JPH069546B2 - Diagnostic device using pulse wave and / or heartbeat collected from body surface - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION (a) Field of Industrial Application The present invention relates to a diagnostic device that uses a pulse wave and / or heartbeat collected from a body surface.

(B) Conventional Technology With the progress of electronics technology, it has been practiced to diagnose physical and mental abnormalities based on electrical measurement results such as an electroencephalogram and an electrocardiogram.

(C) Problems to be Solved by the Invention However, at present, only a doctor observes the above electroencephalogram or electrocardiogram to make a diagnosis, and the arithmetic processing of the above measurement data is performed. It is not done to discover the order contained in the same data, to derive some conclusions from the order, and to derive the diagnosis from the conclusions.

(D) Means for Solving the Problems In the present invention, a pulse wave and / or electrocardiographic sensor that can be mounted on the body surface of a subject, and a pulse wave waveform and / or a pulse wave waveform collected by the sensor
Or, a chaotic attractor that digitizes the electrocardiographic waveform with an A / D converter and embeds the digitized discrete data in a number space, which is a virtual space created by a mathematical operation using the Takens embedding method, is used. The calculation means for projecting onto the two-dimensional number space, the display means for displaying the projection on the two-dimensional number space, and the degree of sharp dependence on the initial value, which is one of the characteristics of chaos, by the chaos attractor as an index. Collected from the body surface, comprising: a calculating unit for calculating the Lyapunov exponent which is the indicated numerical value, a display unit for displaying the Lyapunov exponent, and a storage unit for storing the discrete data and / or the Lyapunov exponent. It is intended to provide a diagnostic device using the pulse wave and / or the heartbeat.

Further, the pulse wave sensor, a bottomed cylindrical cover portion into which the finger tip of the subject can be inserted, an inner surface of the cover portion, an infrared light emitting diode capable of contacting the finger pad of the finger tip portion, respectively. It is also characterized in that a phototransistor is provided, and the infrared light emitting diode and the phototransistor are configured by a photosensor in which the optical axes of the phototransistor intersect at an angle of 20 ° to 30 ° inside the fingertip portion.

(E) Actions / Effects First, chaos will be described. In the present invention, chaos is a chaotic or anti-ordering system used in contrast to cosmos, which is an orderly integrator. Meaning, having rules and laws,
It is a mathematically and physically well-defined concept, and although the law itself follows causality, it is an uncertain phenomenon in which the future prediction of the outcome cannot be captured by probability.

That is, although it is deterministic, a slight error is actually a phenomenon that is amplified and becomes unpredictable under nonlinear influence, and deterministically generated randomness is chaos. .

Therefore, chaos shows that there is a fundamental limit to the predictability, and it is predictable in the sense that it is possible to derive an ordered structure from a considerable phenomenon that was previously thought to be captured only stochastically. It indicates that there is.

A topology that characterizes the long-term behavior of chaos is called a chaotic attractor, which is a mathematical structure in which the behavior of a system that generates chaos converges.

From this viewpoint, as a result of analyzing the pulse wave and / or the electrocardiographic waveform data collected from the body surface, these belong to the above chaos, and therefore, the pulse wave and / or the electrocardiographic waveform data can be predicted. It belongs to the phenomenon, especially the chaotic attractor that embedded the pulse wave data collected from the fingertip in the number space, and the sharp dependence on the initial value that is one of the characteristics of chaos, that is, the initial value dependence. We found that the Lyapunov index, which indicates the degree of sex, is closely related to the information processing in the brain of the subject.

Based on such findings, pulse waves and / or
Alternatively, the electrocardiographic waveform data can be 4
Embed it in the three-dimensional space, project it into the three-dimensional space,
Then, by projecting onto a two-dimensional number space, it can be displayed by a display means such as a CRT.

In addition, the chaotic attractor is arithmetically processed to obtain the Lyapunov exponent, which can be used as a diagnostic material.

Between the chaotic attractor and / or the Lyapunov exponent displayed in this way and the state of the subject, the 8a
Since there are correspondences shown in FIGS. 11 to 11b, it is possible to accurately grasp the state of mind and body including the psychological state of the subject.

In addition, it is considered that the pulse wave data collected from the fingertip is closely related to the information processing in the brain for the following reason.

Blood flow and autonomic nerves are concentrated in the fingertips of the hand.

