KR20170050033A - Apparatus and method for detecting tension of pulsation - Google Patents

Abstract

The present invention relates to an apparatus and method for detecting the degree of tension of a vein, and a computing auxiliary apparatus according to an embodiment of the present invention extracts at least one information from a measured pulse wave, such as skewness, kurtosis, A processor for classifying a pattern for the pulse wave based on at least one of an extraction part, a skewness, a kurtosis, and a starting point extracted by the extraction part, And a calculation unit for calculating the output.

Description

[0001] APPARATUS AND METHOD FOR DETECTING TENSION OF PULSATION [0002]

The present invention relates to a technical idea of detecting a tension of a pulse through various pattern changes on a pulse wave measurement and providing a clinical meaning in consideration of the detected tension of the pulse.

In modern society, interest in health is increasing day by day. Along with this era of interest, as technologies such as data analysis method and tool by real-time data collection are advanced, it becomes possible to monitor health condition and provide personalized health care service.

In addition, convenience and customization of health services and related systems are being strengthened due to diversification of customers 'demands and improvement of expectations according to changes in consumers' consciousness. Based on accumulated personal health data, prevention of lifestyle- Personalized health care projects such as weight management are rapidly growing.

If the past health care services were centered on healthcare, which is limited to the treatment of illnesses in hospitals and medical institutions, patients' health care needs in recent years have been limited to the prevention of disease And health maintenance.

In this trend, various technologies for monitoring health are being developed. For example, the tension of a pulse means the degree of resistance of the blood vessel due to pressing the radial artery, and it is known that it is highly related to a pulse, a long pulse, and the like related to the tension of the pulse in the oriental pulse. In addition, it shows clinically meaningful patterns according to disease or constitution, and it can provide various clinical meaning to the user by measuring the tension of the pulse.

Korean Patent Registration No. 10-1002079 Korean Patent Registration No. 10-1115499

The computing assist apparatus according to an embodiment of the present invention includes an extraction unit for extracting at least one of information from a measured pulse wave such as skewness, kurtosis, and starting point, a skewness, a kurtosis, And a starting point, and a calculating unit for calculating a tense index of the pulse wave based on the classified pattern.

The pulse wave according to one embodiment includes the magnitude of a pulse wave that varies with the pressing of the constant velocity.

The extracting unit extracts a degree of diopter and a degree of diopter by checking whether the measured pulse wave has a magnitude greater than or equal to a threshold value in a specific direction centering on a maximum pulse wave, And classifies the pattern on the basis of the degree of direction and the degree of distortion.

The extraction unit may be configured to determine whether the measured pulse wave size distribution indicates a kurtosis at or above a reference value or below a reference value and, And the processing section classifies the pattern based on the extracted kurtosis level.

The extracting unit according to an embodiment extracts, at the starting point, a point at which the pulse wave size starts to be measured at a predetermined ratio or more from the maximum pressure pulse wave.

A computing auxiliary apparatus according to an exemplary embodiment of the present invention includes a database in which at least one pattern classified according to at least one of a skewness, a kurtosis, and a starting point of a pulse wave and disease information corresponding to the pattern are recorded, An extracting unit for extracting at least one piece of information from skewness, kurtosis, and starting point from the measured pulse wave, at least one of information extracted from the extracted skewness, kurtosis, And a detector for detecting the disease information from the database by using the classified pattern.

The extracting unit extracts a degree of diopter and a degree of diopter by checking whether the measured pulse wave has a magnitude greater than or equal to a threshold value in a specific direction centering on a maximum pulse wave, And classifies the pattern on the basis of the degree of direction and the degree of distortion.

The extraction unit may determine whether the pulse wave is indicative of a kurtosis at or above a reference value or a kurtosis at a reference value or less by checking the distribution of the measured pulse wave, Extracts a kurtosis level with respect to the measured pulse wave size, and the processing section classifies the pattern based on the extracted kurtosis level.

The method includes extracting at least one of a skewness, a kurtosis, and a starting point from a measured pulse wave. The extracted skewness, kurtosis, Classifying the pattern for the pulse wave based on at least one piece of information among the classified patterns, and calculating a tense index of the Mac based on the classified pattern.

The extracting step may include extracting a degree of diopter and a degree of diopter by checking whether the measured pulse wave has a magnitude greater than or equal to a threshold value in a specific direction centering on a maximum impulse wave, The classifying step includes classifying the pattern based on the extracted distortion direction and distortion level.

