U.S. patent application number 16/326789 was filed with the patent office on 2019-07-25 for pain estimating device, pain estimating method, and pain classification.
This patent application is currently assigned to Osaka University. The applicant listed for this patent is Osaka University. Invention is credited to Aya Nakae.
Application Number | 20190223783 16/326789 |
Document ID | / |
Family ID | 61246089 |
Filed Date | 2019-07-25 |
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United States Patent
Application |
20190223783 |
Kind Code |
A1 |
Nakae; Aya |
July 25, 2019 |
PAIN ESTIMATING DEVICE, PAIN ESTIMATING METHOD, AND PAIN
CLASSIFICATION
Abstract
Provided is a pain estimating device with which it is possible
to estimate objectively and accurately pain being experienced by a
subject. A pain estimating device 110 estimates the magnitude of
pain being experienced by a subject, on the basis of brainwaves of
the subject, and is provided with: a measurement unit 111 which
acquires a plurality of items of brainwave data or analysis data
thereof by measuring brainwaves from the subject a plurality of
times; and an estimation unit 112 which estimates the relative
magnitude of the pain being experienced when the brainwave
measurements were conducted a plurality of times, from the
plurality of items of brainwave data or the analysis data thereof
(for example the amplitude), on the basis of the linearity of a
relationship between the brainwave data or the analysis data
thereof (for example the amplitude) and the pain. The present
invention also provides a pain estimating method and device with
which it is possible to estimate objectively and accurately pain
being experienced by a subject, and to classify simply the quality
and quantity thereof. The present invention also provides a method
for generating a pain classification value for classifying pain
being experienced by the subject, on the basis of brainwaves of the
subject.
Inventors: |
Nakae; Aya; (Osaka,
JP) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Osaka University |
Osaka |
|
JP |
|
|
Assignee: |
Osaka University
Osaka
JP
|
Family ID: |
61246089 |
Appl. No.: |
16/326789 |
Filed: |
August 22, 2017 |
PCT Filed: |
August 22, 2017 |
PCT NO: |
PCT/JP2017/029991 |
371 Date: |
February 20, 2019 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/04014 20130101;
A61B 5/04012 20130101; A61B 5/4824 20130101; A61B 5/7264 20130101;
A61B 5/0476 20130101; A61B 5/0484 20130101; G06K 9/00543
20130101 |
International
Class: |
A61B 5/00 20060101
A61B005/00; A61B 5/0484 20060101 A61B005/0484; A61B 5/04 20060101
A61B005/04; G06K 9/00 20060101 G06K009/00 |
Foreign Application Data
Date |
Code |
Application Number |
Aug 22, 2016 |
JP |
2016-162195 |
Jul 7, 2017 |
JP |
2017-133424 |
Claims
1. A pain estimation apparatus for estimating a magnitude of pain
of an object being estimated based on a brainwave of the object
being estimated, comprising: a measurement unit for measuring a
brainwave a plurality of times from the object being estimated to
obtain a plurality of brainwave data or analysis data thereof; and
an estimation unit for estimating a relative magnitude of pain upon
the measurement of a brainwave a plurality of times from the
plurality of brainwave data or analysis data thereof, based on a
pain function for a relationship between brainwave data or analysis
data thereof and pain.
2. The pain estimation apparatus of claim 1, wherein the plurality
of brainwave data or analysis data thereof comprise first brainwave
data or analysis data thereof and second brainwave data or analysis
data thereof, wherein the estimation unit (i) estimates that first
pain corresponding to the first brainwave data or analysis data
thereof is greater than second pain corresponding to the second
brainwave data or analysis data thereof if the first brainwave data
is greater than the second brainwave data, and (ii) estimates that
the first pain is less than the second pain if the first brainwave
data or analysis data thereof is less than the second brainwave
data or analysis data thereof.
3. The pain estimation apparatus of claim 2, wherein the plurality
of brainwave data or analysis data thereof further comprise third
brainwave data or analysis data thereof and fourth brainwave data
or analysis data thereof, wherein the estimation unit further
estimates a relative amount of change between a first change from
the first pain to the second pain, and a second change from third
pain corresponding to the third brainwave data to fourth pain
corresponding to the fourth brainwave data, based on a first value
of difference between the first brainwave data or analysis data
thereof and the second brainwave data or analysis data thereof, and
a second value of difference between the third brainwave data or
analysis data thereof and the fourth brainwave data or analysis
data thereof.
4. The apparatus of claim 1, further comprising: a pain classifier
generation unit for fitting the pain and the brainwave data or
analysis data thereof to a pain function to obtain a pain function
specific to the object being estimated, and identifying a pain
classifier for separating a pain level to at least two based on the
pain function; and a pain classification unit for classifying a
pain level of the object being estimated by fitting the brainwave
data or analysis data thereof to the pain classifier; and/or
wherein the brainwave data or analysis data thereof is an amplitude
of a brainwave; and/or wherein the pain function comprises a linear
function or a sigmoid function.
5-6. (canceled)
7. A pain estimation method for estimating a magnitude of pain of
an object being estimated based on a brainwave of the object being
estimated, comprising: a measurement step for measuring a brainwave
a plurality of times from the object being estimated to obtain a
plurality of brainwave data or analysis data thereof; and an
estimation step for estimating a relative magnitude of pain upon
the measurement of a brainwave a plurality of times from the
plurality of brainwave data or analysis data thereof based on a
pain function for a relationship between brainwave data or analysis
data thereof and pain.
8. The method of claim 7, wherein the brainwave data or analysis
data thereof is an amplitude of a brainwave; and/or wherein the
estimation step comprises classifying a pain level of the object
being estimated by fitting the brainwave data or analysis data
thereof to a predetermined pain function, and the pain classifier
is obtained by fitting the brainwave data or analysis data thereof
of the object being estimated to a pain function; and/or wherein
the pain function comprises a linear function or a sigmoid
function.
9-11. (canceled)
12. A storage medium for storing a program implementing a pain
estimation method for estimating a magnitude of pain of an object
being estimated based on a brainwave of the object being estimated
on a computer, the method comprising: a measurement step for
measuring a brainwave a plurality of times from the object being
estimated to obtain a plurality of brainwave data or analysis data
thereof; and an estimation step for estimating a relative magnitude
of pain upon the measurement of a brainwave a plurality of times
from amplitudes of the plurality of brainwave data based on a pain
function for a relationship between brainwave data or analysis data
thereof and pain.
13. A pain estimation apparatus for estimating a magnitude of pain
of an object being estimated based on a brainwave of the object
being estimated, comprising: a measurement unit for measuring a
brainwave from an object being estimated sequentially inflicted
with stimulation of a plurality of magnitudes to obtain brainwave
data or analysis data thereof corresponding to stimulation of each
magnitude; and an identification unit for identifying an upper
limit value and a lower limit value of the brainwave data or
analysis data thereof of the object being estimated based on the
brainwave data or analysis data thereof, wherein the measurement
unit further measures a brainwave from the object being estimated
to obtain object's brainwave data or analysis data thereof, and
wherein the pain estimation apparatus further comprises an
estimation unit for estimating a value of a magnitude of pain
corresponding to the object's brainwave data or analysis data
thereof based on a relative size of a value of the object's
brainwave data or analysis data thereof with respect to the upper
limit value and the lower limit value.
14. The pain estimation apparatus of claim 13, wherein the
estimation unit estimates a ratio of a value of difference between
a value of the object's brainwave data or analysis data thereof and
the lower limit value to a value of difference between the upper
limit value and the lower limit value as the value of the magnitude
of pain; and/or wherein the brainwave data or analysis data thereof
comprises an amplitude.
15. The apparatus of claim 13, further comprising: a pain
classifier generation unit for fitting the pain and the brainwave
data or analysis data thereof to a pain function to obtain a pain
function specific to the object being estimated, and identifying a
pain classifier for separating a pain level to at least two based
on the pain function; and a pain classification unit for
classifying the brainwave data or analysis data thereof to a pain
level of the object being estimated based on the pain
classifier.
16. (canceled)
17. The apparatus of claim 15, wherein the pain function comprises
a linear function or a sigmoid function.
18. A pain estimation method for estimating a magnitude of pain of
an object being estimated based on a brainwave of the object being
estimated, comprising: a first measurement step for measuring a
brainwave from the object being estimated sequentially inflicted
with stimulation of a plurality of magnitudes to obtain brainwave
data or analysis data thereof corresponding to stimulation of each
magnitude; an identification step for identifying an upper limit
value and a lower limit value of the brainwave data or analysis
data thereof of the object being estimated based on the brainwave
data or analysis data thereof; a second measurement step for
measuring a brainwave from the object being estimated to obtain
object's brainwave data or analysis data thereof; and an estimation
step for estimating a value of a magnitude of pain corresponding to
the object's brainwave data, based on a relative size of a value of
the object's brainwave data or analysis data thereof to the upper
limit value and the lower limit value.
19. The method of claim 18, wherein the brainwave data or analysis
data thereof comprises an amplitude.
20. The method of claim 18, wherein the estimation step comprises
classifying the brainwave data or analysis data thereof to a pain
level of the object being estimated based on a predetermined pain
classifier, wherein the pain classifier is obtained by fitting the
brainwave data or analysis data thereof of the object being
estimated to a pain function.
21. The method of claim 20, wherein the pain function comprises a
linear function or a sigmoid function; and/or wherein the pain
function comprises a linear function with a linear approximation of
a modulation range, or a more comprehensive sigmoid function
encompassing the same.
22-23. (canceled)
24. A storage medium storing a program for implementing a pain
estimation method for estimating a magnitude of pain of an object
being estimated based on a brainwave of the object being estimated
on a computer, the method comprising: a first measurement step for
measuring a brainwave from the object being estimated sequentially
inflicted with stimulation of a plurality of magnitudes to obtain
brainwave data or analysis data thereof corresponding to
stimulation of each magnitude; an identification step for
identifying an upper limit value and a lower limit value of the
brainwave data or analysis data thereof of the object being
estimated based on the brainwave data or analysis data thereof; a
second measurement step for measuring a brainwave from the object
being estimated to obtain object's brainwave data or analysis data
thereof; and an estimation step for estimating a value of a
magnitude of pain corresponding to the object's brainwave data,
based on a relative size of a value of the object's brainwave data
or analysis data thereof to the upper limit value and the lower
limit value.
25-57. (canceled)
Description
TECHNICAL FIELD
[0001] The present invention relates to a pain estimation apparatus
and a pain estimation method for estimating the magnitude of pain
of an object being estimated based on a brainwave of the object
being estimated. The present invention also relates to applying a
biological signal such as a brainwave obtained from an object being
estimated to a pain function (e.g., linear function with a linear
approximation of the modulation range, or a more comprehensive
sigmoid function encompassing the same) and classifying the quality
and amount of pain using a characteristic value obtained thereby.
More specifically, the present invention relates to objectively
showing a pain level (e.g., weak pain, strong pain, or the like),
which varies by individual, from a signal value.
BACKGROUND ART
[0002] Pain is intrinsically subjective, but objective evaluation
is desirable for therapy. Patients often suffer from an adverse
experience by underestimating pain. In this regard, a method for
objectively estimating pain using a brainwave has been proposed
(see, for example, Patent Literature 1).
[0003] However, the intensity of pain is subjective, such that
objective evaluation is challenging. Whether the pain is
particularly unbearable or tolerable to a certain extent cannot be
expressed only by a subjective opinion of "painful". The expression
also varies by individual, such that objective evaluation is
challenging. While classification of pain is desirable for
observing therapeutic effects, such a technology has not been
provided.
CITATION LIST
Patent Literature
[PTL 1] Japanese National Phase PCT Laid-open Publication No.
2010-523226
SUMMARY OF INVENTION
Solution to Problem
[0004] The present invention has solved the problem of being unable
to accurately estimate pain, when using data indicating brainwave
activity of multipole individuals in a plurality of pain as
reference data, by using a pain function (e.g., linear function
with a linear approximation of the modulation range, or a more
comprehensive sigmoid function encompassing the same).
[0005] In other words, the present invention provides a pain
estimation apparatus that can objectively and accurately estimate
pain of an object being estimated.
[0006] The present invention also provides a pain estimation method
and apparatus that can objectively and accurately estimate pain of
an object being estimated and readily classify the quality and
amount thereof. The present invention also provides a technology
for generating a pain classifier for such pain classification.
