KR20170071009A - Apparatus and method for providing classifying of mibyou using measured index of fractal dimension - Google Patents

Apparatus and method for providing classifying of mibyou using measured index of fractal dimension Download PDF

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KR20170071009A
KR20170071009A KR1020150178829A KR20150178829A KR20170071009A KR 20170071009 A KR20170071009 A KR 20170071009A KR 1020150178829 A KR1020150178829 A KR 1020150178829A KR 20150178829 A KR20150178829 A KR 20150178829A KR 20170071009 A KR20170071009 A KR 20170071009A
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이시우
진희정
이영섭
유종향
심은보
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한국 한의학 연구원
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Abstract

The present invention relates to a computing device and method for assisting in classification of a disease by calculating a fractal dimension index. The auxiliary computing device according to an embodiment of the present invention calculates a fractal dimension value from time series data of a subject's heart rate And a determination unit for determining an infirmity for the subject based on the calculated fractal dimension value and the previously calculated index.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a computing apparatus and a method for computing a fractal dimension index,

It is about the technical idea of calculating the indicator that distinguishes the infectious disease and judging the infectious condition of the subject by using it.

The disease is not a disease but refers to a condition that complains of uncomfortable symptoms such as fatigue and pain, indigestion, and abnormal sleep. In other words, it can be interpreted as the state of restoration of the human body whose health is not restored by daily rest. According to the data of National Health Insurance Corporation, 61.8% of the total anti-health group, which exceeded the normal range in 2010, increased significantly from 35% in 2004. This means that the number of infected people is increasing.

On the other hand, current Korean medical science considers fatigue, pain, anger, dyspepsia, depression, sleeping disorder, and anxiety as 7 symptoms of infectious disease.

Especially, there is a common phenomenon in the elderly, but its causes and phenomena are very complex.

Korean Patent Registration No. 10-1510522 Korean Patent Publication No. 10-2012-0119948

A secondary computing device according to an embodiment includes a calculation unit for calculating a fractal dimension value from time series data of a heart rate of a subject and a calculation unit for calculating a fractal dimension index reflecting a clinical result by the analysis population, And a determination unit for comparing the value of the fractal dimension with the value of the fractal dimension to determine the infirmity with respect to the subject, wherein the fractal dimension index includes an average of the fractal dimension values extracted from the analysis population of the infinite state, And the average value of the fractal dimension values extracted from the analysis population of the steady state.

The calculation unit may calculate a box number and a box size according to a box counting algorithm for the time series data, calculate a line of best generated by the calculated box number and box size, the fractal dimension value is calculated by reflecting the slope of the fit.

The auxiliary computing device according to an embodiment further includes a processing unit for training the clinical result by the analysis population on the basis of the calculated fractal dimension value and the infective state for the subject, And updates the fractal dimension index by reflecting a training result.

The determination unit may determine an incapacity for the subject by considering the calculated fractal dimension value and the personal data about the subject.

The personal data according to an embodiment includes at least one of the key, age, sex, weight, and sasang constitution for the subject.

The determination unit may determine an infirmity for the subject by considering at least one of the computed fractal dimension value and the subject's disease state, disease history, and infectious factor.

A secondary computing device according to an embodiment includes a calculation unit for calculating fractal dimension values from time series data of heart rate for an analysis population capable of classifying a health state or an infective state, and a calculator for calculating the fractal dimension ) Values of the fractal dimension values extracted from the analysis population of the steady-state, and the determination unit determines the fractal dimension index based on the average value of the fractal dimension values extracted from the analysis population of the steady- Calculate the average value of the fractal dimension values extracted from the analysis population.

The determination unit according to an embodiment determines the fractal dimension index between the value of the fractal dimension of the infinite state and the average value of the fractal dimension values of the steady state.

The auxiliary computing device according to an embodiment further includes a processing unit and is configured to calculate a clinical result by the analysis population based on the fractal dimension value calculated from the time series data of the subject's heart rate and the infectious condition for the subject And updates the fractal dimension index by reflecting the training result.

The method according to an embodiment includes the steps of calculating a fractal dimension value from time series data of a subject's heart rate and calculating a fractal dimension index reflecting the clinical result by the analysis population and the calculated fractal dimension and comparing the value of the fractal dimension to the subject.

The calculating step may include calculating a box number and a box size according to a box counting algorithm with respect to the time series data, and calculating an optimal line generated by the calculated box number and box size, and calculating the fractal dimension value by reflecting the slope of the line of best fit.

According to an embodiment of the present invention, the determining may include comparing a fractal dimension index reflecting the clinical result by the analysis population with the calculated fractal dimension value to determine the infirmity of the subject .

