KR101890546B1 - Diagnostic device for low back pain -based electromyogram - Google Patents

Abstract

According to an embodiment of the present invention, a device to diagnose lumbago based on the electromyogram of an examinee is capable of converting a consecutive signal of an electromyogram, which is measured or collected from a patient group or normal group for a predetermined time, into second frequency information through frequency analysis, thereby diagnosing lumbago based on the electromyogram of the examinee by using the converted information. The device includes: a determining part determining an integral section, in which a frequency average included in first frequency information is used as a central axis and a corresponding standard tabular difference is used as the length of a horizontal axis, when receiving the first frequency information regarding a preset activity of the examinee; a first calculating part calculating a first integral value (P(I¦H)) of the normal group and a second integral value of the patient group (P(I¦B)), which correspond to the integral section, from each normal distribution curve included in the second frequency information; and a second calculating part calculating a lumbago diagnosis probability (P(H¦I)) based on the electromyogram of the examinee by conducting Bayes′ theorem on a first probability (P(H)) with which the examinee belongs to the normal group, a second probability (P(B)) with which the examinee belongs to the patient group, and the first and second integral values.

Description

TECHNICAL FIELD [0001] The present invention relates to a diagnostic apparatus for diagnosing back pain based on electromyography of a subject,

One embodiment of the present invention is an apparatus for diagnosing low back pain based on an electromyogram of an examinee, and more particularly, to an apparatus for diagnosing a back pain based on an electromyogram of a subject, The present invention relates to a method for diagnosing an electromyography-based back pain which is capable of diagnosing back pain by means of Bayes' theorem.

The electromyogram is a record of the motor activity potentials that are conducted along the muscle fibers and occurs when the muscle contracts and relaxes.

In particular, muscle fatigue results in increased amplitude and a longer cycle.

In the analysis of muscle fatigue using surface EMG, root mean square (RMS), amplitude EMG, and integral EMG are used in the time domain. In the frequency domain, mean frequency, median frequency ) Are used.

Muscle fatigue analysis using EMG in this frequency region shows that when the muscle fatigue occurs due to muscle contraction, the frequency spectrum of the EMG shifts to the left and the center frequency and the average frequency value decrease. The frequency of the EMG moves to the low frequency and the center frequency and the average frequency decrease.

In addition, when the EMG is analyzed in the time domain, the aEMG increases with the accumulation of muscle fatigue, and it is known that there is a direct correlation between the two variables. In such cases, IEMG analysis also shows that IEMG increases as muscle fatigue increases.

The EMG values are closely related to muscle weakness, fatigue and damage.

On the other hand, Low Back Pain refers to the pain of the back region caused by a dysfunction of the vertebrae, discs, joints, ligaments, nerves, and blood vessels and difficulty in coordinating each other.

In particular, back pain is the cause of muscular weakness, fatigue and damage, and muscle weakness is most common.

Accordingly, the present invention proposes a low back pain diagnostic apparatus FMF for diagnosing back pain based on the electromyogram of the subject based on the electromyogram of the muscle closely related to the back pain.

Korean Patent No. 10-1788457 (Oct. 13, 2017)

It is an object of the present invention to provide a low back pain diagnostic apparatus for diagnosing low back pain based on an electromyogram of a subject so as to diagnose the possibility of low back pain according to an electromyogram measured from a predetermined activity of the subject.

Another object of the present invention is to analyze the electromyographic relationship between the subject, the patient group, and the normal group through Bayes' theorem, so that it is possible to more accurately and finely diagnose the diagnosis of back pain of the subject.

According to another aspect of the present invention, there is provided a device for diagnosing a back pain on the basis of an electromyogram of a subject according to an embodiment of the present invention, Wherein the first frequency information for the predetermined activity of the subject is transmitted to the second frequency information, and the first frequency information is converted into the second frequency information through the frequency analysis, and the second frequency information is used to diagnose the back pain based on the electromyogram of the subject, A determination unit for determining an integration period in which the frequency average included in the first frequency information is a central axis and the standard deviation is an abscissa length, a determination unit for determining, from each of the normal distribution curves included in the second frequency information, A first calculation unit for calculating a first integrated value P (I | H) of the normal group and a second integrated value P (I┃B) of the patient group, (Bayes' theorem) with respect to the first probability (P (H)) that the examiner belongs to the normal group, the second probability P (B) belonging to the patient group and the first and second integral values And a second calculation unit for calculating an electromyogram-based low back pain diagnostic probability (P (H┃I)) of the subject through the second calculation unit.