[0007] A pain estimation apparatus according to one embodiment of
the invention is a pain estimation apparatus for estimating a
magnitude of pain of an object being estimated based on a brainwave
of the object being estimated, comprising: a measurement unit for
obtaining a plurality of brainwave data or analysis data thereof
(including, for example, amplitude) by measuring a brainwave a
plurality of times from the object being estimated; and an
estimation unit for estimating a relative magnitude of pain upon
the measurement of brainwave a plurality of times from the
plurality of brainwave data or analysis data thereof (including,
for example, amplitude) based on a pain function (where examples of
pain function patterns include linear and non-linear patterns, so
that the function can be based on linearity) for the relationship
between the brainwave data or analysis data thereof (e.g.,
amplitude) and pain. Linearity can be a value other than amplitude
such as a frequency or wavelet processing value, as long as the
value is a characteristic amount of a brainwave. Linearity in a
modulation range is found not only in the characteristic amount of
a brainwave, but also in subjective evaluation.
[0008] This configuration can estimate the relative magnitude of
pain upon measurements of a brainwave a plurality of times from a
plurality of brainwave data or analysis data thereof (including,
for example, amplitude) based on linearity in the relationship
between the brainwave or analysis data thereof (including, for
example, amplitude) and pain. Existence of linearity between
brainwave or analysis data thereof (including, for example,
amplitude) and pain is a phenomenon elucidated by the inventor. The
magnitude of pain can be estimated without using the magnitude of
pain declared by the object being estimated by utilizing the
linearity in the relationship between brainwave or analysis data
thereof (including, for example, amplitude) and pain, so that pain
of the object being estimated can be objectively and accurately
estimated. Furthermore, brainwave data does not need to be
collected in advance from the object being estimated or the like,
such that the magnitude of pain can be more readily estimated.
[0009] For example, the plurality of brainwave data comprise first
brainwave data and second brainwave data, and the estimation unit
can (i) estimate that first pain corresponding to the first
brainwave data or analysis data thereof (including, for example,
amplitude) is greater than second pain corresponding to the second
brainwave data or analysis data thereof (including, for example,
amplitude) if the first brainwave data is greater than the second
brainwave data, and (ii) estimate that the first pain is less than
the second pain if the first brainwave data or analysis data
thereof (including, for example, amplitude) is less than the second
brainwave data or analysis data thereof (including, for example,
amplitude).
[0010] This configuration can compare the first brainwave data or
analysis data thereof (including, for example, amplitude) with the
second brainwave data or analysis data thereof (including, for
example, amplitude) to estimate which pain is greater between the
first pain corresponding to the first brainwave data or analysis
data thereof and the second brainwave data or analysis data thereof
(including, for example, amplitude). For example, the magnitude of
pain before and after therapy can therefore be compared to evaluate
a therapeutic effect by measuring brainwave data or analysis data
thereof (including, for example, amplitude) before and after
therapy.
[0011] For example, the plurality of brainwave data or analysis
data thereof (including, for example, amplitude) further comprise
third brainwave data or analysis data thereof and fourth brainwave
data or analysis data thereof (including, for example, amplitude).
The estimation unit can further estimate a relative amount of
change between a first change from the first pain to the second
pain and a second change from third pain corresponding to the third
brainwave data or analysis data thereof (including, for example,
amplitude) to fourth pain corresponding to the fourth brainwave
data or analysis data thereof (including, for example, amplitude),
based on a first value of difference between the first brainwave
data or analysis data thereof (including, for example, amplitude)
and the second brainwave data or analysis data thereof (including,
for example, amplitude) and a second value of difference between
the third brainwave data or analysis data thereof (including, for
example, amplitude) and the fourth brainwave data or analysis data
thereof (including, for example, amplitude).
[0012] This configuration can compare the value of difference in
data such as a pair of amplitudes of two sets of brainwave data or
analysis data thereof to estimate the relative amount of change in
pain. For example, a change in pain due to first therapy can
therefore be compared to a change in pain due to second therapy to
evaluate the relative effect of the first therapy and the second
therapy.
[0013] A pain estimation apparatus according to another embodiment
of the invention is a pain estimation apparatus for estimating a
magnitude of pain of an object being estimated based on a brainwave
of the object being estimated, comprising: a measurement unit for
obtaining brainwave data corresponding to stimulation of each
magnitude by measuring a brainwave from the object being estimated
sequentially inflicted with stimulation of a plurality of
magnitudes; and an identification unit for identifying an upper
limit value and a lower limit value of brainwave or analysis data
thereof (including, for example, amplitude) of the object being
estimated, wherein the measurement unit further measures a
brainwave from the object being estimated to obtain object's
brainwave data, and the pain estimation apparatus further comprises
an estimation unit for estimating a value of the magnitude of pain
corresponding to the object's brainwave data based on a relative
magnitude of the object's brainwave data or analysis data thereof
(including, for example, amplitude) with respect to the upper limit
value and the lower limit value.
[0014] This configuration can estimate a value of magnitude of pain
corresponding to the object's brainwave data based on the relative
magnitude of object's brainwave data or analysis data thereof
(including, for example, amplitude) with respect to the upper limit
value and lower limit value of brainwave of the object being
estimated or analysis data thereof (including, for example,
amplitude), such that the magnitude of pain can be quantified.
Neither identification of the upper limit value and the lower limit
value nor estimation of a value of the magnitude of pain requires
the use of the magnitude of pain declared by the object being
estimated. Thus, pain of the object being estimated can be
objectively estimated.
[0015] For example, the estimation unit can also estimate a ratio
of a value of difference between the object's brainwave data or
analysis data thereof (including, for example, amplitude) and the
lower limit value to a value of difference between the upper limit
value and the lower limit value as a value of the magnitude of
pain.
[0016] This configuration can estimate the value of the magnitude
of pain by using a ratio of a value of difference between object's
brainwave data or analysis data thereof (including, for example,
amplitude) and the lower limit value to a value of difference
between the upper limit value and the lower limit value. Therefore,
a value of the magnitude of pain can be estimated more readily.
[0017] These comprehensive or specific embodiments can be
materialized with a system, method, integrated circuit, computer
program, or a storage medium such as a computer readable CD-ROM or
any combination of a system, method, integrated circuit, computer
program and storage medium.
[0018] The present invention also provides the following.
(A1) A pain estimation apparatus for estimating a magnitude of pain
of an object being estimated based on a brainwave of the object
being estimated, comprising:
[0019] a measurement unit for measuring a brainwave a plurality of
times from the object being estimated to obtain a plurality of
brainwave data or analysis data thereof; and
[0020] an estimation unit for estimating a relative magnitude of
pain upon the measurement of a brainwave a plurality of times from
the plurality of brainwave data or analysis data thereof, based on
a pain function for a relationship between brainwave data or
analysis data thereof and pain.
(A2) The pain estimation apparatus of item A1,
[0021] wherein the plurality of brainwave data or analysis data
thereof comprise first brainwave data or analysis data thereof and
second brainwave data or analysis data thereof,
[0022] wherein the estimation unit
(i) estimates that first pain corresponding to the first brainwave
data or analysis data thereof is greater than second pain
corresponding to the second brainwave data or analysis data thereof
if the first brainwave data is greater than the second brainwave
data, and (ii) estimates that the first pain is less than the
second pain if the first brainwave data or analysis data thereof is
less than the second brainwave data or analysis data thereof. (A3)
The pain estimation apparatus of item A1 or A2,
[0023] wherein the plurality of brainwave data or analysis data
thereof further comprise third brainwave data or analysis data
thereof and fourth brainwave data or analysis data thereof,
[0024] wherein the estimation unit further estimates a relative
amount of change between a first change from the first pain to the
second pain and a second change from third pain corresponding to
the third brainwave data to fourth pain corresponding to the fourth
brainwave data, based on a first value of difference between the
first brainwave data or analysis data thereof and the second
brainwave data or analysis data thereof and a second value of
difference between the third brainwave data or analysis data
thereof and the fourth brainwave data or analysis data thereof.
(A4) The apparatus of any one of items A1 to A3, further
comprising:
[0025] a pain classifier generation unit for fitting the pain and
the brainwave data or analysis data thereof to a pain function to
obtain a pain function specific to the object being estimated, and
identifying a pain classifier for separating a pain level to at
least two based on the pain function; and
[0026] a pain classification unit for classifying a pain level of
the object being estimated by fitting the brainwave data or
analysis data thereof to the pain classifier.
(A5) The apparatus of any one of items A1 to A4, wherein the
brainwave data or analysis data thereof is an amplitude of a
brainwave. (A6) The apparatus of item A4 or A5, wherein the pain
function comprises a linear function or a sigmoid function (e.g., a
linear function with a linear approximation of a modulation range,
or a more comprehensive sigmoid function encompassing the same).
(A6A) The apparatus of any one of items A1 to A6, wherein the
estimation being based on the pain function comprises being based
on a pain function pattern including linearity or non-linearity and
preferably being based on linearity. (A7) A pain estimation method
for estimating a magnitude of pain of an object being estimated
based on a brainwave of the object being estimated, comprising:
[0027] a measurement step for measuring a brainwave a plurality of
times from the object being estimated to obtain a plurality of
brainwave data or analysis data thereof; and
[0028] an estimation step for estimating a relative magnitude of
pain upon the measurement of a brainwave a plurality of times from
the plurality of brainwave data or analysis data thereof based on a
pain function for a relationship between brainwave data or analysis
data thereof and pain.
(A8) The method of item A7, wherein the brainwave data or analysis
data thereof is an amplitude of a brainwave. (A9) The method of
item A7 or A8, wherein the estimation step comprises classifying a
pain level of the object being estimated by fitting the brainwave
data or analysis data thereof to a predetermined pain function, and
the pain classifier is obtained by fitting the brainwave data or
analysis data thereof of the object being estimated to a pain
function curve. (A10) The method of item A9, wherein the pain
function comprises a linear function or a sigmoid function (e.g., a
linear function with a linear approximation of a modulation range,
or a more comprehensive sigmoid function encompassing the same).
(A10A) The method of any one of items A7 to A10, further comprising
one or more features of items A1 to A6 and A6A. (A11) A program for
implementing a pain estimation method for estimating a magnitude of
pain of an object being estimated based on a brainwave of the
object being estimated on a computer, the method comprising:
[0029] a measurement step for measuring a brainwave a plurality of
times from the object being estimated to obtain a plurality of
brainwave data or analysis data thereof; and
[0030] an estimation step for estimating a relative magnitude of
pain upon the measurement of a brainwave a plurality of times from
the plurality of brainwave data or analysis data thereof based on a
pain function for a relationship between brainwave data or analysis
data thereof and pain.
(A11A) The program of item A11, further comprising one or more
features of items A1 to A6, A6A, A7 to A10, and A10A. (A12) A
storage medium for storing a program implementing a pain estimation
method for estimating a magnitude of pain of an object being
estimated based on a brainwave of the object being estimated on a
computer, the method comprising:
[0031] a measurement step for measuring a brainwave a plurality of
times from the object being estimated to obtain a plurality of
brainwave data or analysis data thereof; and
[0032] an estimation step for estimating a relative magnitude of
pain upon the measurement of a brainwave a plurality of times from
amplitudes of the plurality of brainwave data based on a pain
function for a relationship between brainwave data or analysis data
thereof and pain.
(A12A) The storage medium of item A12, further comprising one or
more features of items A1 to A6, A6A, A7 to A10, A10A, A11, and
A11A. (A13) A pain estimation apparatus for estimating a magnitude
of pain of an object being estimated based on a brainwave of the
object being estimated, comprising:
[0033] a measurement unit for measuring a brainwave from an object
being estimated sequentially inflicted with stimulation of a
plurality of magnitudes to obtain brainwave data or analysis data
thereof corresponding to stimulation of each magnitude; and
[0034] an identification unit for identifying an upper limit value
and a lower limit value of the brainwave data or analysis data
thereof of the object being estimated based on the brainwave data
or analysis data thereof,
[0035] wherein the measurement unit further measures a brainwave
from the object being estimated to obtain object's brainwave data
or analysis data thereof, and
[0036] wherein the pain estimation apparatus further comprises an
estimation unit for estimating a value of a magnitude of pain
corresponding to the object's brainwave data or analysis data
thereof based on a relative size of a value of the object's
brainwave data or analysis data thereof with respect to the upper
limit value and the lower limit value.