Also, the hand is called the second brain, and from a developmental point of view, the hand and the brain have a close relationship such that the development of the brain and the development of the hand are simultaneously performed.

The ratio of the area of the hand (fingertip) to the somatosensory and motor areas of the brain is very large.

In addition, a bottomed cylindrical cover portion into which the subject's fingertip portion can be inserted, and an infrared light emitting diode and a phototransistor that can respectively contact the finger pad of the fingertip portion are provided on the inner surface of the cover portion. Then, the optical axes of the infrared light emitting diode and the phototransistor are 20 ° to 30 ° inside the fingertip portion.
By crossing at an angle of, the infrared rays projected from the infrared light emitting diode are reflected inside the fingertip portion while shielding the outside light by the cover portion, and the infrared rays are made incident on the phototransistor, whereby the fingertip portion It is possible to output from the same sensor a voltage having a waveform accurately corresponding to the pulse wave.

(F) Example An example of the present invention will be described with reference to the accompanying drawings.

FIG. 1 shows a diagnostic device (A). The diagnostic device includes a pulse wave sensor (1), an electrocardiograph (2), an operational amplifier (3), an A / D converter (4), a computing means and It is composed of a small computer (5) as a storage means, a CRT display (6) and a printer (7) as a display means, and a power supply (8) for supplying electric power to these.

The pulse wave sensor (1) is attached to the fingertip of the subject and is used to collect the pulse wave from the fingertip. As shown in FIG. 2, the pulse wave sensor (1) is flexible and has light-shielding properties such as black sponge rubber. A cover part (12) formed by forming a material having a cylindrical shape with a bottom so that a subject's fingertip part (11) can be inserted, and a photosensor (14) provided on the inner surface of the cover part (12). It consists of and.

The photo sensor (14) is attached to the inner surface of the cover (12), and an infrared light emitting diode (15) and a phototransistor (15) that can be brought into contact with the finger pad (13) of the subject's finger tip (11), respectively. (16) composed of and infrared light emitting diode
The optical axes (15a) and (16a) of (15) and the phototransistor (16) are intersected at an angle of 20 ° to 30 ° inside the fingertip (11), and the infrared light emitting diode (15) Projected wavelength 940n
The infrared rays of m are reflected inside the fingertip (11), and the reflected light is incident on the phototransistor (16) to measure the pulse wave at the fingertip (11) of the subject, Can be output as

In addition, infrared light emitting diode (15) and phototransistor
A convex lens is attached to each of the tip ends of (16). (17) is a stabilized power supply.

FIG. 3 is a circuit diagram of the photo sensor (14).

The pulse wave sensor (1) is configured as described above, the cover part (12) to be attached to the fingertip part (11) of the subject is formed of a flexible and light-shielding material into a cylindrical shape with a bottom. And fingertip (1
It can be easily attached to and detached from the 1), the measurement of the pulse wave is not affected by external light, the mounting position of the photo sensor (14) is stabilized, and the pulse wave can be accurately measured.

The operational amplifier (3) accurately amplifies the output voltage of the photo sensor (14) or the electrocardiograph (2) at a constant amplification factor and outputs it to the next A / D converter (4). is there.

The A / D converter (4) converts the output voltage of the operational amplifier (3) into a 12-bit digital signal and outputs it to the next small computer (5) as discrete data of pulse wave and electrocardiographic waveform. The digital signal is output every time the output request signal from the small computer (5) is input to the A / D converter (4).

The small computer (5) has various calculation functions described below, a function of outputting the calculation results to the CRT display (6) and the printer (7), and a pulse wave input from the A / D converter (4). And / or has a function of storing the discrete data of the electrocardiographic waveform and the result of the above calculation.

First, the overall processing procedure of the small computer (5)
It will be described with reference to the drawings.

When the small computer (5) is started (100), the initial setting (101) is first performed, and the line-of-sight direction of the four-dimensional number space described later is set (102) to process the pulse wave and / or heartbeat discrete data. Constants frequently used for various calculations are calculated in advance and stored (103), and the CRT display (6) is opened (104).
Then, the menu is displayed (105).

