JP2003290174A - Method for monitoring living body organ - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a method for monitoring living both organs which minotors cardiopulmonary functions while determining them by sampling repeated actions in the cardiopulmonary functions of the living body as overwrite information. <P>SOLUTION: Repeated actions in the cardiopulmonary functions are plotted in one cycle, sampling locations on a time axis are set dense or coarse in terms of the usefulness from medical standpoints and memory parts 5 are arranged correspondingly to the respective sampling locations to make the memory parts 5 store sampling data while a standard deviation value is calculated to set upper and lower limit values. Actually measured values of the repeated actions in the cardiopulmonary functions are compared with upper and lower waveforms (data) with respect to the standard deviation value per sampling location, thereby monitoring life sustaining activities. This also allows the detection of abnormality at each of the sampling locations thereby achieving quicker treatment. <P>COPYRIGHT: (C)2004,JPO

Description

Detailed Description of the Invention

[0001]

TECHNICAL FIELD The life support of a living body (animal) is
It is maintained by repeating respiratory activity and blood circulation exercises. Regarding the movement of the lungs for breathing and the movement of the heart for blood circulation, the presence or absence of abnormality can be known by a spirometry test, an electrocardiogram test, or the like. In the present invention, with respect to the organs that perform a periodic operation, the average value and the standard deviation value of the data measured for each of the same periodic measurement points are calculated,
The present invention relates to a method for monitoring a living organ using these as criteria.

[0002]

2. Description of the Related Art Conventionally, in the living body, for example, in human beings, the lungs and heart work to maintain life. this house,
The heart beats about 100,000 times a day, and the beat speed also changes depending on the environment. Electrocardiogram examination is effective in understanding such movements of the heart. The inspection method involves placing a person on a bed, attaching a plurality of electrodes to the body, detecting changes in the weak current, inputting this detected current into an electrocardiographic recorder, and recording it on a sheet fed at a constant speed. This electrocardiogram device is complicated because it uses a plurality of electrodes, but there is one that uses a non-contact radio wave sensor for the human body that utilizes the Doppler effect to inspect the heart rate (Application No. 10-276).
(See No. 500). In this inspection process, after the motion signal from the radio wave sensor is A / D converted, a waveform signal is generated by fast Fourier transform and used as data.

By the way, the waveform of the electrocardiogram and the pulse are closely related, and the three spike waves of P, QRS, and T are shown as a waveform that changes with one heartbeat. In the electrocardiographic recorder described above, the x-axis is the time axis and the y-axis is the magnitude of the detection signal, and the waveform is output in analog display. It is also possible to store a plurality of numerical data in the memory circuit with the time axis of the measured waveform as a plurality of measurement points and reproduce the numerical data for diagnosis.

Therefore, the interval from the pulse of the heart beat to the pulse is 1
In the case of taking one cycle of sample data as a cycle, the time axis is set on an equal scale and measurement points (sample points) are set on this scale. Then, each measured value is stored in the storage circuit in time series, and the data of the first cycle is used as temporary reference data. The maximum value and the minimum value are saved by comparing each measured value measured in time series of the next one cycle with the data stored for each sample point. Hereinafter, the measurement is performed a plurality of times, and the replacement operation is performed as necessary to obtain the maximum value and the minimum value of each sample point. For example, 1 cycle of data is 1000
Repeatedly draw a maximum value waveform that connects the maximum values of each sample point and a minimum value waveform that connects the minimum values of each sample point, and compare the maximum value waveform and the minimum value waveform with the actual measurement value of the living body. Understand the condition of the living body.

That is, one cycle of digital data is analogically displayed as a maximum value waveform M and a minimum value waveform m on a display screen of a device for monitoring the operation of an organ of a living body, and the operation of the organ is measured at sample points. The measured values are plotted on the pattern. When the actually measured value waveform R intersects the maximum value waveform M or the minimum value waveform m at a certain point in time, it is often determined that the organ to be measured is abnormal.

[0006]

However, the number of heart beats is 86,400 times a day when 60 times a minute, and about 260,000 times a month. In such organs, 1
It may be considered that the subject has a long constraint time to determine the maximum value and the minimum value to be compared with the sample number of 000 (about 17 minutes in a row). Also, if the number of samples is too small,
There is a chance element that there was a large (small) value that happened to be very stable during the measurement.