(A14) The pain estimation apparatus of item A13,
[0037] wherein the estimation unit estimates a ratio of a value of
difference between a value of the object's brainwave data or
analysis data thereof and the lower limit value to a value of
difference between the upper limit value and the lower limit value
as the value of the magnitude of pain.
(A15) The apparatus of item A13 or A14, further comprising:
[0038] a pain classifier generation unit for fitting the pain and
the brainwave data or analysis data thereof to a pain function to
obtain a pain function specific to the object being estimated, and
identifying a pain classifier for separating a pain level to at
least two based on the pain function; and
[0039] a pain classification unit for classifying the brainwave
data or analysis data thereof to a pain level of the object being
estimated based on the pain classifier.
(A16) The apparatus of any one of items A13 to A15, wherein the
brainwave data or analysis data thereof comprises an amplitude.
(A17) The apparatus of any one of items A13 to A16, wherein the
pain function comprises a linear function or a sigmoid function.
(A17A) The apparatus of any one of items A13 to A17, further
comprising one or more features of items A1 to A6, A6A, A7 to A10,
A10A, A11, A11A, A12, and A12A. (A18) A pain estimation method for
estimating a magnitude of pain of an object being estimated based
on a brainwave of the object being estimated, comprising:
[0040] a first measurement step for measuring a brainwave from the
object being estimated sequentially inflicted with stimulation of a
plurality of magnitudes to obtain brainwave data or analysis data
thereof corresponding to stimulation of each magnitude;
[0041] an identification step for identifying an upper limit value
and a lower limit value of the brainwave data or analysis data
thereof of the object being estimated based on the brainwave data
or analysis data thereof;
[0042] a second measurement step for measuring a brainwave from the
object being estimated to obtain object's brainwave data or
analysis data thereof; and
[0043] an estimation step for estimating a value of a magnitude of
pain corresponding to the object's brainwave data, based on a
relative size of a value of the object's brainwave data or analysis
data thereof to the upper limit value and the lower limit
value.
(A19) The method of item A18, wherein the brainwave data or
analysis data thereof comprises an amplitude. (A20) The method of
item A18 or A19, wherein the estimation step comprises classifying
the brainwave data or analysis data thereof to a pain level of the
object being estimated based on a predetermined pain classifier,
wherein the pain classifier is obtained by fitting the brainwave
data or analysis data thereof of the object being estimated to a
pain function. (A21) The method of item A20, wherein the pain
function comprises a linear function or a sigmoid function. (A22)
The method of item A20 or A21, wherein the pain function comprises
a linear function with a linear approximation of a modulation
range, or a more comprehensive sigmoid function encompassing the
same. (A22A) The method of any one of items A18 to A22, further
comprising one or more features of items A1 to A6, A6A, A7 to A10,
A10A, A11, A11A, A12, A12A, A13 to A17, and A17A. (A23) A program
for implementing a pain estimation method for estimating a
magnitude of pain of an object being estimated based on a brainwave
of the object being estimated on a computer, the method
comprising:
[0044] a first measurement step for measuring a brainwave from the
object being estimated sequentially inflicted with stimulation of a
plurality of magnitudes to obtain brainwave data or analysis data
thereof corresponding to stimulation of each magnitude;
[0045] an identification step for identifying an upper limit value
and a lower limit value of the brainwave data or analysis data
thereof of the object being estimated based on the brainwave data
or analysis data thereof;
[0046] a second measurement step for measuring a brainwave from the
object being estimated to obtain object's brainwave data or
analysis data thereof; and
[0047] an estimation step for estimating a value of a magnitude of
pain corresponding to the object's brainwave data, based on a
relative size of a value of the object's brainwave data or analysis
data thereof to the upper limit value and the lower limit
value.
(A23A) The program of item A23, further comprising one or more
features of items A1 to A6, A6A, A7 to A10, A10A, A11, A11A, A12,
A12A, A13 to A17, A17A, items A18 to A22, and A22A. (A24) A storage
medium storing a program for implementing a pain estimation method
for estimating a magnitude of pain of an object being estimated
based on a brainwave of the object being estimated on a computer,
the method comprising:
[0048] a first measurement step for measuring a brainwave from the
object being estimated sequentially inflicted with stimulation of a
plurality of magnitudes to obtain brainwave data or analysis data
thereof corresponding to stimulation of each magnitude;
[0049] an identification step for identifying an upper limit value
and a lower limit value of the brainwave data or analysis data
thereof of the object being estimated based on the brainwave data
or analysis data thereof;
[0050] a second measurement step for measuring a brainwave from the
object being estimated to obtain object's brainwave data or
analysis data thereof; and
[0051] an estimation step for estimating a value of a magnitude of
pain corresponding to the object's brainwave data, based on a
relative size of a value of the object's brainwave data or analysis
data thereof to the upper limit value and the lower limit
value.
(A24A) The storage medium of item A24, further comprising one or
more features of items A1 to A6, A6A, A7 to A10, A10A, A11, A11A,
A12, A12A, A13 to A17, A17A, items A18 to A22, A22A, A23, and A23A.
(A25) A method of generating a pain classifier for classifying pain
of an object being estimated based on a brainwave of the object
being estimated, comprising the steps of: a) stimulating the object
being estimated with a plurality of levels of stimulation
intensities; b) obtaining brainwave data or analysis data thereof
of the object being estimated corresponding to the stimulation
intensities; c) plotting, and fitting to a pain function, the
stimulation intensities or a subjective pain sensation level
corresponding to the stimulation intensities and the brainwave data
or analysis data thereof to obtain a pain function specific to the
object being estimated; and d) identifying a pain classifier for
separating a pain level to at least two based on the specific pain
function when a regression coefficient for fitting to the specific
pain function is equal to or greater than a predetermined value.
(A26) The method of item A25, further comprising the step of
calibrating the pain classifier so that classification of the pain
level is at a maximum. (A27) The method of item A25 or A26, wherein
the pain classifier is determined based on an infection point of a
pain function or a median value, and the method optionally further
comprises the step of calibrating the pain classifier so that
classification of the pain level is at a maximum. (A28) The method
of any one of items A25 to A27, wherein the classification
classifies whether there is pain or no pain based on a subjective
view of the object being estimated. (A29) The method of any one of
items A25 to A28, wherein the stimulation intensities comprise at
least one intensity that is highly invasive to the object being
estimated. (A30) The method of any one of items A25 to A29, wherein
the stimulation intensities do not comprise an intensity that is
highly invasive to the object being estimated. (A31) The method of
any one of items A25 to A30, wherein the brainwave data or analysis
data thereof comprises at least one selected from the group
consisting of amplitude data and a frequency property. (A32) The
method of any one of items A25 to A31, wherein the pain function
comprises a linear function or a sigmoid function. (A33) The method
of any one of items A25 to A32, wherein the pain function comprises
a linear function with a linear approximation of a modulation
range, or a more comprehensive sigmoid function encompassing the
same. (A34) An apparatus for generating a pain classifier for
classifying pain of an object being estimated based on a brainwave
of the object being estimated, comprising: A) a stimulation unit
for stimulating the object being estimated with a plurality of
levels of stimulation intensities; B) a brainwave data obtaining
unit for obtaining brainwave data or analysis data thereof of the
object being estimated corresponding to the stimulation
intensities; and C) a pain classifier generation unit for plotting,
and fitting to a pain function, the stimulation intensities or a
subjective pain sensation level corresponding to the stimulation
intensities and the brainwave data or analysis data thereof to
obtain a pain function specific to the object being estimated, and
identifying a pain classifier for separating a pain level to at
least two based on the specific pain function. (A35) The apparatus
of item A34, further having one or more features of items A25 to
A33. (A36) A method of classifying pain of an object being
estimated based on a brainwave of the object being estimated,
comprising the steps of: a) stimulating the object being estimated
with a plurality of levels of stimulation intensities; b) obtaining
brainwave data or analysis data thereof of the object being
estimated corresponding to the stimulation intensities; c)
plotting, and fitting to a pain function, the stimulation
intensities or a subjective pain sensation level corresponding to
the stimulation intensities and the brainwave data or analysis data
thereof to obtain a pain function specific to the object being
estimated; d) identifying a pain classifier for separating a pain
level to at least two based on the specific pain function when a
regression coefficient for fitting to the specific pain function is
equal to or greater than a predetermined value; e) obtaining the
brainwave data or analysis data thereof of the object being
estimated; and f) classifying the brainwave data or analysis data
thereof to a pain level of the object being estimated based on the
pain classifier. (A37) The method of item A36, wherein the
classification of the brainwave data or analysis data thereof based
on the pain classifier is characterized by using a mean value.
(A38) The method of A37, wherein the mean value is characterized by
using a mean value between about 15 seconds to 120 seconds. (A39)
The method of any one of items A36 to A38, wherein the pain
function comprises a linear function or a sigmoid function. (A39A)
The method of any one of items A36 to A39, further having one or
more features of items A25 to A35. (A40) An apparatus for
classifying pain of an object being estimated based on a brainwave
of the object being estimated, comprising: A) a stimulation unit
for stimulating the object being estimated with a plurality of
levels of stimulation intensities; B) a brainwave data obtaining
unit for obtaining brainwave data or analysis data thereof of the
object being estimated; C) a pain classifier generation unit for
plotting, and fitting to a pain function, the stimulation
intensities or a subjective pain sensation level corresponding to
the stimulation intensities and the brainwave data or analysis data
thereof to obtain a pain function specific to the object being
estimated, and identifying a pain classifier for separating a pain
level to at least two based on the specific pain function; and D) a
pain classification unit for classifying the brainwave data or
analysis data thereof to a pain level of the object being estimated
based on the pain classifier. (A41) The apparatus of item A40,
further having one or more features of items A25 to A39. (A42) A
method of classifying pain of an object being estimated based on a
brainwave of the object being estimated, comprising the steps of:
e) obtaining brainwave data or analysis data thereof of the object
being estimated; and f) classifying a pain level of the object
being estimated by fitting the brainwave data or analysis data
thereof to a predetermined pain classifier;
[0052] wherein the pain classifier is obtained by fitting the
brainwave data or analysis data thereof of the object being
estimated to a pain function.
