JP2018183550A - Prevention management support apparatus and system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an electronic apparatus and system which estimate the degree of the symptom and the possibility of the sudden onset of a cardiovascular disease of an analyte by noninvasive measurement.SOLUTION: The apparatus comprises: a sensor which acquires a sound and pressure or acceleration information inside the living body of an analyte; and a control part which extracts fractal information from the acquired data, and on the basis of an index having probability density distribution calculated from the fractal information as an element, estimates the degree of the occurrence and the probability of the onset of a disease of the analyte. The apparatus gives notification to the analyte.SELECTED DRAWING: Figure 1

Description

The present invention relates to a preventive management support apparatus and system.

In recent years, as a health management device and a health management system, the need for a health management device and a health management system that objectively predicts and prevents the risk of sudden onset of vascular diseases in advance has attracted attention.

For example, it is possible to objectively predict and prevent in advance the risk of sudden onset of diseases such as heart failure, myocardial infarction, angina pectoris, cerebral infarction, ischemic anemia, anemia, etc. If possible, it can not only save the lives of one human being, but also indirectly save many human lives that may be involved, and in terms of economics, such a health management device and health If management systems become widespread, it will be possible to contribute to reducing medical costs at the national level.

By the way, in the health management device and the health management system for estimating the health condition of the subject of Patent Document 1, the blood fluidity and the glucose or lipid metabolism status from the blood glucose level of the subject obtained by noninvasive pulse wave measurement. A method of estimating and presenting it has been proposed.

However, when predicting a blood glucose level from in vivo information of a subject obtained by noninvasive measurement and analyzing and predicting a more detailed medical condition of a vascular disease based on the prediction, Thus, when explanatory variables such as propagation speed and the ratio of time difference and amplitude difference between forward wave and reflected wave of pulse wave are dealt with by deterministic analysis method, these variables are the subject's body temperature, pulse, It is easily affected by external disturbances such as the outside air temperature. Therefore, various corrections are required for the analysis, the system configuration becomes complicated, and the accuracy of the analysis is limited.
In addition to predicting blood glucose levels, the risk of sudden onset of vascular diseases such as heart failure, myocardial infarction, angina pectoris, cerebral infarction, ischemic anemia, anemia, etc. In order to make an objective prediction, not only a few explanatory variables are difficult to analyze, but even if the explanatory variables are increased, It is expected that correction will be more complicated and impossible to analyze due to accuracy problems.

International Publication No. WO 2016/174839 A1

Problems to be solved by the present invention

The present invention is for solving the above-described problems, and an analysis method for objectively predicting the degree of occurrence of a disease state of vascular system disease and the possibility of onset, together with prediction of a blood glucose level of a subject, and based thereon A health management device and a health management system are provided.