The extracting step may further include the steps of: determining a distribution of the magnitude of the measured pulse wave to determine whether the pulse wave exhibits a kurtosis at or above a reference or below a reference; and, Extracting a kurtosis level with respect to the measured magnitude of the pulse wave, and the classifying step includes classifying the pattern based on the extracted kurtosis level.

According to an embodiment, the extracting step includes extracting, at the starting point, a point at which a pulse wave size starts to be measured from a maximum specified pulse wave frequency by a predetermined ratio or more on the basis of a time point when the pulse wave sensor contacts the skin.

A program according to an embodiment includes a set of instructions for extracting at least one of information from skewness, kurtosis, and starting point from a measured pulse wave, the extracted skewness, kurtosis, A set of instructions for classifying a pattern for the pulse wave based on at least one of the starting points, and a set of instructions for calculating a tension index of a Mac based on the classified pattern.

1 is a view for explaining a computing auxiliary apparatus according to an embodiment.
2 is a view for explaining the pattern change of the pulse wave size according to the pressure for the pulse tension index analysis.
3 is a view for explaining the pulse wave amplitude according to the degree of tension of the pulse.
Fig. 4 is a view for explaining the distortion and the kurtosis of the veins and the pattern changes thereof. Fig.
5 is a view for explaining signal processing of a computing auxiliary apparatus according to an embodiment.
6 is a view for explaining the kinds of various patterns of the Mac.
FIG. 7 is a diagram illustrating a computing auxiliary apparatus according to another embodiment.
8 is a diagram for explaining a method of detecting the tension of a vein.

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 a computing assistance apparatus 100 according to an embodiment.

The computing assist device 100 according to an exemplary embodiment analyzes the pulse tension index through the pattern change of the pulse wave size according to the pressure, thereby providing various clinical meanings.

To this end, the computing assistant apparatus 100 according to an embodiment may include an extracting unit 110, a processing unit 120, and a calculating unit 130. [

The extracting unit 110 extracts at least one of a skewness, a kurtosis, and a starting point from the measured pulse wave.

The degree of distortion is a measure of the degree of asymmetry of the distribution, ie, the direction in which the distribution is tilted and the degree to which the distribution is tilted. If the distribution of the pulse wave is shifted to either the left or the right can see.

For example, if the calculated value is '0', it has symmetrical distribution. If it is smaller than '0', it has a negative distortion. If it is larger than '0', it has a positive distortion. The larger the absolute value of the calculated value, the greater the degree of asymmetry of the distribution.

The extracting unit 110 may extract the degree of diopter and the degree of diopter by checking whether the measured pulse wave is distributed over a threshold value in a specific direction centering on the reference. The degree of vorticity can be classified into a predetermined level as an index indicating the degree of vignetting.

On the other hand, the extracting unit 110 can check the distribution of the size of the measured pulse wave to determine whether or not it exhibits the kurtosis above the reference or below the reference. Further, if the result of the determination indicates the kurtosis above the reference or below the reference, the kurtosis level with respect to the measured pulse wave size can be extracted.

The kurtosis can be interpreted as a pointed form of the distribution as one of the values obtained by varying the shape of the measured value in the measurement evaluation.

The extracting unit 110 may check the distribution of the size of the measured pulse wave to determine whether or not it indicates the kurtosis at or above the reference. For example, if the result of the determination indicates the kurtosis above the reference or below the reference, the kurtosis level with respect to the measured pulse wave size can be extracted. The kurtosis level is an index indicating the degree of kurtosis and can be divided into predefined steps.

Next, the processing section 120 classifies the pattern for the pulse wave based on at least one of the extracted skewness, kurtosis, and starting point.

For example, the processing unit 120 may classify the pattern based on the extracted directional and distortion level, or classify the pattern based on the extracted kurtosis level. It is also possible to classify patterns by considering both the degree and the kurtosis.

Next, the calculation unit 130 can calculate the stress index of the Mac based on the classified pattern. The tension index of a Mac can also be interpreted as a tension.

The tension of the pulse means the degree of change of the pulse wave according to the pressurization. The larger the tension, the smaller the pulse wave size change due to the pressurization. The smaller the tension, the greater the pulse wave change due to the pressurization. The tension of the arteries can be interpreted as an index reflecting the degree of resistance of the blood vessels against the force to pressurize the radial artery. In general, the blood vessel tension tends to increase with the presence of cardiovascular disease or older age.