According to one embodiment of the present invention, the collection server connected to the back-lining diagnostic apparatus converts the continuous signals of the EMG signals into frequency conversion information having respective amplitudes by frequency through the frequency analysis using fast Fourier transform (FFT) And dividing the frequency conversion information according to a predetermined frequency band, and when the ratio between the amplitude of the previous frequency band and the amplitude of the next frequency band corresponds to a preset ratio, And the second frequency information.

According to an example of the present invention, the determination unit derives a normal distribution curve included in each second frequency information, and the normal distribution curve is obtained by the following equation (1) Lt; / RTI >

(1),

(One),

Here, when m is a frequency average of the patient group, σ is a corresponding standard deviation of the patient group, and when m is a frequency average of the normal group, σ is a corresponding standard deviation of the normal group.

According to one example related to the present invention, the second calculation unit calculates the possibility (P (H┃I)) of back pain diagnosis based on the electromyogram of the subject through the following equation (2)

(2),

(2),

Here, P (I | H) is the first integral value, and P (I) is a probability that the first frequency information belongs to the integration period.

According to one example related to the present invention, the probability P (I) that the first frequency information of the subject belongs to the integration period is derived through the following equation (3)

(3),

(3),

Here, P (I? B) is the second integral value.

The apparatus for diagnosing a back pain based on the electromyogram of the subject according to one embodiment of the present invention, which is configured as described above according to one embodiment of the present invention, It is possible to diagnose the possibility.

In addition, since the EMG relationship between the subject, the patient group, and the normal group is analyzed through the Bayesian theorem, there is an effect that the diagnosis of back pain of the subject can be more accurately and finely diagnosed.

BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 is a diagram of a system for diagnosing low back pain according to an embodiment of the present invention.
FIG. 2A is an example of second frequency information of the biceps femoris muscle of the patient group and the normal group stored in the collection server of FIG. 1, and is an example of the second frequency information of the side gait of the patient group and the normal group.
3 is a flow chart for diagnosing an electromyography-based backache associated with an embodiment of the present invention.

Hereinafter, an apparatus for diagnosing electromyography-based back pain according to the present invention will be described in detail with reference to the drawings. The suffix "module" and " part "for the components used in the following description are given or mixed in consideration of ease of specification, and do not have their own meaning or role. In the present specification, the same or similar reference numerals are given to different embodiments in the same or similar configurations. As used herein, the singular forms "a", "an" and "the" include plural referents unless the context clearly dictates otherwise.

It is to be understood that the drawings herein illustrate conceptual aspects of exemplary circuits embodying the principles of the invention. That is, it should be understood that the functions shown in the figures may be substantially represented on a computer-readable medium and performed by various processes performed by the computer or processor, whether or not the computer or processor is explicitly shown.

And, each function can be provided not only with dedicated hardware, but also with the use of hardware capable of executing software in connection with appropriate software. When provided by a processor, the functions may be provided by a single dedicated processor, a single shared processor, or a plurality of individual processors, some of which may be shared.

In the following description, well-known functions or constructions are not described in detail since they would obscure the invention in unnecessary detail. Hereinafter, a preferred embodiment of the present invention will be described in detail with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 is a diagram of a system for diagnosing low back pain according to an embodiment of the present invention.

Referring to FIG. 1, the backbone diagnostic system 1000 may include an EMG measuring device 100, a low back pain diagnostic device 200, and a collection server 300.

First, the electromyography measuring apparatus 100 includes an electromyography (EMG) sensor, an electrodermal activity sensor, a skin temperature meter, a blood volume pulse meter, an electrocardiogram (ECG) sensor, a respiration sensor a blood pressure sensor, a blood pressure meter, and a heart rate meter.

