JP4588423B2 - State analysis device and software program - Google Patents

Description

  The present invention relates to a state analysis device and a software program, and more particularly to a state analysis device and a software program that can more reliably determine the state of an object.

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. As a typical example, there is a monitoring device that monitors a sleeper's breathing by calculating a pattern movement amount from an image obtained by continuously projecting the projected pattern onto a sleeper on the bed. there were. (For example, refer to Patent Document 1).
JP 2002-175582 A (page 5-9, FIG. 1-13)

  In the conventional apparatus as described above, for example, it is possible to measure and monitor the increase and decrease of the respiratory motion of the patient and the amount thereof, and to detect the presence or absence of respiration.

  However, for example, when monitoring the breathing of a patient who is ventilating with a ventilator, especially when monitoring the spontaneous breathing or the breathing of a patient with poor communication ability, An apparatus capable of discriminating was desired. For example, if a patient has almost no spontaneous breathing and cannot breathe due to disconnection of the artificial respiration circuit, that is, if the patient's breathing has stopped, or if the patient has certain spontaneous breathing, artificial ventilation will not work substantially In order to prevent the problem of artificial respiration directly leading to a serious accident such as in the case of an accident, there has been a demand for a device that can determine the state of an object as accurately and as quickly as possible. . In response to such a request, in the monitoring device described in Patent Document 1, the simplest method is to compare the generated respiratory motion signal with a certain fixed threshold value to stop the respiratory motion. Although it is possible to determine the decrease, the sensitivity and noise level of respiratory motion detection may vary depending on the posture of the patient, the state of the top, and other environmental factors, so the state determination is not always performed accurately There was a thing.

  Therefore, an object of the present invention is to provide a state analysis apparatus and a software program that can more reliably determine the state of an object.

  In order to achieve the above object, the state analysis apparatus according to the first aspect of the present invention is based on the measurement data indicating the state of the object 2 having a periodic movement, as shown in FIG. A spectrum calculating unit 24 for calculating a spectrum for a predetermined period; a spectrum comparing unit 25 for comparing a low frequency component and a high frequency component of the spectrum based on the spectrum; and an object based on a comparison result of the spectrum comparing unit 25 And a state determining means 26 for determining two abnormalities.

  If comprised in this way, a spectrum calculation means will calculate a spectrum based on the measurement data which show the state of the target object with a periodic motion, and a spectrum comparison means will compare the low frequency component and high frequency component of a spectrum. Since the state determination means determines the abnormality of the object based on the comparison result, it is possible to provide a state analysis device that can more reliably determine the state of the object.

  Further, as described in claim 2, in the state analysis apparatus according to claim 1, the state determination unit 26 continuously adds the spectrum of the low-frequency component to the spectrum of the high-frequency component for a second predetermined period. It may be configured to determine that the object 2 is abnormal when it falls.

  If comprised in this way, since a state discrimination | determination means will discriminate | determine that a target object is abnormal when the sum total of the spectrum of a low frequency component falls below the sum total of the spectrum of a high frequency component continuously for the 2nd predetermined period. In addition, the state of the object can be determined more reliably. For example, when the object is a person who is breathing, the low frequency component is a component having a frequency close to respiration, and the high frequency component is typically a component having a noise frequency other than breathing.

  Further, as described in claim 3, in the state analysis apparatus according to claim 1, the state determination unit 26 determines that the average of the total sum of the low-frequency component spectrum is equal to the sum of the high-frequency component spectrum. When the average of the third predetermined period falls below, the object 2 may be determined to be abnormal.

  If comprised in this way, when the average of the 3rd predetermined period of the sum total of the spectrum of a low frequency component will fall below the average of the 3rd predetermined period of the sum total of the spectrum of a high frequency component, it will be said that a target object is abnormal. Since the determination is made, the state of the object can be determined reliably and quickly.

  Further, as described in claim 4, in the state analysis apparatus according to any one of claims 1 to 3, for example, as shown in FIG. 1, it occurs with periodic movement based on measurement data. A non-periodic motion detecting means 28 for determining and detecting an aperiodic movement; the state determining means 26 is determined by the aperiodic motion detecting means 28 to determine that the motion at the determination target time point is an aperiodic movement. In this case, it may be configured not to determine that the object 2 is abnormal.

  With this configuration, the non-periodic motion detection unit determines and detects a non-periodic motion that occurs along with the periodic motion based on the measurement data, and the state determination unit detects that the motion at the determination target time point is aperiodic. Since it is not determined that the object is abnormal when it is determined by the target movement detection means that the movement is aperiodic, it is possible to prevent erroneous determination. For example, when the object is a breathing person, the non-periodic movement is a body movement such as turning over.

  In order to achieve the above object, a state analysis apparatus according to a fifth aspect of the present invention is a periodic analysis based on measurement data indicating the state of an object 2 having a periodic motion, as shown in FIG. Aperiodic motion detection means 28 for determining and detecting aperiodic movement that occurs with movement; and a period in which aperiodic motion is detected by the aperiodic motion detection means 28 among periods before and after the determination target time point. A threshold value calculating unit 29 for calculating a threshold value for determining whether or not there is a modulation in the periodic motion based on the measurement data of the fourth predetermined period excluding; a motion at the determination target time point is an aperiodic motion detecting unit 28 And a state discriminating means 27 for discriminating an abnormality of the judgment target object 2 by comparing the magnitude of the periodic motion at the judgment target time point with a threshold when it is judged that the movement is not aperiodic movement. Configured.

  If comprised in this way, an aperiodic motion detection means will determine and detect the aperiodic motion which arises with a periodic motion based on measurement data, and a threshold value calculation means has modulation in a periodic motion. The threshold value for determining whether or not is calculated based on the measurement data of the fourth predetermined period excluding the period in which the aperiodic motion is detected. When the state determination unit determines that the movement at the determination target time point is not a non-periodic movement by the non-periodic movement detection unit, the state determination unit compares the threshold value with the magnitude of the periodic movement at the determination target time point. Since the abnormality of the determination target is determined, it is possible to provide a state analysis apparatus that can more reliably determine the state of the target without making an erroneous determination. For example, when the object is a breathing person, the non-periodic movement is a body movement such as turning over.

  Further, as described in claim 6, in the state analysis device according to claim 5, as shown in FIG. 2, for example, the magnitude of the periodic movement at the determination target time is changed from zero cross before the determination target time to zero cross. You may comprise so that the value obtained by integrating the measurement data until may be used.

  With this configuration, the value obtained by integrating the measurement data from the zero cross to the zero cross before the determination target time is used for the periodic motion magnitude at the determination target time, so the amplitude and period of the measurement data Both changes can be reflected in the magnitude of the periodic movement, and the determination by the state determination means can be performed with higher accuracy. Here, “from zero cross to zero cross before the determination target time” typically means from zero cross to zero cross just before the determination target time.

  Further, as described in claim 7, in the state analysis device according to claim 5 or 6, the threshold value is determined based on the representative value, and the representative value is a periodic value within a fifth predetermined period. You may comprise so that it may be the average value of a magnitude | size of movement, or an adjustment average value.

  If comprised in this way, since the determination of a state will be performed based on the magnitude | size of the respiration of a past fixed period, the state of a target object can be determined more reliably. In particular, when the adjusted average value is used, for example, when there is a possibility that specific data is included due to an error or a disturbance, the influence can be reduced.

  Further, as described in claim 8, in the state analysis device according to claim 5 or 6, the threshold value is determined based on the representative value, and the representative value is a periodic value within a sixth predetermined period. You may comprise so that it may be a several average value from the larger magnitude | size of a motion.

  With this configuration, for example, even when there is modulation in the periodic motion, an average value of the magnitude of the original periodic motion when there is no modulation can be obtained, and a threshold value is based on the average value. Therefore, the determination by the state determination unit can be performed more reliably.

  Further, as described in claim 9, in the state analysis device according to any one of claims 5 to 8, for example, as shown in FIG. It may be configured to discriminate that the object 2 has been modulated when the magnitude of a small movement falls below a threshold value.

  With this configuration, the state determination unit first determines that the object has been modulated when the magnitude of the periodic movement at the determination target time falls below a threshold, and for example, immediately determines that the object is abnormal. Since no judgment is made, erroneous judgments can be prevented.

  Further, as described in claim 10, in the state analysis device according to claim 9, as illustrated in FIG. 1, for example, the state determination unit 27 determines that the object is modulated, and then fixes the threshold value. The magnitude of the periodic motion at the determination target time point may be configured to determine that the target object 2 is abnormal when the fixed threshold value continuously falls below the seventh predetermined period. .