Therefore, conversely, in the case of the overwriting method, if the number of sample measurements is too large, the data when unstable or in a bad state is mixed, so that it is difficult to monitor accurately. That is, as shown in FIG. 15, for the waveform (a) in which the heart rate is overwritten with a small number of measurements,
The waveform (b) of the average value, the maximum value, and the minimum value obtained from these calculations may be a distorted waveform like the maximum value waveform portion indicated by the arrow A due to chance. Further, when the average value, the maximum value, and the minimum value waveform (d) are obtained from the overwritten waveform (c), this waveform becomes a considerably dull waveform. Also, assuming that the human breathing is once every three seconds, it becomes about 28,000 times a day. Also here, if the number of samples is reduced and the average value, the maximum value and the minimum value are taken, accidental factors are introduced and the reliability is lowered as described above.
In addition, as shown in FIG. 16, when the waveform (e) indicating the respiratory state of the lung is overwritten to obtain the waveform (f) of the maximum value and the minimum value of the data, the waveform may become dull overall. It is not suitable as sampling data.

In addition, since the conventional allowable operating range of organs is set as continuous upper limit data and continuous lower limit data, the judgment cannot be obtained until the inspection for one cycle is completed. I can't respond quickly.
Therefore, there has been a demand for an apparatus capable of detecting an abnormality even during the measurement of one cycle.

Since the purpose of the present invention is to grasp the state of each life-sustaining activity that is repeatedly performed in the organ of the living body and to monitor and play a part in medical treatment, it is possible to grasp the state of the organ of the living body. An object of the present invention is to provide a method for monitoring a living organ, in which a sensor is provided, the output of the sensor is calculated to create monitoring data, and the monitoring data is compared with the actual measurement value at the present time of the living body. .

[0010]

In order to achieve the above-mentioned object, the present invention provides the invention according to claim 1 in which the cycle from the setting start point to the setting end point of the repetitive motion of an organ in a living body is defined as one cycle. A plurality of points that are useful as data are set as sampling points respectively, and the amount of change at each sampling point for each cycle of the device is measured multiple times and the data is saved, and the standard deviation value is set for each sampling point. It is characterized in that the measured value of the organ is collated with the standard deviation value to determine the presence or absence of abnormality of the organ. As an example of the above configuration, among signal changes of repetitive activity (1 cycle),
Important parts useful for diagnosing abnormalities of living organs are sampled at short time intervals (example: 1 ms), and unimportant parts are sampled at long time intervals (example: 10 ms) to save data.

According to a second aspect of the present invention, in the first aspect of the present invention, the sampling data is individually stored in each storage section of the storage circuit, and the actual measurement value of the sampling location is judged to be normal or abnormal in the storage section and is notified. A feature is that an arithmetic program is provided. Referring to the above example, since the sampling execution time is sufficiently shorter than 1 ms, the program is executed during that time.

According to a third aspect of the present invention, in the first or second aspect of the invention, a large number of sampling points on the time axis are set in a useful sampling area, and a small number of sampling points on the time axis are set in a non-useful area. It is characterized by

According to a fourth aspect of the invention, in any one of the first to third aspects of the invention, the number of sampling points is increased in the important area of the medical finding during one cycle of the operation of the organ of the living body. Is characterized by.

According to a fifth aspect of the present invention, in any one of the first to fourth aspects of the present invention, the arithmetic program obtains an average value and standard deviation value of sampling data, and the average value or standard deviation value and the organ. It is characterized in that the presence or absence of abnormality of the organ is determined by comparing the measured value with.

According to a sixth aspect of the invention, in any one of the first to fifth aspects of the invention, the standard deviation value is multiplied by a coefficient to set an upper limit value and a lower limit value, and the actually measured value of the organ is set to the upper limit value. It is characterized in that the presence / absence of abnormality of the organ is determined in correspondence with the actual measurement permissible range of the value and the lower limit value.