Here, the electromyographic sensor senses the action potential of the muscles, the dermatologically active sensor measures the conductance of the skin, the skin temperature meter senses the temperature of the skin surface, and the blood volume meter measures the amount of blood flowing in the blood vessel , The ECG sensor senses the potential associated with the heartbeat on the body surface, the respiration sensor measures how quickly it breathes, and the heart rate monitor can measure the number of times the heart is beating for a unit of time.

That is, the EMG measuring apparatus 100 can detect at least one of EMG, blood volume, electrocardiogram, respiration, heart rate, body temperature and blood pressure.

The EMG measuring apparatus 100 may include a wireless communication module (not shown) to transmit at least one of the sensed EMG, blood volume, ECG, respiration, heart rate, body temperature and blood pressure to the backbone diagnosis apparatus 200 have.

Here, the communication module (not shown) may be a wired communication module including at least one of a power line communication (PLC), a USB communication, an Ethernet, a serial communication and an optical / coaxial cable, (WLAN), Digital Living Network Alliance (DLNA), Wireless Broadband (Wibro), World Interoperability for Microwave Access (WIMAX), High Speed Downlink Packet AccesB (HSDPA), High Speed Uplink Packet Which includes at least any one of a mobile terminal, an access terminal, an access terminal, an access terminal, an access terminal, an access terminal, an access terminal, an access terminal, an access terminal, an access terminal, and an access terminal. Module and Bluetooth, Radio Frequency Identification (RFID), Infrared Data Association (IrDA), UWB (Ultra Wideband), ZigBee, Near Field Communication (NFC), Ultra Sound Communicati a local area communication module including at least one of on-line communication (USC), visible light communication (VLC), Wi-Fi, and Wi-Fi direct.

In addition, the EMG measuring device 100 may measure a biceps femoris B or a lateral gastrocnemius B, which is a predetermined part of a body part of a subject to be diagnosed with respect to a back pain through an electromyographic sensor (not shown) Zigbee (not shown), which realizes the electromyogram of the action potential of the muscle in real time and has a low data rate among the communication module (not shown) but has high node scalability, wide coverage, and low power consumption It is possible to transmit the electromyogram of the subject to the backbone diagnosis apparatus 200 by interlocking with the backbone diagnosis apparatus 200 through the backbone diagnosis apparatus 200.

According to the embodiment of the present invention, the electromyography measuring apparatus 100 measures an electromyogram for a biceps femoris muscle or a lateral gastrocnemius for a predetermined time according to a predetermined activity of the subject, for example, a walking motion, and performs a fast Fourier transform (FFT) The continuous signal of the electromyogram according to the time for the biceps femoris muscle or the lateral gastrocnemius is converted into the first frequency information and the converted first frequency information such as 50.70, 55.25, 49.17 To the low back pain diagnosis apparatus 200.

Here, at least two first frequency information may include a frequency average and a corresponding standard deviation.

That is, when the at least two first frequency information for the biceps femoris muscle of the subject transformed through the frequency analysis is 50.70, 55.25, 49.17, the electromyogram measuring apparatus 100 calculates the frequency average, for example, When the at least two first frequency information for the lateral gastrocnemius of the subject transformed through the frequency analysis is 59.92, 54.23, 57.16, the frequency average, For example, 57.10 and the corresponding standard deviation, for example, ± 2.82, can be transmitted to the lower back diagnosis apparatus 200 as the first frequency information of the subject.

Next, the low back pain diagnostic apparatus 200 transmits data from the electromyogram measuring apparatus 100 so that the first frequency information on the electromyogram of the subject, for example, the frequency average of the subject for the predetermined activity and the standard deviation, Wireless communication device.

That is, the back-lash diagnosis apparatus 200 is generally a computer such as a desktop PC (Personal Computer), a notebook PC, or the like, but is not limited thereto. And may be a wired / wireless communication device capable of data communication with the mobile terminal 300.

