JP2015100525A - Diagnostic data generation apparatus and diagnostic apparatus - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a diagnostic data generation apparatus capable of determining a mental disease such as depression in a simplified manner.SOLUTION: A diagnostic data generation apparatus includes m-dimensional average number-of-vectors calculation means 7 and m+1-dimensional average number-of-vectors calculation means 8 which respectively determine the m-dimensional average number of vectors and the m+1-dimensional average number of vectors, with a distance d changed. Disorder calculation means 9 determines microscopic disorder of pulse waves, on the basis of the m+1-dimensional average number of vectors and the m-dimensional average number of vectors with the distance d changed. Generation means 10 generates a change graph of the microscopic disorder of the pulse waves. The microscopic disorder of the pulse waves is thus determined from the m-dimensional average number of vectors and the m+1-dimensional average number of vectors with the distance d changed, so that the tendency of the change of microscopic disorder enables the diagnosis of mental diseases.

Description

  The present invention relates to a diagnostic data generation apparatus, and more particularly to an objective determination process for a mental illness.

  Today, there is known a recognition technique for checking the working condition of a gene of a protein that activates brain cells as a recognition based on objective data of depression.

  Patent Document 1 discloses a computer system that performs chaos analysis on acquired biological information and measures mental immunity such as communication and dementia.

JP 2006-204502

  However, the method of checking the working condition of the above gene requires a judgment after sending it to a laboratory after a blood test, which is complicated and takes time.

  It is an object of the present invention to provide a diagnostic data generation apparatus that can easily determine a mental illness such as depression.

  1) A diagnostic data generating apparatus according to the present invention includes: A) pulse wave storage means for storing pulse wave data that changes depending on blood flow flowing in a measurement site; B) m-dimensional average vector number calculation means having: b1 ) Combination generating means for generating a plurality of combinations composed of m consecutive elements for N pieces of individual data from the pulse wave data, b2) Distance between the vectors with each combination as an m-dimensional vector M-dimensional vector distance computing means for computing m, b3) For each of the m-dimensional vectors, compute the number of vectors existing in the distance d, and compute the statistical average value for the number of m-dimensional vectors Dimensional average vector number calculating means, b4) calculating the number of vectors existing in the distance d for each of the m-dimensional vectors, and calculating a statistical average value for the number of m-dimensional vectors. M + 1-dimensional average vector number calculation means for calculating, C) m + 1-dimensional average vector number calculation means having the following: c1) N pieces of individual data from the pulse wave data are composed of continuous m + 1 elements. A combination generating means for generating a plurality of combinations, c2) m + 1 dimensional vector distance calculating means for calculating the distance between each vector with each combination as an m + 1 dimensional vector, c3) the m + 1 dimensional For each vector, m + 1 dimensional average vector number calculating means for calculating the number of vectors existing within the distance d and calculating a statistical average value for the m + 1 dimensional vector number, c4) the m + M + 1-dimensional average vector number calculating means for calculating the number of vectors existing within the distance d and calculating a statistical average value for the m + 1-dimensional vector number for each one-dimensional vector, D) The number of m-dimensional average vectors and Randomness calculating means for obtaining microscopic randomness of the pulse wave based on the number of m + 1 dimensional average vectors, the m dimensional average vector number calculating means and the m + 1 dimensional average vector number calculating means Obtains the number of m-dimensional average vectors and the number of m + 1-dimensional average vectors when the distance d is changed, and the randomness computing means obtains the fineness of the pulse wave when the distance d is changed. It has a generating means for obtaining a visual randomness and further generating a change graph of the microscopic randomness of the pulse wave.

  Thus, by determining the microscopic randomness of the pulse wave when the distance d is changed, and generating the change graph of the microscopic randomness, an objective judgment for mental illness Data can be obtained.

  2. In the diagnostic data generation device according to the present invention, the distance d is obtained by multiplying a standard deviation of blood flow in the N pieces of individual data by a coefficient, and the randomness calculation means changes the coefficient. By changing the distance d, the microscopic randomness of the pulse wave is obtained. Therefore, it is possible to obtain determination data in consideration of the individual fluctuation width of the blood flow.

  3. In the diagnostic data generation device according to the present invention, the pulse wave data is time-series data of pulse waves. Therefore, judgment data based on the pulse wave can be obtained.

  4. In the diagnostic data generation device according to the present invention, the pulse wave data is time-series data of differential pulse wave data. Therefore, determination data based on the differential pulse wave data can be obtained.

  5. In the diagnostic data generation device according to the present invention, m = 2 or m = 3.

  6. In the diagnostic data generation apparatus according to the present invention, A) pulse wave storage means for storing pulse wave data that changes depending on the blood flow flowing through the measurement site, B) calculation means having the following b1) to b3), b1) For N pieces of individual data from the pulse wave data, a plurality of sets composed of continuous m elements are generated, and the difference in the set characteristic values determined from the values of the elements of each set is the first threshold value. First set number calculation means for determining the number of sets of sets that are less than d as the first set number, b2) For the N pieces of individual data, a plurality of sets composed of consecutive m + 1 elements are generated. , Second set number calculation means b3 for obtaining the set number of sets whose difference in set characteristic value determined from the values of the elements of each set is less than the first threshold value d as the second set number; A microscopic randomness calculating means for determining the microscopic randomness of the pulse wave based on the second set number; and C) the microscopic randomness calculation. Means for calculating a microscopic randomness of the pulse wave when the first threshold value d is changed, and D) a pulse when the first threshold value d is changed. Means for generating a change graph of the microscopic randomness of the waves. As described above, the microscopic randomness of the pulse wave when the first threshold value d is changed is obtained, and the change graph of the microscopic randomness is generated. Judgment data can be obtained.

  7. In the diagnostic data generation device according to the present invention, the first threshold value d is obtained by multiplying a standard deviation of blood flow in the N pieces of individual data by a coefficient, and the change microscopic randomness calculation means. Changes the first threshold value d by changing the coefficient to determine the microscopic randomness of the pulse wave. Therefore, it is possible to obtain determination data in consideration of the individual fluctuation width of the blood flow.