[0016]

BEST MODE FOR CARRYING OUT THE INVENTION Embodiments of the present invention will be described below with reference to the accompanying drawings. First, the organ of the living body, for example,
As shown in FIG. 12, a monitoring device for grasping or monitoring the functional state of the heart is provided with a plurality of sensors 1 and an interface 2 thereof, and the output of the interface 2 is an arithmetic circuit controlled by a control circuit 3. 4 is connected. A storage circuit 6 including a plurality of storage units 5 and a display unit 7 are connected to the arithmetic circuit 4. Further, the control circuit 3 controls each part according to the setting of the operation unit 8 to adjust the environmental equipment (chair, bed, etc.) 9 and the posture of the subject placed on the environmental equipment 9.
Camera to observe the situation (CCD camera, radio wave sensor, etc.)
The adjustment of 10 is also controlled by the instruction of the operation unit 8. Therefore, since the monitoring device calculates the output of the sensor 1, the type of the sensor 1 and the mounting position of the sensor 1 largely depend on various measuring machines.

Further, the monitoring device is provided with an abnormality notifying circuit 11 for detecting mechanical failure of the sensor input section, and the output thereof is inputted to the control circuit 3. The control circuit 3 also has a self-diagnosis function. Further, the exchange of data inside the monitoring device can be operated from the outside by the external connection unit 12, and further the data can be output to the outside. Further, as shown in FIG. 13, it is possible to transmit a plurality of pieces of detection data to a plurality of test subjects to the outside and connect them to an external model. The bed (environmental tool 9) shown in FIG. 13 includes a pressure-sensitive sensor (piezoelectric-type / differential pressure sensor, etc.) 13, an electroencephalogram sensor 14, a pulse / electrocardiogram sensor 15, and an interface 2 for monitoring a breathing motion. There is.

According to the present invention, when monitoring life-sustaining activities of a living body, sample data to be compared is the posture of the target living body,
It is carried out in a configuration that can eliminate major involvement in mental and physical conditions. Then, one breath and one heartbeat are overlapped as one cycle unit, and as will be described later, the average value and the standard deviation value are calculated for each measurement point in one cycle, and the actual measurement value is monitored by using these as the monitoring reference. is there. First, the collection of sampling data for monitoring the function of a living body organ will be described. Here, when the lung is used as the organ, the pressure sensor 13 is used. When the heart is used as the organ, the sensor used is the pulse / electrocardiogram sensor 15. When collecting data for one cycle, pulse processing is performed. This explanation will be easier to understand by taking an electrocardiogram waveform as an example. That is, as shown in FIG. 14, when all the waveforms are used in the series of electrocardiographic waveforms (a), the waveforms are shifted due to the difference in pulse intervals, which is not preferable as described in the prior art overwriting. Therefore, this waveform is pulsed in the arithmetic circuit (b).
do. Then, preferable pulse data is selected by measuring the time between pulses.

More specifically, in the previous step, the plurality of pulsed waveforms are made to come to the same position in one cycle at the time of measurement, and the electrocardiogram waveform is sampled in time series,
A plurality of cycles of sampling data are stored in a plurality of storage units. Then, of the collected pulse intervals, for example,
The pulse interval suitable for medical treatment (high frequency) is set to 1 (see FIG. 10). Alternatively, the data in the excited state or the resting state may be prepared in advance, and the pulse intervals (high frequency) suitable for medical treatment in this state may be assigned as the setting 2 and the setting 3. Next, in the pulse interval shown in FIG. 14B, the pulse intervals before and after the electrocardiogram waveform are different from others. In this case, the electrocardiogram waveform is to be checked, and by deleting the waveform in order to align the sampling data and deleting the waveforms in the previous stage and the waveform in the subsequent stage, the electrocardiogram waveform used as shown in FIG. 14C. Have the same waveform, and the same pulse-to-pulse time.

In this way, the sampling waveform is selected and the monitoring data is created.

Next, in the acquisition of waveform data of a plurality of cycles, which part is useful and important for the purpose of monitoring will be described.

First, in setting the measurement conditions, the activity of one cycle of the organ of the living body is finely sampled once in a time series, and the change amount for each sampling during the sampling is detected to detect the medical and waveform changes. The dense and coarse areas of sampling are determined in consideration, and the number of samplings between them is set. The medical consideration is to shorten the sampling time interval and increase the amount of information for the important part of the life support activity useful for medical treatment in monitoring the life support activity of the living body. In addition, since the data transition is small in the region where the change in life-sustaining activity is gradual, the sampling time interval is lengthened and the minimum sampling is performed.
Effectively use the storage section to save measurements.