Means for solving the problem

In order to solve the above-described problems, the present invention provides the following preventive management support apparatus and system.
(1) A sensor unit that acquires sound, pressure, or acceleration information in the living body of a subject, and an index that extracts fractal information from the acquired data and uses a probability density distribution calculated therefrom as an element A preventive management support device that has a control unit that estimates the degree of occurrence of a medical condition and the possibility of onset based on the subject and notifies the subject.
(2) The control unit extracts a linear prediction coefficient of the acquired sound, pressure or acceleration information as the fractal information, and based on an index having a probability density distribution calculated therefrom as an element, (1) The preventive management support apparatus which has a control part which estimates the generation | occurrence | production degree of this disease state, and the possibility of onset, and alert | reports to a test subject.
(3) The control unit extracts a linear prediction coefficient of the acquired sound, pressure or acceleration information as the fractal information, and based on an index having a probability density distribution calculated therefrom as an element, The preventive management support apparatus according to (1) or (2), which has a control unit that estimates the blood glucose level of the subject and notifies the subject.
(4) The control unit extracts a linear prediction coefficient of the acquired sound, pressure or acceleration information as the fractal information, and based on an index having a probability density distribution calculated therefrom as an element, A preventive management support apparatus according to any one of (1) to (3), which has a control unit that estimates the degree of occurrence of a sudden illness of the vascular disease and the possibility of onset, and notifies the subject.
(5) The control unit extracts a linear prediction coefficient of the acquired sound, pressure, or acceleration information as the fractal information, and based on an index having a probability density distribution calculated therefrom as an element, (1) to (4), a preventive management support apparatus that has a control unit that estimates the degree of occurrence of a disease state of the nervous system disease and the possibility of onset, and notifies the subject.
(6) The control unit extracts a linear prediction coefficient of the acquired sound, pressure or acceleration information as the fractal information, and based on an index having a probability density distribution calculated therefrom as an element, A preventive management support apparatus according to any one of (1) to (5), which has a control unit that distinguishes whether the pathological condition is a vascular disease or a nervous system disease and notifies the subject.
(7) A sensor unit that acquires sound, pressure, or acceleration information in a subject's living body, and an index that extracts fractal information from the acquired data and uses a probability density distribution calculated therefrom as an element. A control unit that estimates the degree of the medical condition of the subject and the possibility of onset, and includes a prevention support device that informs the subject, and continuously the sound and pressure in the living body of the subject at arbitrary time intervals Alternatively, a preventive management management system that supports the above prevention while acquiring acceleration information.
(8) A sensor unit that acquires sound, pressure, or acceleration information in the living body of the subject, and an index that extracts fractal information from the acquired data and uses the probability density distribution calculated therefrom as an element Based on the subject, having a control unit that estimates the degree of occurrence of the medical condition of the subject and the possibility of onset, including a prevention support device that informs the subject, the sound in the subject's living body continuously at an arbitrary time interval, A preventive management support system comprising a notification device that provides the above-mentioned preventive support and issues a warning sign to a subject according to the necessity of the subject's medical condition while acquiring pressure or acceleration information.

Effect of the invention

ADVANTAGE OF THE INVENTION According to this invention, the electronic device and system which can estimate the disease state of the vascular system disease state of a subject and the possibility of sudden onset easily and non-invasively can be provided.

It is explanatory drawing which showed this preventive management assistance apparatus.
It is a figure of a measurement part and a power supply part. It is the figure which attached the measurement part to the neck. It is a figure which shows the function block diagram of a measurement part and a power supply part. It is a figure explaining the fractal information of the linear prediction coefficient. It is the figure which displayed the measured value of the blood glucose level of the healthy test subject (I), the healthy test subject (II), the test subject of diabetes, and the test subject of diabetes, and the calculated value for every hour. The figure which measured the position in Am-Bm phase space of each state of three healthy test subjects and two test subjects who are healthy but have a slightly high blood pressure for 5 hours. It is. It is the figure which measured the fluctuation | variation of Am and Bm value in the Am and Bm phase space of the test subject of diabetes before causing a cerebral infarction, and the fluctuation | variation of As and Bs value over 10 hours for every hour. Changes in Am and Bm values in the Am and Bm phase spaces of subjects who are treated after the onset of cerebral infarction and exhibiting blood glucose spikes, and changes in As and Bs values every hour for 11 hours It is the figure measured over. At the position in the Am-Bm phase space of the unaffected subject group and the subject group who has developed a vascular disease, the unaffected state and the diseased state cannot be distinguished from each other by the Am and Bm indices alone. It is a figure which shows that the presence or absence of the possibility of onset of a test subject cannot be estimated. It is the figure which showed the transition of the medical condition of the cervical spine hernia, the position in the Am-Bm phase space of the test subject of an early stage lung cancer, and the magnitude | size of dispersion | distribution As and Bs. The example of the MM matrix computed from the data acquired from the test subject is shown. Fig. 2 shows a flowchart of the operation of the system.