2 is a view for explaining the pattern change of the pulse wave size according to the pressure for the pulse tension index analysis.

The extraction unit may determine an initial time point (ITP) for extracting a time point at which a pulse wave size starts to be measured at a predetermined ratio or more from a maximum pressure pulse wave on the basis of a time point when the pulse wave sensor contacts the skin have.

For example, as shown in the graph of reference numeral 210, an initial time point (ITP) for a pulse wave size pattern is set to a point 211 at which the pulse wave size starts at 20% or more of the maximum pulse wave 212 . For the starting point comparison between different subjects, the x axis of the starting point can be set to 0 as the contact point at which the pulse sensor contacts the skin. It is possible to match with the oriental medicine pulse by using the degree of the measured pulse wave, the kurtosis, and the distribution position on the ITP 3 axis.

The graph 220 may also be set to a point 221 that starts at 20% or more of the maximum pressure pulse wave 222, like the graph 210.

When the patterns identified by the graph 210 and the patterns 220 are compared with each other, it can be seen that the graph 210 has a higher level of kurtosis than the graph 220. In addition, it is confirmed that the patterns identified by the graph 210 have the magnitudes of the pulse waves significantly shifted to the right with respect to the maximum pressure pulse wave. Therefore, the pattern identified by the graph 210 can determine that the distortion has occurred, and can determine the distortion level according to the degree of smoothing.

3 is a view for explaining the pulse wave amplitude according to the degree of tension of the pulse.

The graphs in Figure 3 show the pulse wave amplitude versus applied pressure.

The graph of the reference numeral 310 is broader than the graph of the reference numeral 320, and the section resistant to pressure is widely distributed. In other words, it can be seen that the degree of change in the pulse wave size due to pressurization is small. This indicates that the degree of tension is relatively large, and the tendency of the blood vessel tensions to be higher with or without cardiovascular disease.

The reference numeral 320 is narrower than the reference numeral 310 in terms of resistance against pressure. That is, it can be seen that the degree of change of the pulse wave size according to the pressurization is large. This indicates that the degree of tension is relatively small.

Fig. 4 is a view for explaining the distortion and the kurtosis of the veins and the pattern changes thereof. Fig.

The computing auxiliary device according to an exemplary embodiment may classify patterns into various types of pulses in consideration of the degree of swelling and the kurtosis of the collected pulse waves.

FIG. 4 shows an embodiment in which patterns are classified by classification according to degree of accent and degree of kurtosis. First, the pattern according to the reference numeral 410 corresponds to a pattern of the negative skew in which the distribution of the pulse waves is shifted to the right (negative skew), and the pattern according to the reference numeral 420 corresponds to the pattern of the positive skew in which the distribution of the pulse waves is shifted to the left.

On the other hand, the pattern according to reference numeral 430 corresponds to a pattern with a kurtosis not more than a reference, and the pattern according to a reference numeral 440 corresponds to a pattern with a kurtosis not less than a reference. In the pattern of kurtosis, the criterion may be an average of the kurtosis considering the characteristics of each healthy person and body type.

The computing assistant apparatus according to the present invention may designate various patterns according to the combination of the reference numerals 410 to 440 and correspond to a pattern having a shape closest to the measured pulse wave with a pulse wave pattern to be measured.

5 is a view for explaining signal processing of a computing auxiliary apparatus according to an embodiment.

The computing auxiliary device according to an embodiment collects the amplitude of the pulse wave over time and collects the raw data 501. In addition, the collected road data 501 can be processed to extract a corresponding pattern. To this end, the computing assistant first detects the peak value of each pulse wave and performs envelope processing using the detected peak values (502). In addition, the computing auxiliary device may perform the merging of the missing values. At this time, the merging process for the missing images is performed using various methods such as interpolating the adjacent measured values and merging the missing values (step 503).

Next, the computing assist device may match the viewpoint using the contact point to the merged pulse wave (504), and may set the x-axis based on the data having the maximum length (505). In addition, the computing aiding device may unify the length of the x-axis by replacing the values after the last value for the individual data with the initial peak value (506).

Thereafter, the computing auxiliary device performs a data smoothing process using a smoothing function (507), calculates a degree, a kurtosis, and a starting point using the result of the calculation, and classifies the pattern of the pulse wave using the calculation result (508).