During the sampling, when the organ of the living body is in a stationary state, the amount of change in the sampling data of the first cycle measured in advance is detected to determine the sampling time interval. In the next cycle, data is collected while correcting the sampling time interval based on the data of the first cycle.

The graph shown in FIG. 1A shows the pressure sensor 1
The subject breathes in a bed in which 3 is placed on his / her back, and the pressure change signal of the pressure-sensitive sensor 13 generated in response to the breathing motion is continuously measured. The monitoring device performs data processing. , A screen image of the display unit 7. The part in the figure is the region where the value of the pressure sensor starts to rise due to the inspiratory action, the part of the region where the inflation and contraction of the lungs changes due to breathing, the part to the region where the exhaling action begins to descend, and the next part Is an area where the breathing motion is stationary.

In the measurement shown in FIG.
The time interval for sampling the region is made different as necessary based on the data collected in advance. Further, the number of sampling data in one cycle from the first breathing motion to the next breathing motion is set to 332, and the sampling data is performed every 2.6 seconds. In addition, the waveform shown in FIG.
This is an application of the conventional technique of sampling four regions in a time series at equal intervals (10 ms). The number of samples in the region is 50, and the number of samples in the lung fluctuation region in the region is 30. The number of samples is 60, and the region in which the breathing motion is stopped has a long time interval, so the number of samples is 120.
Therefore, the number of samples in one cycle is 260, which shows that the sampling method of the present invention is appropriate.

The portion shown in FIG. 1 (a) is an upstream slope monitoring area, the sampling time interval is 5 ms, the number of samplings is 100, and the portion shown as a fluctuation monitoring area is 3 ms, the sampling number is several hundred. The part of, as the down slope monitoring area,
Sampling time interval is 5ms, sampling number is 1
Twenty are flat monitoring regions, the sampling time interval is 100 ms, and the number of samples is twelve. The number of sampling data in one cycle from the first breathing motion to the next breathing motion is 332, which is performed every 2.6 seconds. In addition, although FIG. 1B is also performed every 2.6 seconds, the former can finely monitor a region in which the waveform change is larger in the same time band.

Next, each area from FIG. 1 will be described. The area (2) is an upslope monitoring area in which the movement of the human body due to the inspiratory movement is measured by the pressure sensor, and is stored for every three cycles. Point 1 to point 100
If the interval time of sampling up to is 5 ms, the required time is 0.5 seconds (5 ms × 100 = 0.5 s).

The region (2) is the central portion of the respiratory action of inhalation and exhalation of the human body, and is a variation monitoring region in which the movement is measured by the pressure sensitive sensor, so that all cycles are stored in the storage unit. Point 10
If the interval time of sampling from 1 to point 200 is 3 ms, the required time is 0.3 seconds (3 ms x 10
0 = 0.3 s). Since the area (2) is a downward slope monitoring area in which the movement of the human body due to the exhaust operation is measured by the pressure sensor, it is saved in the storage unit every 5 cycles. Point 2
If the sampling interval time from 01 to point 320 is 5 ms, the required time is 0.6 seconds (5 ms x 1
20 = 0.6 s). Since the area (1) is a flat monitoring area in which the inspiratory movement associated with the breathing movement of the human body is not performed, it is not stored in the storage unit or is stored in the storage unit every 50 cycles. Assuming that the sampling interval time from the points 321 to 332 is 100 ms, the required time is 1.
It takes 2 seconds (100 ms × 12 = 1.2 s).

When data is collected in this way, in the connected storage circuit, as shown in FIG.
The data measured at the predetermined sample points according to the amount of the sample numbers of ,, and will be stored in the corresponding storage units. In addition, when the regions and are converted into waveforms in FIG. 2, a continuous waveform of only the breathing motion is obtained.

Next, the sampling process will be described with reference to FIGS. First, in the condition setting of step 1, the sampling speed and the number of times of sampling are set in the order of regions based on the existing or latest measurement waveform.
If necessary, set conditions with external devices and external output settings. In step 2, sampling is started. In step 3 (and 4, 5), the position, posture, environmental condition, etc. of the subject are checked. If the condition is kept constant (the process goes to step 4 or 5 depending on the condition), the process proceeds to step 6. For example, step 3 is a constant supine position,
Step 4 is categorized as a right sideways state, and step 5 as a state in which the legs are bent. Also, instruct the subject to meet this condition.