This device is a plate-shaped hard plastic resin (or aluminum, etc.) that is a light, small digital microphone, pressure sensor, or acceleration sensor that is curved along the roundness of the neck and has a thickness of 1 mm, a width of 8 mm, and a length of about 25 mm. Are fixed to a position of about 50 mm from one end of this pair (this position may be adjusted according to the thickness of the subject's neck), and the digital microphone is connected to the left carotid artery (actually, in vivo sound, pressure or It can be anywhere where acceleration information can be obtained, but for convenience, it is fixed so that it hits the upper part of the left carotid artery), and the other end of the plate-like hard plastic resin (or aluminum, etc.) is a pair The other plate-like hard plastic resin (or aluminum or the like) is joined by a leaf spring so that the neck is softly sandwiched. At this time, a pair of plate-like hard plastic resin (or aluminum, etc.) will allow the digital microphone to be properly fixed to the carotid artery so that the other part will not swing to the neck as much as possible. It is preferable to make it into a shape to prevent stuffiness and allergies. Moreover, the part which touches a neck may wind the cloth like gauze. This measuring unit is very lightweight of about 20 g, and is disconnected from the power source unit of the subject's chest (or hand) and connected by wiring, so the weight of the measuring unit hardly places a burden on the neck. .

The digital microphone obtains in-vivo sound information from the left carotid artery (which may be pressure or acceleration information, but has been considered as sound information in consideration of the simplicity of the entire device) by operating for 10 seconds at regular intervals. From the end of the color of the measuring unit, recording is performed in the memory unit of the power source unit of the subject's chest (or hand) connected by wiring of an appropriate length, and at the same time, the data is transferred from the transmitting unit to the control unit using WiFi or BlueTooth. The control unit may be a server, the display unit may be a smartphone, or the control unit may be integrated as a tablet terminal or a personal computer, but for continuous measurement, the control unit is a server in consideration of standby power. The display unit is preferably a smartphone.

When starting this device, first specify the number of times the subject should measure on the counter of the power supply at the chest, and when the switch is turned on, the five timers built in the power supply unit are interlocked every hour. A series of operations are performed.
(1) The scramble timer T1 turns on the vibration motor and the scramble timer T2 approximately two minutes before the measurement after one hour to inform the subject in advance of the start of the measurement and at the same time, the subject uses the smartphone application. Prompt to start the software.
(2) After 2 minutes, the scramble timer T2 is activated, and the digital microphone, LED, recorder, and scramble timer T3 are turned on via the photocoupler, and the in-vivo sound information from the left carotid artery of the subject is measured. Simultaneously with the start, the LED blinks to inform the subject that measurement is in progress, and the in vivo information of the subject is converted into a wav file and stored in the recorder.
(3) When the scramble timer T2 is stopped after 10 seconds, the scramble timer T3 is started, the LED is turned off via the photocoupler, and the subject is informed that the measurement is completed, and the scramble timer T4 is turned on. Turn on the reproduction of the wav file and turn on WiFi or BlueTooth of the transmission unit built in the power supply unit, and transmit the wav file to the smartphone.
(4) After 10 seconds, the scramble timer T4 is activated, and the delete of the previous wav file is turned on via the photocoupler to return the recording unit to the initial state.
(5) After 10 seconds, the after idle timer is started, the scramble timer T1 is turned on and the counter is turned on via the photocoupler, and the number of measurements is counted. When the counter reaches the specified number of measurements, the series of measurements ends and the power supply is automatically switched off. Otherwise, the measurement is repeated every other hour.
On the other hand, on the smartphone side, the wav file transmitted every hour is further transmitted to the server. On the server side, a series of analysis is performed, and the result is sent back to the smartphone side and displayed. At this time, if it is estimated that the subject's medical condition is at risk of sudden onset, the display is flashed in red to alert the subject.

FIG. 12 shows the flowchart described above.

In this preventive management support device and system, each measurement time is set to 10 seconds so that the subject can perform intermittent repeated measurement for a long time, and the subject is informed of the start of measurement by a vibration motor in advance. While the LED is blinking, non-invasive measurement and blood sugar level calculation are possible without any other conditions, only by measuring conditions that do not speak and are not placed in extreme noise environments. . This is based on an index that extracts fractal information from information obtained by acquiring sound, pressure, or acceleration signal information in the living body of a subject for a certain period of time (about 10 seconds) and uses a probability density distribution calculated therefrom as an element. This is because the sound, pressure, or acceleration signal information is not easily affected by disturbances. As a result, the accuracy of blood glucose level calculation can be improved, the measurement conditions can be relaxed, and the measurement device can be simplified. Coupled with non-invasive measurement, the burden on the subject can be reduced, and the device can be made smaller, lighter and more portable. In addition, continuous changes in the degree of progression of vascular and neurological conditions can be measured, the risk of onset can be analyzed with an objective scale, and the risk of onset can be avoided in advance. To. It is desirable to measure these measurements at a fixed time interval, a total of 5 to 10 times, for a more accurate understanding of the medical condition.