6 is a view for explaining the kinds of various patterns of the Mac.

Specifically, FIG. 6A shows various combinations of patterns 600 considering the level of kurtosis together with the direction and level of the distortion. On the other hand, FIG. 6B shows a three-dimensional scattering plot 610 with the axis of distortion, kurtosis, and start point as respective axes.

For example, the following classification can be made according to the pattern of the degree of pulse wave change due to pressurization. For example, for a pulse wave having a steepness, it can be classified into a pattern of E009 type. In addition, for a pulse wave having almost no distortion on the kurtosis, the pattern can be classified into the patterns of E004 and E010. In addition, for a pulse wave whose starting point is slow on the kurtosis, it can be classified into patterns of the patterns of E002, E011, and E019.

In this case, each of the patterns can provide clinically meaningful medicinally.

For example, the pattern of the E009 pattern corresponds to a pulse wave having a high degree of tension of the pulse wave, and the probability of the pulse wave is long. Also, in case of E004 and E010, the pulse is rapidly detected, and as the pressure is increased, the size of the pulse wave is rapidly increased and then the pulsation disappears. In addition, the pattern of E002, E011, E019 pattern corresponds to the pulse of the characteristic that the pulse is delayed and disappears soon, and it can be classified as the pulse wave of the oriental medicine, the vein of the medicine, or the pulse of the pulse.

Based on the pattern thus classified, the clinical significance based on oriental medicine can be provided.

FIG. 7 is a diagram illustrating a computing auxiliary apparatus according to another embodiment.

The computing auxiliary device 700 according to another embodiment may construct a database of pulse wave patterns and disease information corresponding to each pattern in a database, and may provide corresponding disease information from pulse waves collected by referring to a database.

The computing aid device 700 may include a database 710, an extractor 720, a processor 730, and a detector 740.

The database 710 according to one embodiment records disease information corresponding to at least one pattern and pattern classified according to at least one of the skewness, kurtosis, and starting point of a pulse wave.

For example, the computing auxiliary device 700 may measure a patient's pulse wave for each disease, and extract a pattern of the measured pulse wave and register the same in a database.

The extraction unit 720 extracts at least one of skewness, kurtosis, and starting point from the measured pulse wave, and the processing unit 730 extracts the extracted skewness, The pattern for the pulse wave can be classified based on at least one of the following information: kurtosis, and starting point.

The detection unit 740 according to one embodiment can detect disease information from the database 710 using the classified pattern. At this time, the detecting unit 740 can search the database 710 for a pattern matching the classified pattern as an index. In addition, disease information corresponding to the searched pattern is read, and disease information can be detected using the classified pattern.

For example, the extracting unit 720 determines whether the measured pulse wave size is distributed over a threshold value in a specific direction centering on a reference, determines whether or not the pulse wave is distorted, You can even extract levels. In addition, the processing unit 730 can classify the patterns based on the extracted distortion direction and distortion level.

On the other hand, the extracting unit 730 determines whether the kurtosis is greater than the reference value by checking the distribution of the measured pulse wave. If the kurtosis is greater than the reference value, the kurtosis level of the measured pulse wave is extracted can do. Similarly, the processing unit 730 at this time can classify the pattern based on the extracted kurtosis level.

8 is a diagram for explaining a method of detecting the tension of a vein.

The method for detecting the tension of a pulse according to an embodiment extracts at least one of a skewness, a kurtosis, and a starting point from a measured pulse wave (step 801). Next, a method for detecting the tension of a pulse according to one embodiment classifies a pattern for a pulse wave based on at least one of the extracted skewness, kurtosis, and starting point (step 802) .

The degree of distortion is a measure of the degree of asymmetry of the distribution, ie, the direction in which the distribution is tilted and the degree to which the distribution is tilted. If the distribution of the pulse is shifted to either the left or the right center of the reference, have. On the other hand, the kurtosis can be interpreted as a pointed form of the distribution of the distribution as one of the values obtained by varying the shape of the measurement value in the measurement evaluation. The method of detecting the tension of the pulse can be used to judge whether the pulse wave is acute or steep after signal processing for the collected pulse wave, and classify the pattern by considering the starting point.

The method for detecting the degree of tension of a vein according to an embodiment calculates the stress index of the vein based on the classified pattern (step 803).