  With this configuration, the state determination unit determines that the object has been modulated, and then the target is detected when the magnitude of the periodic movement at the determination target time point continuously falls below the threshold for the seventh predetermined period. A state that is inappropriate for calculating a threshold value that determines that an object is abnormal and that the target object actually becomes abnormal and the measurement data fluctuates in the seventh predetermined period. Since the threshold value can be avoided from being influenced by the above-described case, it is possible to more reliably determine the abnormality of the object.

  Further, as described in claim 11, in the state analysis apparatus according to claim 9, for example, as shown in FIG. 1, the state determination unit 27 determines that the target is modulated, and further determines the determination target time point. It may be configured to determine that the target object 2 is abnormal when the period of the periodic movement of fluctuates.

  With this configuration, after determining that the object 2 has been modulated, the state determination unit 27 further determines that the object is abnormal when the period of periodic movement at the determination target time fluctuates. Therefore, erroneous determination can be prevented, and abnormality of the object can be determined more reliably.

  Further, as described in claim 12, the state analysis apparatus according to any one of claims 1 to 11 includes a measurement apparatus 10 that acquires measurement data, for example, as shown in FIG. The apparatus 10 projects a plurality of bright spots 11b (see, for example, FIG. 8) onto the target area 3 (see, for example, FIG. 8), and a target area in which the plurality of bright spots 11b (see, for example, FIG. 8) are projected. 3 (for example, see FIG. 8), and the amount of movement between two frames of a plurality of bright spots 11b (for example, see FIG. 8) is calculated from two frames of images acquired at different time points by the imaging device 12. The movement amount calculation unit 141 may be configured to include the movement amount waveform generation unit 142 that generates movement amount waveform data in which the movement amounts are arranged in time series.

  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, a software program according to a thirteenth aspect of the present invention is a software program that is installed in a computer and operates as a state analysis device, for example, as shown in FIG. A spectrum for the first predetermined period is calculated based on the measurement data indicating the state of the target object having a typical movement (S108); based on the spectrum, the low frequency component and the high frequency component of the spectrum are compared ( S110); based on the comparison result, an abnormality of the object is determined (S110, S114).

  If comprised in this way, a spectrum will be calculated based on the measurement data which show the state of the target object with a periodic motion, and the low frequency component and high frequency component of the spectrum concerned are compared. Further, since the abnormality of the object is determined based on the comparison result, a software program that can more reliably determine the state of the object can be provided.

  In order to achieve the above object, the software program according to the fourteenth aspect of the present invention is a software program for installing in a computer and operating the computer as a state analysis device, for example, as shown in FIG. Based on the measurement data indicating the state of the target object having a typical movement, the non-periodic movement that occurs with the periodic movement is determined and detected (S106, S104); Based on the measurement data of the fourth predetermined period excluding the period in which the target movement is detected, a threshold value for determining whether or not the periodic movement is modulated is calculated (S120); When it is determined that the movement is not a periodic movement, an abnormality of the determination target is determined by comparing the magnitude of the periodic movement at the determination target time point with a threshold (S 12, S116) as configured.

  With this configuration, the non-periodic motion that occurs along with the periodic motion is determined and detected based on the measurement data, and a threshold value for determining whether the periodic motion is modulated is determined as the aperiodic motion. Is calculated based on the measurement data of the fourth predetermined period excluding the detected period. Furthermore, when it is determined that the movement at the determination target time point is not an aperiodic movement, the abnormality of the determination target object is determined by comparing the threshold value with the magnitude of the periodic movement at the determination target time point. It is possible to provide a software program that can more reliably determine the state of an object without making an incorrect determination.

  As described above, according to the present invention, the spectrum calculation means for calculating the spectrum for the first predetermined period based on the measurement data indicating the state of the object having a periodic motion, and the spectrum based on the spectrum. The state of the object is configured to include a spectrum comparison unit that compares the low-frequency component and the high-frequency component, and a state determination unit that determines abnormality of the object based on the comparison result of the spectrum comparison unit. It is possible to provide a state analysis apparatus that can more reliably discriminate.

  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 an 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 an FG sensor 10 described later, a spectrum compared by a spectrum comparison unit 25 described later, a first state determination unit 26 described below, and a second state. The state determination unit 27 determines the state of the object 2. In this figure, the input device 35 and the display 40 are illustrated as being externally attached to the arithmetic device 20, but may be incorporated. The FG sensor 10 will be described in detail with reference to FIG.

  The computing device 20 is based on the spectrum calculation unit 24 as a spectrum calculation unit that calculates the spectrum of the first predetermined period based on the measurement data indicating the state of the object 2 having a periodic motion, and on the basis of the spectrum. A spectrum comparison unit 25 as a spectrum comparison unit that compares the low-frequency component and the high-frequency component of the spectrum, and a first state determination unit as a state determination unit that determines abnormality of the object 2 based on the comparison result of the spectrum comparison unit 25. The state discriminating unit 26 is provided.

  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. Based on the measurement data of the fourth predetermined period excluding the period in which the non-periodic motion is detected by the body movement detection unit 28 among the periods before and after the determination target time point The threshold value calculation unit 29 as a threshold value calculation unit for calculating a threshold value for determining whether there is modulation in the movement and the movement of the target object 2 at the determination target time are not non-periodic movements by the body movement detection unit 28. A second state discriminating unit 27 as a state discriminating unit for discriminating an abnormality of the judgment target object 2 by comparing the magnitude of the periodic movement at the judgment target time point with a threshold value. .

  The main predetermined period of this embodiment includes the first predetermined period to the eleventh predetermined period. These predetermined periods will be sequentially described later (however, the order is not necessarily the number order). FIG. 7 shows a summary of the first predetermined period to the tenth predetermined period. In the following description, FIG. 7 will be referred to as appropriate.

  Further, here, based on the comparison result of the spectrum comparison unit 25, the first state determination unit 26 that determines abnormality of the object 2 and the movement of the object 2 at the determination target time point are the body movement detection unit 28. When it is determined that the movement is not aperiodic movement, the magnitude of the periodic movement at the determination target time point is compared with the threshold value calculated by the threshold value calculation unit 29, thereby determining the abnormality of the determination target object 2. The second state determination unit 27 is described as being configured separately, but may be configured integrally.

  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 performing artificial respiration on the bed 3 will be described. Here, the periodic motion is, for example, the respiratory motion of the human 2, and the non-periodic motion is, for example, a body motion other than the respiratory motion of the human 2 (hereinafter referred to as “body motion unless otherwise noted”). "). It should be noted that the respiratory motion of the human 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.

  Here, the person 2 is abnormal in the present embodiment, a state in which the artificial respiration circuit of the person 2 performing artificial respiration is abnormal, typically a state in which artificial respiration does not substantially work. Say. That is, the respiration monitor 1 is a device that detects an abnormality in the artificial respiration circuit of the person 2 who is performing artificial respiration, and monitors artificial respiration such as a muscular dystrophy patient or an amyotrophic lateral sclerosis (ALS) patient. However, the device is intended to prevent accidents of artificial respiration.

  Although artificial respiration plays an important role in prolonging the lives of patients with muscular dystrophy and amyotrophic lateral sclerosis (ALS), troubles of artificial respiration frequently occur. Especially for patients with low spontaneous breathing and communication ability, ventilator troubles directly lead to serious accidents, so not only rely on the ventilator (not shown) alarm function, but also double and triple safety measures. is necessary.

  Therefore, the respiratory monitor 1 that monitors artificial respiration needs to detect some abnormalities and dangers. For example, the first is detection of respiratory stop when a patient who has little spontaneous breathing, such as a patient undergoing artificial respiration by tracheostomy, becomes unable to breathe due to disconnection of an artificial respiration circuit or the like. In this case, a respiratory motion waveform acquired by the FG sensor 10 to be described later disappears, and discovery and notification are required as soon as possible. The second is a case where artificial respiration substantially fails in a patient with a certain spontaneous breathing. In this case, the patient's breathing state changes, in other words, modulation of breathing is expected, but is not as clear and difficult to detect as breathing arrest. Instead, it is desirable to find it as early as possible, but even if it takes a little time to find it, it will not cause a serious accident immediately.

  More specifically, in the present embodiment, the person 2 is abnormal, for example, a state in which the respirator of the person 2 without spontaneous breathing has been removed and the breathing has stopped, or a person with some spontaneous breathing This is a state in which the artificial respiration of the person 2 is in trouble, in other words, a state in which the artificial respiration of the person 2 has a trouble.