Vascular diseases of the subject of the present invention include sudden pathologies such as left atrial failure, stress heart failure, myocardial infarction, angina pectoris, cerebral infarction, ischemic heart failure, ischemic anemia, and the like. The nervous system diseases of the subject of the present invention include cervical hernia, spinal hernia, nerve compression by lung cancer tumor, and so on.

An index having the probability density distribution as an element will be specifically described.
First, information acquired in a predetermined time (about 10 seconds) via a sound (or pressure or acceleration) signal in the living body of the subject in the measurement unit is recorded and stored in the memory unit. From this recorded data, a total of about 40,000 linear prediction coefficients of arbitrary dimensions (10 dimensions are desirable in consideration of accuracy and calculation speed) are calculated. This linear prediction coefficient is used as fractal information and digitized with an arbitrary width (here, 0.1) for each dimension, and a matrix with the probability density distribution normalized for each dimension as an element (hereinafter referred to as MM matrix) Is used as an index.

The fractal information of the linear prediction coefficient will be described with reference to FIG.
FIG. 4 shows a time-series repeating pattern of 10-dimensional linear prediction coefficients obtained from the sound signal information sampled at 8000 cycles / sec of the subject. Each linear prediction coefficient for each dimension varies randomly, but the fluctuation has a subject-specific boundary region (attractor) at that time, and the linear prediction coefficient is random so as to fill the boundary region. In vivo information is conveyed as a whole while fluctuating. This is because the nerve transmission information is the same, and the influence of the disturbance of each value is absorbed in the entire random information to make it less susceptible to the influence of the disturbance. Therefore, the linear prediction coefficient for each dimension is replaced with probability density information to convey in vivo information.

FIG. 11 shows an example of an MM matrix calculated from data acquired from a 64-year-old female subject.

Next, a method for calculating a blood glucose level from the calculated MM matrix will be described.
First, the maximum probability density for each dimension of the MM matrix and the vectors having the centroid of the probability density as elements are A and B, the average of these vector elements is Am, Bm, the variance is As, Bs, and so on. The parameter group is obtained from three equations that are intertwined with each other as shown in the formulas BloodSugar1, BloodSugar2, and BloodSugar3, with the blood glucose level as an explanatory variable. This is because fluctuations in blood sugar levels of healthy subjects, changes in blood sugar levels of diabetic reserves (subjects exhibiting blood sugar spikes), and fluctuations in blood sugar levels of diabetic subjects have different amplitude characteristics and periodic characteristics. This is because they partially overlap each other. Therefore, the blood glucose level is calculated by changing the blood glucose level of the healthy subject to the region 1, the blood glucose level changing region of the diabetic subject to the region 2, and the blood glucose level of the diabetic reserve army (subject showing the blood glucose spike). The above three formulas are applied in an overlapping manner according to each area, with the area as area 3.
The above three formulas are shown below.

An example in which the above formula is applied is shown below.
Example 1
The graph of FIG. 5 (a) shows the actual blood glucose level of a healthy subject (I) (thick solid line: a value measured with a TERUMO medium-safe mini GR-102) and a value calculated by the above formula for calculating blood glucose ( A thin solid line) is displayed every hour. Although the scale of the vertical axis of the graph is enlarged, the fluctuation of the blood glucose level is actually small and gently changing.

Example 2
The graph of FIG. 5 (b) shows an actual blood glucose level of a healthy subject (II) (thick solid line: a value measured with a TERUMO medium safe mini-GR-102) and a value calculated by the above formula for calculating blood glucose ( A thin solid line) is displayed every hour. The scale of the vertical axis of the graph is also enlarged here, but the fluctuation of the blood glucose level is actually small and smoothly changing.