The method according to an exemplary embodiment of the present invention includes training a clinical result by the analysis population based on the calculated fractal dimension value and an infective state for the subject, And updating the fractal dimension indicator.

A program according to an embodiment includes a set of instructions for calculating a fractal dimension value from time series data of a subject's heart rate and a set of instructions for calculating a fractal dimension index reflecting a clinical result by the analysis population, and a command set for comparing the value of the fractal dimension to determine the infirmity of the subject.

FIG. 1 is a diagram illustrating a secondary computing device for determining an infective state according to an embodiment.
2 is a view for explaining time-series data on heart rate.
3A is a view for explaining an embodiment of calculating a box size and a box number by applying a box counting algorithm to time series data on heart rate.
FIG. 3B is a view for explaining an embodiment in which an optimal line is calculated using the calculated box size and the number of boxes.
4 is a view for explaining time-series data on the subject's heart rate.
5 is a diagram for explaining an embodiment for calculating an optimal line from time series data of a subject.
6 is a diagram illustrating a secondary computing device for estimating an uninjured indices according to one embodiment.
FIG. 7 is a view for explaining an operation method of an auxiliary computing apparatus for determining an infective state according to an embodiment.

It is to be understood that the specific structural or functional descriptions of embodiments of the present invention disclosed herein are presented for the purpose of describing embodiments only in accordance with the concepts of the present invention, May be embodied in various forms and are not limited to the embodiments described herein.

Embodiments in accordance with the concepts of the present invention are capable of various modifications and may take various forms, so that the embodiments are illustrated in the drawings and described in detail herein. However, it is not intended to limit the embodiments according to the concepts of the present invention to the specific disclosure forms, but includes changes, equivalents, or alternatives falling within the spirit and scope of the present invention.

The terms first, second, or the like may be used to describe various elements, but the elements should not be limited by the terms. The terms may be named for the purpose of distinguishing one element from another, for example without departing from the scope of the right according to the concept of the present invention, the first element being referred to as the second element, Similarly, the second component may also be referred to as the first component.

It is to be understood that when an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, . On the other hand, when an element is referred to as being "directly connected" or "directly connected" to another element, it should be understood that there are no other elements in between. Expressions that describe the relationship between components, for example, "between" and "immediately" or "directly adjacent to" should be interpreted as well.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, the terms "comprises ", or" having ", and the like, are used to specify one or more of the features, numbers, steps, operations, elements, But do not preclude the presence or addition of steps, operations, elements, parts, or combinations thereof.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Terms such as those defined in commonly used dictionaries are to be interpreted as having a meaning consistent with the meaning of the context in the relevant art and, unless explicitly defined herein, are to be interpreted as ideal or overly formal Do not.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. However, the scope of the patent application is not limited or limited by these embodiments. Like reference symbols in the drawings denote like elements.

1 is a diagram illustrating an auxiliary computing device 100 for determining an infective state according to an embodiment.

A fractal structure is one in which self-duplication is repeated cyclically under certain rules and interpreted in terms of self-similarity and repeatability to the shape. The fractal structure is evaluated as a fractal dimension that represents the degree and complexity of the fill in the existing space. It is characterized by the appearance of a few numbers. It can be used as a means to analyze complex shapes such as surface irregularities and topographies .

The auxiliary computing device 100 according to an embodiment can extract the fractal dimension analysis from the fact that the fractal dimension value of the time series data of the heart rate can be used as an index indicating various health states, .

Specifically, the auxiliary computing device 100 according to an exemplary embodiment includes a calculation unit 110, and a determination unit 120.

First, the calculation unit 110 calculates a fractal dimension value from time series data of the subject's heart rate.

For example, the calculation unit 110 calculates a box number and a box size according to a box counting algorithm for time series data, and calculates a line of best fit generated by the calculated box number and box size ) To reflect the slope of the fractal dimension.

Next, the determination unit 120 determines the infirmity of the subject based on the calculated fractal dimension value. For example, the determination unit 120 may compare the calculated fractal dimension with the fractal dimension index reflecting the clinical result by the analysis population, and determine the infantile disease to the subject by comparing the calculated fractal dimension.

Specifically, the determination unit 120 may compare the fractal dimension value calculated from the current subject based on the fractal dimension index calculated from the analysis population.

For example, the determination unit 120 may determine that the current subject is in a normal state when the calculated fractal dimension value is greater than or equal to the fractal dimension index. In addition, the determination unit 120 may determine that the current subject is in the infinite state when the calculated fractal dimension value is less than or equal to the fractal dimension index.