  8. A diagnostic data output device according to the present invention includes any one of the diagnostic data generation devices and an output unit that outputs the generated change graph.

  9. The diagnostic device according to the present invention includes notifying means for notifying any one of the diagnostic data generating device and the change graph when the trend is not simply decreasing.

  10. In the diagnostic data generation apparatus according to the present invention, pulse wave storage means for storing pulse wave data that changes depending on the blood flow flowing through the measurement site, differential pulse wave data obtained by differentiating the pulse wave data twice or more. A plurality of differential data calculating means for calculating, and chaos attractor generating means for generating a chaos attractor from the differential pulse wave data, wherein the embedding dimension is n and the embedding delay is τ. The trajectory of such a chaotic attractor differs in shape between healthy and mentally ill. Therefore, objective judgment data for mental illness can be obtained.

  11. A diagnostic data output device according to the present invention includes the diagnostic data generation device and output means for outputting the generated orbit shape of the generated chaotic attractor.

  12. In the diagnostic data generation device according to the present invention, pulse wave storage means for storing pulse wave data that changes depending on the blood flow flowing through the measurement site, differential pulse wave data obtained by differentiating the pulse wave data four times or more. A plurality of differential data calculating means for calculating, a mental illness degree calculating means indicating that there is a mental illness tendency, obtaining a wave height ratio b / a from the differential pulse wave data, and using this as a first parameter, Mental illness degree calculating means for determining that the mental illness degree is larger as the value of the first parameter is smaller is provided. Thus, objective judgment data for mental illness can be obtained by judging the degree of mental illness from the first parameter.

  13. In the diagnostic data generating apparatus according to the present invention, the mental illness degree calculating means obtains a pulse height ratio d / b from the differential pulse wave data, and uses this as a second parameter, and the first parameter and the It is judged that the degree of the mental illness is larger as the difference in the second parameter is smaller. Thus, objective judgment data for mental illness can be obtained by judging the degree of mental illness from the difference between the first parameter and the second parameter.

  14. In the diagnostic data generation device according to the present invention, pulse wave storage means for storing pulse wave data that changes depending on the blood flow flowing through the measurement site, differential pulse wave data obtained by differentiating the pulse wave data four times or more. A plurality of differential data calculating means for calculating, a mental illness degree calculating means indicating that there is a mental illness tendency, obtaining a wave height ratio d / b from the differential pulse wave data, and using this as a second parameter, Mental illness degree calculating means for determining that the degree of the mental illness is larger as the value of the second parameter is smaller is provided. Thus, objective judgment data for mental illness can be obtained by judging the degree of mental illness from the second parameter.

  15. The diagnostic data output device according to the present invention includes the diagnostic data generation device and output means for outputting the generated trajectory shape of the chaotic attractor.

  In this specification, the “statistical average value” includes an average value, a median value, and a mode value in a narrow sense. In addition, the “average value” in the narrow sense includes various arithmetic methods such as geometric average, harmonic average, generalized average, and weighted average. “Differential pulse wave data” includes data obtained by differentiating pulse wave data at least once.

2 is a functional block diagram of a diagnostic data generation device 1. FIG. It is a figure which shows the hardware constitutions of the diagnostic data raw apparatus. It is an example of the waveform of pulse wave data. The flowchart of graph generation is shown. It is a detailed flowchart of a sample entropy calculation process. Indicates the calculation result (healthy person) when r is changed. Indicates the calculation result (with mental illness) when r is changed. It is a figure explaining the significance of sample entropy. It is a graph at the time of changing a sampling rate. It is an acceleration pulse wave in 2nd Embodiment. It is a graph which shows the track of a healthy person's attractor in a 2nd embodiment. It is a graph which shows the locus | trajectory of the attractant of a mental patient in 2nd Embodiment. 3 is a functional block diagram of an attractor generation device 210. FIG. It is a flowchart of attractor generation. It is a figure which shows the process which produces | generates a chaotic attractor from fingertip pulse wave data. The 4th derivative data of a healthy person are shown. The 4th derivative data of a mentally ill person are shown. The attractant of a healthy person is shown. The attractor of the mentally ill person is shown. It is the analysis result about the wave height ratio. It is a figure for demonstrating the difference between a patient and a healthy person about wave height ratio b / a. It is a figure for demonstrating the difference between a patient and a healthy person about crest ratio b / d. 4 is a functional block diagram of a diagnostic data generation device 410. FIG. It is a flowchart in the case of diagnosing from a crest ratio. It is a functional block diagram of diagnostic data generation device 520.

1 ... Diagnostic data generator 23 ... CPU
27 ... Memory

Functional Block Diagram FIG. 1 shows a functional block diagram of a diagnostic apparatus 100 having a diagnostic data generation apparatus 1 according to the present invention. The diagnostic device 100 includes a finger plethysmogram measurement unit 3, a diagnostic data generation device 110, a notification unit 11, and an output unit 12.

  The finger plethysmogram measuring means 3 measures the finger plethysmogram and measures pulse wave data that changes depending on the blood flow flowing through the measurement site.