Example 3
The graph of FIG.5 (c) shows the actual value (thick solid line: the value measured with TERUMO medium safe mini-GR-102) of the blood glucose level of the diabetic subject, and the value (thin solid line) calculated by the above formula for calculating the blood glucose level. Is displayed every hour. In contrast to the above two cases, in general, in the case of a diabetic subject, the blood glucose level fluctuates at a high position, but in the case of a diabetic subject under treatment (the subject in the graph of FIG. 5C), a relatively low position. If there is a gentle transition, a sudden sudden rise or fall fluctuation (in this case, sudden sudden rise) is often caused, but the above formula accurately grasps the tendency of the fluctuation.

Example 4
The graph in FIG. 5 (d) shows the actual blood glucose level of the subject with the urinary tract reserve (thick solid line: a value measured with the TERUMO medium-safe mini GR-102) and the value calculated by the above formula for calculating the blood glucose level. (Thin solid line) is displayed every hour. Obviously, the test subject showed a symptom of a blood glucose level spike, and the blood glucose level repeatedly fluctuated before and after the meal, but again, the above calculation formula accurately captures the tendency of the fluctuation.

In fact, the blood glucose level calculated by the control unit of the present preventive management system is much simpler than the calculation system of Patent Literature 1 and the measurement conditions are much gentler, but the blood glucose level calculated in Patent Literature 1 is much gentler. It is higher than the accuracy of the value and has a wide measuring range. The absolute value of the difference between the blood glucose level calculated by the present preventive management system and the prevention support apparatus and the blood glucose level actually measured by the measuring device (TERUMO medium safe mini GR-102) is in the blood glucose level range from 83 mg / dl to 229 mg / dl. 16.9 mg / dl or less, which is a practical value. The calculated blood glucose level is displayed on the display unit in different colors according to the degree of risk.
Table 1 shows the measured values, the calculated values by this algorithm, and the errors.

The control unit of the present preventive management system analyzes the vascular disease and the nervous system disease of the subject in parallel with the calculation of the blood glucose level. At this time, the blood flow and blood pressure of the subject are redefined by the average values Am and Bm of the vector elements of the two vectors generated from the MM matrix, the maximum probability density vector A and the probability density centroid vector B, respectively. Thus, the fluctuations in the expanded blood flow vector and blood pressure vector, and the variances As and Bs of the expanded blood flow vector element and blood pressure vector element at the time, respectively, indicate the state of the subject's medical condition. Estimating and clarifying the progress from non-disease to onset and the dispersion thresholds Ax and Bx are greatly useful for prevention.

The above will be specifically described.
In the phase space created by the average values Am and Bm of the elements of the vectors A and B, the variation of the blood flow vector Am and the blood pressure vector Bm defined by the subject and the blood flow vector element defined by expansion at that time Each of the variances As and Bs of the blood pressure vector element fluctuates according to the condition of the subject, but when the subject is a healthy body, the blood flow vector Am and the blood pressure vector Bm within the phase space created by Am and Bm. Are hardly moving from a specific position, and at the same time, the distribution As and Bs of the blood flow vector element and the blood pressure vector element are extremely small, and vascular diseases (heart failure, stress heart failure, myocardial infarction, angina pectoris, Far below the threshold in the process from illness to onset of cerebral infarction, ischemic anemia, ischemic heart failure, cerebral hyperemia, etc., ie, dispersion thresholds Ax and Bx. On the other hand, in the case of a subject who has some vascular disease or nervous system disease state, or a sign of a disease state (this applies to diabetic patients and their reserves), the blood flow vector Am and the blood pressure vector Bm are According to the degree of progression of the disease state, away from the position H of the healthy subject, the irregular spiral fluctuation is repeated several times within a day, and at the same time, the respective distributions As and Bs of the blood flow vector element and the blood pressure vector element are repeated. Also repeats irregular fluctuations. What is important at this time is that, in the Am-Bm phase space, the position of the subject's (Am, Bm) point is in the lower left of the position H of the healthy subject, and the subject's variance Bs is the variance threshold Bx (= 2.0 ), The risk of developing vascular disease (heart failure, stress heart failure, myocardial infarction, angina pectoris, cerebral infarction, ischemic anemia, ischemic heart failure, cerebral hyperemia, etc.) is high. Bm) When the position of the point is on the upper right side from the position H of a healthy subject and the subject's variance As exceeds the variance threshold Ax, the risk of developing a vascular disease (brain overflow) is high. It is inferred from the embodiment of the present preventive management system shown.