(A43) A method of item A42, wherein the pain function comprises a
linear function or a sigmoid function. (A43A) The method of item
A42 or A43, further having one or more features of items A25 to
A39, A39A, and A40 to A41. (A44) An apparatus for classifying pain
of an object being estimated based on a brainwave of the object
being estimated, comprising: X) an amplitude data obtaining unit
for obtaining brainwave data or analysis data thereof of the object
being estimated; and Y) a pain classification unit for classifying
a pain level of the object being estimated by fitting the brainwave
data or analysis data thereof to the pain classifier, wherein the
pain classifier is obtained by fitting the brainwave data or
analysis data thereof of the object being estimated to a pain
function. (A45) The apparatus of item A44, wherein the pain
function comprises a linear function or a sigmoid function. (A45A)
The apparatus of item A44 or A45, further having one or more
features of items A25 to A39, A39A, A40 to A43, and A43A. (A46) A
program for having a computer perform a method of generating a pain
classifier for classifying pain of an object being estimated based
on a brainwave of the object being estimated, the method comprising
the steps of: a) stimulating the object being estimated with a
plurality of levels of stimulation intensities; b) obtaining
brainwave data or analysis data thereof of the object being
estimated corresponding to the stimulation intensities; c)
plotting, and fitting to a pain function, the stimulation
intensities or a subjective pain sensation level corresponding to
the stimulation intensities and the brainwave data or analysis data
thereof to obtain a pain function specific to the object being
estimated; and d) identifying a pain classifier for separating a
pain level to at least two based on the specific pain function when
a regression coefficient for fitting to the specific pain function
is equal to or greater than a predetermined value. (A47) The
program of item A46, wherein the pain function comprises a linear
function or a sigmoid function. (A47A) The program of item A46 or
A47, further having one or more features of items A25 to A39, A39A,
A40 to A43, A43A, A44 to A45, and A45A. (A48) A storage medium
comprising a program for having a computer perform a method of
generating a pain classifier for classifying pain of an object
being estimated based on a brainwave of the object being estimated,
the method comprising the steps of: a) stimulating the object being
estimated with a plurality of levels of stimulation intensities; b)
obtaining brainwave data or analysis data thereof of the object
being estimated corresponding to the stimulation intensities; c)
plotting, and fitting to a pain function, the stimulation
intensities or a subjective pain sensation level corresponding to
the stimulation intensities and the brainwave data or analysis data
thereof to obtain a pain function specific to the object being
estimated; and d) identifying a pain classifier for separating a
pain level to at least two based on the pain function when a
regression coefficient for fitting to the specific pain function is
equal to or greater than a predetermined value. (A49) The storage
medium of item A48, wherein the pain function comprises a linear
function or a sigmoid function. (A49A) The storage medium of item
A48 or A49, further having one or more features of items A25 to
A39, A39A, A40 to A43, A43A, A44 to A45, A45A, A46 to A47, and
A47A. (A50) A program for having a computer execute a method of
classifying pain of an object being estimated based on a brainwave
of the object being estimated, the method comprising the steps of:
a) stimulating the object being estimated with a plurality of
levels of stimulation intensities; b) obtaining brainwave data or
analysis data thereof of the object being estimated corresponding
to the stimulation intensities; c) plotting, and fitting to a pain
function, the stimulation intensities or a subjective pain
sensation level corresponding to the stimulation intensities and
the brainwave data or analysis data thereof to obtain a pain
function specific to the object being estimated; d) identifying a
pain classifier for separating a pain level to at least two based
on the specific pain function when a regression coefficient for
fitting to the specific pain function is equal to or greater than a
predetermined value; e) obtaining the brainwave data or analysis
data thereof of the object being estimated; and f) classifying the
brainwave data or analysis data thereof to a pain level of the
object being estimated based on the pain classifier. (A51) The
program of item A50, wherein the pain function comprises a linear
function or a sigmoid function. (A51A) The program of item A50 or
A51, further having one or more features of items A25 to A39, A39A,
A40 to A43, A43A, A44 to A45, A45A, A46 to A47, A47A, A48 to A49,
and A49A. (A52) A storage medium comprising a program for having a
computer execute a method of classifying pain of an object being
estimated based on a brainwave of the object being estimated, the
method comprising the steps of: a) stimulating the object being
estimated with a plurality of levels of stimulation intensities; b)
obtaining brainwave data or analysis data thereof of the object
being estimated corresponding to the stimulation intensities; c)
plotting, and fitting to a pain function, the stimulation
intensities or a subjective pain sensation level corresponding to
the stimulation intensities and the brainwave data or analysis data
thereof to obtain a pain function specific to the object being
estimated; d) identifying a pain classifier for separating a pain
level to at least two based on the specific pain function when a
regression coefficient for fitting to the specific pain function is
equal to or greater than a predetermined value; e) obtaining the
brainwave data or analysis data thereof of the object being
estimated; and f) classifying the brainwave data or analysis data
thereof to a pain level of the object being estimated based on the
pain classifier. (A53) The storage medium of item A52, wherein the
pain function comprises a linear function or a sigmoid function.
(A53A) The storage medium of item A52 or A53, further having one or
more features of items A25 to A39, A39A, A40 to A43, A43A, A44 to
A45, A45A, A46 to A47, A47A, A48 to A49, A49A, A50 to A51, and
A51A. (A54) A program for having a computer execute a method of
classifying pain of an object being estimated based on a brainwave
of the object being estimated, the method comprising the steps of:
e) obtaining brainwave data or analysis data thereof of the object
being estimated; and f) classifying a pain level of the object
being estimated by fitting the brainwave data or analysis data
thereof to a predetermined pain classifier;
[0053] wherein the pain classifier is obtained by fitting the
brainwave data or analysis data thereof of the object being
estimated to a pain function.
(A55) The program of item A54, wherein the pain function comprises
a linear function or a sigmoid function. (A55A) The program of item
A54 or A55, further having one or more features of items A25 to
A39, A39A, A40 to A43, A43A, A44 to A45, A45A, A46 to A47, A47A,
A48 to A49, A49A, A50 to A51, A51A, A52 to A53, and A53A. (A56) A
storage medium comprising a program for having a computer execute a
method of classifying pain of an object being estimated based on a
brainwave of the object being estimated, the method comprising the
steps of: e) obtaining brainwave data or analysis data thereof of
the object being estimated; and f) classifying a pain level of the
object being estimated by fitting the brainwave data or analysis
data thereof to a predetermined pain classifier;
[0054] wherein the pain classifier is obtained by fitting the
brainwave data or analysis data thereof of the object being
estimated to a pain function.
(A57) The storage medium of item A56, wherein the pain function
comprises a linear function or a sigmoid function. (A57A) The
storage medium of item A56 or A57, further having one or more
features of items A25 to A39, A39A, A40 to A43, A43A, A44 to A45,
A45A, A46 to A47, A47A, A48 to A49, A49A, A50 to A51, A51A, A52 to
A53, A53A, A54 to A55, and A55A.
[0055] The present invention is intended so that one or more of the
aforementioned features can be provided not only as the explicitly
disclosed combinations, but also as other combinations thereof.
Additional embodiments and advantages of the present invention are
recognized by those skilled in the art by reading and understanding
the following detailed description as needed.
[0056] The present invention also provides the following.
(B1) A pain estimation apparatus for estimating a magnitude of pain
of an object being estimated based on a brainwave of the object
being estimated, comprising:
[0057] a measurement unit for measuring a brainwave a plurality of
times from the object being estimated to obtain a plurality of
brainwave data; and
[0058] an estimation unit for estimating a relative magnitude of
pain upon the measurement of a brainwave a plurality of times from
an amplitude of the plurality of brainwave data, based on a
linearity in a relationship between the amplitude of a brainwave
and pain.
(B2) The pain estimation apparatus of item B1,
[0059] wherein the plurality of brainwave data comprise first
brainwave data and second brainwave data,
[0060] wherein the estimation unit
(i) estimates that first pain corresponding to the first brainwave
data is greater than second pain corresponding to the second
brainwave data if an amplitude of the first brainwave data is
greater than an amplitude of the second brainwave data, and (ii)
estimates that the first pain is less than the second pain if the
amplitude of the first brainwave data is less than the amplitude of
the second brainwave data. (B3) The pain estimation apparatus of
item B2,
[0061] wherein the plurality of brainwave data further comprise
third brainwave data and fourth brainwave data,
[0062] wherein the estimation unit further estimates a relative
amount of change between a first change from the first pain to the
second pain and a second change from third pain corresponding to
the third brainwave data to fourth pain corresponding to the fourth
brainwave data, based on a first value of difference between an
amplitude value of the first brainwave data and an amplitude value
of the second brainwave data and a second value of difference
between an amplitude value of the third brainwave data and an
amplitude value of the fourth brainwave data.
(B4) A pain estimation method for estimating a magnitude of pain of
an object being estimated based on a brainwave of the object being
estimated, comprising:
[0063] a measurement step for measuring a brainwave a plurality of
times from the object being estimated to obtain a plurality of
brainwave data; and
[0064] an estimation step for estimating a relative magnitude of
pain upon the measurement of a brainwave a plurality of times from
an amplitude of the plurality of brainwave data based on a
linearity in a relationship between the amplitude of brainwave and
pain.
(B5) A pain estimation apparatus for estimating a magnitude of pain
of an object being estimated based on a brainwave of the object
being estimated, comprising:
[0065] a measurement unit for measuring a brainwave from an object
being estimated sequentially inflicted with stimulation of a
plurality of magnitudes to obtain brainwave data corresponding to
stimulation of each magnitude; and
[0066] an identification unit for identifying an upper limit value
and a lower limit value of a brainwave amplitude of the object
being estimated based on the brainwave data;
[0067] wherein the measurement unit further measures a brainwave
from the object being estimated to obtain object's brainwave data,
and
[0068] wherein the pain estimation apparatus further comprises
[0069] an estimation unit for estimating a value of a magnitude of
pain corresponding to the object's brainwave data based on a
relative size of an amplitude value of the object's brainwave data
with respect to the upper limit value and the lower limit value.
(B6) The pain estimation apparatus of item B5, wherein the
estimation unit estimates a ratio of a value of difference between
the amplitude value of the object's brainwave data and the lower
limit value to a value of difference between the upper limit value
and the lower limit value as a value of the magnitude of pain. (B7)
A pain estimation method for estimating a magnitude of pain of an
object being estimated based on a brainwave of the object being
estimated, comprising:
[0070] a first measurement step for measuring a brainwave from the
object being estimated sequentially inflicted with stimulation of a
plurality of magnitudes to obtain brainwave data corresponding to
stimulation of each magnitude;
[0071] an identification step for identifying an upper limit value
and a lower limit value of a brainwave amplitude of the object
being estimated based on the brainwave data;
[0072] a second measurement step for measuring a brainwave from the
object being estimated to obtain object's brainwave data; and
[0073] an estimation step for estimating a value of magnitude of
pain corresponding to the object's brainwave data, based on a
relative size of an amplitude value of the object's brainwave data
to the upper limit value and the lower limit value.
[0074] The present invention is intended so that one or more of the
aforementioned features can be provided not only as the explicitly
disclosed combinations, but also as other combinations thereof.
Additional embodiments and advantages of the present invention are
recognized by those skilled in the art by reading and understanding
the following detailed description as needed.
[0075] The present invention further provides the following.
(C1) A method of generating a pain classifier for classifying pain
of an object being estimated based on a brainwave of the object
being estimated, comprising the steps of: a) stimulating the object
being estimated with a plurality of levels of stimulation
intensities; b) obtaining brainwave data or analysis data thereof
of the object being estimated corresponding to the stimulation
intensities; c) plotting, and fitting to a sigmoid function
pattern, the stimulation intensities or a subjective pain sensation
level corresponding to the stimulation intensities and the
brainwave data or analysis data thereof to obtain a sigmoid curve
specific to the object being estimated; and d) identifying a pain
classifier for separating a pain level to at least two based on the
sigmoid curve when a regression coefficient for fitting to the
sigmoid function pattern is equal to or greater than a
predetermined value. (C2) The method of item C1, further comprising
the step of calibrating the pain classifier so that classification
of the pain level is at a maximum. (C3) The method of item C1 or
C2, wherein the pain classifier is determined based on an infection
point of the sigmoid curve, and the method optionally further
comprises the step of calibrating the pain classifier so that
classification of the pain level is at a maximum. (C4) The method
of any one of items C1 to C3, wherein the classification classifies
whether there is pain or no pain based on a subjective view of the
object being estimated. (C5) The method of any one of items C1 to
C4, wherein the stimulation intensities comprise at least one
intensity that is highly invasive to the object being estimated.
(C6) The method of any one of items C1 to C5, wherein the
stimulation intensities do not comprise an intensity that is highly
invasive to the object being estimated. (C7) The method of any one
of items C1 to C6, wherein the brainwave data or analysis data
thereof comprises at least one selected from the group consisting
of amplitude data and a frequency property. (C8) An apparatus for
generating a pain classifier for classifying pain of an object
being estimated based on a brainwave of the object being estimated,
comprising: A) a stimulation unit for stimulating the object being
estimated with a plurality of levels of stimulation intensities; B)
a brainwave data obtaining unit for obtaining brainwave data or
analysis data thereof of the object being estimated corresponding
to the stimulation intensities; and C) a pain classifier generation
unit for plotting, and fitting to a sigmoid function pattern, the
stimulation intensities or a subjective pain sensation level
corresponding to the stimulation intensities and the brainwave data
or analysis data thereof to obtain a sigmoid curve specific to the
object being estimated, and identifying a pain classifier for
separating a pain level to at least two based on the sigmoid curve.