  In the present embodiment, the measurement data is data measured by the FG sensor 10. 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 the measurement results obtained by measuring the movement of the person 2 at a plurality of points, and is the first measurement data based on the sum of the measurement values at the plurality of points. And / or body motion data as second measurement data, which is data of the sum of absolute values of measurement values at a plurality of points. The sum of absolute values is a concept including the sum of the squares of the measured values. 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 in which the measurement value has changed more than a certain value.

  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 the 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.

The measurement data acquired from the FG sensor 10 may be acquired collectively for a certain period, or may be acquired in real time as the state of the person 2 changes. In the present embodiment, as described above, since the respiratory monitor 1 is a device that detects an abnormality in the artificial respiration circuit of the person 2 who is performing artificial respiration, when it is acquired in real time with the state transition of the person 2 I will explain it.
Hereinafter, each of the above-described configurations will be described in detail with reference to FIG. 1 again.

  The spectrum calculation unit 24 calculates the spectrum for the first predetermined period based on the measurement data indicating the state of the person 2 in motion of breathing, here, the breathing data (FIG. 2A). Here, the spectrum may be an amplitude spectrum or a power spectrum. In the present embodiment, the spectrum uses a power spectrum corresponding to a value obtained by squaring the amplitude spectrum. Since the frequency distribution of the power spectrum is emphasized more than the amplitude spectrum, it is suitable for determining an abnormality of the person 2 by comparing the low frequency component and the high frequency component of the spectrum.

  Specifically, the spectrum calculation unit 24 performs discrete Fourier transform on the respiration data (FIG. 2A) for the first predetermined period immediately before the determination target time, typically FFT (Fast Fourier Transform, Fast Fourier Transform). The power spectrum at the determination target time is calculated by conversion.

  Here, the determination target time point is a time point at which the abnormality of the person 2 is determined, and the time point sequentially moves with time. 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.

  Further, the first predetermined period is a time for accumulating respiratory data (FIG. 2A) for executing FFT, for example, a period including at least one cycle before the determination target time point, for example, 5 to 30 seconds, preferably 5 to 15 seconds, here 8 seconds immediately before the determination target time point.

  FIG. 3 is a diagram illustrating an example of a power spectrum calculated by the spectrum calculation unit 24 of the respiratory monitor 1 according to the present embodiment. When there is actually normal breathing motion, the power spectrum of the frequency in the range close to the normal number of breathing increases, and when there is no normal breathing motion, the power spectrum decreases. On the other hand, the change in the power spectrum of the frequency that is not in the respiratory motion, that is, the frequency in the noise range is small regardless of the presence or absence of normal respiratory motion. Here, the frequency in the range close to the normal number of breaths is, for example, a frequency component of 10 to 50 times / minute, and the frequency in the range of other noise components is typically the number of normal breaths. It becomes a frequency component of a range higher than a near frequency component. Therefore, by comparing the low frequency component and the high frequency component of the power spectrum, it is possible to determine the abnormality of the object 2, that is, here, the presence or absence of normal breathing of the person 2.

  Here, typically, the range of the low frequency component is 60 times / minute or less, and the range of the high frequency component is 60 times / minute or more. In the present embodiment, the range of the low frequency component is 10 as described above. -50 times / minute, and the range of the high frequency component is 80-120 times / minute. The range of the low frequency component and the range of the high frequency component may partially overlap. For example, the range of the low frequency component is 10 to 70 times / minute, and the range of the high frequency component is 60 to 120 times. / Min.

  Specifically, the spectrum comparison unit 25 sums the power spectrum of the low-frequency components (hereinafter referred to as “signal component” unless otherwise specified) and the sum of the power spectra of the high-frequency components (hereinafter, unless otherwise specified). (Referred to as “noise component”). The sum of the power spectrum of each component may be simply added up from the power spectrum of each component or may be calculated by integrating the power spectrum of each component with respect to the frequency. The spectrum comparison unit 25 compares the calculated signal component with the noise component. The spectrum comparison unit 25 transmits a signal of the comparison result to the display 40 and displays the comparison result on the display 40.

  FIG. 4 shows a predetermined time (about 6 minutes) of the waveform pattern of the respiratory data acquired by the actual measurement of the respiratory monitor 1 according to the present embodiment, the signal component compared by the spectrum comparison unit 25, and the noise component data. This is an example.

  The first state determination unit 26, when the signal component continuously falls below the noise component for a second predetermined period based on the comparison result between the signal component compared by the spectrum comparison unit 25 and the noise component. The object 2 is abnormal. Here, the object 2 is configured to be determined as having stopped breathing.

  The second predetermined period is a period sufficient to evaluate the cessation of breathing. Typically, the period includes at least one cycle before the determination target time, for example, 5 to 30 seconds, preferably 5 seconds. ~ 15 seconds, here 8 seconds. It should be noted that, here, continuously falling for the second predetermined period does not necessarily mean that the condition must not be deviated from once, but it may be substantially continuous, and even if it deviates instantaneously. If there is no effect on The same applies to the following description unless otherwise specified.

  As described above, the first state determination unit 26 determines, for example, the stop of breathing based on the relative comparison result between the signal component and the noise component without using one fixed threshold value. Even when the detection sensitivity and noise level of measurement data acquired by the FG sensor 10 to be described later change due to environmental conditions such as the posture of the person 2, the situation of the top cover, the situation of ambient light, etc., the change is appropriately handled. More accurate discrimination can be made. In addition, since the first state determination unit 26 performs the determination based on the relative comparison result between the signal component and the noise component, for example, when the artificial respiration circuit is disconnected at the time of the posture change of the person 2 or removed at the time of care If the person 2 forgets or the posture of the person 2 changes due to body movement such as turning over, it is not necessary to set a threshold again over a certain amount of time, and the respiratory stop of the person 2 can be detected quickly. .

  In the above description, the first state determination unit 26 determines that the respiration of the person 2 is abnormal when the signal component continuously falls below the noise component for the second predetermined period. In addition to the method of simply comparing the magnitude of the noise component, various comparison methods such as using a difference or a ratio can be selected. The relative relationship between the signal component and the noise component may differ depending on how to select the range of the low frequency component and the range of the high frequency component of the power spectrum. Therefore, for example, by using a difference or a ratio according to how to select each of the ranges, it is possible to determine a breathing abnormality of the person 2 before the signal component falls below the noise component.

  In the above description, the first predetermined period (data accumulation time) for calculating the power spectrum from the respiration data by discrete Fourier transform is about 8 seconds. As shown in FIG. 3, when a power spectrum is obtained by sampling respiratory data for 8 seconds, the power spectrum can be obtained with a respiration rate resolution of about 7.5 times / minute. If you want to know more precise breathing rate, it is necessary to take a long sampling time of data, and with such a short sampling time, you can not know the precise precise breathing rate, but here the rough high frequency component and low frequency component Since it is sufficient to know the amount, it is possible to sufficiently determine respiration abnormality with data obtained by short-time sampling. Therefore, as a result, it is possible to more quickly determine abnormalities in breathing. In the embodiment described above, the first predetermined period (data accumulation time) is about 8 seconds, the second predetermined period (discriminating evaluation period) is about 8 seconds, and the stoppage of breathing is determined in about 16 seconds in total. Is possible.

  In addition, the first state determination unit 26 applies the target when the average of the third predetermined period of the sum of the low-frequency component spectra falls below the average of the third predetermined period of the sum of the high-frequency component spectra. You may comprise so that it may discriminate | determine that the thing 2 is abnormal.

  That is, in this case, the spectrum comparison unit 25 calculates the signal component and the noise component at the determination target time point, and further uses the signal component and the noise component within the third predetermined period immediately before the determination target time point to calculate the respective averages. Calculate the value. The signal component and noise component within the third predetermined period immediately before the determination target time point are signal components and noise components calculated at the past determination target time point. The spectrum comparison unit 25 compares the calculated average value of the signal components with the average value of the noise components.

  Here, the third predetermined period only needs to include a plurality of past determination target time points, for example, 5 to 30 seconds, preferably 5 to 15 seconds, typically the same as the second predetermined period. 8 seconds. More preferably, it is a time that does not interfere with real-time monitoring of the person 2 by the respiratory monitor 1.

  By doing in this way, the first state determination unit 26 shortens the second predetermined period described above depending on the length of the third predetermined period or, in an extreme case, the second predetermined period. When the period is set to 0 second and the average value of the signal components falls below the average value of the noise components, it is determined that the person 2 is abnormal without waiting for the second predetermined period (discrimination evaluation period) to elapse. It can be constituted as follows.