Example 5
FIG. 6 shows three healthy subjects in their 20s to 50s and two healthy subjects who are 64 and 86 years old, but who have a slightly high blood pressure (around 140). The position in the Am-Bm phase space is measured every hour for 5 hours.
The former three healthy subjects do not move at all from the same H point (Am (H), Bm (H)), while the latter two from the same point slightly lower right than the H point Didn't move. This is consistent with the condition that the latter two are healthy but have slightly higher blood pressure (ie, Bm> Bm (H) and Am <Am (H)). Furthermore, the variances As and Bs of the former three subjects are smaller than the variances As and Bs of the latter two subjects. This confirms that the magnitudes of the variances As and Bs as well as the position in the Am-Bm phase space are indicators of the health condition.

Example 6
The star-shaped plot in FIG. 7 and the rectangle surrounding it are the variation of Am and Bm values in the Am and Bm phase space of the diabetic subject (88-year-old female) K before causing cerebral infarction, and the As and Bs values. The variation was measured every hour for 10 hours.
As described above, in the case of a subject having a vascular disease or a nervous system disease or a symptom of the disease (this applies to a diabetic patient or the reserve army), the blood flow vector Am and the blood pressure vector Bm are According to the degree of progression of the disease state, away from the position H of the healthy subject, the irregular spiral fluctuation is repeated, and at the same time, the respective distributions As and Bs of the blood flow vector element and the blood pressure vector element are also irregular. Although the variance Bs of the subject k has increased, the variance threshold Bx = 2.0 that may cause cerebral infarction has not been exceeded at this point. (However, the value of Bx varies depending on how the reference value of the index Bm is taken.)

Example 7
The square plot in FIG. 8 and the rectangle surrounding it are Am in the Am, Bm phase space of a subject (66-year-old male) F who is undergoing treatment after onset of a relatively mild cerebral infarction and is showing a blood glucose spike. , Bm value fluctuations, and As, Bs value fluctuations measured every hour for 11 hours.
In the case of this test subject F, although it has a medical condition similar to that of the test subject K described above, unlike the test subject K, taking a drug that improves blood flow for treatment, the Am value of the test subject F is It is inferred that the value is higher than the Am value of K. Although the variance Bs of the subject F is large, at this time, the variance threshold Bx = 2.0 that may cause cerebral infarction is no longer exceeded. (However, as described above, the value of Bx varies depending on how the reference value of the index Bm is taken.)

FIG. 9 shows the fluctuating positions (star plot or triangle plot) in the Am-Bm phase space of a group of subjects without disease, stress heart failure, left atrial failure, myocardial infarction, angina pectoris, cerebral infarction, imaginary At the fluctuating position (square plot) in the Am-Bm phase space of a group of subjects who have developed vascular diseases such as blood anemia, the non-disease state and the onset state are interlaced with each other only with the Am and Bm indices. This indicates that the subject cannot predict whether or not the subject is likely to be ill.

As described above, in FIGS. 6 and 7, the squares at the positions of the indicators Am and Bm of healthy and unaffected subjects represent the variances As and Bs, all of which are variances with respect to Bs. The threshold Bx = 2.0 is not exceeded. (However, as described above, the value of Bx varies depending on how the reference value of the index Bm is taken.)

In FIG. 8, the respective squares at the positions (star plots) of the indices Am and Bm of subjects who are treated after the onset of cerebral infarction represent the variances As and Bs, all of which are the variance threshold Bx = It does not exceed 2.0. (However, as described above, the value of Bx varies depending on how the reference value of the index Bm is taken.)