In addition, the auxiliary computing device 100 according to an exemplary embodiment may further include a processing unit 130. [ For example, the processor 130 trains the clinical results by the analysis population based on the computed fractal dimension value and the subject's incapacitated state, and reflects the training results to the fractal dimension it is possible to update the fractal dimension index.

The determination unit 120 may determine a non-infectious disease to the subject in consideration of the calculated fractal dimension value as well as the personal data about the subject. To this end, the secondary computing device 100 may collect personal data about the subject from the outside and may collect data, such as key, age, gender, weight, and sasang constitution for the subject, for example. In addition, the secondary computing device 100 may further collect information such as the subject's disease status, disease history, and infectiousness factor. That is, the auxiliary computing device 100 can determine the infectious disease to the subject in addition to the computed fractal dimension value, further considering the subject's disease state, disease history, and infectious factor.

Hereinafter, an embodiment for calculating the fractal dimension index will be described with reference to FIGS. 2 and 3, and an embodiment for calculating the fractal dimension value for the subject through FIGS. 4 and 5 will be described.

2 is a view for explaining time-series data on heart rate.

Baish et al. (2000) suggested that analysis of cancer by fractal dimension index is possible. Cipra (2003) showed that time-series data on heartbeat of healthy hearts have a higher fractal dimension than that of unhealthy heartbeats. Based on this, physiological indicators that can quantify the infectious disease can be presented and estimated.

Figure 2 corresponds to the time series data for various heart rates presented by Cipra (2003).

Time series data for the heart rate corresponding to the reference numerals 210 and 230 correspond to congestive heart disease, reference numeral 240 denotes an arrhythmia, and reference numeral 220 denotes a normal heartbeat. That is, the most irregularity is the time series data on the heart rate measured from the subjects in the normal state

In the present invention, a fractal dimension index can be calculated to quantify the irregularity.

FIG. 3A is a view for explaining an embodiment for calculating a box size and a box number by applying a box counting algorithm to time series data on a heart rate, FIG. 3B is a diagram for calculating an optimal line using a calculated box size and a box number Fig.

More specifically, FIG. 3A shows that the box size (L) is reduced and the number of boxes to be analyzed is included in the box (N), as shown in FIGS. 310 to 330. FIG. The box size (L) and the number of boxes (N) are shown in a log-log graph and the slope of the optimal line is calculated.

The slope of the optimal line shown in graph 340 of FIG. 3B is -1.09, and the slope of the optimal line corresponds to the fractal dimension. Based on this, it can be judged that the irregularity increases as the fractal dimension moves away from -1.0.

There are various ways to obtain the fractal dimension, but it is often used as a calculation method by the box counting algorithm.

Assuming that the box counting algorithm includes all the points, curves, or shapes on the plane with N (e) grid of size e, the smaller the size e, the greater the number 1 / e And the relationship can be expressed as the following equation (1).

[Equation 1]

Figure pat00001

Here, k is the ratio of the box size, and D can be changed to the following formula (2) by taking the logarithm of the dimension formula of the element [1].

&Quot; (2) "

Figure pat00002

The number of gratings e1 and e2 having different sizes with respect to [Equation 2] can be calculated as Equation (3).

&Quot; (3) "

Figure pat00003

In Equation (3), D b can be interpreted as a dimension according to the applause counting.

If it is assumed that e is infinitely small in Equation (1), Equation (3) can be simplified as Equation (4).

&Quot; (4) "

Figure pat00004

However, in general, the number of points to be analyzed is limited, so if e is very small, one element is included in one grid. At this time, N (e) is the number of elements belonging to the set S and is expressed by Equation (5).

&Quot; (5) "

Figure pat00005

Where D c is the volume dimension.

If the distance between any two elements is

Figure pat00006
, The correlation integral C (r) is calculated, and the equation of the correlation integral coefficient for the N samples thus created is expressed by Equation (6).

&Quot; (6) "

Figure pat00007

In Equation (6), H is a heaviside step function and can be calculated as follows.

Figure pat00008

If Equation (6) is used instead of log∥S∥ in Equation (5), it can be expressed as Equation (7).

&Quot; (7) "

Figure pat00009

The fractal analysis according to the present invention is a method for quantitatively calculating the irregularity of a shape. The fractal dimension value corresponding to D 0 is generated when a change in value according to a change in measurement interval is converted into a log-log form , And the larger the value of D 0 , the more the measured characteristics are irregularly or variably distributed.

According to the present invention, in order to develop a fractal dimension index for the unaffected group, a fractal dimension evaluation was performed on the time series data of the heart rate of three existing target groups.