  Moreover, the data (refer FIG. 2) which show the body movement mentioned above appear in the part enclosed with the broken line in the figure of FIG. For example, when there is body movement due to body posture change or nursing care, the noise component may increase as shown by the portion surrounded by a broken line in the figure. At this time, since the noise component may exceed the signal component, it may be erroneously determined that breathing has stopped. Therefore, the first state determination unit 26 determines that the movement at the determination target time point is the body movement by the body movement detection unit 28 (see FIG. 1) described later, that is, the body movement described later. If it is a period, it may not be determined whether to stop breathing. Thereby, erroneous determination can be prevented. At that time, once body movement is detected, it may not be determined whether or not to stop breathing for a certain period (for example, 3 to 5 seconds) after the body movement is not detected until it is settled.

  When the person 2 is a patient having a respiratory abnormality that is not caused by artificial respiration, such as sleep apnea, the second stop is performed so that the respiratory stop of the person 2 is not erroneously determined due to the respiratory abnormality. The predetermined period (discriminant evaluation period) may be longer than the longest apnea time of the patient. That is, the second predetermined period (discriminant evaluation period) may be arbitrarily adjusted according to the characteristics of the object 2 by operating the input device 35 (see FIG. 1). In addition, even if the period during which the signal component is below the noise component continues for the second predetermined period, if the continuous period is shorter than the longest apnea time, the determination by the first state determination unit 26 is performed. It may not be possible. Thereby, similarly, it is possible to prevent an error in determination by the first state determination unit 26. In this case, the third predetermined period described above is also preferably slightly longer than the longest apnea time.

  Returning to FIG. 1, the body motion detection unit 28, as described above, is based on the measurement data indicating the state of the object having a periodic motion, that is, the state of the person 2 having the respiratory motion, and the non-periodic motion that occurs with the respiratory motion. The body movement period, which is a period in which data indicating the body movement of the object exists, is further detected 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 an eighth predetermined period immediately before the determination target time point. . The eighth 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 value) in the eighth 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 carried out sequentially from the data of the determination target time points that change in real time, it is known that the previous determination target time point is a body movement, so it is known at the next determination target time point. It is preferable to exclude the time point, that is, the period indicating the body movement, from the eighth predetermined period as in the fourth 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.

  Here, since the respiratory monitor 1 according to the present embodiment monitors the person 2 in real time, it is preferable that the body motion reference value used for the determination target time has already been calculated at the determination target time. In the present embodiment, the body motion reference value that is actually used at the determination target time point is calculated using data within the eighth predetermined period up to the previous time point (the determination target time point that goes back one point in the past). The body movement reference value is used. That is, in other words, the body motion reference value used at the determination target time point is different from the body motion reference value calculated using the data within the eighth predetermined period until the determination target time point.

  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 ninth 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 ninth predetermined period is in a time zone having a certain width enough 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. Furthermore, since the ninth predetermined period is an apparatus in which the respiratory monitor 1 according to the present embodiment monitors the state transition of the person 2 in real time, typically, the ninth predetermined period may be a period immediately before the determination target time point. The body movement determination value is a secondary parameter at each time point obtained from measurement data (for example, body movement data).

  Further, 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 ninth 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 refers to the entropy used in physics as the uncertainty of an event, and if the decrease in uncertainty due to certain information is the amount of information, the relative value of uncertainty before and after receiving 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. If it is known that the first eye has come out, 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. Specifically, the information entropy H (X) is calculated by the following equation (1), for example. 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. 5, 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. In the rightmost class (N class) in the figure, since all data of N class or more are counted, the frequency is large.

  In this case, as shown in FIG. 5, 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 ninth 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 ninth predetermined period including or adjacent to the determination target time point, and is typically the ninth predetermined period immediately before 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). The distribution of a plurality of body movement determination values for each determination target time point that changes in real time, or the body movement determination values for one night (about 7 to 10 hours) measured in advance. It may be determined from the distribution. 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 an eighth 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, for example, by setting a body movement threshold value from the distribution of measurement data (for example, body movement data) in the eighth predetermined period and setting it as a 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 value Thm (shown in FIG. 5) is, for example, the mode up to a position where the frequency of the mode value in the histogram of FIG. 5 becomes a certain percentage (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 a device in which the respiration monitor 1 monitors the state transition of the person 2 in real time. Therefore, typically, the body motion detection unit 28 performs body motion detection in a period before the determination target time point. Based on the measurement data of the fourth predetermined period excluding the body movement period that is the detected period, a threshold value for determining whether or not there is modulation in the movement of breathing is calculated.

  Also here, as with the body movement reference value, the threshold value actually used for determination at the determination target time point is different from the threshold value calculated at the determination target time point. That is, the second state determination unit 27 described later uses a threshold value calculated at a previous time point (a determination target time point that goes back one time point) as a threshold value that is actually used at the determination time point.

  The fourth predetermined period may be, for example, one minute or one hour, and may be a time sufficient for calculating the threshold value. Preferably, the fourth predetermined period represents the most recent state of the person 2, and is preferably about 1 minute to several minutes, and more preferably a time that does not interfere with real-time monitoring of the person 2 by the breathing monitor 1. It is good to be. In the present embodiment, the fourth predetermined period is, for example, 2 minutes. Here, as shown in FIG. 6A, when the body motion period detected by the body motion detection unit 28 overlaps with the fourth predetermined period, the remaining data excluding the measurement data of the body motion period. Measurement data for a period corresponding to the excluded period is added to the measurement data to obtain measurement data for the fourth predetermined period. Specifically, for example, when the body movement period overlaps with the fourth predetermined period, the measurement data of the period corresponding to the period excluded from the further past measurement data of the fourth predetermined period (2 minutes) Is preferably added (see FIG. 6B). Similarly, when the body movement period overlaps the fourth predetermined period, such as when monitoring of the person 2 is not in real time, from the measurement data on the future side following the determination target time of the fourth predetermined period (2 minutes). Measurement data for a period corresponding to the excluded period may be added (FIG. 6C). That is, measurement data for two consecutive minutes (fourth predetermined period) not including the body movement period can be secured.

  Referring back to FIG. 1, the threshold value calculated by the threshold value calculation unit 29 and used to determine whether there is a modulation in the respiratory motion is determined based on a representative value described later. In other words, the threshold value calculation unit 29 calculates a representative value based on measurement data, here, respiration data, and calculates a threshold value based on the representative value. As described above, the second state determination unit 27 determines that the movement of the person 2 at the determination target time point is not a body movement by the body movement detection unit 28, that is, the determination target time point is a body movement period. If it is determined that there is not, the magnitude of the periodic movement at the determination target time point, that is, the magnitude of the respiratory movement of the person 2 at the determination target time point is determined as the previous time point The abnormality of the person 2 is determined by comparing with the threshold value calculated at the time point), that is, the threshold value calculated with the measurement data of the fourth predetermined period until the previous determination target time point.

  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 value, bottom value, 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.

  In the present embodiment, the magnitude of the respiratory movement of the person 2 at the determination target time is obtained by integrating measurement data from the zero cross to the zero cross before the determination target time, in this case, the respiration data (see FIG. 2A). (Hereinafter simply referred to as “pseudo tidal volume” unless otherwise specified). Here, “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.

  By using the pseudo tidal volume as the magnitude of the respiratory movement of the person 2 at the determination target time point, changes in both the amplitude and interval of the respiratory 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.

  The second state determination unit 27 is configured to calculate a pseudo tidal volume obtained by integrating measurement data from the zero cross immediately before the determination target time point to the zero cross, in this case, respiration data (see FIG. 2). Is done.

Note that the second state determination unit 27 determines that the determination target time point is not the body movement period, but is within the pseudo tidal volume calculation period, that is, the determination target time point from the zero cross two times before the determination target time point. When the body movement period is included in the period up to the zero crossing immediately before, it is preferable that the pseudo tidal volume is not calculated and abnormality is not determined at the determination target time point. With this configuration, the magnitude of the respiratory motion of the person 2 at the determination target time point can be calculated with high accuracy, and the state determination by the second state determination unit 27 can be performed more accurately.
In the following description, unless otherwise specified, the magnitude of the breathing motion of the person 2 will be described in the case of a pseudo tidal volume.

  The representative value described above is calculated based on the magnitude of the periodic movement of the object 2, that is, the magnitude of the movement of the person 2 breathing. Specifically, the threshold value calculation unit 29 may calculate the representative value as an average value of the magnitude of respiratory motion of the person 2 within the fifth predetermined period or an adjusted average value.