The diagnostic data generation device 110 includes a pulse wave storage unit 5, an m-dimensional average vector number calculation unit 7, an m + 1-dimensional average vector number calculation unit 8, a randomness calculation unit 9, and a generation unit 10. The m-dimensional average vector number calculation means 7 is a combination generation means for generating a plurality of combinations composed of consecutive m elements for N pieces of individual data from the pulse wave data. M-dimensional vector distance computing means for computing the distance between each vector as a vector, for each of the m-dimensional vectors, computing the number of vectors existing within the distance d, and a statistical average for the number of m-dimensional vectors M-dimensional average vector number calculation means for calculating a value, and m for calculating the number of vectors existing within the distance d and calculating a statistical average value for the number of m-dimensional vectors for each of the m-dimensional vectors Dimensional average vector number calculation means,
The m + 1 dimensional average vector number calculating means 8 is a combination generating means for generating a plurality of combinations composed of consecutive m + 1 elements for the N individual data from the pulse wave data, and each of the combinations M + 1 dimensional vector distance computing means for computing the distance between the vectors as m + 1 dimensional vectors, for each of the m + 1 dimensional vectors, computing the number of vectors present in the distance d, An m + 1 dimensional average vector number calculating means for calculating a statistical average value for the number of m + 1 dimensional vectors, for each m + 1 dimensional vector, calculating the number of vectors existing within the distance d, M + 1 dimensional average vector number calculating means for calculating a statistical average value for the m + 1 dimensional vector number.

  The randomness calculation means 9 obtains the microscopic randomness of the pulse wave based on the number of m + 1 dimensional average vectors and the number of m dimensional average vectors.

  Further, the m-dimensional average vector number calculating means 7 and the m + 1-dimensional average vector number calculating means 8 obtain the m-dimensional average vector number and the m + 1-dimensional average vector number when the distance d is changed. The randomness calculation means 9 obtains the microscopic randomness of the pulse wave when the distance d is changed. The generation means 10 generates a change graph of the microscopic randomness of the pulse wave.

  Therefore, the microscopic randomness of the pulse wave can be obtained based on the number of m-dimensional average vectors and the number of m + 1-dimensional average vectors when the distance d is changed.

  The notifying means 11 notifies the change graph when it is not a simple decreasing tendency. The output unit 12 outputs the generated change graph.

2. Hardware Configuration The hardware configuration of the diagnostic apparatus 100 will be described with reference to FIG. FIG. 1 is an example of a hardware configuration in which the diagnostic apparatus 100 is configured using a CPU.

  The diagnostic apparatus 100 includes a CPU 23, a memory 27, a hard disk 26, a monitor 30, an optical drive 25, an input device 28, a communication board 31, a pulse wave sensor 36, and a bus line 29. The CPU 23 controls each unit via the bus line 29 according to each program stored in the hard disk 26.

  The hard disk 26 stores an operating system program 26o (hereinafter abbreviated as OS) and a main program 26p. The processing of the main program 26p will be described later.

  The pulse wave sensor 36 is a general fingertip pulse wave measuring instrument. In the present embodiment, a finger plethysmograph is used that measures blood flow using infrared rays and obtains a finger plethysmogram from this blood flow. Specifically, the infrared light emitted from the light emitting element is reflected by the finger to be measured and received by the light receiving element. The intensity of the reflected light represents the blood flow rate. Therefore, the signal output from the light receiving element is a fingertip volume pulse wave. The signal from this light receiving element is output as digital data.

  Data from the pulse wave sensor 36 is taken into the memory 27 and stored in the pulse wave storage unit 26m.

  FIG. 3A shows an example of the finger plethysmogram output from the finger plethysmograph 36. Although it is actually digital data, it is shown as a waveform in FIG. 3A.

  The result storage unit 26k stores sample entropy obtained from the pulse wave measured as described later.

  In this embodiment, Windows 7 (registered trademark or trademark) is used as the operating system program (OS) 26o, but the present invention is not limited to this.

  Each of the above programs is read from the CD-ROM 25a storing the program via the optical drive 25 and installed in the hard disk 26. In addition to the CD-ROM, a program such as a flexible disk (FD) or an IC card may be installed on a hard disk from a computer-readable recording medium. Furthermore, it may be downloaded using a communication line.

  In the present embodiment, the program stored in the CD-ROM is indirectly executed by the computer by installing the program from the CD-ROM to the hard disk 26. However, the present invention is not limited to this, and the program stored in the CD-ROM may be directly executed from the optical drive 25. Note that programs that can be executed by a computer are not only programs that can be directly executed by being installed as they are, but also programs that need to be converted into other forms (for example, those that have been compressed) In addition, those that can be executed in combination with other module parts are also included.

3. Flow chart (3.1 Diagnostic data generation process)
FIG. 4 shows an overall flowchart of the diagnostic data generation process. The user brings the finger to be measured into contact with the pulse wave sensor 33 and causes the diagnostic apparatus 100 to execute a measurement start process. As a result, the CPU 23 starts reading and storing the finger plethysmogram (step S1 in FIG. 4). The result is stored in the memory 27. The stored pulse wave data represents the horizontal axis time and the vertical axis blood flow.

  The CPU 23 performs N samplings from the pulse wave data (step S3). In the present embodiment, data for 3 minutes is employed at a sampling rate of 200 times per second (N = 36000).

  The CPU 23 sets the allowable value r as an initial value (step S5). In the present embodiment, the initial value of the allowable value r is set to 0.1.

  The CPU 23 generates a set B composed of m elements and a set A composed of m + 1 elements while shifting N sampling values one by one (step S7). For example, N sampling values are k (1) for the first, k (2) for the second, k (3) for the third, k (4) for the fourth, k (5) for the fifth, ... If the nth is k (n) and m = 2, the set B is composed of m elements, so B (1) = (k (1), k (2)), B ( 2) = (k (2), k (3)), B (3) = (k (3), k (4)) ..., B (Nm) = (k (Nm), k (Nm) ) Of Nm. On the other hand, since the set A is composed of m + 1 elements, A (1) = (k (1), k (2), k (3)), A (2) = (k (2) , K (3), k (4)), A (3) = (k (3), k (4), k (5))..., A (Nm) = (k (Nm), k ( N-m + 1), k (N)) and Nm sets. Here, as for the set B, the same number of sets as the set A are generated in order to match the number of sets of both as described later.

  The CPU 23 calculates sample entropy, assuming that the set A is expressed by an m-dimensional vector and the set B is expressed by an m-dimensional vector (step S9). The sample entropy calculation method employed in this embodiment will be described with reference to FIG.