In the menu, as shown in FIG. 5, the A / D converter (4)
Power-on sequence (107) that receives the power-on response from the diagnostic device (A) to enable it, clears the chaotic attractor display window (108), and enlarges the chaotic attractor display size.
9), zoom out to reduce the display size of chaos attractor (110), pulse wave (111) to display data so that it fits in the window, and save data (11
2), reading the saved data and displaying the chaotic attractor of the data (116), rotation of the chaotic attractor by changing the line-of-sight direction of the four-dimensional number space (113) (In addition, the pulse found in the trial up to now) Waves and / or 3 patterns of angles that make it easy to grasp the size of the electrocardiographic waveform are registered so that these can be specified easily.) Discrete data of 10,000 pulse waves and / or heartbeats at a sampling frequency of 200 Hz Are collected and the next data (114) drawn on the window as a chaotic attractor, END (115) for ending the operation of the diagnostic device (A), and the like.

Next, regarding the calculation for displaying the chaotic attractor,
A pulse wave will be described as an example. The processing of the electrocardiographic waveform is similar to that of the pulse waveform.

FIG. 6 shows an outline of the above calculation, in which the pulse wave waveform is
It is decomposed into 12 bits at the sampling frequency of 00Hz (1
20) Create discrete data, and embed this discrete data in a four-dimensional number space by the Turkens embedding method to create a chaotic attractor (121).

The chaotic attractor of the four-dimensional number space is projected onto the three-dimensional number space (122).

The chaotic attractor projected in the three-dimensional number space is projected in the two-dimensional number space and output to the screen (123). It is done in that order.

During the above calculation, a menu is called to rotate the chaotic attractor so that it can be seen from any direction, and the chaotic attractor can be enlarged or reduced and discrete data can be saved. It is possible to read the data and display the chaotic attractor calculated from the discrete data.

Further, in order to increase the calculation speed, the discrete data from the A / D converter (4) is of integer type, and constants frequently used for the above calculation are calculated in advance and stored.

In the above calculation, embedding the discrete data in the four-dimensional number space by the embedding method of the Turkens means that the numerical value of the discrete data input to the small computer (5) one after another at a certain time is the numerical value of the first axis. X, from this point (for example, if the constant interval τ = 10), the tenth numerical value is the second axis numerical value Y, and the twentieth numerical value is the third axis numerical value Z, 30.
The four-dimensional vector is formed by these numerical values with the numerical value W of the fourth axis as the numerical value W of the fourth axis, and in this way, the next vector is the first, eleventh, twenty-first, and thirty-first A chaotic attractor calculated from discrete data with a large number of vectors formed by numerical values X, Y, Z, and W in this way is 4
It is to form in the dimensional space.

Then, the unit vector n1 = (n in the viewing direction of the four-dimensional number space
In order to match (1, n2, n3, n4) with the fourth axis, the following matrix calculation is performed, and each vector (X, Y, Z, W) is converted into (X ′, Y ′, Z ′, W). ′)

By the way, since the chaotic attractor formed in the four-dimensional number space cannot be displayed as a figure on the CRT display (6), the following matrix calculation is performed and the coordinates X ″, Y of the projection point in the three-dimensional number space are calculated. ", Z" is calculated.

Then, the coordinates X and Y obtained by projecting the coordinates of the three-dimensional number space onto the two-dimensional number space are calculated by the calculation of the following equation.

X = Y ″ cosβ−X ″ cosα Y = Z ″ −Xtanβ where α and β are angles formed by the x and y axes of the three-dimensional number space and the x and y axes of the two-dimensional number space.

A CRT display (6) as a display means or a printer using the coordinates X and Y of the two-dimensional number space thus obtained
Output it to (7) and use it as a diagnostic material.

Next, the Lyapunov index will be described.

Chaos has a property of strongly depending on the initial value. This property is called initial value dependence, and the numerical value indicating the degree of initial value dependence by an index is called the Lyapunov exponent.

The Lyapunov exponent can be calculated by calculating how far the distance between two adjacent trajectories drawn by the chaotic attractor is when a certain unit time elapses.

Next, a theoretical calculation method for obtaining the Lyapunov exponent will be described.

As shown in FIG. 12, the reference trajectory is x (t) t = {0,
1,2 ... nτ}, x (0) is the point ahead τ time
(Τ), y (τ) is a point near x (0), y (t) is the orbit where y (0) exists, and y (τ) is the point ahead of τ time from y (0),
When the distance between x (0) and y (0) is d (0), x (τ) and y
The distance d (τ) from (τ) can be expressed by the following equation.

d (τ) = d (0) e ^λτ This means that the distance between orbits that were initially close to each other exponentially expands with time, and the expansion ratio of this distance is expressed as an exponential value λ. Is the Lyapunov index.

