calculating the cross-correlation function, and the first measurement data or the first data calculated by the process of calculating the auto-correlation function Including a process of calculating a third evaluation value corresponding to the phase difference based on a comparison result between the measured data of 2 and the bottom position of the autocorrelation function;
Software program. The process of determining the state of the object includes at least one measurement data selected from the group consisting of the first measurement data, the second measurement data, and overall measurement data corresponding to substantially the entire object. A fourth evaluation value for similarity to time lag;
At least one selected from the group consisting of fifth evaluation values indicating symmetry with respect to time of at least one measurement data selected from the group consisting of the first measurement data, the second measurement data, and the total measurement data. Further including a process of determining the state of the object based on the evaluation value;
The software program according to claim 16.