The CPU 23 initializes the process number i (step S21 in FIG. 5). The CPU 23 calculates the difference between the i-th set and all other sets with each set of the set B as an m-dimensional vector, and the vector specified by the i-th set exists within the distance σ · r. An average vector number B ^m _i (r) of vectors to be calculated is obtained (step S23). Note that σ is a standard deviation of the sampled N sampling values. For the average number of vectors existing within the distance σ · r, a difference is calculated for each vector, and the number of vectors within which the difference is within the distance σ · r may be obtained.

  Here, since i = 1, the CPU 23 obtains a distance between the vector B (1) and another vector B (2) = (k (2), k (3)). In the present embodiment, the maximum value of the difference between corresponding elements is adopted as the distance between vectors. For example, in the case of vector B (1) and vector B (2), the difference between k (2) of vector B (2) and k (1) of vector B (1) and k ( Of the differences between 3) and k (2) of vector B (1), the larger value is the distance. As for the vector distance, other vector distance calculation methods (for example, Euclidean distance, etc.) may be employed.

The CPU 23 sets the number of vectors in the distance σ · r to “1” if the calculated distance is smaller than the distance σ · r, and “0” otherwise. This is to determine how many vectors other than itself exist near the vector B (1). Similarly, the distance between the vector B (1) = (k (1), k (2)) and the vector B (3) = (k (3), k (4)) Repeat to find the distance between the vectors B (1) and B (Nm). The obtained number is totaled and the average is obtained. In this embodiment, since the distance to itself is not obtained, (Nm-1) distances are totaled and the average of them is obtained. Thereby, the average number of neighbors of the vector B ² ₁ (0.1) is obtained.

Similarly, each set of the set A is set as an m + 1-dimensional vector, and the difference between the i-th set and all other sets is calculated, and the distance σ · r is calculated for the vector specified by the i-th set. The average vector number A ^{m + 1} _i (r) of the existing vectors is obtained (step S25).

The CPU 23 increments the process number i and determines whether or not the process number i has been completed. In this case, since the process is not completed, the processes in steps S23 and S25 are repeated. As a result, the average number of neighbors of the vector B ² ₂ (0.1) and the vector A ² ₂ (0.1) is obtained. This gives the average number of neighbors for all vectors.

In step S29, when the CPU 23 obtains the average number of neighbors for all the sets for the sets A and B, the average vector number A ^{m + 1 for} all the sets. (r) and average vector number B ^m (r) is obtained (step S31). For this ^purpose , the average vector numbers B ^m2 ₁ (0.1), B ^m2 ₂ (0.1), and B ^m2 ₃ (0.1) may be summed up to find the average. The same applies to the average vector number A ^{m + 1} _i (r).

  The CPU 23 calculates the average vector number A for all sets.^{m + 1} (r) and average vector number B^m The sample entropy is obtained from (r). In the present embodiment, (A^{m + 1} (r) / B^m (r)) was obtained, and the value obtained by multiplying the natural logarithm by -1 was taken as the sample entropy.

SampEn (m, r, N) =-ln (A ^{m + 1} (r) / B ^m (r)) ... (Formula 1)
Here, m is the number of elements constituting the set, r is a distance threshold, and N is the number of samples.

  In this way, the sample entropy of the set A and the set B when the allowable value r = 0.1 is obtained. The CPU 23 adds the increment r1 to the permissible value r (step S11 in FIG. 4), determines whether the permissible value r exceeds the set value (step S13), and if not, the value increased by r1. The sample entropy of set A and set B is obtained. Similarly, the sample entropy when the allowable value r increases by r1 is obtained and stored in the result storage unit 26k (see FIG. 2). In this embodiment, r1 = 0.01, but the present invention is not limited to this.

  If the allowable value r exceeds the set value in step S13, the process is terminated. In the present embodiment, the setting value is set to 0.5, but is not limited to this. In this way, sample entropy is obtained when the allowable value r is sequentially changed.

  The CPU 23 displays a graph of the change in sample entropy when the allowable value r is sequentially changed. FIG. 6 is a case of a healthy person, and FIG. 7 is a measurement result of a patient with major depressive disorder (single type). In the graph of a healthy person shown in FIG. 6, the value of sample entropy decreases monotonously as the allowable value r increases. This is because a large allowable range means that the sample entropy is small even when the variation is large. On the other hand, when there is a mental illness shown in FIG. 7, the sample entropy does not decrease monotonously but tends to increase once in the middle even if the allowable value r increases. The reason for this is not clear, but the inventor considered that there was some relationship between disturbance of the pulse wave and mental illness. When this tendency was examined for patients with other psychiatric disorders, similar field results were obtained. In the present embodiment, Asperger syndrome, major depressive disorder (single type), major depressive disorder (single type) acute stress disorder, major depressive disorder / primary sleep disorder, major Depressive disorder Unspecified anxiety disorder, major depressive disorder repetitive / burnout syndrome, major depressive disorder (repetitive), schizophrenic disorder (depressive), bipolar II disorder, eating disorder, mood Circulatory disorder, organic psychotic disorder (Basedoh's disease), mood circulatory disorder, eating disorder organic psychotic disorder hypothyroidism, short-term psychotic disorder (with significant stress factor), organic psychotic disorder・ About hypothyroidism, mood circulatory disorder, obsessive-compulsive disorder, PMS organic psychotic disorder, mood modulation disorder PTSD chemical hypersensitivity, schizophrenia (delusion type), obsessive-compulsive disorder, schizophrenia, etc. The same trend was observed.

  In this way, by displaying the change in sample entropy when the allowable value r is sequentially changed in a graph, it is possible to easily and objectively determine whether the subject has a mental illness.

  Although the case where m = 2 has been described in the present embodiment, the inventor further examined m = 3, m = 4,... And m as shown in FIGS. It was found that there was a similar tendency. Thus, the above-mentioned tendency is remarkable when m = 2 or m = 3, but may occur even when m = 4 or more.