The Lyapunov exponent λ can be calculated by the following equation.

d (τ) = d (0) e ^λτ e ^λτ = d (τ) / d (0) λτ = log _e {d (τ) / d (0)} λ = log _e {d (τ) / d ( 0)} / τ Next, a specific method of calculating the Lyapunov exponent based on the above theory will be described.

When there is a reference trajectory x (t) t = {0,1,2 ... nτ}, a point that is orthogonal to the vector x (0) x (1) and is separated by a certain unit distance is y ₀ (0), The orbit where y ₀ (0) exists is y ₀ (t).

The distance between x (0) and y ₀ (0) is d ₀ (0), the point τ time after point x (0) is x (τ), and the point τ time after y ₀ (0) is y
The distance between ₀ (τ), x (τ) and y (τ) is d (τ).

Then, in the same direction as the vector x (τ) y ₀ (τ),
A point separated by a certain unit distance is y ₁ (0), a trajectory where y ₁ (0) exists is y ₁ (t), and a distance between x (τ) and y ₁ (0) is d.
The point after τ time of ₁ (0), x (τ) is x (2τ), y ₁ (0)
Point after τ time of y ₁ (τ), x (2τ) and y ₁ (τ)
The distance from and is d ₁ (τ).

Next, points in the same direction as the vector x (2τ) y ₁ (τ) and separated by a certain unit distance are y ₂ (0), x (2τ)
And y ₂ (0) is the distance between d ₂ (0), x (2τ) after τ time is x (3τ), and y ₂ (0) after τ time is y.
The distance between ₂ (τ), x (3τ) and y ₂ (τ) is d ₂ (τ)
And

By repeating this operation, d in the first τ time
₀ (τ) / d ₀ (0) times, and in the next τ time, d ₁ (τ) /
d ₁ (0) times, and in the next τ time, d ₂ (τ) / d ₂ (0)
The distance between the x (t) orbit and the adjacent orbit is expanded by a factor of two, and nτ
It can be seen that after time, it becomes d _n-1 (τ) / d _n-1 (0).

The Lyapunov exponent λ is the average of the expansion ratios of the distances between the orbits per unit time, and can be calculated by the following equation.

The above is a theoretical calculation method of the Lyapunov exponent λ, but with this calculation method, it is difficult to calculate the Lyapunov exponent of the discrete data actually collected from the body surface of the subject.

The reason is that the data used as the model of the logical Lyapunov exponent calculation method generates chaos data by an appropriately defined formula (mathematical model), and thus can take continuous infinite data. So
Each of the following conditions can be satisfied.

On the other hand, in the discrete data actually collected from the body surface of the subject, it is not always possible to take data points at desired positions in the number space, and the number of data is finite. Therefore, the vector A'at the point A with the four-dimensional vector orbit
It is necessary that the (unit vector) exists at the orthogonal position of the trajectory, but this does not always exist in the discrete data actually collected from the body surface of the subject.

Further, the next vector B ″ (unit vector) at a certain point B needs to exist at the orthogonal position of the trajectory at the point B, but this is not always found in reality.

Further, it is necessary that the angle formed by the vectors B ′ and B ″ is small, but it is not necessarily small in reality.

Therefore, the closest vector at each point is searched. For example, when searching for the vector B ″, if the search is performed simply on the condition that it is close to B ′, the vector at the point next to the point B may be obtained. is there.

Since the number of data is finite, if point A is near the end of the discrete data group, the next data point (point A plus τ)
I can't take it.

The pulse wave and / or the electrocardiographic waveform becomes 12-bit discrete data by A / D conversion, but since this is also a finite number, it is possible that different data points have the same value.

Therefore, in the present invention, the conditions for adopting the data points used in the calculation of the Lyapunov exponents are defined as follows.

That is, in a four-dimensional space, a vertex is placed on the above trajectory, and a cone with a small apex angle with the vector B'as the center line is set, and the vector within this cone is adopted.

From the above, it is possible to avoid the possibility of taking the vector of the adjacent point unless the vector B ″ is very small and is not substantially in the same direction as the trajectory direction.