On the other hand, in FIG. 8, the index of untreated subjects in the group of subjects who developed vascular diseases such as stress heart failure, left atrial failure, myocardial infarction, angina pectoris, cerebral infarction, ischemic anemia, etc. The squares at the Am and Bm positions (square plots) all exceeded the variance threshold Bx = 2.0 for Bs, but the subject under treatment did not exceed the variance threshold Bx = 2.0. . (However, as described above, the value of Bx varies depending on how the reference value of the index Bm is taken.)

From the above, it is concluded that the index for predicting the possibility of the onset of vascular disease in the subject is the magnitude of the variances As and Bs together with the position in the Am-Bm phase space.

Example 8
FIG. 10 shows changes in the pathology of a nervous system disease (cervical hernia) in a 60-year-old male N, the position of a 67-year-old male G in the early lung cancer in the Am-Bm phase space, and the magnitudes of dispersion As and Bs. It is shown by. The transition of the pathology of the vascular disease in the Am, Bm phase space is to move away from the position H of the healthy subject while repeating irregular spiral fluctuations. Depending on whether each threshold value is exceeded, it indicates whether any vascular disease such as heart failure, left atrial failure, myocardial infarction, angina pectoris, cerebral infarction, ischemic anemia, etc. may occur. On the other hand, the transition of the position (square point) of the subject N in the Am-Bm phase space of the subject N having a nervous system disease (cervical spinal hernia), and the subject Am-Bm whose part of the nervous system is compressed by cancer Positions in phase space (points of inverted triangles) are characterized by slow pathogenesis along a regular helix. This is because when a nervous system disease or nerve (such as cervical hernia, spinal hernia, etc.) is being compressed for some reason, there is a slight phase shift from the normal state of the nerve's inhibitory and active effects. This is presumed to be due to phase fluctuations in flow and blood pressure. The pain symptoms of subjects N and G and discomfort from the esophagus to the periphery of the heart were very similar. Based on the above, Am-Bm phase space is used to determine whether a subject's medical condition is a vascular disease, a nervous system disease, or whether a nerve is compressed for some reason (for example, cancer). In which position of the variance As or Bs is close to the respective threshold, and at the same time, what trajectory the subject's pathology is drawing in the Am-Bm phase space Is an important factor.

110. Color 120. Digital microphone or pressure sensor or acceleration sensor 150. Leaf spring or spring 180. Wiring 200. Power supply unit 210. Timer section 220. Counter 230. LED 240. Vibration motor 250. Recording unit 260. Transmitter (WiFi or BlueTooth) 300. Smartphone and server or PC or tablet terminal

The apparatus compact and lightweight digital microphone or pressure sensor, or an acceleration sensor, a thickness of 1mm curved along the neck roundness, width 8 mm, the longitudinal length 25mm plate-shaped hard plastic resin (or aluminum, etc. ) Are fixed at a position of about 50 mm from one end (this position may be adjusted according to the thickness of the neck of the subject), and the digital microphone is connected to the left carotid artery (actually, in-vivo sound, pressure) Or any place where acceleration information can be obtained, but for convenience, the left carotid artery is fixed so that it hits the upper part), and the other end of the plate-shaped hard plastic resin (or aluminum, etc.) a pair of the other plate-shaped hard plastic resin (or the like aluminum), are coupled by a plate spring, sandwich soften the neck structure Going on. Plate-shaped hard plastic resin in this case pairs (or like aluminum) is, to the extent that the digital microphone phone is properly secured to the carotid artery portion, the other part to the extent that touches soft and gaps as possible not to shake the neck In order to prevent stuffiness and allergies, it is preferable to make the shape to make. Moreover, the part which touches a neck may wind the cloth like gauze. This measuring unit is very light, about 20 g, and is connected by a wiring extending from the control unit of the subject's chest (or hand) and the power supply unit, so the weight of the measuring unit almost places a burden on the neck. Absent.

The digital microphone obtains in-vivo sound information from the left carotid artery (which may be pressure or acceleration information, but has been considered as sound information in consideration of the simplicity of the entire device) by operating for 10 seconds at regular intervals. From the end of the color of the measurement unit, the data is recorded in the memory unit of the control unit of the subject's chest (or hand) connected by wiring of an appropriate length, and simultaneously transferred from the transmission unit to the display unit by WiFi or BlueTooth . To continuous measurement, in consideration of the standby power, it is desirable to smartphone display unit.