Sampling is possible in each posture of the subject, and the storage unit can also store sampling data for each condition. Therefore, since the same process is followed for the outputs of steps 3, 4 and 5, only the process subsequent to the output A will be described with reference to FIG.
If a signal to detect that the posture has changed due to camera data is received during the processing from step 6 onward, the procedure forcibly returns to step 3 (4, 5) and the intermediate data of the latest 1 cycle during measurement is Do not save.

In step 6 of FIG. 4, when there is no input of a passage trigger (internal trigger) or another trigger (external trigger), a standby state is entered here. When the passage trigger is generated, in step 7, the initially set sampling speed is set. In step 8, it is judged whether the sampling point has been reached, and if it has reached (YE
(In the case of S), measurement is performed in step 9, and the sampling data is stored in the storage unit 5.

Here, according to the set value n, step 7
Repeat steps 10 to 10 to measure and save. The setting of n is as follows. n = 1 saves data for all measurement cycles. When n = 2, the data is saved by skipping one cycle. When n = 5, data is stored once every 5 cycles. When n = X, data is stored once every X cycles. When the number of set data is reached in step 10, the process proceeds to step 11 and the sampling speed is set to 5 ms, 3 m.
s, 5 ms, 100 ms.

In step 12, it is judged whether or not the breathing motion has been able to collect one cycle of data at a series of sampling rates, and if not, the procedure returns to step 7 to continue sampling at the changed sampling rate. Then, if the data for one cycle can be collected, it is determined in step 13 whether or not the data for the set number of cycles has been obtained.

When all the data are judged and stored in step 13, the measurement is completed (step 14), and if the data for the set cycle has not been taken yet,
The process returns to step 6 and waits for a trigger. When the measurement is completed (step 14), the data is calculated in step 15. It should be noted that, due to a time margin, it is possible to perform an operation according to a program at the time of storing data in the storage unit 5 shown in FIG. 12, and store the result and data together in the storage unit 5. Further, the command may be sent together with the data processed in step 16 according to the capability of the receiving side.

Next, with reference to FIGS. 5 and 6, the creation of the reference waveform to be compared with the actually measured value will be described. Here, for ease of explanation, the area where the above-mentioned set value n is set to 1 and the data of all the measurement cycles are stored are used. Further, m: total number of cycles measured z: number of points in one cycle; I is a number from 1 to z m × z: total number of data measured. Here, 320000 pieces z = data quantity + data quantity + data quantity. Here, z = 320. The number of times of measurement of the number of breathing motions in the normal backless state measured is
000 times (the number of cycle measurements is 1000 = m).

In FIG. 5, the standard deviation value for each of the same measurement points of the 1000 cycle data measured and stored in step 17 is obtained, and the standard deviation value is set to be three times that value. The calculation is started in step 18. Step 1
In step 9, I = 1 is set in order to perform the calculation for obtaining the standard deviation value at each first point of the 1000 cycle data. If I = 2, the standard deviation value at each second point of 1000 cycle data is obtained. If I = x, the standard deviation value at each x point of 1000 cycle data is obtained.

In step 20, the standard deviation value is calculated,
In step 21, the calculated standard deviation value and the calculated value that appears during the calculation are saved. In step 22, the standard deviation value of the calculation result is multiplied by the coefficient A, and in step 23, the value is stored. In step 24, it is checked whether the value of I has reached the number of data in one cycle. If not reached, I = I + 1 is calculated in step 25, one point is added, and the process returns to step 20 to continue the calculation. If so, the calculation is completed up to the last point of one cycle, and the process ends in step 26.

FIG. 6 is a table of the explanation of FIG.
The standard deviation value is calculated by calculating the data from the first cycle to the m-th cycle of I = 1 side by side. Similarly, the standard deviation value is calculated by calculating the data from the 1st cycle to the mth cycle in a row with I = 2. This is repeated until I = z. When I = z, 320 standard deviation values obtained from s1 to sz for all data of m (1000) cycles for each measurement point are created. The 320 pieces are multiplied by the coefficient A to create a standard deviation value waveform.