(C9) The apparatus of item C8, further having one or more features
of items C2 to C7. (C10) A method of classifying pain of an object
being estimated based on a brainwave of the object being estimated,
comprising the steps of: a) stimulating the object being estimated
with a plurality of levels of stimulation intensities; b) obtaining
brainwave data or analysis data thereof of the object being
estimated corresponding to the stimulation intensities; c)
plotting, and fitting to a sigmoid function pattern, the
stimulation intensities or a subjective pain sensation level
corresponding to the stimulation intensities and the brainwave data
or analysis data thereof to obtain a sigmoid curve specific to the
object being estimated; d) identifying a pain classifier for
separating a pain level to at least two based on the sigmoid curve
when a regression coefficient for fitting to the sigmoid function
pattern is equal to or greater than a predetermined value; e)
obtaining the brainwave data or analysis data thereof of the object
being estimated; and f) classifying the brainwave data or analysis
data thereof to a pain level of the object being estimated based on
the pain classifier. (C11) The method of item C10, wherein the
fitting of the brainwave data or analysis data thereof to the pain
classifier is characterized by using a mean value. (C12) The method
of C10, wherein the mean value is characterized by using a mean
value between about 15 seconds to 120 seconds. (C13) An apparatus
for classifying pain of an object being estimated based on a
brainwave of the object being estimated, comprising: A) a
stimulation unit for stimulating the object being estimated with a
plurality of levels of stimulation intensities; B) a brainwave data
obtaining unit for obtaining brainwave data or analysis data
thereof of the object being estimated (wherein brainwave data
corresponding to the stimulation intensities and actual brainwave
amplitude data or analysis data thereof can be obtained); C) a pain
classifier generation unit for plotting, and fitting to a sigmoid
function pattern, the stimulation intensities or a subjective pain
sensation level corresponding to the stimulation intensities and
the brainwave data or analysis data thereof to obtain a sigmoid
curve specific to the object being estimated, and identifying a
pain classifier for separating a pain level to at least two based
on the sigmoid curve; and D) a pain classification unit for
classifying the brainwave data or analysis data thereof to a pain
level of the object being estimated based on the pain classifier.
(C14) The apparatus of item C13, further having one or more
features of item C11 and C12. (C14A) The apparatus of any one of
items C11 to C14, further having one or more features of item C2 to
C7. (C15) A method of classifying pain of an object being estimated
based on a brainwave of the object being estimated, comprising the
steps of: e) obtaining brainwave data or analysis data thereof of
the object being estimated; and f) classifying the brainwave data
or analysis data thereof to a pain level of the object being
estimated based on a predetermined pain classifier;
[0076] wherein the pain classifier is obtained by fitting the
brainwave data or analysis data thereof of the object being
estimated to a sigmoid curve.
(C15A) The method of item C15, further having one or more features
of items C2 to C7 and C11 to C12. (C16) An apparatus for
classifying pain of an object being estimated based on a brainwave
of the object being estimated, comprising: X) an amplitude data
obtaining unit for obtaining brainwave data or analysis data
thereof of the object being estimated; and Y) a pain classification
unit for classifying the brainwave data or analysis data thereof to
a pain level of the object being estimated based on the pain
classifier, wherein the pain classifier is obtained by fitting the
brainwave data or analysis data thereof of the object being
estimated to a sigmoid curve. (C16A) The apparatus of item C16,
further having one or more features of items C2 to C7 and C11 to
C12. (C17) A program for having a computer perform a method of
generating a pain classifier for classifying pain of an object
being estimated based on a brainwave of the object being estimated,
the method comprising the steps of: a) stimulating the object being
estimated with a plurality of levels of stimulation intensities; b)
obtaining brainwave data or analysis data thereof of the object
being estimated corresponding to the stimulation intensities; c)
plotting, and fitting to a sigmoid function pattern, the
stimulation intensities or a subjective pain sensation level
corresponding to the stimulation intensities and the brainwave data
or analysis data thereof to obtain a sigmoid curve specific to the
object being estimated; and d) identifying a pain classifier for
separating a pain level to at least two based on the sigmoid curve
when a regression coefficient for fitting to the specific pain
function is equal to or greater than a predetermined value. (C17A)
The program of item C17, further having one or more features of
items C2 to C7 and C11 to C12. (C18) A storage medium comprising a
program for having a computer perform a method of generating a pain
classifier for classifying pain of an object being estimated based
on a brainwave of the object being estimated, the method comprising
the steps of: a) stimulating the object being estimated with a
plurality of levels of stimulation intensities; b) obtaining
brainwave data or analysis data thereof of the object being
estimated corresponding to the stimulation intensities; c)
plotting, and fitting to a sigmoid function pattern, the
stimulation intensities or a subjective pain sensation level
corresponding to the stimulation intensities and the brainwave data
or analysis data thereof to obtain a sigmoid curve specific to the
object being estimated; and d) identifying a pain classifier for
separating a pain level to at least two based on the sigmoid curve
when a regression coefficient for fitting to the sigmoid function
pattern is equal to or greater than a predetermined value. (C18A)
The storage medium of item C18, further having one or more features
of items C2 to C7 and C11 to C12. (C19) A program for having a
computer execute a method of classifying pain of an object being
estimated based on a brainwave of the object being estimated, the
method comprising the steps of: a) stimulating the object being
estimated with a plurality of levels of stimulation intensities; b)
obtaining brainwave data or analysis data thereof of the object
being estimated corresponding to the stimulation intensities; c)
plotting, and fitting to a sigmoid function pattern, the
stimulation intensities or a subjective pain sensation level
corresponding to the stimulation intensities and the brainwave data
or analysis data thereof to obtain a sigmoid curve specific to the
object being estimated; d) identifying a pain classifier for
separating a pain level to at least two based on the sigmoid curve
when a regression coefficient for fitting to the sigmoid function
pattern is equal to or greater than a predetermined value; e)
obtaining the brainwave data or analysis data thereof of the object
being estimated; and f) classifying a pain level of the object
being estimated by fitting the brainwave data or analysis data
thereof to the pain classifier. (C19A) The program of item C19,
further having one or more features of items C2 to C7 and C11 to
C12. (C20) A storage medium comprising a program for having a
computer execute a method of classifying pain of an object being
estimated based on a brainwave of the object being estimated, the
method comprising the steps of: a) stimulating the object being
estimated with a plurality of levels of stimulation intensities; b)
obtaining brainwave data or analysis data thereof of the object
being estimated corresponding to the stimulation intensities; c)
plotting, and fitting to a sigmoid function pattern, the
stimulation intensities or a subjective pain sensation level
corresponding to the stimulation intensities and the brainwave data
or analysis data thereof to obtain a sigmoid curve specific to the
object being estimated; d) identifying a pain classifier for
separating a pain level to at least two based on the sigmoid curve
when a regression coefficient for fitting to the sigmoid function
pattern is equal to or greater than a predetermined value; e)
obtaining the brainwave data or analysis data thereof of the object
being estimated; and f) classifying the brainwave data or analysis
data thereof to a pain level of the object being estimated based on
the pain classifier. (C20A) The storage medium of item C20, further
having one or more features of items C2 to C7 and C11 to C12. (C21)
A program for having a computer execute a method of classifying
pain of an object being estimated based on a brainwave of the
object being estimated, the method comprising the steps of: e)
obtaining brainwave data or analysis data thereof of the object
being estimated; and f) classifying the brainwave data or analysis
data thereof to a pain level of the object being estimated based on
a predetermined pain classifier;
[0077] wherein the pain classifier is obtained by fitting the
brainwave data or analysis data thereof of the object being
estimated to a sigmoid curve.
(C21A) The program of item C21, further having one or more features
of items C2 to C7 and C11 to C12. (C22) A storage medium comprising
a program for having a computer execute a method of classifying
pain of an object being estimated based on a brainwave of the
object being estimated, the method comprising the steps of: e)
obtaining brainwave data or analysis data thereof of the object
being estimated; and f) classifying the brainwave data or analysis
data thereof to a pain level of the object being estimated based on
a predetermined pain classifier;
[0078] wherein the pain classifier is obtained by fitting the
brainwave data or analysis data thereof of the object being
estimated to a sigmoid curve.
(C22A) The storage medium of item C22, further having one or more
features of items C2 to C7 and C11 to C12.
[0079] The present invention is intended so that one or more of the
aforementioned features can be provided not only as the explicitly
disclosed combinations, but also as other combinations thereof.
Additional embodiments and advantages of the present invention are
recognized by those skilled in the art by reading and understanding
the following detailed description as needed.
[0080] It is also intended that the features of any one or more of
A series, B series, and C series can be provided in combination
with one another. Additional embodiments and advantages of the
present invention are recognized by those skilled in the art by
reading and understanding the following detailed description as
needed.
[0081] In one aspect, the present invention provides a pain
estimation method and apparatus. The apparatus is for estimating
and classifying a magnitude of pain of an object being estimated
based on a brainwave of the object being estimated. The apparatus
and method measure a brainwave a plurality of times from the object
being estimated to obtain a plurality of brainwave data or analysis
data thereof, and fit a relationship between an amplitude of a
brainwave or frequency property and pain to a pain function to
generate a pain classifier from the amplitude, frequency, or the
like of the plurality of brainwave data. Optionally, a pain
classifier is calibrated, and then a relative pain level is
estimated using the pain classifier.
[0082] This configuration can estimate a relative magnitude of pain
upon measurement of a brainwave a plurality of times to estimate
the pain level from an amplitude or frequency property of the
plurality of brainwave data or analysis data thereof, based on a
relative relationship between the amplitude of brainwave data and
pain. The existence of a certain relative relationship between the
amplitude or frequency property of a brainwave and pain is a
phenomenon elucidated by the inventor. The present invention can
further classify the magnitude and level of pain without using the
magnitude of pain declared by the object being estimated by fitting
the relative relationship to a pain function, such that pain of the
object being estimated can be objectively and accurately
classified. Furthermore, the quality of pain can be classified by
"unbearable" pain, "bearable" pain, "comfortable pain" and the
like, so that the therapeutic effect can be more accurately
evaluated.
[0083] For example, the plurality of brainwave data or analysis
data thereof comprise a sufficient number of brainwave data or
analysis data thereof to enable fitting to an exemplified pain
function such as a linear function or a sigmoid curve. For such
fitting, brainwave data or analysis data thereof for at least three
stimulation intensities, preferably brainwave data or analysis data
thereof for 4, 5, 6, 7, or more stimulation intensities can be
used.
[0084] Once such fitting to a pain function is completed in the
present invention, a regression coefficient is optionally evaluated
to determine whether the fitting is appropriate. Generally, 0.5 or
greater, or preferably 0.6 or greater can be used as a threshold
value of a regression coefficient. If a suitable regression
coefficient is accomplished, the fitting is suitable, so that
analysis proceeds to the next analysis. If a regression coefficient
is evaluated to be unsuitable, additional brainwave data or
analysis data thereof can be obtained for re-fitting to a pain
function with the existing data, brainwave data or analysis data
thereof is obtained again for re-fitting to a pain function only
with the newly obtained data.
[0085] Once fitting is completed, a pain classifier is generated
based on a pain function. A pain classifier refers to a specific
value of brainwave data (e.g., amplitude) or analysis data thereof
for classifying pain into at least two classes. For example, a pain
classifier can be a value for classification into "weak pain" and
"strong pain". It is desirable to be able to clinically classify
pain into "unbearable pain requiring therapy" and "bearable pain
that does not require therapy". Such a pain classifier can be
determined by considering an inflection point or the like.
[0086] A pain classifier that is generated can be utilized
directly, but the classifier can be optionally calibrated. For
example, calibration is materialized, when classified into "weak
pain" and "strong pain", by fitting the classifier to actually
obtain brainwave data (e.g., amplitude) or analysis data thereof
and change the classifier to a value with less deviation (i.e.,
classification into a different class, including determination as
strong pain when it should be weak pain or vice versa) or to a
value with the least deviation.
[0087] For brainwave data or analysis data thereof, (i) first pain
corresponding to first brainwave data (e.g., amplitude) or analysis
data thereof can be estimated to be greater than second pain
corresponding to second brainwave data (e.g., amplitude) or
analysis data thereof if the first brainwave data or analysis data
thereof is greater than the second brainwave data or analysis data
thereof, which is different from the first brainwave data or
analysis data thereof, and (ii) the first pain can be estimated to
be less than the second pain if the first brainwave data (e.g.,
amplitude) or analysis data thereof is less than the second
brainwave data or analysis data thereof, in which case the data can
be further compared to a pain classifier to classify which level of
pain (e.g., strong pain, weak pain, or the like) the first and
second pain falls under.
[0088] This embodiment can estimate which of the first pain
corresponding to the first brainwave data or analysis data thereof
(e.g., amplitude) and the second pain corresponding to the second
brainwave data or analysis data thereof (e.g., amplitude) is
greater by comparing the first brainwave data or analysis data
thereof with the second brainwave data or analysis data thereof.
For example, the magnitude of pain before, during, and/or after
therapy can therefore be compared to evaluate a therapeutic effect
by measuring brainwave data or analysis data thereof before and
after therapy.