[Table 1] to [Table 3] show fractal dimension analysis results of three groups (analysis population of 20 persons per group). Among the three groups, s3 groups Corresponding to the normal group, and has the largest fractal dimension value on an absolute value basis as expected.

[Table 1]

Figure pat00010

[Table 2]

Figure pat00011

[Table 3]

Figure pat00012

FIG. 4 is a diagram showing time series data 400 about a subject's heart rate, and FIG. 5 is a diagram illustrating an example of calculating an optimal line from time series data of a subject.

The computing assistant apparatus according to the present invention can calculate the optimal line identified by reference numeral 500 by applying a box counting algorithm from the inputted time series data 400 of the subject. In addition, the calculated slope (1.254) of the optimal line can be used as a fractal dimension value to determine the infantile condition for the subject in contrast to the fractal dimension index.

According to the present invention, according to the analysis result that the fractal dimension value of the time series data according to the heart rate of a normal person is larger than 1.2 on the basis of the absolute value, the analysis result of the fractal dimension If it is smaller than 1.2, it can be judged that there is a possibility of an incurable disease.

That is, according to the present invention, the fractal dimension value '1.2' can be determined as a fractal dimension index for determining the infinite state. That is, the subject corresponding to the time series data 400 of FIG. 4 represents a fractal dimension value of 1.2 or more, which is a fractal dimension index, and thus can be determined as a normal state.

FIG. 6 is a diagram illustrating a secondary computing device 600 for computing a virgin indicator in accordance with one embodiment.

Referring to FIG. 1, an apparatus for determining a critical state by comparing a fractal dimension index with a fractal dimension value of a current subject is described. In FIG. 6, a device for generating a fractal dimension index .

The auxiliary computing device 600 may include a calculation unit 610 and a determination unit 620. [

First, the calculation unit 610 calculates fractal dimension values from the time series data of the heart rate for the analysis population capable of classifying the health state or the infective state. In addition, the determination unit 620 determines a fractal dimension index based on the calculated fractal dimension values.

Specifically, the calculation unit 610 can calculate the average value of the fractal dimension values extracted from the analysis population of the steady state and the average value of the fractal dimension values extracted from the analysis population of the infinite state. have. The determiner 620 may also determine a fractal dimension index between the value of the non-diseased fractal dimension and the average value of the steady-state fractal dimension values.

The secondary computing device 600 according to one embodiment may further include a processing unit 630.

The processing unit 630 can train the clinical result by the analysis population based on the fractal dimension value calculated from the time series data of the subject's heart rate and the infantile condition for the subject. In addition, the processing unit 630 may update the fractal dimension index reflecting the training result.

FIG. 7 is a view for explaining an operation method of an auxiliary computing apparatus for determining an infective state according to an embodiment.

The operation method of the auxiliary computing device for determining the infective state according to an embodiment can calculate the fractal dimension value from the time series data of the subject's heart rate.

To this end, the method of operation of the secondary computing device may measure or collect time series data on the subject's heart rate, and collect data such as key, age, gender, weight, and sasang constitution as personal data along with time series data Step 701).

Thus, the method of operation of the auxiliary computing device may calculate a fractal dimension value through analysis of the fractal dimension for the heart rate time series data (step 702).

Meanwhile, the operation method of the auxiliary computing device receives the individual disease state and the infectious disease factor of the subject in parallel with the heart rate time series data (step 703), and can check the disease or infectious condition (step 704).

In addition, the operation method of the auxiliary computing device may determine whether the subject is currently infected by referring to the fractal dimension calculated after the analysis and the subject's disease or infirmity history (step 705). In addition, the method of operation of the secondary computing device may determine whether the subject is currently infected (706) or not (707), according to the determination result of step 705.

As a result, using the present invention, it is possible to calculate scientific and objective indicators necessary for distinguishing infectious diseases, and it is possible to quickly and accurately judge whether the subject belongs to the US Army or the Non-US Army by utilizing the calculated indicator.

The apparatus described above may be implemented as a hardware component, a software component, and / or a combination of hardware components and software components. For example, the apparatus and components described in the embodiments may be implemented within a computer system, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA) , A programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For ease of understanding, the processing apparatus may be described as being used singly, but those skilled in the art will recognize that the processing apparatus may have a plurality of processing elements and / As shown in FIG. For example, the processing unit may comprise a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as a parallel processor.