For example, the low back pain diagnosis apparatus 200 includes various mobile terminals for communicating via a wireless Internet or a portable Internet. In addition, the back pain diagnostic apparatus 200 may include a Palm Personal Computer, a smart phone (Bmart phone), a mobile game- (Digital Multimedia Broadcasting) phone, tablet PC, and iPad with communication function.

At this time, the back-lid diagnostic apparatus 200 is a smart phone based on an open operating system that can be freely used and deleted by downloading various application programs desired by the user unlike a general mobile phone (aka a feature phone) All mobile phones with mobile office functions or communication (including voice communication), including all Internet phones or Tablet PCs that can access the Internet, It is desirable to understand it as a device.

The backbone diagnosing apparatus 200 may be implemented as a smart phone equipped with various open operating systems. Examples of the open operating system include Symbian of NOKIA, BlackBerry of RIMB, Apple Unlike mobile phones that have a closed operating system, such as the iPhone of Microsoft Corp., Windows Mobile of MB (MB), Android of Google (Google) and the sea of Samsung Electronics, users can install and manage various application programs .

According to the embodiment of the present invention, when the first frequency information on the predetermined activity of the subject is transmitted through the intervention with the electromyography measuring apparatus 100, The second frequency information corresponding to the predetermined activity of the patient group and the normal group, respectively.

Here, the collection server 300 can receive and collect continuous signals of an electromyogram measured for a predetermined time, for example, an electromyogram according to time, from the group of the back pain patients and the group of the back pain which have been previously measured or collected separately through the electromyography measuring apparatus 100 have.

More specifically, the collection server 300 performs frequency analysis using Fast Fourier Transform (FFT) on the electromyogram measured for a certain period of time from the group of the back pain patients and the normal group that have been previously measured or collected separately through the electromyography measuring apparatus 100 Converted into frequency conversion information having respective amplitudes by frequency, and frequency conversion information having amplitudes of the respective frequencies can be collected or stored.

Here, the frequency conversion information may be a frequency spectrum having a frequency-dependent amplitude in which the continuous signal of the EMG signal is converted.

According to the embodiment, the collection server 300 divides the collected frequency conversion information according to a predetermined frequency band, and when the ratio between the amplitude of the previous frequency band and the amplitude of the next frequency band falls within a predetermined ratio, , The frequency conversion information may be stored in advance as each second frequency information for a predetermined activity, for example, a motion such as walking.

Here, the second frequency information may be a normal distribution curve including frequency averages and corresponding standard deviations for predetermined activities of the patient group and the normal group.

FIG. 2A is an example of second frequency information of the biceps femoris muscle of the patient group and the normal group stored in the collection server of FIG. 1, and is an example of the second frequency information of the side gait of the patient group and the normal group.

That is, as shown in FIG. 2A, the second frequency information includes information about a predetermined activity of the patient group and the normal group stored from the collection server 300, for example, A frequency average of about 27, a corresponding standard deviation of 26, a frequency average of about 48 of the normal group of the biceps femoris, and a corresponding standard deviation of 23 can be included in the distribution curve.

2B, the second frequency information is a normal distribution curve of each of the patient group stored in the collection server 300 and a predetermined gesture of the normal group, for example, lateral gypsum for movement such as walking , Wherein the frequency of the lateral gastrocnemius of the patient group is about 21, the standard deviation is 22, the average frequency of the normal group is about 59, and the standard deviation is about 45.

More specifically, the collection server 300 can derive a normal distribution curve included in each second frequency information.

At this time, each normal distribution curve can derive the respective normal distribution curves through the following equation (1) with the frequency average included in the first frequency information of the subject as the variable χ.

Here, the following expression (1)

(1) 이고,

(1)

Here, when m is a frequency average of the patient group, the sigma is a corresponding standard deviation of the patient group, and when m is a frequency average of the normal group, the sigma may be a corresponding standard deviation of the normal group.