[0089] For example, the plurality of brainwave data or analysis
data thereof can obtain additional brainwave data or analysis data
thereof.
[0090] The pain estimation apparatus and method according to
another embodiment of the invention is a pain estimation apparatus
and method for estimating a magnitude of pain of an object being
estimated based on a brainwave of the object being estimated,
measuring a brainwave from the object being estimated inflicted
with a plurality of magnitudes of stimulation to obtain brainwave
data (amplitude, frequency property, or the like) or analysis data
thereof corresponding to stimulation of each magnitude (in the
apparatus, a measurement unit performs the action of obtaining) and
identifying brainwave data (also referred to as a brainwave
property; includes amplitude, frequency property and the like) or
analysis data thereof corresponding to one or more magnitudes of
stimulation based on the brainwave data or analysis data thereof as
a pain classifier (in the apparatus, a pain classifier generation
unit performs this action). Preferably, the pain estimation
apparatus and method further measure a brainwave from the object
being estimated, obtain new brainwave data, classify the newly
obtained brainwave data or analysis data thereof based on the pain
classifier, and classify the magnitude of pain corresponding to the
object's brainwave data. Such classification is executed by a pain
classification unit in the apparatus.
[0091] This configuration can classify a magnitude of pain based on
a pain classifier. By utilizing a pain classifier, a magnitude of
pain can be classified without using a magnitude of pain declared
by an object being estimated, so that pain of the object being
estimated can be expressed and classified objectively and
accurately. Furthermore, stimulation corresponding to strong pain
does not need to be collected, or can be kept to a minimum, from an
object being estimated or the like. Thus, a magnitude of pain can
be readily estimated without inflicting pain on an object. For
example, the magnitude of pain before, during, and/or after therapy
can be compared and/or classified to evaluate a therapeutic effect
by measuring brainwave data or analysis data thereof before,
during, and/or after therapy.
[0092] Certain embodiments provide a method and apparatus for
generating a pain classifier for classifying pain of an object
being estimated based on a brainwave of the object being estimated,
where the object being estimated is stimulated at a plurality of
levels of stimulation intensities. As the number of stimulation
intensity levels (magnitudes), a sufficient number that enables
fitting to a pain function is provided. At least two, preferably at
least 3, preferably at least 4, preferably at least 5, preferably
at least 6 or more levels (magnitudes) of stimulation intensities
can be used for fitting to a pain function.
[0093] These comprehensive or specific embodiments of the invention
can be materialized with a system, method, integrated circuit,
computer program, or a storage medium such as a computer readable
CD-ROM or any combination of a system, method, integrated circuit,
computer program, and storage medium.
[0094] The present invention is intended so that one or more of the
aforementioned features can be provided not only as the explicitly
disclosed combinations, but also as other combinations thereof.
Additional embodiments and advantages of the present invention are
recognized by those skilled in the art by reading and understanding
the following detailed description as needed.
Advantageous Effects of Invention
[0095] The pain estimation apparatus according to one embodiment of
the invention can be objectively and accurately estimate pain of an
object being estimated.
[0096] The present invention can readily classify pain. A preferred
embodiment provides a pain classifier without inflicting strong
pain to an object, or by using a minimum number of pain. This can
be used to perform various treatments without inflicting strong
pain, or with minimum pain, or classify a therapeutic effect.
BRIEF DESCRIPTION OF DRAWINGS
[0097] FIG. 1 is a graph showing the relationship between
electrical stimulation and pain level (VAS).
[0098] FIG. 2 is a graph showing the relationship between
electrical stimulation and pain level (paired comparison).
[0099] FIG. 3 is a graph showing the relationship between
electrical stimulation and brainwave amplitude.
[0100] FIG. 4 is a graph showing an example of a brainwave
waveform.
[0101] FIG. 5 is a graph showing a linear relationship between the
pain level (VAS) from electrical stimulation and brainwave
amplitude.
[0102] FIG. 6 is a graph showing the linear relationship between
pain level (paired comparison) from electrical stimulation and
brainwave amplitude.
[0103] FIG. 7 is a graph showing the linear relationship between
pain level (VAS) from thermal stimulation and brainwave
amplitude.
[0104] FIG. 8 is a graph showing the linear relationship between
pain level (paired comparison) from thermal stimulation and
brainwave amplitude.
[0105] FIG. 9 is a block diagram depicting a function configuration
of the pain estimation system according to embodiment 1.
[0106] FIG. 10A is a flow chart depicting processing of the pain
estimation apparatus according to embodiment 1.
[0107] FIG. 10B is a flow chart depicting an example of an
estimation process according to embodiment 1.
[0108] FIG. 10C is a flow chart depicting another example of a pain
estimation process according to embodiment 1.
[0109] FIG. 11 is a block diagram depicting a functional
configuration of the pain estimation system according to embodiment
2.
[0110] FIG. 12 is a flow chart depicting an identification process
in the pain estimation system according to embodiment 2.
[0111] FIG. 13 is a flow chart depicting an estimation process in
the pain estimation system according to embodiment 2.
[0112] FIG. 14 is a graph for explaining the estimation process in
embodiment 2.
[0113] FIG. 15 shows a change in absolute amplitude over time in
low temperature pain stimulation. FIG. 15 shows absolute amplitude
data (one subject) of 18 epochs associated with 6 intensity levels
immediately before averaging for each level in Example 1.
[0114] FIG. 16 shows an intensity-amplitude sigmoid function in low
temperature pain stimulation. FIG. 2 shows standardized absolute
amplitude averaging three epochs in each intensity level. The
horizontal and vertical axes indicate stimulation intensity and
standardized amplitude, respectively.
[0115] FIG. 17 shows a decreasing sigmoid function in a low
temperature pain paradigm. FIG. 3 clearly shows a decreasing
sigmoid function between intensity level and amplitude, which
supports that lower level 2 and higher level 1 have statistically
significantly different amplitudes by a statistical result
(t=2.886, p=0.013)
[0116] FIG. 18 shows a pain classifier based on a sigmoid fitting
function. FIG. 18 shows a binary pain classifier for one
patient.
[0117] FIG. 19 shows a sigmoid fitting function and pain
classification in a reference electrical pain paradigm.
[0118] FIG. 20 shows a prediction of a low temperature pain
intensity based on a reference pain prediction.
[0119] FIG. 21 shows calibration of a classification threshold
related to pain prediction.
[0120] FIG. 22 shows another example related to branching (sigmoid
function pattern) in the change in the amount of brain activity
between the strong pain level and another pain level.
[0121] FIG. 23 is an example of a flow chart depicting the flow of
the invention.
[0122] FIG. 24 is an example of a block diagram depicting the
functional configuration of the invention.
[0123] FIG. 25 is an example of a block diagram depicting the
functional configuration of the invention.
[0124] FIG. 26 is another example of a block diagram depicting the
functional configuration of the invention.
[0125] FIG. 27 depicts a schematic diagram of a linearly
approximated portion of a sigmoid function.
[0126] FIG. 28 shows diversity of linearity in the modulation range
in a sigmoidal pain function in analytical schematic diagram of the
invention. A shows the diversity of the slope of a linear function
in the modulation range, and B shows the diversity of amplitude
differences in the modulation range. C shows an example of a change
in linearity in the modulation range observed when a pain
stimulation presenting method has been changed with one
subject.
DESCRIPTION OF EMBODIMENTS
[0127] The present invention is explained hereinafter. Throughout
the entire specification, a singular expression should be
understood as encompassing the concept thereof in the plural form,
unless specifically noted otherwise. Thus, singular articles (e.g.,
"a", "an", "the", and the like in the case of English) should also
be understood as encompassing the concept thereof in the plural
form, unless specifically noted otherwise. The terms used herein
should also be understood as being used in the meaning that is
commonly used in the art, unless specifically noted otherwise.
Thus, unless defined otherwise, all terminologies and scientific
technical terms that are used herein have the same meaning as the
general understanding of those skilled in the art to which the
present invention pertains. In case of a contradiction, the present
specification (including the definitions) takes precedence.
Definition
[0128] The terms and the general technologies used herein are first
explained.
[0129] As used herein, "object" is used synonymously with patient
and subject and refers to any organism or animal which is subjected
to the technology in the disclosure such as pain measurement and
brainwave measurement. An object is preferably, but is not limited
to, humans. As used herein, an object may be referred to an "object
undergoing estimation" when estimating pain, but this has the same
meaning as object or the like.
[0130] As used herein, "brainwave" has the meaning that is commonly
used in the art and refers to a current generated by a difference
in potential due to neurological activity of the brain when a pair
of electrodes is placed on the scalp. Brainwave encompasses
electroencephalogram (EEG), which is obtained from deriving and
recording temporal changes in the current. A wave with an amplitude
of about 50 .mu.V and a frequency of approximately 10 Hz is
considered the primary component at rest. This is referred to an a
wave. During neurological activity, a waves are suppressed and a
fast wave with a small amplitude of 17 to 30 Hz appears, which is
referred to as a P wave. During a period of shallow sleep, a waves
gradually decrease and 8 waves of 4 to 8 Hz appear. During a deep
sleep, 5 waves of 1 to 4 Hz appear. These brainwaves can be
described by a specific amplitude and frequency power. In the
present invention, analysis of amplitudes can be important.
[0131] As used herein, "brainwave data" is any data related to
brainwaves (also referred to as "amount of brain activity", "brain
characteristic amount", or the like), such as amplitude data (EEG
amplitude, frequency property, or the like). "Analysis data" from
analyzing such brainwave data can be used in the same manner
referred to as "brainwave data or analysis data thereof" herein.
Examples of analysis data include mean amplitude and peak amplitude
of brainwave data (e.g., Fz, Cz, C3, C4), frequency power (e.g.,
Fz(.delta.), Fz(.theta.), Fz(.alpha.), Fz(.beta.), Fz(.gamma.),
Cz(.delta.), Cz(.theta.), Cz (.alpha.), Cz(.beta.), Cz(.gamma.),
C3(.delta.), C3(.theta.), C3(.alpha.), C3(.beta.), C3(.gamma.),
C4(.delta.), C4(.theta.), C4(.alpha.), C4(.beta.), C4(.gamma.)) and
the like. Of course, this does not exclude other data commonly used
as brainwave data or analysis data thereof.
[0132] As used herein, "amplitude data" is one type of "brainwave
data" and refers to data for amplitudes of brainwaves. This is also
referred to as simply "amplitude" or "EEG amplitude". Since such
amplitude data is an indicator of brain activity, such data can
also be referred to as "brain activity data", "amount of brain
activity", or the like. Amplitude data can be obtained by measuring
electrical signals of a brainwave and is indicated by potential
(can be indicated by .mu.V or the like). Amplitude data that can be
used include, but are not limited to, mean amplitude.
[0133] As used herein, "frequency power" expresses frequency
components of a waveform as energy and is also referred to as power
spectrum. Frequency power can be calculated by extracting and
calculating frequency components of a signal embedded in a signal
contained in noise in a time region by utilizing fast Fourier
transform (FFT) (algorithm for calculating discrete Fourier
transform (DFT) on a computer at a high speed). FFT of a signal
can, for example, use the function periodogram in MATLAB to
normalize the output thereof and calculate the power spectrum
density PSD or power spectrum, which is the source of measurement
of power. PSD indicates how power of a time signal is distributed
with respect to frequencies, and the unit thereof is watt/Hz. Each
point of PSD is integrated over the range of frequencies where the
points are defined (i.e., over the resolution bandwidth of PSD) to
calculate the power spectrum. The unit of a power spectrum is watt.
The value of power can be read directly from power spectrum without
integration over the range of frequencies. PSD and power spectrum
are both real numbers, so that no phase information is included. In
this manner, frequency power can be calculated with a standard
function in MATLAB.
[0134] "Pain" refers to a sensation that is generated as
stimulation, generally upon intense injury such as
damage/inflammation to a body part. In humans, this includes common
sensation as sensation accompanying unpleasant feeling. In
addition, cutaneous pain and the like also has an aspect of
external receptor, which plays a role in determining the quality
such as hardness, sharpness, hotness (thermal pain), coldness (cold
pain), or spiciness of an external object in cooperation with other
skin sensation or taste. The sensation of pain of humans can occur
at almost any part of the body (e.g., pleura, peritoneum, internal
organs (visceral pain, excluding the brain), teeth, eyes, ears, and
the like) except the skin and mucous membrane, which can all be
sensed as a brainwave or a change thereof in the brain.