In FIG. 7A, the standard deviation value (s) obtained at each sampling point is multiplied by a coefficient A (for example, 3), and the value of 3 × s at each sampling point is connected along the point. A continuous 3s curve (standard deviation value waveform) (making an upper limit value on the plus side and a lower limit value on the minus side with respect to the average value waveform) and a maximum value waveform / minimum value waveform obtained by a known method are shown. Sampling point A shown in FIG.
Is a point obtained by multiplying the standard deviation value (s1) by a coefficient 3 as shown in FIG. 7B, and the sampling point B is the standard deviation value (s2) as shown in FIG. 7C. This is the point multiplied by the coefficient 3. 7 (b), (c)
The vertical axis of is the number of samples, and the width of the standard deviation value (s1) is narrow in the histogram of FIG. 7B, and the number of sampled data is also gathered near the average value. Also, FIG.
In the histogram of (c), the standard deviation value (s2) has a wide range, but the number of sampling data is mostly near the average value. In this way, even when the waveform is viewed fragmentally, it is easy to compare the average value with the maximum and minimum values obtained by a known method and the actual measurement values at that time.

The histogram and the value of the standard deviation value (s) are different at each sampling point, and the upper and lower limit waveforms created by connecting the standard deviation value of each point by multiplying the coefficient inevitably have high accuracy. The upper and lower limit values become narrower. From this, even if the number of sampling measurements is small, a 3s waveform (upper and lower limit value waveform) representing the accuracy and state more accurate than the conventional maximum value waveform and minimum value waveform can be obtained.

Further, the statistical defective rate can be set without measuring all the cycle numbers by the statistical method. That is, in the 1 s waveform of the coefficient 1, 68 to 70% of the whole falls within this determination range, and the respiratory activity is good. Further, it is possible to create a continuous 2s curve in which 2 × s values of coefficient 2 are connected along points, and a continuous 3s curve in which 3 × s values of coefficient 3 are connected along points. Monitoring width is 1s, 2s, 3s
Besides, the width of s multiplied by another coefficient may be used. Plus / minus 3s in the deviation value waveform centered around the average value waveform
When the value exceeds the deviation value of, the waveform becomes more suitable for the breathing action than the maximum value waveform obtained by connecting the conventional maximum value waveforms, and 99.8% of the whole in this monitoring width is a good breathing action.

Here, the storage section 5 of the storage circuit 6 that stores each sampling data will be described. When the sampling data stored in the storage unit 5 is captured at intervals of 3 ms and 5 ms, the capturing time is shorter, and the calculation and transfer / communication processing can be sufficiently performed in the remaining time. A communication program is provided. Therefore, these results are also stored in the storage unit 5. Further, the display unit 7 is not to show a table of numerical data and compare them, but rather to display the measured value, the upper limit value and the lower limit value on the screen screen by color-coding them by RGB processing, which is an abnormal breathing motion actually measured. You can easily see if
Reduce monitoring work. If there is a unique waveform, it is recorded (stored in a dedicated storage unit).

Next, the determination and processing when an abnormality occurs will be described. As shown in FIG. 8, the average value waveform and the upper and lower limit waveforms are color-coded and displayed on the screen screen.
A down slope monitoring area and a fluctuation monitoring area are set.
As a result, the actual measurement values are plotted between the upper and lower limit waveforms when the trigger is started, making it easy to see. When the normal mode is set, as shown in FIG. 13, it is always connected to a personal computer or a server.

The condition for the abnormality determination is, for example, in the up / down slope monitoring area during one cycle, when 5 points or more consecutively exceeding the upper limit or the lower limit occurs for 20 consecutive cycles, it is determined as an abnormality. And transmit a caution signal. In the variation monitoring area during one cycle, if 10 points or more continuously exceeding the upper limit or the lower limit occur continuously for 10 cycles, it is determined to be abnormal and a caution signal is transmitted. In the fluctuation monitoring area during one cycle, if 100 points or more continuously exceeding the upper limit or the lower limit occur for 20 consecutive cycles, it is determined to be abnormal, and an emergency signal is transmitted. In addition, it performs a pre-programmed process. The upper and lower limit waveforms in which the standard deviation values of 5 × s of the coefficient 5 are connected are created, and a waveform exceeding the upper limit waveform is regarded as a motion different from the respiratory motion of the monitoring target, and the monitoring can be invalidated. In this way, the condition of the subject can be grasped and the life support activity of the lung can be monitored by the monitoring device equipped with various sensors.