  It was also found that when a plurality of curves were drawn for a plurality of values of m, the mentally ill person had a tendency to intersect with other curves (see FIG. 7). Therefore, when the sample entropy obtained by changing the allowable value r as shown in FIG. 4 is obtained, it is possible to more objectively determine whether or not the patient is mentally ill by repeating the process of changing m.

  In addition, it may be determined whether or not the obtained change graph has a simple decreasing tendency, and if it is not a simple phenomenon tendency, this may be notified. Further, when a plurality of curves are drawn with respect to the plurality of values of m described above, when intersecting with other curves, this may be detected and notified.

  The difference from the conventional method for using the sample entropy in this embodiment will be described.

  Conventionally, a method for obtaining a sample entropy and determining a mental state based on the value is known. Specifically, in the conventional calculation of sample entropy, the calculation is performed with the number of samples N, the allowable range r, and the continuous length m being fixed, and the determination is made based on the values. When time-series change data that periodically changes while fluctuating with the passage of time, such as a blood flow rate, is divided into a plurality of sample sections, the following relationship is obtained.

The values of the elements constituting the set B indicate continuous data changes as shown in FIG. By grasping this as an m-dimensional vector, the number of vectors distributed in σ · r, that is, as a section changing from a certain value k (P) to k (P + 1) as shown in FIG. 8B It shows how many similar sections exist. In addition, the value of the elements that make up the set A further changes from a certain value k (P) to k (P + 1), and then how many similar sections change to k (P + 2) Indicates whether there is. (A ^{m + 1 in} the logarithm of the above formula (1) (r) / B ^m (r)) is (A ^{m + 1} (r) / (Nm-1) / B ^m (r) / (Nm-1)) is obtained by reducing / (Nm-1). Where B ^m (r) / (Nm-1) is the probability of changing from a certain value k (P) to k (P + 1) as A ^{m + 1} (r) / (Nm−1) obtains the probability of further changing to k (P + 2) after changing from a certain value k (P) to k (P + 1). That is, for these ratios, when a change from a certain value k (P) to k (P + 1) is 1, a ratio of further changing to k (P + 2) is obtained. Equation (1) finds the logarithm of this value. In the past, this value was used to analyze the relationship with mental illness.

  On the other hand, the inventor considered whether it was possible to make a judgment by a statistical method, rather than making a judgment from such a single numerical value. Therefore, the sample entropy in which the allowable range r with respect to the length m of each series in the number N of samples was changed was obtained. In general, the larger the allowable range r, the larger the number of sample entropies that enter the neighborhood of a certain sequence m, and the larger the number that enters the neighborhood of the adjacent sequence m + 1. Is expected to converge monotonically to a certain value. In the case of a healthy person, it actually decays monotonously and converges. On the other hand, it was found that for mentally ill people, such a change does not attenuate monotonously but tends to increase once (the graph rises). Thus, the point which judges with the shape of a graph when the tolerance | permissible_range r is changed differs from the past.

  In the present embodiment, the sampling number N = 36000, but the value of N is not limited to this. The same tendency is detected even when N = 600 as shown in FIG. 9A and even when N = 100 as shown in FIG. 9B. Therefore, almost the same measurement result can be obtained with a measurement time of 3 seconds and further with a measurement time of 0.5 seconds.

  In the present embodiment, the case where the value of m is set to m and m + 1 has been described for the sets B and A, but is not limited thereto, and may be m and m + 2, for example.

  In the present embodiment, the sample entropy is obtained by the processes shown in FIGS. 3 and 4, but the present invention is not limited to this, and the sample entropy may be obtained by another calculation method.

  In the present embodiment, the same number of sets as the set A are generated for the set B. However, the set B may be configured by N−m + 1 sets. This is because, if N is about 100 or more, the ratio of the sample entropy between the two is almost the same as the difference in error.

  In this embodiment, in step S23 and step S25 in FIG. 5, the average is obtained by summing (Nm-1) distances, and the average is obtained. However, the present invention is not limited to this. The distances 0 may be added, and (Nm) distances may be summed up to obtain an average of them.

  In the present embodiment, the sample entropy is obtained. However, any arithmetic expression representing general redundancy, such as an entropy arithmetic expression of Kolomogorov or Shannon, can be employed.

  In this embodiment, the pulse wave data is adopted, but a velocity pulse wave or an acceleration pulse wave obtained by differentiating the pulse wave data may be used. The velocity pulse wave can be obtained by differentiating the pulse wave data once, and the acceleration pulse wave can be obtained by differentiating the pulse wave data twice. Further, differential pulse wave data obtained by differentiating acceleration pulse waves one or more times may be used.

4. Second Embodiment As for the acceleration pulse wave as shown in FIG. 10 as well, the trajectory tendency of sample entropy is different between a mentally ill person and a healthy person. The inventor has generated the chaotic attractor on the assumption that there is a possibility that some difference may be caused by displaying the chaotic attractor of the acceleration pulse wave. As a result, as shown in FIGS. 11A and 12A, the shape of the attractor is clearly different between the healthy person and the mentally ill person.

  FIG. 10A shows the pulse wave of a healthy person and its acceleration pulse wave, and FIG. 10B shows the pulse wave of a mentally ill person and its acceleration pulse wave. FIG. 11B shows a chaotic attractor generated from the pulse wave shown in FIG. 10A, and FIG. 12B shows a chaotic attractor generated from the pulse wave shown in FIG. 10B. Thus, there is no clear difference between the two chaotic attractors generated from the pulse wave. On the other hand, as described above, with regard to the chaotic attractor of the acceleration pulse wave, while the healthy person has a sharp shape (see FIG. 11A), the graph of the sick person is round and complicated. Yes (see FIG. 12A). Therefore, by displaying the trajectory of the chaotic attractor of the acceleration pulse wave as described above, it is possible to objectively determine whether or not the person is mentally ill.