Additionally, internal sensation of pain represented by visceral
pain is also encompassed by sensation of pain. The aforementioned
sensation of pain is referred to as somatic pain relative to
visceral pain. In addition to somatic pain and visceral pain,
sensation of pain called "referred pain", which is a phenomenon
where pain is perceived at a surface of a site that is different
from a site that is actually damaged, is also reported. This can
also be classified in the present invention.
[0135] For sensation of pain, there are individual differences in
sensitivity (pain threshold), as well as qualitative difference due
to a difference in the receptor site or how a pain stimulation
occurs. Sensation of pain is classified into dull pain, sharp pain,
and the like, but sensation of pain of any type can be measured,
estimated, and classified in the present disclosure. The disclosure
is also compatible with fast sensation of pain (A sensation of
pain), slow sensation of pain (B sensation of pain), (fast) topical
pain, and (slow) diffuse pain. The present invention is also
compatible with abnormality in sensation of pain such as
hyperalgesia. Two nerve fibers, i.e., "A5 fiber" and "C fiber", are
known as peripheral nerves that transmit pain. For example, when a
hand is hit, the initial pain is transmitted as sharp pain from a
clear origin (primary pain: sharp pain) by conduction through the
A5 fiber. Pain is then conducted through the C fiber to feel
throbbing pain (secondary pain; dull pain) with an unclear origin.
Pain is classified into "acute pain" lasting 4 to 6 weeks or less
and "chronic pain" lasting 4 to 6 weeks or more. Pain is an
important vital sign along with pulse, body temperature, blood
pressure, and breathing, but is difficult to express as objective
data. Representative pain scales VAS (Visual Analogue Scale) and
faces pain rating scale are subjective evaluation methods that
cannot compare pain between patients. Meanwhile, the inventor has
focused on brainwaves which are hardly affected by the peripheral
circulatory system as an indicator for objectively evaluating pain,
arriving at the conclusion that any type of pain can be determined
and classified by observing the change in amplitude/latency to pain
stimulation and fitting this to a sigmoid curve, which is one of
the pain functions. While instantaneous stimulation and sustained
stimulation can both be detected, one of the advantages is that
sustained stimulation is remarkably detected in particular.
[0136] One of the important points of the present invention is in
the ability to determine whether pain is pain "requiring therapy",
rather than the intensity in itself. Therefore, it is important
that "pain" can be clearly categorized based on the concept of
"therapy". For example, this leads to "qualitative" classification
of pain such as "pleasant/unpleasant" or "unbearable". For example,
the position of a "pain classifier", infection point, width of a
classifier, and the relationship thereof can be defined. In
addition to a case of n=2, cases where n=3 or greater can also be
envisioned. When n is 3 or greater, pain can be separated into "not
painful", "comfortable pain", and "painful". For example, pain can
be determined as "unbearable, need therapy", "moderate", "painful,
but not bothersome". The determination using a sigmoid function of
the invention can identify "unbearable" and "painful but bearable,
no need for therapy".
[0137] As used herein, "subjective pain sensation level" refers to
the level of sensation of pain of the object, and can be expressed
by conventional technology such as computerized visual analog scale
(COVAS) or other known technologies such as Support Team Assessment
Schedule (STAS-J), Numerical Rating Scale (NRS), Faces Pain Scale
(FPS), Abbey pain scale (Abbey), Checklist of Nonverbal Pain
Indicators (CNPI), Non-communicative Patient's Pain Assessment
Instrument (NOPPAIN), Doloplus 2, or the like.
[0138] As used herein, "pain classifier" refers to a value or range
of brainwave data (e.g., amplitude) or analysis data thereof
identified for classifying the type of pain. In this disclosure, a
unit, apparatus, or instrument for generating a "pain classifier"
(and thus predicting pain) can also be referred to as a "pain
classification instrument", "pain prediction instrument", or the
like. In this disclosure, a pain classifier can be determined by,
for example, but not limited to, utilizing an inflection point or
the like based on a specific pain function (e.g., also referred to
as a linear function or sigmoid curve specific to an object being
estimated) obtained by stimulating the object being estimated and
plotting, and applying and fitting into a pain function,
stimulation intensity or a subjective pain sensation level
corresponding thereto from data such as amplitude data of
brainwaves obtained therefrom. A pain classifier, once generated,
can be improved by calibration. A pain classifier can be denoted as
pain classifier, pain predictor, or the like, which are synonymous.
It is possible to determine whether it is "change within the strong
level of pain" or "qualitative change indicating low level of pain,
which is a deviation from the strong level of pain" by using a
"pain classifier". If there is a reaction with a deviation beyond a
change within the strong level of pain, this can be determined from
a change within the strong level of pain using the pain classifier
of the invention. If there is a change within the strong level of
pain, a change that is not an error can be identified, and anything
beyond this can be processed as a deviating reaction.
[0139] As used herein, "pain function" is the relationship between
pain level and stimulation level expressed in a mathematical
function of a dependent variable (variable Y) and an independent
variable (variable X), where the function expresses the
relationship based on the "broadly defined" linearity between
brainwave or analysis data thereof (including, for example,
amplitude) and pain elucidated by the inventor as a function. In
view of this relationship, (i) first pain corresponding to first
brainwave data or analysis data thereof (including, for example,
amplitude) can be estimated to be greater than second pain
corresponding to second brainwave data or analysis data thereof
(including, for example, amplitude) if the first brainwave data is
greater than the second brainwave data, and (ii) the first pain can
be estimated to be less than the second pain if the first brainwave
data or analysis data thereof (including, for example, amplitude)
is less than the second brainwave data or analysis data thereof
(including, for example, amplitude). Any function that can express
this is understood to be within the scope of the pain function.
Examples of such a pain function include linear functions and
sigmoid functions. More specific examples include linear functions
with linear approximation of the modulation range and more
comprehensive sigmoid functions encompassing the same. Linearity
can also be, in addition to amplitude, any brainwave characteristic
amount such as frequency or wavelet processing value. The present
invention has discovered that linearity in the modulation range can
be found in not only brainwave characteristic amounts, but also in
subjective evaluation.
[0140] As used herein, "stimulation" includes any stimulation that
can cause sensation of pain. Examples thereof include electrical
stimulation, cold stimulation, thermal stimulation, physical
stimulation, chemical stimulation, and the like. In the present
invention, stimulation used for generating a pain classifier can be
any stimulation, but temperature stimulation (cold stimulation or
thermal stimulation) or electrical stimulation is generally used. 3
or more stimulation levels are generally used, preferably 4 or
more, more preferably 5 or more, and still more preferably 6 or
more, or more types of stimulation can be used. For temperature,
for example when low temperature stimulation is used, temperature
can be reduced to an appropriate sensation in the range of, for
example, -15.degree. C. to 10.degree. C. When 6 points are
obtained, the temperature can be reduced by 5.degree. C. to
generate stimulation of 6 temperature levels. Evaluation of
stimulation can be matched with subjective pain sensation levels
using, for example, conventional technology such as computerized
visual analog scale (COVAS) or other known technologies such as
Support Team Assessment Schedule (STAS-J), Numerical Rating Scale
(NRS), Faces Pain Scale (FPS), Abbey pain scale (Abbey), Checklist
of Nonverbal Pain Indicators (CNPI), Non-communicative Patient's
Pain Assessment Instrument (NOPPAIN), Doloplus 2, or the like.
Examples of values that can be employed as stimulation intensity
include nociceptive threshold (threshold for generating
neurological impulses to nociceptive fiber), pain detection
threshold (intensity of nociceptive stimulation that can be sensed
as pain by humans), pain tolerance threshold (strongest stimulation
intensity among experimentally tolerable nociceptive stimulation by
humans), and the like.
[0141] As used herein, "sigmoid function" or "sigmoid curve" refers
to a real function exhibiting a sigmoidal shape. In the present
invention, normalized subjective pain intensity and normalized EEG
amplitude can be used to generate a sigmoid curve, which has been
actually demonstrated.
[0142] A sigmoid function is generally represented by
.sigma..sub.a(x)=1/(1+e.sup.-ax=(tanh(ax+2)+1)/2. A decreasing
sigmoid function can be expressed by subtraction from 1 or a
reference value. A monotonically increasing continuous function of
(-.infin., .infin.).fwdarw.(0, 1) has one inflection point. This
function has an asymptote at y=0 and y=1. In such a case, the
inflection point is (0, 1/2). For setting an asymptote, the
function may not have an asymptote at 0 or 1 depending on the
measured (and optionally normalized) amplitude data (EEG
amplitude), but the maximum value and minimum value can be used as
an asymptote in such a case.
[0143] As used herein, "fitting" to a linear function, sigmoid
function, or the like refers to a technique of fitting measured
values or a curve obtained therefrom to approximate a pain function
(e.g., linear function or nonlinear function), which can be
performed based on any approach. For example, a known sigmoid
function can be used. Examples of such fitting include least square
fitting, nonlinear regression fitting (MATLAB nlinfit function or
the like), and the like. After fitting, a regression coefficient
can be calculated for the approximated sigmoid curve to determine
whether the sigmoid curve can be used or preferable in the present
invention. For a regression coefficient, a regression model is
effective. The adjusted coefficient of determination (R.sup.2) is
desirable with a numerical value closer to "1" such as 0.5 or
greater, 0.6 or greater, 0.7 or greater, 0.8 or greater, 0.85 or
greater, 0.9 or greater, or the like. A higher numerical value has
higher confidence. The precision of fitting can be investigated by
using a specific threshold value to categorize and compare an
estimated value and an actual measurement value (this is referred
to as precision of determination in the analysis of the
invention).
[0144] As used herein, "calibration", for a pain classifier, refers
to any step for correcting the pain classifier or a corrected value
thereof generated by fitting to a pain function more in line with
the classification of the object of measurement. Examples of such
calibration include an approach to increase/decrease the value so
that the classification of a pain level is at a maximum and the
like. Other examples include, but are not limited to, approaches
such as a method of applying a specific reference stimulation at a
specific time interval and weighting using a coefficient or the
like from the change in the amount of brain activity to correct the
determination of a change in pain within an individual.
[0145] As used herein, "classification" of pain can be made from
various viewpoints. Representative examples include classification
to whether it is "painful" or "not painful" for the object being
estimated, and additionally include, but are not limited to,
classification of feeling pain, but with quantitative distinction
between strong pain and weak pain, as well as qualitative
distinction ("bearable" pain and "unbearable" pain).
PREFERRED EMBODIMENTS
[0146] The preferred embodiments of the present invention are
explained hereinafter. It is understood that the embodiments
provided hereinafter are provided to better facilitate the
understanding of the present invention, so that the scope of the
invention should not be limited by the following descriptions.
Thus, it is apparent that those skilled in the art can refer to the
descriptions herein to make appropriate modifications within the
scope of the invention. It is also understood that the following
embodiments of the invention can be used individually or as a
combination.
[0147] Each of the embodiments explained below describes a
comprehensive or specific example. The numerical values, shapes,
materials, and constituent elements, arrangement positions and
connection forms of the constituent elements, steps, orders of
steps, and the like in the following embodiments are one example,
which is not intended to limit the Claims. Further, the constituent
elements in the following embodiments that are not recited in the
independent claims indicating the most superordinate concept are
explained as an optional constituent element.
[0148] (Linearity)
[0149] The inventor has found that the technology in Patent
Literature 1 described in the section of "Technical Field" results
in the following problem.
[0150] Patent Literature 1 classifies a set of reference data
indicating brainwave activity of a plurality of individuals into
reference data of several pain states and compares the
characteristics extracted from each classified reference data with
characteristics extracted from brainwave data to estimate pain from
the brainwave data. In this regard, the pain states used in
classifying reference data are based on pain declared by subjects.
Therefore, it is difficult to suitable classify reference data
unless the declared pain is accurate.
[0151] Pain is often declared by subjects according to Visual
Analog Scale (VAS). VAS is a method for a subject to declare a pain
level by indicating which position, on a 10 cm straight line
representing pain levels, from 0 to 100 the current pain level is.
However, in VAS, declared pain levels are dependent on the past
experience to pain of a subject, such that it is difficult to
objectively evaluate pain.