Further, the angle θ of each vector with respect to the trajectory is (X, Y, Z, W), where the vector of each data is (X ', Y', Z ', W'), and It is possible to check the orthogonality between the vector and the trajectory by this.

Also, set the upper and lower limits of the absolute value of each vector,
By adopting a vector within this range, it is possible to avoid the possibility of taking a vector of an adjacent point even if the vector B ″ is very small and is substantially in the same direction as the trajectory direction. it can.

In the above range, observing the chaotic attractor,
The value with the best convergence can be set.

The elongation rate of each vector thus obtained is converted into a logarithm whose base is 2, and the arithmetic mean value thereof is defined as the first Lyapunov exponent λ1.

In order to carry out the above calculation, the program (50) shown in FIGS. 7a and 7b is stored in the small computer (5).

That is, when the calculation of the Lyapunov exponent λ1 is started (51), first, a point A, which is a reference for judging the adoption condition of the data point, is set near the start end of the vector trajectory (52). This point A is postponed as the calculation progresses.

Next, it is judged whether there is a margin to take the next point B (point after τ) (53), and if there is a margin (53Y), this point B is adopted as the next point (54). Find a temporary data point at this point B
(55) If it is found (55Y), a temporary data point is searched for from the next point (56), and if not found, the discrete data is searched from the beginning (57).

Then, if the data point found in this way meets the above-mentioned adoption conditions (58Y), this data point is adopted (5
9), if not (58N), update the vector size range in the data point adoption condition (60), and if it does not deviate from the upper limit of this range (61N), return to step (55) and deviate. While doing (61Y), the reference point A is moved to the next point B (62), and the step
Return to (53). The data point thus adopted is judged whether or not there is a margin to develop the above-mentioned vector A ′ into the vector B ′ (63), and if there is a margin (63Y), it is determined as the trajectory of the vector B ′. Check for orthogonality (64). If there is no margin in step (63) (63N) and if there is no orthogonality in step (64) (64N), the process returns to step (55).

Then, it is judged whether or not the retry described below is in progress (6
5) If it is during retry (65Y), calculate the angle with the previous vector (66), and if this angle is large (66L), return to step (55), and if it is small (66S) and Step
If the retry is not being performed in (65) (65N), the vector B'is determined as a vector developed from the vector A '(6
7).

Then, the point A and the vector A'are stored for the retry (68).

At the end of the calculation, if there is no room to obtain the next point in step (53) (53N), the calculation is terminated (E).

Next, the temporary vector B ″ at the point B is searched (70), and if the vector B ″ does not meet the data point adoption condition (71N), the range of the vector size of the above condition is updated.
(72) If the upper limit is still exceeded (73Y), the retry flag is set (74) and the process returns to step (55).

If the upper limit is not exceeded in step (73) (73
N), return to step (70).

Then, if there is a vector B ″ that meets the data point adoption condition in step (71) (71Y), it is judged whether or not this vector B ″ is orthogonal to the trajectory (75), and if it is not orthogonal,
(75N), returning to step (70), if they are orthogonal (75Y), the angle formed by the vectors B ′ and B ″ at point B is calculated (7
6) Judge whether this angle is sufficiently small, that is, whether B ″ is in the above cone (77). If the angle is not small (77N), proceed to step (70). Returning, if it is small (77Y), the elongation rate from vector A'to vector B'because of moving from point A to point B is converted into a logarithm with base 2 (78), and this numerical value is arithmetically averaged. As the first Lyapunov exponent λ1 (79).

Then, it is judged whether or not the point B currently calculated is the end of the discrete data (80), and if it is not the end (80N), the above point B is set as the reference point for the next calculation (to the above point A). Equivalent)
(81) Convert the vector B ″ into a unit vector and substitute it into the reference vector for the next calculation (corresponding to the vector B ′ above).
(82) The point B and the vector B'are stored for the retry (83), the point next to the point B is calculated (84), and the vector developed from the calculated reference vector of the next point is calculated. Then, (85), the coordinates for orthogonal check of the vector used in the next calculation are updated (86), the process returns to step (70), and the above calculation is repeated.