  FIG. 13 shows a functional block diagram of the diagnostic apparatus 200 according to the second embodiment. The diagnostic apparatus 200 includes a finger plethysmogram measurement unit 203, a diagnostic data generation unit 201, and an output unit 213.

  The fingertip pulse wave measuring means 203 is the same as the fingertip pulse wave measuring means 3 shown in FIG. The diagnostic data generation device 201 calculates an embedding dimension from the acceleration data obtained by the acceleration pulse wave computing unit 203 and the acceleration pulse wave computing unit 203 that computes acceleration data of the pulse wave data measured by the fingertip pulse wave measuring unit 203. And a chaos attractor generating means 209 for generating a chaos attractor, and a result storage means 211 for storing the generated chaos attractor. The output means 213 outputs the generated orbit shape of the chaotic attractor.

  Since the hardware configuration when the second embodiment is realized by a CPU and a program is the same as that of the first embodiment, a description thereof will be omitted.

  The processing of the CPU 23 according to the program in the second embodiment will be described with reference to FIG.

  The CPU 23 gives a command to measure the pulse wave to the finger plethysmograph 36 and measures the pulse wave (step S41 in FIG. 14). When the measurement pulse wave is given from the fingertip pulse wave measuring device 36, it is stored in the memory 27.

  The CPU 23 calculates an acceleration pulse wave from the pulse wave stored in the memory 27 (step S43 in FIG. 14). This is obtained by differentiating the measured pulse wave twice as in the prior art. The obtained acceleration pulse wave is shown in FIG. 3B.

  The CPU 23 initializes the target block number j (step S45). The CPU 23 extracts data of the jth block (step S47). In the present embodiment, the sampling rate is set to 200 times per second as in the first embodiment, and P = 3500 pieces of data are extracted. In this case, since j = 1, 3500 pieces of data from the top are extracted as the first block data. In this embodiment, although P = 3500, it is not limited to this. In this way, 3500 points of data from the top are extracted, and these become the data of the first block B1.

  The CPU 23 reconfigures the chaos attractor for the finger plethysmogram data of the target block, with the embedding dimension as n and the embedding delay as τ according to the Takens embedding theorem (step S49).

  The procedure of the chaotic attractor structure from fingertip pulse wave data is shown using FIG. Time-series fingertip pulse wave data is represented by w (t) (FIG. 15A). Based on the fingertip pulse wave data, the CPU 23 generates vectors P (i) = w (i), w (i + τ), and w (i + 2τ) (see FIG. 15A). Here, τ is an embedding delay.

  This vector P (i) is sequentially plotted in the n-dimensional reconstruction phase space as shown in FIG. 15B. Here, for illustration, a three-dimensional vector is used. In this case, the coordinate axes of the n-dimensional reconstruction phase space are Xi = w (i), Yi = (i + τ), and Zi = (i + 2τ). In this way, an attractor as shown in FIG. 15C can be obtained.

  CPU23 judges whether the attractor was calculated | required about all the blocks of a finger plethysmogram (step S51). Here, since j = 1 and there is an unprocessed block, the process returns to step S7 to increment the process block number j. Thus, j = 2. The CPU 23 extracts the second block data (step S47). In the present embodiment, the top of the j-th block data is set to a position shifted by 200 from the top of the previous J-1th block data. The CPU 23 performs the calculation of step S49 for this target block (step S49).

  Thus, CPU23 calculates | requires an attractor similarly about 2nd block B2. The CPU 23 records the attractor (vector P (i)) calculated in this way on the hard disk 26.

  If the CPU 23 determines that attractors have been obtained for all blocks, the CPU 23 displays the recorded attractors on the display 26 (step S55). In addition, various representations are possible for the display of the attractor. For example, in the case of a four-dimensional attractor, it may be displayed by vertical / horizontal / height and color.

  Thus, it can also be comprised as a diagnostic apparatus containing the said diagnostic data generation apparatus.

  In this embodiment, the embedding dimension n is 4 and the embedding delay τ is 10 points (10 sampling points). Note that the embedding dimension n and the embedding delay τ may be other values as long as the differences shown in FIGS. 11 and 12 appear in the acceleration of the fingertip pulse wave.

  For human pulse waves, a delay time of 50 ms is preferable. Therefore, even when the sampling rate is changed, the embedding delay τ may be determined so that the delay time is 50 ms.

  In the present embodiment, the case where the acceleration pulse wave data is employed has been described, but data obtained by further differentiating the acceleration pulse wave once or twice or more may be used. FIG. 16 and FIG. 17 show pulse wave data (fourth differential pulse wave data) obtained by further differentiating the acceleration pulse waves of two healthy subjects and two mentally ill people two or more times, respectively. FIG. 18 and FIG. 19 show the attractors of the four-time differential pulse wave data, respectively. Thus, even if the data is obtained by further differentiating the acceleration pulse wave twice, the trajectory of the attractor differs between a normal person and a mentally ill person.

5. Third Embodiment In the second embodiment, an acceleration pulse wave attractor is generated, and it is determined whether the person is mentally ill depending on whether the shape is round or cornered. The inventor has examined which of the characteristics of the acceleration pulse wave has such a tendency. As a result, the wave height ratio b / a and the wave height ratio b / d of the 4th derivative data obtained by differentiating the pulse wave data 4 times for the subjects examined by the inventor (465 healthy persons and 199 patients with mental illness) are: It was found that there is a correlation in the judgment of mental illness. That is, it was found that the larger the wave height ratio b / d of the 4th derivative data, the more apt to be mental illness, and the smaller the wave height ratio b / a of the 4th derivative data, the more apt to be mentally ill. 20 to 22 show the analysis results. It can be seen from the p-values in FIGS. 21 and 22 that the crest ratio b / a and crest ratio b / d of the four-time differential data have a correlation with mental illness.

  Further, regarding the subject, the relationship using both the wave height ratio b / a and the wave height ratio b / d of the four-time differential data can be expressed by the following relational expression.