The Tension Index of a Mac can indicate a pattern change of the pulse wave size according to the pressurization. The method of detecting the tension of the pulse wave can analyze the pulse tension index according to the pattern change, and thereby provide various clinical meaning.

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 (14)

Implemented at least temporarily by the computer:
An extracting unit for extracting at least one of a skewness, a kurtosis, and a starting point from the measured pulse wave;
A processor for classifying a pattern for the pulse wave based on at least one of the extracted skewness, kurtosis, and starting point; And
A calculating unit for calculating a tense index of the vein based on the classified pattern;
And a computing device.
The method according to claim 1,
The pulse wave,
And a size of a pulse wave that changes in accordance with the pressure of the constant velocity.
The method according to claim 1,
Wherein the extracting unit extracts a degree of diopter and a degree of diopter by checking whether the measured pulse wave has a magnitude greater than or equal to a threshold value in a specific direction about a reference,
And the processing section classifies the pattern based on the extracted distortion direction and distortion level.
The method according to claim 1,
Wherein the extracting unit determines whether the measured pulse wave size distribution indicates a kurtosis at or above a reference value or below a reference value and, And extracts the corresponding kurtosis level,
And the processing section classifies the pattern based on the extracted kurtosis level.
The method according to claim 1,
Wherein the extracting unit extracts, as the starting point, a time point at which a pulse wave size starts to be measured at a predetermined ratio or more from a maximum pressure pulse wave on the basis of a time when the pulse wave sensor contacts the skin.
Implemented at least temporarily by the computer:
A database in which at least one pattern classified according to at least one of a skewness, a kurtosis, and a starting point of a pulse wave and disease information corresponding to the pattern is recorded;
An extracting unit for extracting at least one of a skewness, a kurtosis, and a starting point from the measured pulse wave;
A processor for classifying a pattern for the pulse wave based on at least one of the extracted skewness, kurtosis, and starting point; And
And a detector for detecting the disease information from the database using the classified pattern,
And a computing device.
The method according to claim 6,
Wherein the extracting unit extracts a degree of diopter and a degree of diopter by confirming whether the measured pulse wave is distributed over a threshold value in a specific direction about a reference,
And the processing section classifies the pattern based on the extracted distortion direction and distortion level.
The method according to claim 6,
Wherein the extracting unit determines whether the pulse wave is indicative of a kurtosis at or above a reference or a reference at a reference or less by checking a distribution of the measured pulse wave with reference to a time when the pulse wave sensor contacts the skin, Extracts a kurtosis level with respect to the measured pulse wave size,
And the processing section classifies the pattern based on the extracted kurtosis level.
In a method at least temporarily implemented by a computer,
Extracting at least one of a skewness, a kurtosis, and a starting point from the measured pulse wave;
Classifying the pattern for the pulse wave based on at least one of the extracted skewness, kurtosis, and starting point; And
Calculating a stress index of the Mac based on the classified pattern
≪ / RTI >
10. The method of claim 9,
Wherein the extracting comprises:
Determining whether the measured pulse wave size is distributed more than a threshold value in a specific direction centering on a reference, and extracting a distortion direction and a distortion level
Lt; / RTI >
Wherein said classifying comprises:
Classifying the pattern based on the extracted distortion direction and distortion level
≪ / RTI >
10. The method of claim 9,
Wherein the extracting comprises:
Determining a distribution of the measured pulse wave size and determining whether the pulse wave is indicative of a kurtosis at or above a reference; And
Extracting a kurtosis level with respect to the measured magnitude of the pulse wave when the result of the determination indicates a kurtosis above the reference or below the reference value
Lt; / RTI >
Wherein said classifying comprises:
Classifying the pattern based on the extracted kurtosis level
≪ / RTI >
10. The method of claim 9,
Wherein the extracting comprises:
Extracting, at the starting point, a time point at which a pulse wave size starts to be measured at a predetermined ratio or more from a maximum pressure pulse wave on the basis of a time when the pulse wave sensor contacts the skin
≪ / RTI >
A computer-readable recording medium on which a program for performing the method of any one of claims 9 to 12 is recorded.
21. A program stored on a recording medium, the program being executable on a computing system:
A set of instructions for extracting at least one of a skewness, a kurtosis, and a starting point from a measured pulse wave;
A command set for classifying a pattern for the pulse wave based on at least one of the extracted skewness, kurtosis, and starting point; And
A command set for calculating a stress index of the Mac based on the classified pattern;
≪ / RTI >

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