  The fifth predetermined period is typically the same period as the fourth predetermined period, and may be, for example, one minute or one hour, as long as a plurality of past determination target time points are included. Preferably, since the fifth predetermined period represents the most recent state of the person 2, approximately one minute to several minutes is appropriate. In the present embodiment, the fifth predetermined period is the fourth predetermined period. The same 2 minutes. In the present embodiment, the fifth predetermined period is the same as the above-described fourth predetermined period, except when the body movement period is included in the period, the period corresponding to the excluded period is excluded. In addition, since the fifth predetermined period is set, the threshold value calculation unit 29 can calculate the average value of the respiratory motion of the person 2 as the representative value or the adjusted average value with high accuracy. The state determination by the state determination unit 27 can be performed more accurately.

  Here, the fifth predetermined period includes a plurality of past determination target time points. In other words, a plurality of pseudo tidal volumes can be obtained from the respiration data of the fifth predetermined period. The threshold value calculation unit 29 calculates a representative value by calculating an average value or an adjusted average value of the plurality of pseudo tidal volumes within the fifth predetermined period.

  Here, the adjusted average value is a plurality of pseudo tidal volumes within the fifth predetermined period, each pseudo tidal volume that becomes the maximum value and minimum value, and a constant value that is close to the maximum value and the minimum value. It means the average value obtained from the remaining pseudo tidal volume excluding the proportion or a certain number of pseudo tidal volumes. In this way, when there is a possibility that specific data is included due to an error or disturbance, the influence can be reduced.

  Note that the threshold value calculation unit 29 may calculate the representative value as a plurality of average values from the larger one of the respiratory motion magnitudes of the person 2 within the sixth predetermined period. That is, the representative value can be an average of a certain number, for example, 3 to 5 pseudo tidal volumes, from the one with the larger value of the pseudo tidal volume within the fourth predetermined period. By doing so, it is effective when the pseudo tidal volume originally obtained by artificial respiration is to be obtained when breathing is unstable. However, it is also possible to set the average value of 3 to 5 pseudo tidal volumes from the largest in consideration of specific noise. Further, as described above, 3 to 5 may be used from the larger one of the remaining pseudo tidal volumes excluding a certain ratio of a value close to the maximum value or a certain number of pseudo tidal volumes.

  Similar to the fifth predetermined period, the sixth predetermined period is typically the same period as the fourth predetermined period, and may be, for example, one minute or one hour. It only has to be included. Preferably, since the sixth predetermined period represents the most recent state of the person 2, approximately one minute to several minutes is appropriate. In the present embodiment, the sixth predetermined period is the fourth predetermined period. The same 2 minutes. In the present embodiment, the sixth predetermined period is the same as the above-described fourth predetermined period, except for the body movement period when the period includes the body movement period, and a period corresponding to the excluded period. In addition, since the sixth predetermined period is set, the threshold value calculation unit 29 can accurately calculate a plurality of average values from the larger one of the respiratory movements of the person 2 as the representative value, and the second state determination unit 27 can be more accurately determined.

  When the amplitude, peak value, and bottom value of the respiration data (see FIG. 2A) are used as the magnitude of the respiration movement of the person 2, the threshold value calculation unit 29 responds appropriately as described above. The representative value to be calculated may be calculated.

  The threshold value calculation unit 29 calculates a threshold value by multiplying the obtained representative value by a certain coefficient. For example, the threshold value calculation unit 29 calculates a value of about 20% to 80% of the obtained representative value as the threshold value at the determination target time point. In the present embodiment, the threshold value is a value that is about 60% of the calculated representative value. When it is determined that the determination target time point is not a body movement period, the second state determination unit 27 calculates a pseudo tidal volume at the determination target time point calculated by the second state determination unit 27 and a threshold value calculation unit. The abnormality of the person 2 is discriminated by comparing with the threshold value calculated at the previous time point calculated by 29 (the determination target time point going back one time point).

  Specifically, the second state determination unit 27 determines that the person 2 has been modulated when the pseudo tidal volume at the determination target time falls below a threshold value. Furthermore, after determining that the person 2 has been modulated, the second state determination unit 27 fixes the threshold value when the pseudo tidal volume falls below the threshold value, and the pseudo tidal volume at the determination target time point is When the fixed threshold value continuously falls for the seventh predetermined period, it is determined that the person 2 is abnormal.

  The seventh predetermined period may be a time sufficient for determining the abnormality of the person 2, and is, for example, about one breath (5 to 15 seconds) to several minutes. In the present embodiment, the seventh predetermined period is about 60 seconds.

  Here, after determining that the person 2 has been modulated, the second state determination unit 27 fixes the threshold value when the pseudo tidal volume falls below the threshold value, so that in the seventh predetermined period, For example, if the person 2 is actually in an abnormal state and the measurement data fluctuates thereby, a state inappropriate for calculating the representative value is included. It can be avoided. For example, when an abnormality actually occurs in the person 2, the magnitude of respiration decreases, and the measurement data when the respiration decreases are sequentially entered as measurement data within the above-described fourth predetermined period. The representative value gradually decreases, the threshold value does not decrease, and an abnormality cannot be detected.

  Although nasal mask ventilation depends on the ventilator's mode of operation, the patient controls the timing of inspiration and the intake volume is controlled by the patient. There is considerable modulation. Some of these modulations are consciously controlled during arousal, while others are unconscious, and especially during sleep, respiratory abnormalities with apnea are also found. In the presence of these modulations, it is necessary to detect that artificial respiration has stopped working and has shifted to spontaneous breathing.

  For example, a patient with spontaneous breathing begins spontaneous breathing when the patient's nasal mask is being ventilated or the ventilator tube is disconnected and sufficient air is not being delivered to the patient. However, since the spontaneous respiration is generally weak, the tidal volume is lower than that during artificial respiration.

  In addition, under normal artificial respiration, the patient's control frequently results in a temporary decrease in respiration. However, on average, a constant respiration rate is secured to maintain a small respiration rate. It is rare to continue for a certain time. That is, it is usually predicted that a large amount of respiration will be mixed with a small amount of respiration. On the other hand, when the ventilator is removed or becomes illegal as described above, the patient himself / herself does not have the ability to take a large breath, so that small breaths continue. Therefore, as described above, when the artificial tidal volume at the determination target time point continuously falls below the threshold for the seventh predetermined period, that is, when a small breath continues for a certain period, the artificial respiration is normally performed. It is possible to determine whether the breathing is being performed or whether the artificial breath is removed and the patient is spontaneously breathing, and it is possible to determine that the patient's breathing is abnormal.

  Further, as described above, when sufficient air cannot be sent to the patient, a patient with spontaneous breathing starts spontaneous breathing, but since the spontaneous breathing is generally weak, the number of breathing increases. That is, the respiratory cycle at the determination target time tends to be short.

  Therefore, the second state determination unit 27 determines that the pseudo tidal volume at the determination target time is below the threshold and has been modulated, and further, when the respiration cycle (respiration interval) at the determination target time fluctuates. The person 2 may be determined to be abnormal. The respiration cycle (breathing interval) at the determination target time point may be calculated from the respiration data (see FIG. 2A) for the tenth predetermined period immediately before the determination target time point. Instead of the breathing cycle (breathing interval), the person 2 may be determined to be abnormal when the reciprocal of the breathing cycle, that is, the number of breathing (breathing rate) fluctuates.

  The tenth predetermined period may typically be a period including at least one normal breathing cycle, for example, about 5 to 60 seconds. Here, the tenth predetermined period is about 15 seconds. Incidentally, the range of normal breathing for an adult is about 2 to 6 seconds for a breathing cycle (breathing interval) and about 10 to 30 times per minute for a breathing rate (breathing rate).

  The second state determination unit 27 has a breathing cycle (breathing interval) at the determination target time point that is, for example, about ± 20% or more with respect to the breathing cycle (breathing interval) at the previous determination target time point. It is configured so that it is determined that there has been a change when it deviates.

  The second state discriminating unit 27 is configured to detect the modulation and further discriminate that the person 2 is abnormal when the respiratory cycle (respiration interval) at the determination target time point fluctuates. If there is no change, it is evidence that the artificial respiration is normally performed. Therefore, even if only the pseudo tidal volume is changed, it can be determined that no abnormality of the artificial respiration has occurred. As a result, if it is difficult to determine abnormalities in breathing based on whether or not the pseudo tidal volume has fallen below the threshold, for example, the amount of breathing is reduced despite normal artificial respiration. In such a case, erroneous discrimination can be prevented.