F = b / d * 0.89-b / a * 1.265-2.02 (Formula 2)
Center of gravity Y = 40 * (-0.567) + 47 * 0.482 = 0
When F <0, it can be determined that the subject is healthy and the others are mentally ill.

  The relationship between the crest ratio b / a and the crest ratio b / d and the mentally ill person can be represented by F = K1 * (b / d) −K2 (b / a) −K3. That is, it can be said that the greater the value b / d, the stronger the tendency of the mentally ill and the smaller the value b / a, the stronger the tendency of the mentally ill. In other words, it is possible to determine whether or not the patient is a mentally ill person based on the difference between the 4th differential data wave height ratio b / d and the wave height ratio b / a.

Note that the Lyapunov index in FIG. 20 can be obtained as follows. Here, the Lyapunov exponent is a dynamical system of x _{n + 1} = f (x _n ), which indicates how far two orbits {x _n } starting from two adjacent points move away when n → infinity. It is a measure to measure. The Lyapunov index of each dimension of the attractor calculated by the following formula can be calculated.

  The invention disclosed in the present embodiment can be understood as an invention if it is constituted by the means shown in FIG.

  Fingertip pulse wave measuring means 403 for measuring pulse wave data that changes depending on the blood flow flowing through the measurement site, pulse wave storage means 405 for storing the pulse wave, and four times obtained by differentiating the pulse wave data four times. Differential data calculation means 406 for calculating differential data, differential data storage means 407 for storing the obtained four-time differential data, and means for indicating that there is a mental illness tendency, the four times obtained by the differential data calculation means Diagnosis comprising: a wave height ratio b / a from differential data, and using this as a first parameter, a mental illness degree calculating means 409 for determining that the lower the value of the first parameter is, the higher the mental illness degree is Data generator 410.

  The mental illness degree calculating means 409 obtains the crest ratio d / b from the four-time differential data, and uses this as the second parameter. The smaller the difference between the first parameter and the second parameter, the smaller the mental illness is. It may be determined that the degree is large.

  The mental illness degree calculating means 409 is a means for indicating that there is a mental illness tendency, and obtains a crest ratio d / b from the four-time differential data, and uses this as the second parameter, the second parameter. You may make it judge that the said mental illness degree is so large that the value of is small.

  Since the hardware configuration when the third embodiment is realized by a CPU and a program is the same as that of the first embodiment, the description thereof is omitted.

  The program in this case is as shown in FIG. Briefly, when a measured pulse wave is given from the finger plethysmograph 36, the CPU 23 stores it in the memory 27 (step S61). The CPU 23 calculates a differential pulse wave that has been differentiated four times from the pulse stored in the memory 27 (step S63).

  The CPU 23 calculates the wave height ratio d / b and the wave height ratio d / b, and calculates the mental illness level. Specifically, the average of the wave height ratio d / b and the wave height ratio d / b may be obtained and applied to the equation (2).

The CPU 23 determines whether or not F <0 (step S66). If F <0, the CPU 23 displays on the display 26 that there is a healthy tendency (step S55). If F <0, the display 26 displays that there is a mental illness tendency (step S55).
Similarly to the first embodiment, the diagnosis device 400 may be configured by connecting a notification unit and / or an output unit to the diagnosis data generation device.

  In the present embodiment, the case where the four-time differential data obtained by further differentiating the acceleration pulse wave data twice has been described, but the data obtained by further differentiating the four-time differential data one or more times may be used.

6. Other Embodiments The invention disclosed in the first embodiment can be grasped as a diagnostic data generation apparatus as shown in FIG.

  Calculation means 510 having fingertip pulse wave measurement means 503 for measuring pulse wave data that changes depending on the blood flow flowing through the measurement site, pulse wave storage means 505 for storing the pulse wave, and b1) to b3) below. B1) For N pieces of individual data from the pulse wave data, a plurality of sets composed of continuous m elements are generated, and the difference in the set characteristic values determined from the values of the elements of each set is first. First set number calculation means 513 for obtaining the number of sets of sets that are less than the threshold value d as the first set number, b2) For the N pieces of individual data, a plurality of sets composed of consecutive m + 1 elements Second set number calculation means 515 for obtaining a set number of sets whose difference in set characteristic value determined from the element values of each set is less than the first threshold value d as the second set number, b3) the first Based on the number of sets and the second number of sets, the microscopic randomness calculation means 511 for obtaining the microscopic randomness of the pulse wave. C) Change microscopic randomness calculation means 523, D) for causing the microscopic randomness calculation means 511 to calculate the microscopic randomness of the pulse wave when the first threshold value d is changed. A diagnostic data generation apparatus 500 comprising: a generation unit 525 that generates a change graph of microscopic randomness of a pulse wave when the first threshold value d is changed.

  Similarly to the first embodiment, a notification device and / or an output device may be connected to this diagnostic data generation device to constitute a diagnostic device.

  In the present embodiment, the fingertip pulse wave is used as the time-series change data detected from the human body. However, in addition to the pulse wave, the present invention can be similarly applied to the brain wave and the like.

  Further, the terminal computer may be a mobile terminal or the like instead of a personal computer. Alternatively, the terminal computer may not perform the calculation, but may transmit data necessary for the calculation to a network-connected computer and transmit the calculation result of the computer to the terminal computer.

  In the above embodiment, the CPU is used to realize the function shown in FIG. 1, and this is realized by software. However, some or all of them may be realized by hardware such as a logic circuit.

In addition, you may make it make an operating system (OS) process a part of said program.