When it is determined that the point B is the end of the discrete data in step (80) (80Y), the calculation is ended (E). That is, in steps (52) to (68), a reference point that meets the data point adoption condition is searched mainly by referring to the vector B ′ at the next point, and in steps (70) to (77), the same condition is found. It is designed to search for suitable developed vectors.As described above, by setting the adoption conditions for the data points to be adopted and searching for data points that meet this condition, unqualified data points can be calculated. In order to prevent the inclusion of data points, and if no suitable data point is found, instead of abandoning the data point search at that point, expand the search range of the size of the recruitment condition vector and search. By adopting the suboptimal data point, the first Lyapunov exponent λ1 can be calculated with high accuracy.

Next, the calculation of the second Lyapunov exponent λ2 will be described.

The calculation of the second Lyapunov exponent λ2 is basically the same as the above-described calculation of the first Lyapunov exponent λ1, and at the point A1, the vectors A2 and A3 adapted to the data point adoption condition are obtained.
To form a triangle A1-A2-A3 in the four-dimensional number space, and then the vectors A2 and A3 form a triangle B1-B2-B3 with the vectors B2 and B3 developed to the next point B1. Then, the newly formed eligible vectors B'and B "at the point B1 form a triangle B1-B'-B", and the triangle B1-B2-B3 is formed.
When the angle between the triangle B1-B'-B ″ and the triangle B1-B′-B ″ is sufficiently small, calculate the area expansion rate from the triangle A1-A2-A3 to the triangle B1-B2-B3, and use this as the logarithm with 2 as the base. The second Lyapunov exponent λ2 by converting and arithmetically averaging this number
To calculate.

The angle formed by each triangle is the angle formed by the combined vector of the vectors B2 and B3 and the combined vector of the vectors B'and B ".

Next, the correspondence between the chaotic attractor and the Lyapunov index and the condition of the subject will be described.

FIG. 8a is a chaotic attractor of pulse wave data when a subject (Mr. H, male, healthy) is relaxing, and FIG. 8b is a state in which the subject is reading (magazine). Is.

Comparing the above two figures, the figure of the chaotic attractor tends to shrink when the information processing in the brain is actively performed like reading, and the width reduction in the upper right-lower left direction is slight. However, the width reduction in the upper left-lower right direction is remarkable.

In addition, the spiral local structure in the upper right part of the chaotic attractor is clearly denser than when relaxed.

Fig. 9a shows another subject (Mr. K, woman, health) relaxing, Fig. 9b shows reading (mathematical text), and Fig. 9c shows reading (manga book). Figure 9d is a chaotic attractor of pulse wave data when looking at a vaguely beautiful picture. The size of the figure is small when relaxing and looking at a beautiful picture, when reading a book. Is getting bigger.

But when I'm reading a math textbook and when I'm reading a manga book, the latter figure is smaller,
It can be seen that there is a difference in the degree of consciousness concentration depending on whether the subject is interested or not.

Both of the above two examples are for healthy subjects. As both brain information processing becomes active, the local structure becomes coarser → denser and the concentration of consciousness becomes higher. According to this, it can be seen that the figure shrinks.

In addition, since slight differences in the size of graphics and detailed structures are considered to be individual differences, it is considered that a change in state of the same subject has a greater meaning than a comparison between subjects.

The Lyapunov exponents are shown at the bottom of the drawing. The first Lyapunov exponent λ1 becomes smaller as the concentration of consciousness becomes higher, and the second Lyapunov exponent λ2 becomes smaller as the brain information processing becomes active. And, in the case of healthy subjects, corresponds well with the results obtained from the above chaotic attractors.

FIG. 10a shows the treatment of Mr. S, who has a history of neurosis, before treatment.
Fig. 0b shows the chaotic attractor in the relaxed state after the treatment, and Fig. 10c shows the chaotic attractor in the relaxed state after the treatment.
The figure before treatment is extremely small, and after recovery, the figure expands and is almost the same size as a healthy person. In addition, the spiral local structure moves to the upper right and wraps around the end of the figure, and the difference between before treatment and after recovery is clear.

The first Lyapunov index λ1 is small before the treatment, increases during the treatment, and after recovery is smaller than during the treatment but larger than before the treatment.

Morita therapy is used to treat the above-mentioned neurosis. This treatment method is performed by accommodating a patient in a dark and silent room for a certain period of time and cutting off external stimuli during this period.

Extrapolating the transition of the first Lyapunov index λ1 to the conclusions obtained from healthy persons, it is considered that the symptoms of neurosis may have been exhibited due to excessive concentration of consciousness.