  As described above, the second state determination unit 27 does not use one fixed threshold value, for example, but uses a threshold value that is appropriately calculated according to the determination target time point. Even when the detection sensitivity and noise level of measurement data acquired by the FG sensor 10 to be described later change due to environmental conditions such as the situation of ambient light, the situation of ambient light, etc., a more accurate discrimination that appropriately corresponds to the change is made. be able to. In addition, since each threshold value at the determination target time point is calculated, the period until the determination becomes possible after the change of the posture is longer than the determination in the first state determination unit 26, It is possible to reliably discriminate abnormalities (for example, a state in which the ventilator has been removed) in which the artificial respiration of a patient with spontaneous breathing does not substantially work, which is not clear and difficult to detect as being stopped.

  In this case as well, in the case where the person 2 is a patient having a respiratory disorder that is not caused by artificial respiration, such as sleep apnea, as in the second predetermined period (discriminant evaluation period) described above, The predetermined period (discriminant evaluation period) of the patient may be longer than the longest respiratory depression (including apnea) time of the patient. That is, the seventh predetermined period (discriminant evaluation period) may be arbitrarily adjusted according to the characteristics of the object 2 and the like. In addition, when the period during which the pseudo tidal volume falls below the threshold continues for the seventh predetermined period (discrimination evaluation period), and the continuous period is shorter than the longest respiratory decrease time, the second state determination unit The determination by 27 may not be performed. Thereby, similarly, the determination error by the second state determination unit 27 can be prevented.

  The respiratory monitor 1 according to the embodiment of the present invention is an alarm that issues an alarm when the person 2 is determined to be abnormal by the first state determination unit 26 or the second state determination unit 27. A device 36 may be provided. The alarm device 36 (see FIG. 1) has means for emitting an alarm sound such as a speaker, for example. When the person 2 is determined to be abnormal, the alarm device 36 (see FIG. 1) emits the alarm sound and informs a doctor, nurse, or the like. Notify 2 abnormalities.

  In this case, the first state determination unit 26 continuously reduces the noise component below the noise component for the second predetermined period based on the comparison result between the signal component compared by the spectrum comparison unit 25 and the noise component. When it is determined that the person 2 is abnormal, for example, the person 2 who does not breathe spontaneously has stopped breathing, that is, the state in which the artificial respiration does not substantially work, the warning signal is sent to the warning device 36 (see FIG. 1). ). In addition, as described above, the second state determination unit 27 determines that the pseudo tidal volume at the determination target time point is a threshold value, and the person 2 is continuously abnormal for a seventh predetermined period, for example, some spontaneous breathing. When it is determined that the ventilator of a person 2 is detached and the respiration is modulated and the respiration is reduced, that is, the respiration is substantially not working, the alarm signal is sent to the alarm device 36 (see FIG. 1). ). The alarm device 36 (see FIG. 1) emits an alarm sound when receiving an alarm signal.

  In this case, even if the first state determination unit 26 and the second state determination unit 27 are not configured to generate an alarm signal, they are different from the first state determination unit 26 and the second state determination unit 27. It is good also as a structure which produces | generates an alarm signal by the alarm means not shown of a body. That is, when the first state determination unit 26 or the second state determination unit 27 determines that the person 2 is abnormal, the respiratory monitor 1 generates an alarm signal based on the determination of the abnormality, It is good also as a structure provided with the alarm means which transmits an alarm signal to the alarm device 36 (refer FIG. 1). In FIG. 1, the alarm device 36 is illustrated as being externally attached, but may be provided internally.

  Further, the arithmetic unit 20 (see FIG. 1) may be configured to notify the occurrence of an alarm to the outside via an interface (not shown) when the alarm device 36 (see FIG. 1) is activated. . The notification is based on, for example, the intensity of light including voice, characters, symbols, and indoor lighting, or vibration. The interface (not shown) has a function of connecting to a communication line such as a general telephone line, an ISDN line, a PHS line, or a mobile phone line.

  FIG. 7 is a diagram summarizing the first predetermined period to the tenth predetermined period of the respiratory monitor 1 according to the embodiment of the present invention. Here, the main predetermined periods described above will be summarized with reference to FIG. The first predetermined period is a period (data accumulation time) for the spectrum calculation unit 24 to calculate the power spectrum, and is, for example, 5 to 30 seconds. The second predetermined period is a period for the first state determination unit 26 to determine a person's abnormality (breathing stop) when the signal component falls below the noise component, for example, 5 to 30 seconds. . The third predetermined period is a period for the spectrum comparison unit 25 to calculate the average value of the sum total of the frequency components, and is, for example, 5 to 30 seconds. The fourth predetermined period is a period for the threshold value calculation unit 29 to calculate the threshold value, and is, for example, 1 to several minutes. The fifth predetermined period is a period for calculating the average value of the respiratory movement (pseudo tidal volume) or the adjusted average value as a representative value by the threshold calculation unit 29 (typically, the first 4 is the same period as the predetermined period of 4), for example, 1 to several minutes. The sixth predetermined period is a period (typically, for calculating a plurality of average values from the larger respiratory movement (pseudo tidal volume) as a representative value by the threshold calculation unit 29. The same period as the fourth predetermined period), for example, one to several minutes. The seventh predetermined period is a period for the second state determination unit 27 to determine a person's abnormality when the pseudo tidal volume falls below a threshold, for example, about one breath (5-15) Seconds) to several minutes. The eighth predetermined period is a period for the body motion detection unit 28 to calculate the body motion reference value, and is, for example, 1 to 2 minutes. The ninth predetermined period is a period used when the body motion detection unit 28 calculates the body motion determination value at the determination target time point, and is, for example, 5 to 10 seconds. The tenth predetermined period is a period for the second state determination unit 27 to calculate the respiration cycle at the determination target time, and is, for example, about 5 to 60 seconds.

  FIG. 8 is a schematic perspective view of the FG sensor 10 of the respiratory monitor 1 according to the embodiment of the present invention. 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. 9 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, the interval 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. 8, 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 measurement range 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. 11) becomes long, so that changes 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. 11) will be described. Here, as will be described later with reference to FIG. 11, the FG sensor 10 measures the movement of bright spots forming a pattern. 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. 11, 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. 11) is long, for example, even a slight movement of the object is greatly reflected in the amount of movement of the bright spot. Can be measured, but jumping may occur, for example, when there is a large movement.

  FIG. 10 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 where the measurement range is the plane 102 and a laser beam L1 described later is projected perpendicularly to the plane 102 will be described. 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. 11 is a conceptual perspective view for explaining the concept of movement of the bright spot 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. 10). 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 light beam L1 (see FIG. 10) generated by the light beam generation unit 105 (see FIG. 10) is typically a light beam of an infrared laser. Further, the laser beam L1 (see FIG. 10) 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 measurement range will be described as the plane 102 and the 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. 12, 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. 9 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.

  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 uses the movement amount waveform data generated as described above as measurement data, the spectrum calculation unit 24 (see FIG. 1) of the computing device 20, the second state determination unit 27 (see FIG. 1), This is output to the body motion detection unit 28 (see FIG. 1), the threshold calculation unit 29 (see FIG. 1), and the like. That is, the movement amount waveform generation unit 142 is a body that is movement data waveform data that is at least the above-mentioned movement amount waveform data that is the sum of movement amounts of bright spots and the sum of absolute values of movement amounts of bright spots, for example. Measurement data including both waveform data and dynamic 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. As a case where the posture or position of the person 2 on the bed 3 is affected, for example, the projection device 11 (see FIG. 8) is placed in the bed 3 (see FIG. 8) in order to increase the base line length d (see FIG. 11). The projection of the pattern 11a (see FIG. 8) 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.

  FIG. 13 is a flowchart showing an outline of processing steps by the respiratory monitor 1 according to the embodiment of the present invention. A method for monitoring an abnormality of the person 2 by the respiratory monitor 1 will be described with reference to FIG. Note that FIG. 1 or FIG. 9 is appropriately referred to for the configuration of the respiratory monitor 1, and FIG. 7 is appropriately referred to for explanation of the main predetermined period.

  First, as the bright spot movement amount acquisition step, two frames acquired by the imaging device 12 at different times for each of the plurality of bright spots 11b (see FIG. 8) projected on the bed 3 by the movement amount calculation unit 141. The amount of bright spot movement between the two frames of the plurality of bright spots 11b (see FIG. 8) is calculated from the image (S100). Next, as a measurement data generation step, 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 (S102). Specifically, the movement amount waveform generation unit 142 generates respiratory data that is movement amount waveform data of the sum of bright spot movement amounts and body movement that is movement amount waveform data of the sum of absolute values of movement amounts of bright spots, for example. Generate data and.