In other words, the collection server 300 calculates the second frequency information including the respective normal distribution curves of the patient group and the normal group and the normal distribution curves of the patient group and the side gait of the normal group, And may transmit the related data when requested from the backbone diagnostic device 200. [

In this way, the collection server 300 includes an Oracle (registered trademark), an Informix (registered trademark), and the like, which classify, store and manage second frequency information corresponding to each preset activity from a measured EMG for a certain period of time from a patient group and a normal group, Oriented database management system (OODBMB) such as Infomix, Bybase, DB2, or a relational database management system (RDBMB) such as Gemston, Orion, O2, (Not shown), which may be implemented in accordance with the purpose and has appropriate fields for achieving its function, or may be provided separately.

1, the electromyographic-based back pain diagnostic apparatus 200 according to the embodiment of the present invention may include a determination unit 210, a first calculation unit 220, and a second calculation unit 230 have.

First, the determination unit 210 determines whether or not a continuous signal of an electromyogram measured for a predetermined period of time from a predetermined activity of the subject, such as walking, through interlocking with the electromyogram measuring apparatus 100, When receiving the information, the second frequency information corresponding to the predetermined group of the patient group and the normal group derived from the collection server 300 may be requested and received.

At this time, the determining unit 210 determines a frequency average included in the first frequency information of the subject as a center axis in each normal distribution curve included in the second frequency information corresponding to the predetermined group of activities of the patient group and the normal group , And determine the integral period in which the standard deviation is the abscissa of the hatched area shown in FIGS. 2A and 2B.

Next, the first calculation unit 220 calculates the first integral value P (I < IH >) of the normal group corresponding to the integration period from each normal distribution curve included in the second frequency information of the patient group and the normal group, ) And the second integral value P (I? B) of the patient group.

Here, the first integral value P (I | H) may be a probability that the subject belongs to the normal group and the first frequency information for the predetermined activity of the subject belongs to the integration period, and the second integral The value P (I < I > B) may be a probability that the subject belongs to the patient group and the first frequency information for the predetermined activity of the subject belongs to the integration period.

을 통해 상기 정상군의 제1 적분 값(P(I┃H))인 0.255를 산출할 수 있다. More specifically, the first calculation unit 220 substitutes the first frequency average, for example, 51.71, of the biceps femoris muscle of the subject to the above-mentioned χ in equation (1) of the normal group, Into an integral section, respectively,

(P (I | < 1 > H)) of the normal group.

을 통해 상기 환자군의 제2 적분 값(P(I┃B))인 0.015를 산출할 수 있다. In the equation (1) of the patient group, the first calculation unit 220 substitutes the frequency average of, for example, 57.10 of the first frequency information of the knee's fingers for the frequency information of the examinee into the above-mentioned χ, For example, ± 2.82 is substituted into the integral section,

0.015 < / RTI > which is the second integral value P (I &thetas; B) of the patient group.

Next, the second calculation unit 230 calculates a first probability P (B), a second probability P (H) to belong to the normal group, and a second probability P (P (H? I)) of the subject's electromyogram based on the Bayes' theorem for the two integral values.

Here, the first probability P (B) and the second probability P (H) can be preset to 0.5.

More specifically, the second calculation unit 230 calculates the probability P (H┃I) that the subject belongs to the normal group in the integration period through the following equation (2)

(2),

(2),

Here, P (I | H) in the equation (2) may be the first integral value, and P (I) may be a probability that the first frequency information of the subject belongs to the integration period.

At this time, the second calculation unit 230 calculates the probability P (I) that the first frequency information of the subject belongs to the integration period through the following equation (3)

(3)

(3)

Where P (I ' B) may be the second integral value of the patient group.

Accordingly, the second calculator 230 calculates P (B┃I) = 1-P (H┃I) as the probability P (H┃I) belonging to the normal group in the integration period is calculated (P (B? I)) based on the probability that the subject belongs to the patient group in the integration period (P (B? I)).

3 is a flow chart for diagnosing an electromyography-based backache associated with an embodiment of the present invention.

1 to 3, first, the acquisition server 300 acquires continuous signals of an electromyogram, for example, an electromyogram corresponding to a time measured from a patient group and a normal group through frequency analysis using fast Fourier transform (FFT) And converted into conversion information (S100).