The second Lyapunov index λ2 remained at the same level before and during the treatment, but decreased after the recovery, and after the treatment, the brain information processing was not active. Indicates that information processing in the brain has become active.

Fig. 11a is a healthy person, and Fig. 11b is a chaotic attractor of electrocardiographic waveform data collected by an electrocardiograph (2) from a heart disease patient currently undergoing arrhythmia treatment, both of which are in a relaxed state.

Comparing the two figures, the figure of a healthy person is a bow-shaped shape with clear outlines consisting of two loops and two loops, whereas those of heart disease patients have a complicated shape. In particular, the structure on the upper left side is disordered.

For reference, the first Lyapunov index λ1 of healthy people is 2.9 ±
It is as small as 0.1 and corresponds well to the structure of the figure.

In this embodiment, as described above, the pulse wave sensor (1) and the electrocardiograph (2)
By calculating the pulse wave and heartbeat chaotic attractors and the first and second Lyapunov exponents λ1 and λ2 from the pulse wave data and the electrocardiographic waveform data of the subject respectively collected by and and displaying them on the CRT display. , It is possible to diagnose the physical and mental condition including the psychological state of the subject, and in particular, the chaos that indicates the physical and mental condition is extracted logically from the above seemingly random data and the order is extracted. By expressing the attractor and the Lyapunov index, it is possible to make an extremely objective diagnosis without any difference due to the difference in the diagnosticians.

Since the convergence of the chaotic attractor described above and the first and second Lyapunov indexes λ1 and λ2 correspond well, the first and second Lyapunov indexes λ in the present embodiment.
It proves that the calculation method of 1, λ2 is proper.

[Brief description of drawings]

FIG. 1 is an explanatory diagram showing the configuration of a diagnostic device according to the present invention, FIG. 2 is a sectional explanatory diagram showing the structure of a pulse wave sensor, FIG. 3 is a circuit diagram of a photosensor, and FIG. 4 is a pulse wave and a heart. FIG. 5 is an explanatory diagram showing the overall processing procedure of radio wave type data, FIG. 5 is an explanatory diagram of a menu, FIG. 6 is an explanatory diagram of a calculation procedure for displaying a chaotic attractor, and FIGS. 7a and 7b are Lyapunov. Flowchart of arithmetic processing for obtaining index, FIGS. 8a to 10
Figure c is a pulse wave chaotic attractor, Figures 11a and 1
Fig. 1b is a chaotic attractor for electrocardiographic waveform data, No. 12
The figure is an illustration of Lyapunov exponent calculation. (A): Diagnostic device (1): Pulse wave sensor (2): Electrocardiograph (5): Small computer (arithmetic means, storage means) (6): CRT display (display means) (11): Finger tip (14): Photo sensor (15): Infrared light emitting diode (16): Photo transistor

Claims (2)

[Claims] 1. A) pulse wave and / or electrocardiographic sensor that can be worn on the body surface of a subject; and b) digitize the pulse wave waveform and / or electrocardiographic waveform collected by the sensor by an A / D converter. , A calculation means for projecting a chaotic attractor embedded in a number space, which is a virtual space created by mathematical operation of the digitized discrete data, which is a numerical algorithm, into a two-dimensional number space, and c) Display means for displaying the projection of this two-dimensional number space, and d) calculating the Lyapunov exponent, which is a numerical value indicating the degree of sharp dependence on the initial value, which is one of the characteristics of chaos, from the chaotic attractor. From a body surface characterized by comprising: a calculating means for performing: (e) a display means for displaying the Lyapunov exponent; and a) a storage means for storing the discrete data and / or the Lyapunov exponent. Sorted pulse wave and / or diagnostic device using heartbeat. 2. The pulse wave sensor has a bottomed cylindrical cover into which a subject's finger tip can be inserted, and infrared rays capable of abutting on the inner surface of the cover to the finger pad of the finger tip. A light emitting diode and a phototransistor are provided, and the infrared light emitting diode and the phototransistor are configured by a photosensor in which the optical axes of the infrared light emitting diode and the phototransistor intersect at an angle of 20 ° to 30 ° inside the fingertip portion. The diagnostic device using the pulse wave and / or the heartbeat collected from the body surface according to claim 1.