  Next, the body motion reference value calculated by the body motion detection unit 28 based on measurement data (for example, body motion data) within an eighth predetermined period until the previous determination target time point and the body at the determination target time point The movement data (measured value) is compared to determine whether or not the movement at the determination target time point is a body movement, that is, whether or not the determination target time point is included in the body movement period (S104). Here, when the determination target time point is the body movement period (S104; YES), it is determined whether or not the monitoring of the person 2 is continued without determining the abnormality of the person 2 by the respiration monitor 1 at the present time. (S122). When monitoring is continued (S122; YES), the process returns to the bright spot movement amount acquisition step (S100), and the subsequent processes are repeatedly executed. If the monitoring is not continued (S122; NO), the monitoring is terminated. When the determination target time point is not the body movement period (S104; NO), the process proceeds to the next stage body movement reference value calculation step (S106).

  In the case immediately after the start of monitoring by the respiratory monitor 1, the next body motion reference value calculation step (S106) has not been executed yet, and the body motion reference value has not been calculated. May assume that the period is not a body movement period, and may proceed to the body movement reference value calculation step (S106), or a body movement reference value used only immediately after the start may be set in advance.

  In the body movement reference value calculation step, the body movement reference value is calculated by the body movement detection unit 28 based on measurement data (for example, body movement data) within an eighth predetermined period until the determination target time point (S106). The body movement reference value calculated using data up to the current determination target time (including data at the determination target time) is used for detection of body movement at the next determination target time. The body motion detection unit 28 calculates the body motion reference value without including the period that has already been determined to be the body motion period in the eighth predetermined period. The body movement reference value calculation step (S106) may be performed before the step (S104) of determining whether or not the determination target time point is a body movement period. In this case, instead of using the body motion reference value calculated at the previous time point (determination target time point that goes back one point in time) as the body motion reference value actually used at the determination target time point, immediately before the determination target time point ( If the body motion reference value is calculated based on the measurement data (for example, body motion data) within the eighth predetermined period of time (not including the data at the determination target time) and used as the body motion reference value as it is at the determination target time Good. This is because it is determined in the next stage whether or not the measurement data at the determination target time point is body movement. Here, whether or not the measurement data at the determination target time point may be used for calculation of the body movement reference value. I don't know.

  Next, as the power spectrum calculation step, specifically, the spectrum calculation unit 24 uses the FFT to calculate the determination target time point from the first predetermined period (here, about 8 seconds) immediately before the determination target time point. A power spectrum is calculated (S108). Next, the spectrum comparison unit 25 calculates and compares the signal component by taking the sum of the power spectra of the low frequency components and the noise component by taking the sum of the power spectra of the high frequency components.

  The first state determination unit 26 determines whether or not the signal component falls below the noise component based on the comparison result between the signal component and the noise component by the spectrum comparison unit 25 as a breathing stop determination step (S110). When the signal component does not fall below the noise component (S110; NO), the process proceeds to the next S112. When the signal component falls below the noise component (S110; YES), the first state determination unit 26 determines that the period during which the signal component falls below the noise component is the second predetermined period (here, 8). It is determined whether or not the time has elapsed (S114). When the second predetermined period has not elapsed (S114; NO), the process proceeds to S112. When the second predetermined period has elapsed (S114; YES), the first state determination unit 26 2 is abnormal, here, it is determined that it is a state of respiratory stop, and an alarm signal is transmitted to the alarm device 36, and the alarm device 36 issues an alarm (S118).

  In S112, the second state determination unit 27 integrates respiration data from the zero cross to the zero cross just before the determination target time (for example, one breath, one exhalation, or one inspiration) as a breath reduction determination step. Calculate pseudo tidal volume. Further, the second state determination unit 27 calculates a threshold value at which the pseudo tidal volume at the determination target time point is calculated from data up to the previous determination target time point (including data at the determination target time point) by the threshold value calculation unit 29. It is determined whether or not it falls below (S112). In addition, when using the pseudo tidal volume as the magnitude of the movement of breathing as in the present embodiment, the pseudo tidal volume is obtained only at the determination target time immediately after the appearance of a new zero cross. Until the zero cross appears, the pseudo tidal volume calculated immediately before the determination target time point may be used. By doing in this way, the calculation amount in the respiration monitor 1 can be reduced. The same applies when the amplitude is used as the magnitude of the respiratory motion. Further, since the respiratory data itself (measured value) is updated every time, abnormality may be determined by comparing the respiratory data (measured value) with, for example, the average value of the peak and the average value of the bottom each time. .

  When the pseudo tidal volume at the determination target time point does not fall below the threshold value (S112; NO), the process proceeds to the next representative value / threshold value calculation step (S120). If it has fallen (S112; YES), the second state determination unit 27 continues the period during which the pseudo tidal volume falls below the threshold for a seventh predetermined period (here, about 60 seconds). It is determined whether or not the time has elapsed (S116). If the seventh predetermined period has not elapsed (S116; NO), the process proceeds to the representative value / threshold value calculation step (S120). When the seventh predetermined period has elapsed (S116; YES), the second state determination unit 27 determines that the state of the person 2 is abnormal, in this case, the state in which breathing has decreased, The alarm signal is transmitted to the alarm device 36, and the alarm device 36 issues an alarm (S118).

  In the case of immediately after the start of monitoring by the respiration monitor 1, the representative value and threshold value calculation step (S120) has not yet been executed, and the threshold value for determining whether there is a modulation in respiration has not been calculated. The representative value / threshold value calculation step (S120) may be presumed that there is no modulation at the current determination target time point, or a threshold value used only immediately after the start may be set in advance. As mentioned above, it is desirable to detect as early as possible when artificial ventilation stops working in a patient with a certain spontaneous breathing, but it will soon become a serious accident even if it takes a little time to detect There is nothing. If an abnormality in breathing of the person 2 occurs immediately after the start, if the breathing stops or falls to a level close to the stop, it is detected by the above-described breathing stop determination step (S110), so there is a risk to life. This can be avoided.

  In S120, as a representative value and threshold value calculation step, the threshold value calculation unit 29 calculates a representative value based on measurement data, for example, respiratory data, within a fourth predetermined period until the determination target time point (including data at the determination target time point). The threshold value is calculated by multiplying the representative value by a predetermined coefficient (S120). The threshold value calculated at the current determination target time point is used for determination at the next determination target time point. Note that the threshold value calculation unit 29 calculates a threshold value for determining whether or not there is modulation in respiration based on the measurement data of the fourth predetermined period excluding the body movement period among the period before the determination target time point. To do. The representative value / threshold value calculation step (S120) may be performed before the step (S112) of determining whether or not the pseudo tidal volume is below the threshold value. In this case, instead of using the threshold calculated based on the data up to the previous time point (determination target time pointed back one point in time) as the threshold value actually used for the determination target time point, The threshold value may be calculated based on the measurement data (for example, respiratory data) within the fourth predetermined period of time and used as the threshold value for the determination target time point.

Next, it is determined whether or not the monitoring is continued (S122). When the monitoring is continued (S122; YES), the process returns to the bright spot moving amount acquisition step (S100) and the subsequent processes are repeatedly executed. . If the monitoring is not continued (S122; NO), the monitoring is terminated. Each time the measurement data is acquired in the bright spot movement amount acquisition step (S100) and the measurement data generation step (S102), it is determined whether or not the determination target time is the body movement period (S104). In addition, when the data acquisition is performed a plurality of times during the above determination, instead of performing each step of determination of respiratory abnormalities (S110 to S116), the next threshold value is calculated immediately after the determination. Instead, the threshold value may be calculated using the data before the data to be evaluated in the determination after the required number of times of data acquisition until the next determination.

  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, for example, without using a fixed threshold value, the first state determination unit 26 uses the relative relationship between the signal component and the noise component. By determining the stoppage of breathing based on the comparison result, the detection sensitivity and noise of the measurement data acquired by the FG sensor 10 depending on the environmental conditions such as the posture of the person 2, the top situation, the ambient light situation, etc. Even when the level changes, it is possible to make a more accurate and reliable determination appropriately corresponding to the change. In addition, since the first state determination unit 26 performs the determination based on the relative comparison result between the signal component and the noise component, for example, when the artificial respiration circuit is disconnected at the time of the posture change of the person 2 or removed at the time of care If the person 2 forgets or the posture of the person 2 changes due to body movement such as turning over, it is not necessary to set a threshold again over a certain amount of time, and the respiratory stop of the person 2 can be detected quickly. .