Here, the frequency conversion information may be a frequency spectrum having frequency-specific amplitudes for continuous signals of the EMG signals.

In addition, the collection server 300 may divide the frequency conversion information according to a predetermined frequency band, and when the ratio between the amplitude of the previous frequency band and the amplitude of the next frequency band is less than a predetermined ratio, for example, 0.05, The conversion information may be set as second frequency information of a predetermined activity, for example, a motion such as walking (S110).

Here, the second frequency information may include a frequency average and a corresponding standard deviation as information of a normal distribution curve.

That is, the collection server 300 may store the second frequency information corresponding to the preset activity from the EMG measured from the patient group and the normal group for a predetermined time to DB (DB).

On the other hand, when the continuous signal of the EMG signal is measured for a predetermined time from the frequency information of the subject, the EMG measuring device 100 converts the frequency information into the first frequency information through frequency analysis using Fast Fourier Transform (FFT) And may transmit the converted first frequency information to the determination unit 210. [

At this time, when the determination unit 210 receives a frequency average and a corresponding standard deviation included in the first frequency information transmitted by the user's predetermined activity from the electromyographic measurement apparatus 100, The second frequency information of the patient group and the normal group corresponding to the predetermined activity can be received (S120).

In addition, the determining unit 210 may determine an integration period in which the frequency average included in the first frequency information of the subject is the central axis and the standard deviation is the abscissa length (S130).

Next, the first calculation unit 220 calculates a first integral value P (I < > H) of the normal group corresponding to the integration period from a normal distribution curve included in the second frequency information of the patient group and the normal group, ) And the second integral value P (I┃B) of the patient group (S140).

Thereafter, the second calculator 230 calculates a first probability P (B) that the predetermined subject belongs to the patient group, a second probability P (H) that belongs to the normal group, (P (H? I)) based on the electromyogram of the subject through the Bayes' theorem with respect to the integrated value (S150).

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 array (FPA) 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.

10: Network
100: EMG measuring device
200: EMG diagnostic device
210:
220: first calculation unit
230: second calculation unit
300: collection server
1000: EMG based low back pain diagnosis system

Claims (5)

A low back pain diagnosis apparatus for diagnosing low back pain based on an electromyogram of an examinee using frequency analysis of a continuous signal of an electromyogram measured or collected from a patient group and a normal group for a certain period of time,
The apparatus for diagnosing low back pain comprises:
A determining unit for determining an integration period in which the frequency average included in the first frequency information is a central axis and the standard deviation is an abscissa when the first frequency information for the predetermined activity of the subject is transmitted;
(P (I | H)) of the normal group and the second integral value (P (I | B)) of the patient group corresponding to the integration interval from each normal distribution curve included in the second frequency information, A first calculation unit for calculating a first calculation result; And
A second probability P (B) belonging to the patient group and a first probability P (B) belonging to the predetermined group and a first probability P (H) and a second calculation unit for calculating an electromyogram-based back pain diagnostic probability (P (H < I > I)) of the subject through the theorem.
delete The method according to claim 1,
Wherein the determination unit derives a normal distribution curve included in each second frequency information,
The normal distribution curve is derived by the following equation (1), where the frequency average of the subject is defined as a variable χ,

(One),
Wherein, when m is a frequency average of the patient group, σ is a corresponding standard deviation of the patient group, and when m is a frequency average of the normal group, σ is a corresponding standard deviation of the normal group. Back pain diagnosis device for diagnosing back pain based on the EMG of the patient.
The apparatus according to claim 1,
(P (H? I)) based on the electromyogram of the subject through the following equation (2)

(2),
(I) is a probability that the first frequency information belongs to the integration period, P (I | H) is the first integral value, and P (I) is a probability that the first frequency information belongs to the integration period. .
5. The method of claim 4, wherein the probability (P (I)) that the first frequency information belongs to the integration period is
Is derived through the following equation (3)

(3),
Here, P (I? B) is the second integral value, which diagnoses the back pain based on the electromyogram of the subject.

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