Next, the pulse / electrocardiogram sensor 1 is attached to the subject.
In the method of attaching 5 and monitoring the condition of the subject, electrocardiogram sampling data is required. In sampling, as shown in the explanation in FIG. 14,
Align the same pulse intervals. Therefore, as shown in FIG. 10, in Steps 3, 4, and 5, the pulse interval time range is selected. Further, in this flowchart, the subsequent procedure is the same as that described so far, and therefore will be omitted. Further, in the electrocardiographic data, a large number of sampling points are taken in a region having a large waveform fluctuation, and a small number of sampling points are taken in other parts. Therefore,
In the electrocardiographic waveform according to the present invention, the three spike waves of P, QRS, and T are emphasized and other portions are reduced, so that three convex portions are formed as a waveform as shown in FIG. It will be different from the waveform.

Next, in the method of attaching the electroencephalogram sensor 14 to the subject and monitoring the condition of the subject, the electroencephalogram sampling data is required. In this case as well, the same method as above is used, and therefore different points will be described regarding the case of measuring the electroencephalogram. First, the subject is calmed down, and as shown in FIG. 11, the stable state of the electroencephalogram is checked in step 3 after the start. Next, in order to set one cycle of the electroencephalogram, an "internal pulse" or an artificial "external pulse" obtained by pulse processing the electroencephalogram is used, but in order to select an appropriate one, the pulse interval time range is set in Steps 4 and 5. Select setting 1 or setting 2. When a pulse other than the set pulse is input, the process does not proceed to the next data processing, and the standby state is set in step 4. Since the subsequent data processing is the same, it will be omitted.

Regardless of horses, dogs, humans, etc., the target of the living body can be examined by a human dog or an outpatient in the case of a human, and in the case of a healthy person, a predetermined load may be applied to obtain data.
Further, one cycle can be determined by repeatedly changing the environmental conditions, and the values of blood pressure and body temperature can be taken as data. In addition, the words normal and abnormal were used to distinguish the data, but since sample data may be collected from patients, normal data is read as reference value data, average value data, normal value data, etc. It may be read as departure data.

[0049]

The present invention is as described above. In the invention of the method according to claim 1, when the organ of the living body repeatedly performs life-supporting activity, a plurality of useful sites in one cycle of repeating Is sampled to obtain a standard deviation value for each location, and the actually measured value of the organ is collated with the standard deviation value for monitoring. Therefore, it is possible to accurately monitor the life support activity of the living body. According to the invention of claim 2, in the invention of claim 1, since an arithmetic program is provided for each storage unit that stores sampling data, normality or abnormality can be detected quickly even during sampling, and the monitoring capability can be achieved. Will increase. In the invention of the method according to claim 3, in the invention of claim 1 or 2, a large number of sampling points on the time axis are set in a useful area of sampling in the life support activity of a living body, and the time axis is set in an ineffective area. Since the number of sampling points is set to be small, it is possible to effectively utilize the storage unit, and it is possible to monitor the organs of the living body in detail during life support activities. The invention according to claim 4 is the method according to any one of claims 1 to 3, wherein the number of sampling points is increased in the area of the medical finding of the organ of the living body, so that the life sustaining activity of the living body is uncertain. Can be monitored in detail. According to a fifth aspect of the present invention, in the method of any one of the first to fourth aspects, the arithmetic program obtains an average value and a standard deviation value of sampling data,
Since the average value or the standard deviation value is compared with the actually measured value of the organ, the operation data at that time can be obtained in the storage unit even during sampling, and the state of the living body can be grasped and monitored. . A sixth aspect of the present invention is the method according to any one of the first to fifth aspects, in which sampling data for monitoring life-support activity of a living body is stored in the storage unit as a coefficient (integer) to a standard deviation value. ) Is set to set an upper limit value and a lower limit value, and the actual measurement values of the life-supporting activities of the living body are compared according to the actual measurement allowable range. As a result, it is possible to grasp the state of the living body at that time and determine whether or not there is an abnormality, and it is possible to continuously perform accurate monitoring in accordance with the life support activity of the living body. In addition, since the necessary data is stored in each storage unit, it is possible to statistically grasp the life support activity of the living body as compared with the conventional case of monitoring with the maximum and minimum waveforms.