  Furthermore, according to the respiratory monitor 1 or the software program according to the embodiment of the present invention described above, the second state determination unit 27 does not use one fixed threshold value, for example, the determination target time point. Measurement data acquired by the FG sensor 10 according to environmental conditions such as the posture of the person 2, the state of the top cover, the situation of ambient light, etc. by determining the state of the person 2 using a threshold value that is appropriately calculated according to Even when the detection sensitivity and noise level of the sensor change, it is possible to make a more accurate determination appropriately corresponding to the change. In addition, it is possible to reliably determine abnormalities in which artificial respiration of a patient with spontaneous breathing does not work substantially (for example, a state in which the ventilator is disconnected), which is not as clear and difficult to detect as respiratory arrest. I can do it.

  Further, when the determination target time point is the body movement period, it is possible to prevent erroneous determination by not determining the state of the person 2, and data indicating body movement in calculating the representative value and the threshold value. By not using, more accurate discrimination can be made.

  Therefore, for example, when a patient who has little spontaneous breathing cannot breathe due to disconnection of the artificial respiration circuit, that is, when the patient's breathing stops or when there is a certain spontaneous breathing, It is possible to prevent the trouble of artificial respiration directly leading to a serious accident when it stops working.

  In addition, since the respiratory monitor 1 uses the FG sensor 10 as a measuring device, the measurement data indicating the state of the person 2 can be acquired without touching the person 2, so that the person 2 is not burdened, so that a healthy person In addition to bedtime respiratory monitoring and in-patient respiratory monitoring, it is ideal for monitoring muscular dystrophy and ALS patients who are constantly ventilating. Furthermore, even small movements of the person 2 such as breathing can be accurately measured.

  In the above description, the respiration monitor 1 is a device that detects an abnormality in the artificial respiration circuit of the person 2 who is performing artificial respiration, and is, for example, a device intended to prevent an accident of artificial respiration. Explained as a thing. However, the present invention is not limited to this, and it goes without saying that, for example, an apparatus that simply discriminates a state where breathing has stopped or a state where breathing has fallen may be used, even if the purpose is not to prevent an accident of artificial respiration. That is, in this case, that the person 2 is abnormal means a dangerous breathing state such as a state where the breathing is stopped or a state where the breathing is lowered.

It is a block diagram which shows the structural example of the respiration monitor which concerns on embodiment of this invention. It is the schematic shown about the example of the waveform pattern which the respiration data and body motion data which are used for the respiration monitor which concerns on embodiment of this invention form. It is the figure which showed an example of the power spectrum calculated by the spectrum calculation part of the respiration monitor which concerns on this Embodiment. It is a diagram explaining the respiration data acquired by the actual measurement of the respiration monitor which concerns on this Embodiment, the signal component compared with a spectrum comparison part, and the data of a noise component. It is a diagram which shows the example of the histogram of measurement data used for the respiration monitor which concerns on embodiment of this invention. It is a figure explaining the case where a body movement period overlaps in the 4th predetermined period with the respiration monitor concerning an embodiment of the invention. It is the figure put together about the 1st predetermined period to the 10th predetermined period of the respiration monitor concerning an embodiment of the invention. It is a typical perspective view of the FG sensor of the respiration monitor which concerns on embodiment of this invention. It is a block diagram which shows schematic structure of the FG sensor of the respiration monitor which concerns on embodiment of this invention. It is a typical perspective view explaining the projection apparatus of the respiration monitor which concerns on 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 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 flowchart which shows the outline of the process process by the respiration monitor which concerns on embodiment of this invention.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Respiration monitor 2 Person 3 Bed 10 FG sensor 11 Projection apparatus 11a Pattern 11b Bright spot 12 Imaging device 14 Measurement part 20 Calculation apparatus 24 Spectrum calculation part 25 Spectrum comparison part 26 1st state determination part 27 2nd state determination part 28 Body motion detection unit 29 Threshold acid calculation unit 36 Alarm device 141 Movement amount calculation unit 142 Movement amount waveform generation unit

Claims (13)

Spectrum calculating means for calculating a spectrum for a first predetermined period based on measurement data indicating a state of an object having a periodic movement due to respiration;
Spectrum comparison means for comparing a low frequency component and a high frequency component of the spectrum based on the spectrum;
State determination means for determining abnormality of the object based on the comparison result of the spectrum comparison means;
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 determination means determines that the object is abnormal when the sum of the spectrum of the low-frequency components continuously falls below the sum of the spectrum of the high-frequency components for a second predetermined period.
State analysis apparatus. Spectrum calculating means for calculating a spectrum for a first predetermined period based on measurement data indicating a state of an object having a periodic movement due to respiration;
Spectrum comparison means for comparing a low frequency component and a high frequency component of the spectrum based on the spectrum;
State determination means for determining abnormality of the object based on the comparison result of the spectrum comparison means;
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 determining means detects that the object is abnormal when the average of the third predetermined period of the sum of the spectrum of the low frequency components falls below the average of the third sum of the spectrum of the high frequency components. It is determined that
State analysis apparatus. The state determining means detects that the object is abnormal when the average of the third predetermined period of the sum of the spectrum of the low frequency components falls below the average of the third sum of the spectrum of the high frequency components. It is determined that
The state analysis apparatus according to claim 1. Aperiodic motion detection means for determining and detecting aperiodic motion that occurs with the periodic motion based on the measurement data;
The state determination means does not determine that the object is abnormal when the movement at the determination target time is determined to be aperiodic movement by the aperiodic movement detection means,
The state analysis apparatus according to any one of claims 1 to 3. Based on the measured data, and the non-periodic motion detecting means for detecting to determine the non-cyclic movements occur with the periodic motion;
Whether the periodic motion is modulated based on the measurement data of the fourth predetermined period excluding the period in which the non-periodic motion detecting unit detects the non-periodic motion among the periods before and after the determination target time point Threshold calculating means for calculating a threshold for determining whether or not;
When the movement at the determination target time point is determined not to be a non-periodic movement by the aperiodic movement detection unit, the magnitude of the periodic movement at the determination target time point is compared with the threshold value, thereby State discriminating means for discriminating abnormality of the judgment object;
The state analysis apparatus according to any one of claims 1 to 3 . The magnitude of the periodic movement at the determination target time point uses a value obtained by integrating the measurement data from zero cross to zero cross before the determination target time point.
The state analysis apparatus according to claim 5. The threshold is determined based on a representative value,
The representative value is an average value of the magnitude of the periodic movement within a fifth predetermined period, or an adjusted average value.
The state analysis apparatus according to claim 5 or 6. The threshold is determined based on a representative value,
The representative value is a plurality of average values from the larger one of the magnitudes of the periodic movements within a sixth predetermined period.
The state analysis apparatus according to claim 5 or 6. The state determining means determines that the object is modulated when the magnitude of the periodic movement at the determination target time falls below the threshold.
The state analysis apparatus according to any one of claims 5 to 8. After determining that the object has been modulated, the state determination unit fixes the threshold value, and the magnitude of the periodic movement at the determination target time point continues the fixed threshold value for a seventh predetermined period. When the target falls down, it is determined that the object is abnormal.
The state analysis apparatus according to claim 9. The state determination means determines that the object is modulated, and further determines that the object is abnormal when the period of the periodic movement at the determination target time fluctuates.
The state analysis apparatus according to claim 9. A software program installed on a computer and operating the computer as a state analysis device;
Calculating a spectrum for a first predetermined period based on measurement data indicative of the state of an object with periodic movement due to respiration ;
Based on the spectrum, comparing the low frequency component and the high frequency component of the spectrum;
Based on the comparison result, it determines an abnormality of the object;
Obtaining said measurement data;
In the acquisition of the measurement data, a plurality of bright spots are projected onto the target area, the target area on which the plurality of bright spots are projected is imaged, and two frames of images acquired at different time points by imaging the target area are used. Calculating a movement amount of the plurality of bright spots between the two frames, and generating movement amount waveform data in which the movement amounts are arranged in time series;
In the determination of the abnormality of the object, it is determined that the object is abnormal when the sum of the spectra of the low frequency components continuously falls below the sum of the spectra of the high frequency components for a second predetermined period. ,
Software program. Based on the measured data, it detected to determine the non-periodic motion which occurs with the periodic motion;
Based on the measurement data of the fourth predetermined period excluding the period in which the non-periodic motion is detected among the periods before and after the determination target time point, a threshold for determining whether or not the periodic motion is modulated is calculated. ;
When it is determined that the movement at the determination target time point is not an aperiodic movement, an abnormality of the determination target object is determined by comparing the magnitude of the periodic movement at the determination target time point with the threshold value. ;
The software program according to claim 12 .