The software may include a computer program, code, instructions, or a combination of one or more of the foregoing, and may be configured to configure the processing device to operate as desired or to process it collectively or collectively Device can be commanded. The software and / or data may be in the form of any type of machine, component, physical device, virtual equipment, computer storage media, or device , Or may be permanently or temporarily embodied in a transmitted signal wave. The software may be distributed over a networked computer system and stored or executed in a distributed manner. The software and data may be stored on one or more computer readable recording media.

The method according to an embodiment may be implemented in the form of a program command that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions to be recorded on the medium may be those specially designed and configured for the embodiments or may be available to those skilled in the art of computer software. Examples of computer-readable media include magnetic media such as hard disks, floppy disks and magnetic tape; optical media such as CD-ROMs and DVDs; magnetic media such as floppy disks; Magneto-optical media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine language code such as those produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. For example, it is to be understood that the techniques described may be performed in a different order than the described methods, and / or that components of the described systems, structures, devices, circuits, Lt; / RTI > or equivalents, even if it is replaced or replaced.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (15)

Implemented at least temporarily by the computer:
A calculation unit for calculating a fractal dimension value from time series data of the subject's heart rate; And
A determination unit for comparing the calculated fractal dimension value with the fractal dimension index reflecting the clinical result by the analysis population and determining the infirmity for the subject by comparing the calculated fractal dimension value with the calculated fractal dimension value,
Lt; / RTI >
The fractal dimension indicator is based on the average value of the fractal dimension values extracted from the analysis population of the infinite state and the average value of the fractal dimension values extracted from the analysis population of the steady state. ≪ / RTI >
The method of claim 1, wherein
The calculation unit may calculate,
Calculating a box number and a box size according to a box counting algorithm for the time series data, and reflecting the slope of a line of best fit generated by the calculated box number and box size, A secondary computing device that calculates a fractal dimension value.
The method according to claim 1,
Further comprising a processing unit,
Training the clinical result by the analysis population based on the computed fractal dimension value and the infantile condition for the subject and analyzing the fractal dimension by reflecting the training result, A secondary computing device that updates the indicator.
The method according to claim 1,
Wherein,
And determines the infant mortality for the subject in consideration of the calculated fractal dimension value and the personal data for the subject.
5. The method of claim 4,
The personal data includes:
Age, gender, weight, and sasang constitution for the subject.
The method according to claim 1,
Wherein,
Wherein the at least one of the calculated fractal dimension value and at least one of the subject's disease state, disease history, and infectious factor is considered.
Implemented at least temporarily by the computer:
A calculation unit for calculating fractal dimension values from time-series data on heart rate for an analysis population capable of distinguishing between a healthy state and an untreated state; And
Determining a fractal dimension index based on the calculated fractal dimension values;
Lt; / RTI >
The calculation unit may calculate,
Computing an average value of the fractal dimension values extracted from the analysis population of the steady state and an average value of the fractal dimension values extracted from the analysis population of the infinite state.
8. The method of claim 7,
Wherein,
Determining the fractal dimension index between the non-diseased fractal dimension value and the steady-state average value of the fractal dimension values.
8. The method of claim 7,
Further comprising a processing unit,
Training a clinical result by the analysis population on the basis of a fractal dimension value calculated from time series data of the subject's heart rate and an uninvolved state for the subject and reflecting the training result And updates the fractal dimension indicator.
CLAIMS 1. A method of operating a processor implemented at least temporarily by a computer, the method comprising:
Calculating a fractal dimension value from time series data of the subject's heart rate; And
Comparing the fractal dimension index reflecting the clinical result by the analysis population with the calculated fractal dimension value to judge the infantile disease to the subject
≪ / RTI >
The method of claim 10, wherein
Wherein the calculating step comprises:
Calculating a box number and a box size according to a box counting algorithm for the time series data; And
Calculating the fractal dimension value by reflecting a slope of a line of best fit generated by the calculated box number and box size,
≪ / RTI >
The method of claim 10, wherein
Wherein the determining step comprises:
Comparing the fractal dimension index reflecting the clinical result by the analysis population with the calculated fractal dimension value to judge the infantile disease to the subject
≪ / RTI >
12. The method of claim 11,
Training the clinical result by the analysis population based on the computed fractal dimension value and the virulent condition for the subject;
Updating the fractal dimension index by reflecting the training result
≪ / RTI >
A computer-readable recording medium having recorded thereon a program for performing the method according to any one of claims 10 to 13.
21. A program stored on a recording medium, the program being executable on a computing system:
A set of instructions for calculating a fractal dimension value from time series data of a subject's heart rate; And
A command set for comparing a fractal dimension index reflecting the clinical result by the analysis population with the calculated fractal dimension value to determine the infirmity for the subject;
≪ / RTI >
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