Claims (15)

A) Pulse wave storage means for storing pulse wave data that varies depending on the blood flow flowing through the measurement site,
B) m-dimensional average vector number calculation means having the following:
b1) Combination generation means for generating a plurality of combinations composed of m consecutive elements for N individual data from the pulse wave data,
b2) m-dimensional vector distance computing means for computing the distance between each vector with each combination as an m-dimensional vector,
b3) m-dimensional average vector number calculating means for calculating the number of vectors existing within a distance d for each m-dimensional vector and calculating a statistical average value for the m-dimensional vector number;
b4) m-dimensional average vector number calculating means for calculating the number of vectors existing within the distance d and calculating a statistical average value for the m-dimensional vector number for each of the m-dimensional vectors,
C) m + 1 dimensional average vector number calculation means having the following:
c1) Combination generation means for generating a plurality of combinations composed of m + 1 continuous elements for N pieces of individual data from the pulse wave data,
c2) m + 1 dimensional vector distance computing means for computing the distance between each vector with each combination as an m + 1 dimensional vector,
c3) For each m + 1 dimensional vector, calculate the number of vectors existing within the distance d, and calculate the m + 1 dimensional average vector number for calculating the statistical average value for the m + 1 dimensional vector number Computing means,
c4) For each m + 1 dimensional vector, calculate the number of vectors existing within the distance d, and calculate the m + 1 dimensional average vector number for calculating the statistical average value for the m + 1 dimensional vector number Computing means,
D) Randomness calculation means for obtaining microscopic randomness of the pulse wave based on the m-dimensional average vector number and the m + 1-dimensional average vector number;
With
The m-dimensional average vector number calculating means and the m + 1 dimensional average vector number calculating means determine the m-dimensional average vector number and the m + 1-dimensional average vector number when the distance d is changed,
The randomness calculation means obtains the microscopic randomness of the pulse wave when the distance d is changed,
Furthermore, it has generating means for generating a change graph of the microscopic randomness of the pulse wave,
A diagnostic data generator characterized by the above. In the diagnostic data generation device according to claim 1,
The distance d is obtained by multiplying the standard deviation of blood flow in the N individual data by a coefficient,
The randomness calculation means changes the distance d by changing the coefficient, and determines the microscopic randomness of the pulse wave,
A diagnostic data generator characterized by the above. In the diagnostic data generation device according to claim 1 or 2,
The pulse wave data is pulse wave time-series data,
A diagnostic data generator characterized by the above. In the diagnostic data generation device according to claim 1 or 2,
The pulse wave data is time-series data of differential pulse wave data,
A diagnostic data generator characterized by the above. In the diagnostic data generation device according to any one of claims 1 to 4,
M = 2 or m = 3,
A diagnostic data generator characterized by the above. A) Pulse wave storage means for storing pulse wave data that varies depending on the blood flow flowing through the measurement site,
B) Calculation means having the following b1) to b3)
b1) For N pieces of individual data from the pulse wave data, a plurality of sets composed of continuous m elements are generated, and the difference in the set characteristic values determined from the values of the elements of each set is the first threshold value. a first set number calculating means for obtaining a set number of sets that is less than d as the first set number;
b2) For the N pieces of individual data, a plurality of sets composed of consecutive m + 1 elements are generated, and the difference in the set characteristic value determined from the element values of each set is less than the first threshold d A second set number calculating means for obtaining the set number of sets as the second set number;
b3) a microscopic randomness calculating means for determining the microscopic randomness of the pulse wave based on the first set number and the second set number;
C) A change microscopic randomness calculation unit that causes the microscopic randomness calculation unit to calculate the microscopic randomness of the pulse wave when the first threshold value d is changed,
D) having means for generating a change graph of microscopic randomness of the pulse wave when the first threshold value d is changed;
A diagnostic data generator characterized by the above. In the diagnostic data generation device according to claim 6,
The first threshold value d is obtained by multiplying a standard deviation of blood flow in the N individual data by a coefficient.
The change microscopic messiness calculating means changes the first threshold value d by changing the coefficient to obtain the microscopic messiness of the pulse wave;
A diagnostic data generator characterized by the above. The diagnostic data generation device according to any one of claims 1 to 7,
Output means for outputting the generated change graph;
A diagnostic data output device comprising: The diagnostic data generation device according to any one of claims 1 to 8,
For the change graph, if it is not a simple decrease tendency, a notification means for notifying this,
Diagnostic device with Pulse wave storage means for storing pulse wave data that varies depending on the blood flow flowing through the measurement site;
A plurality of differential data calculation means for calculating differential pulse wave data obtained by differentiating the pulse wave data twice or more;
A chaos attractor generating means for generating a chaos attractor from the differential pulse wave data, wherein the embedding dimension is n and the embedding delay is τ.
A diagnostic data generation apparatus comprising: The diagnostic data generation device according to claim 10,
Output means for outputting the generated orbital shape of the chaotic attractor;
A diagnostic data output device comprising: Pulse wave storage means for storing pulse wave data that varies depending on the blood flow flowing through the measurement site;
A plurality of differential data calculation means for calculating differential pulse wave data obtained by differentiating the pulse wave data four times or more;
A mental illness degree calculating means indicating that there is a mental illness tendency, wherein a wave height ratio b / a is obtained from the differential pulse wave data, and this is used as a first parameter, and the smaller the value of the first parameter is, Mental illness degree calculating means for judging that the degree of mental illness is large,
A diagnostic data generation apparatus comprising: The diagnostic data generation device according to claim 12,
The mental illness degree calculating means obtains a crest ratio d / b from the differential pulse wave data, and uses this as a second parameter. The smaller the difference between the first parameter and the second parameter is, the more the mental illness degree is. Judging it to be big,
A diagnostic data generator characterized by the above. Pulse wave storage means for storing pulse wave data that varies depending on the blood flow flowing through the measurement site;
A plurality of differential data calculation means for calculating differential pulse wave data obtained by differentiating the pulse wave data four times or more;
A mental illness degree calculating means indicating that there is a mental illness tendency, wherein a wave height ratio d / b is obtained from the differential pulse wave data, and this is used as a second parameter, the smaller the value of the second parameter, Mental illness degree calculating means for judging that the degree of mental illness is large,
A diagnostic data generation apparatus comprising: The diagnostic data generation device according to any one of claims 12 to 14,
Output means for outputting the degree of mental illness;
A diagnostic data output device comprising:

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