[Brief description of drawings]

FIG. 1 shows continuous sampling waveforms (a) with different sampling interval times according to the present invention and conventional continuous sampling waveforms (b) with different sampling interval times according to the present invention for monitoring a living organ in the present invention. FIG.

FIG. 2 is a diagram illustrating a sampling point and a method of collecting data for monitoring a living organ according to the present invention.

FIG. 3 is a flow chart for performing breathing sampling according to an embodiment of the present invention.

FIG. 4 is a flow chart of a breathing sampling process according to an embodiment of the invention.

FIG. 5 is a flowchart for calculating a standard deviation value for each point according to the embodiment of the present invention.

FIG. 6 is a diagram illustrating calculation of standard deviation values in the flowchart shown in FIG.

FIG. 7 is a diagram showing a standard deviation value waveform of one cycle, a maximum / minimum waveform (a), and histograms (b) and (c) of each part according to the embodiment of the present invention.

FIG. 8 is a graph showing sampling time intervals in one cycle of breathing motion according to an embodiment of the present invention.

FIG. 9 is a graph showing a pulse / electrocardiogram of a heart among organs of a living body according to an embodiment of the present invention by a waveform of one cycle.

FIG. 10 is a flow chart for sampling a pulse / electrocardiogram according to an embodiment of the present invention.

FIG. 11 is a flowchart for performing electroencephalogram sampling according to an embodiment of the present invention.

FIG. 12 is a block diagram of a monitoring device for grasping / monitoring the activity of a living body organ according to the embodiment of the present invention.

FIG. 13 is a configuration diagram of a monitoring device around the monitoring device according to the embodiment of the present invention.

FIG. 14 is a schematic diagram for explaining an electrocardiogram waveform (a), its pulsation (b) and a waveform selection step (c) therefor for sampling a pulse / electrocardiogram according to an embodiment of the present invention. .

FIG. 15 is an electrocardiogram waveform diagram according to the conventional method of obtaining the maximum and minimum values by overwriting data.

FIG. 16 is a waveform diagram of a breathing motion according to the conventional method of obtaining the maximum and minimum values by overwriting data.

[Explanation of symbols]

5 memory 6 memory circuits

   ─────────────────────────────────────────────────── ─── Continued front page    F-term (reference) 4C017 AA10 AA14 AA18 AA19 AB04                       AB10 AC03 AC16 BB13 BC11                       BC14 BD06 CC02 FF30                 4C027 AA02 AA03 CC00 GG13 GG16                       GG18 KK00 KK01 KK05                 4C038 SS00 ST00 SV01 SX07 VA04                       VA16 VA20 VB33 VB40 VC01                       VC20

Claims (6)

[Claims] 1. A cycle from the setting start point to the setting end point of the repetitive motion of an organ in a living body is set as one cycle, and a plurality of points useful as data are set as sampling points during the one cycle, and one cycle of the organ tube is set. The amount of change at each sampling point is measured multiple times and the data is saved, and the standard deviation value is obtained for each sampling point, and the measured value of the organ is collated with the standard deviation value to determine whether there is an abnormality in the organ. A method for monitoring a living body organ, characterized in that 2. The sampling data is individually stored for each storage unit of the storage circuit, and the storage unit is provided with an arithmetic program for determining whether the actual measurement value at the sampling location is normal or abnormal and notifying the actual measurement value. Item 2. A method for monitoring a living organ according to Item 1. 3. The living body according to claim 1, wherein a large number of sampling points on the time axis are set in a useful sampling area, and a small number of sampling points on the time axis are set in a non-useful area. How to monitor organs. 4. The biological organ according to any one of claims 1 to 3, wherein the number of sampling points is increased in an important area of medical findings during one cycle of the operation of the biological organ. Monitoring method. 5. The arithmetic program determines an average value and a standard deviation value of sampling data, and compares the average value or the standard deviation value with an actual measurement value of the organ so as to determine the presence or absence of abnormality of the organ. The method for monitoring a living organ according to any one of claims 1 to 4, wherein the method is performed. 6. The standard deviation value is multiplied by a coefficient to set an upper limit value and a lower limit value, and the actual measurement value of the organ is made to correspond to the actual measurement allowable range of the upper limit value and the lower limit value to determine the presence or absence of abnormality of the organ. The method for monitoring a living organ according to any one of claims 1 to 5, wherein the determination is performed.