This device, when initially subject to switch on the power supply portion of the chest, to 10 seconds before the measurement of the biological Uchioto information from the subject's left carotid artery every hour is started, the in vibration motor is activated The examiner is informed in advance that the measurement is started, and the LED blinks for 10 seconds during the measurement to inform the subject that the measurement is being performed . Thereafter, the in vivo information of the subject is converted into a wav file by sampling at 8000 cycles / sec by the control unit, a linear prediction coefficient is calculated, an MM matrix is calculated, and the result is stored in the recording unit. Furthermore, the analysis parameter group obtained from MM matrix of that through the W IFI, are sent to the smart phone, a medical condition of the subject, to Gran displayed in colors corresponding to the condition.

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Here, the values of symbols such as Bxl, Bxml, etc. are as follows.

110. Color 120. Digital microphone or pressure sensor or acceleration sensor 150. Leaf spring or spring 180. Wiring 200. Power supply unit 210. Control unit 220. Recording unit 230. Transmitter (WiFi or BlueTooth) 240. Vibration motor 250. LED 300. Smartphone and server or PC or tablet terminal

Claims (8)

Based on a sensor unit that acquires sound, pressure, or acceleration information in the living body of the subject, and fractal information extracted from the acquired data, and an index that uses the probability density distribution calculated therefrom as an element, A preventive management support apparatus which has a control unit characterized by estimating the degree of occurrence of a medical condition of a subject and the possibility of onset, and notifies the subject. The control unit extracts a linear prediction coefficient of the acquired sound, pressure or acceleration information as the fractal information, and based on an index having a probability density distribution calculated therefrom as an element, The prevention management support apparatus according to claim 1, wherein the occurrence degree and the possibility of onset are estimated. The control unit extracts a linear prediction coefficient of the acquired sound, pressure or acceleration information as the fractal information, and calculates a blood glucose level of the subject based on an index having a probability density distribution calculated therefrom as an element. The preventive management support apparatus according to claim 1, wherein the apparatus is estimated. The control unit extracts a linear prediction coefficient of the acquired sound, pressure or acceleration information as the fractal information, and based on an index having a probability density distribution calculated therefrom as an element, the vascularity of the subject The prevention management support apparatus according to claim 1, wherein the degree of occurrence of a sudden disease state of a disease and the possibility of onset are estimated. The control unit extracts a linear prediction coefficient of the acquired sound, pressure or acceleration information as the fractal information, and based on an index having a probability density distribution calculated therefrom as an element, the subject's nervous system The preventive management support apparatus according to claim 1, wherein the occurrence degree of a disease state and the possibility of onset are estimated. The control unit extracts a linear prediction coefficient of the acquired sound, pressure or acceleration information as the fractal information, and based on an index having a probability density distribution calculated therefrom as an element, the medical condition of the subject is determined. The preventive management support apparatus according to claim 1, wherein the apparatus is distinguished from a vascular disease or a nervous system disease. Based on a sensor unit that acquires sound, pressure, or acceleration information in the living body of the subject, and fractal information extracted from the acquired data, and an index that uses the probability density distribution calculated therefrom as an element, A control unit characterized by estimating the degree of occurrence of the medical condition of the subject and the possibility of the onset, and provided with a prevention support device that notifies the subject, and continuously in the living body of the subject at arbitrary time intervals The preventive management management system that supports the above prevention while acquiring sound, pressure or acceleration information. Based on the sensor unit that acquires the sound, pressure, or acceleration information in the subject's living body, and fractal information extracted from the acquired data, and an index that uses the probability density distribution calculated therefrom as an element, A control unit characterized by estimating the degree of occurrence of the disease state and the possibility of the onset, including a prevention support device for informing the subject, and continuously in-vivo sound of the subject at arbitrary time intervals A preventive management support system comprising a notification device that provides the above-mentioned prevention support while acquiring pressure or acceleration information, and issues a warning sign to the subject according to the necessity of the subject's medical condition.

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