[0152] Therefore, it is difficult to objectively and accurately
estimate pain of a subject with the technology of Cited Reference
1, which estimates pain of a subject using reference data
classified with a pain level declared by the subject.
[0153] In this regard, the inventor has elucidated the relationship
between pain and brainwave by evaluating a plurality of types of
pain by a plurality of methods. The relationship between pain and
brainwaves elucidated by the inventor is explained with reference
to the drawings.
[0154] First, the relationship between pain from electrical
stimulation and brainwaves is explained. Data provided below
indicates data of a representative subject among a plurality of
subjects.
[0155] FIG. 1 is a graph showing the relationship between
electrical stimulation and pain level (VAS). FIG. 2 is a graph
showing the relationship between electrical stimulation and pain
level (paired comparison). FIG. 3 is a graph showing the
relationship between electrical stimulation and brainwave
amplitude. FIG. 4 is a graph showing an example of a brainwave
waveform.
[0156] The horizontal axes of FIGS. 1, 2, and 3 indicate a current
value of electrical stimulation. The vertical axis in FIG. 1
indicates the pain level declared by a subject according to VAS.
The vertical axis in FIG. 2 indicates the pain level declared by a
subject according to paired comparison. The vertical axis in FIG. 3
indicates an amplitude value of a brainwave. In FIG. 4, the
horizontal axis indicates time, and the vertical axis indicates a
signal level.
[0157] Paired comparison is a method for a subject to declare which
electrical stimulation is how much more painful for each of the
plurality of sets of electrical stimulation, with electrical
stimulation of two magnitudes as a set, by a numerical value. In
such a case, a pain level is declared by comparing a pair of pain,
so that the effect of past experiment of the subject on pain levels
can be alleviated.
[0158] As shown in FIGS. 1 and 2, the relationship between a
current value of electrical stimulation (i.e., intensity of
stimulation) and pain level exhibits strong linearity in the
intermediate region of electrical stimulation intensities in either
the method by VAS or paired comparison. The linearity in the
intermediate region can be included in a part of a more
comprehensive sigmoid (S-shaped) curve, and the shape thereof
(e.g., upper limit value, lower limit value, and the like) varies
by subjects.
[0159] As shown in FIG. 3, the relationship between a current value
of electrical stimulation and amplitude value of a brainwave also
exhibits strong linearity in the intermediate region of electrical
stimulation intensities. In this regard, a value of difference
between the maximum peak value and the minimum peak value (i.e.,
peak-to-peak value) is used as the amplitude value of a brainwave.
For example in FIG. 4, the maximum value of difference (N1-P1)
among the three values of difference (N1-P1, N2-P2, and N1-P2) is
used as an amplitude value.
[0160] In this manner, both the relationship between the intensity
of electrical stimulation and pain level and the relationship
between the intensity of electrical stimulation and amplitude value
of a brainwave have linearity in the intermediate region. In other
words, the pain level and amplitude of a brainwave both have an
upper limit and lower limit to electrical stimulation and exhibit a
similar change to the intensity of electrical stimulation. When the
relationship between the amplitude value of a brainwave and pain
level was analyzed in this regard, the relationship between the
amplitude value of a brainwave and pain level was represented as in
FIGS. 5 and 6.
[0161] FIG. 5 is a graph showing the relationship between pain
level (VAS) from electrical stimulation and brainwave amplitude.
FIG. 6 is a graph showing the relationship between pain level
(paired comparison) from electrical stimulation and brainwave
amplitude. In FIGS. 5 and 6, the vertical axis indicates the
amplitude of a brainwave, and the horizontal axis indicates the
pain level.
[0162] As shown in FIGS. 5 and 6, the pain level from electrical
stimulation and amplitude value of a brainwave have linearity in
either VAS or paired comparison. In other words, amplitude values
of brainwaves are proportional to pain levels.
[0163] In this disclosure, linearity includes focused linearity
that is partially included in a more comprehensive nonlinear
function in addition to strict linearity. In other words, linearity
includes relationships that can be approximated to a linear
function within a range of a given tolerance for the entire data or
within a given range. The range of a given tolerance is defined,
for example, by a coefficient of determinant R.sup.2 in regression
analysis. The coefficient of determinant R.sup.2 is a value
obtained by subtracting a result of dividing residual sum of
squares with total sum of squares from 1. The range of a given
tolerance is for example a range where R.sup.2 is 0.5 or
greater.
[0164] The relationship between pain from thermal stimulation and
brainwave also has linearity between the pain level and brainwave
amplitude as in the case of electrical stimulation.
[0165] FIG. 7 is a graph showing the relationship between pain
level (VAS) from thermal stimulation and brainwave amplitude. FIG.
8 is a graph showing the relationship between pain level (paired
comparison) from thermal stimulation and brainwave amplitude. In
FIGS. 7 and 8, the vertical axis indicates the amplitude of a
brainwave, and the horizontal axis indicates pain levels.
[0166] As shown in FIGS. 7 and 8, the pain level from thermal
stimulation and amplitude value of a brainwave have linearity in
both VAS and paired comparison. While the upper limit value and the
lower limit value of an amplitude value of a brainwave varies by
subjects, it was found from the experiment of the inventor that the
upper limit value of an amplitude value does not exceed about 60
.mu.V.
[0167] In this manner, the inventor has elucidated that the
amplitude of a brainwave and pain have linearity as a result of
analyzing the relationship between an amplitude value of a
brainwave and pain level from evaluating a plurality of types of
pain by a plurality of methods.
[0168] (Estimation of Pain by Linearity)
[0169] In this regard, the present invention estimates the
magnitude of pain based on linearity in the relationship between an
amplitude of a brainwave and pain. The present invention is
specifically explained below with reference to the drawings based
on the embodiments.
[0170] Each of the embodiments explained below describes a
comprehensive or specific example. The numerical values, shapes,
materials, constituent elements, arrangement positions and
connection forms of the constituent elements, steps, orders of
steps, and the like in the following embodiments are one example,
which is not intended to limit the Claims. Further, the constituent
elements in the following embodiments that are not recited in the
independent claims indicating the most superordinate concept are
explained as an optional constituent element.
Embodiment 1 of Estimation by Linearity
[0171] Embodiment 1 estimates the relative magnitude of pain upon
measurement of brainwaves a plurality of times by utilizing the
characteristic of amplitude of a brainwave and pain having
linearity. Embodiment 1 is explained below with reference to FIGS.
9 to 10C.
[0172] [Configuration of Pain Estimation System]
[0173] FIG. 9 is a block diagram depicting a functional
configuration of the pain estimation system 100 according to
embodiment 1. The pain estimation system 100 comprises a pain
estimation apparatus 110 and a brainwave meter 120.
[0174] The pain estimation apparatus 110 comprises a measurement
unit 111 and an estimation unit 112. The pain estimation apparatus
110 is materialized by, for example, a computer comprising a
processor and a memory. In such a case, the pain estimation
apparatus 110 makes the processor function as the measurement unit
111 and the estimation unit 112 when a program stored in the memory
is executed by the processor. The pain estimation apparatus 110 can
also be materialized by, for example, a dedicated electrical
circuit. A dedicated electrical circuit can be a single integrated
circuit or a plurality of electrical circuits.
[0175] The measurement unit 111 obtains a plurality of brainwave
data by measuring a brainwave a plurality of times from an object
being estimated via the brainwave meter 120. An object being
estimated is an organism generating a change in brainwave by pain
and is not limited to humans.
[0176] The estimation unit 112 estimates a relative magnitude of
pain upon measurement of a brainwave a plurality of times from an
amplitude of a plurality of brainwave data based on linearity in
the relationship between the amplitude of a brainwave and pain. In
other words, the estimation unit 112 estimates the relative
magnitude of pain corresponding to a plurality of brainwave data
based on a property of greater pain for greater amplitude of a
brainwave.
[0177] For example, when a brainwave is measured twice by the
measurement unit 111, the estimation unit 112 can estimate the
relative magnitude of first pain corresponding to first brainwave
data and second pain corresponding to second brainwave data. First,
the estimation unit 112 estimates that the first pain is greater
than the second pain if an amplitude of the first brainwave data is
greater than an amplitude of the second brainwave data. On the
other hand, the estimation unit 112 estimates that the first pain
is less than the second pain if the amplitude of the first
brainwave data is less than the amplitude of the second brainwave
data. The estimation unit 112 estimates that the first pain is the
same as the second pain if the amplitude of the first brainwave
data is the same as the amplitude of the second brainwave data.
[0178] For example, when a brainwave is measured four or more times
by the measurement unit 111, the estimation unit 112 can further
estimate the relative amount of change in pain in the following
manner. First, the estimation unit 112 calculates a first value of
difference between an amplitude value of the first brainwave data
and an amplitude value of the second brainwave data. The estimation
unit 112 further calculates a second value of difference between an
amplitude value of third brainwave data and an amplitude value of
fourth brainwave data. The estimation unit 112 then estimates a
relative amount of change between a first change from the first
pain corresponding to the first brainwave data to the second pain
corresponding to the second brainwave data and a second change from
third pain corresponding to the third brainwave data to fourth pain
corresponding to the fourth brainwave data, based on the first
value of difference and the second value of difference.
[0179] Specifically, the estimation unit 112 estimates that the
amount of the first change is greater than the amount of the second
change if the first value of difference is greater than the second
value of difference. On the other hand, the estimation unit 112
estimates that the amount of the first change is less than the
amount of the second change if the first value of difference is
less than the second value of difference. The estimation unit 112
estimates that the first amount of change is the same as the second
amount of change if the first value of difference is the same as
the second value of difference.
[0180] The brainwave meter 120 measures electrical activity
generated within the brain of the object being estimated with an
electrode on the scalp. The brainwave meter 120 then outputs
brainwave data that is the result of measurement.
[0181] [Processing of Pain Estimation System]
[0182] Next, the processing of the pain estimation system 100
configured in the above manner is explained. FIG. 10A is a flow
chart depicting the processing of the pain estimation system 100
according to embodiment 1. FIG. 10B is a flow chart depicting an
example of the pain estimation process according to embodiment 1.
FIG. 10C is a flow chart depicting another example of the pain
estimation process according to embodiment 1. Specifically, FIGS.
10B and 10C depict the detailed process of step S112 in FIG.
10A.
[0183] First, the measurement unit 111 obtains a plurality of
brainwave data by measuring a brainwave a plurality of times from
an object being estimated via the brainwave meter 120 (S111). In
other words, the measurement unit 111 measures a brainwave at a
plurality of times.
[0184] Next, the estimation unit 112 estimates a relative magnitude
of pain upon measurement of a brainwave a plurality of times from
an amplitude of a plurality of brainwave data based on linearity in
the relationship between the amplitude of a brainwave and pain
(S112). In other words, the estimation unit 112 estimates a
relative magnitude of pain at a plurality of times.
[0185] For example, the estimation unit 112 can estimate the
relative magnitude between first pain at first measurement of
brainwave and second pain at the second measurement of brainwave by
using the first brainwave data obtained by the first measurement of
brainwave and the second brainwave data obtained by the second
measurement of brainwave. Specifically, as depicted in FIG. 10B,
the estimation unit 112 compares a first amplitude of the first
brainwave data and a second amplitude of the second brainwave data
(S121). In this regard, the estimation unit 112 estimates that the
first pain is greater than the second pain if the first amplitude
is greater than the second amplitude (S122) The estimation unit 112
estimates that the first pain is the same as the second pain if the
first amplitude is the same as the second amplitude (S123). The
estimation unit 112 estimates that the first pain is less than the
second pain if the amplitude of the first brainwave data is less
than the amplitude of the second brainwave data (S124).
[0186] For example, the estimate unit 112 can also estimate a
relative amount of change between a first change from the first
pain at the first measurement of brainwave to the second pain at
the second measurement of brainwave and a second change from third
pain at the third measurement of brainwave to fourth pain at the
fourth measurement of brainwave. Specifically, as depicted in FIG.
10C, the estimation unit 112 first calculates a first value of
difference between an amplitude value of first brainwave data and
an amplitude value of second brainwave data (S131) The estimation
unit 112 further calculates a second value of difference between an
amplitude value of third brainwave data and an amplitude value of
fourth brainwave data (S132) The estimation unit 112 then compares
the first value of difference with the second value of difference
(S133). In this regard, the estimation unit 112 estimates that the
first amount of change is greater than the second amount of change
if the first value of difference is greater than the second value
of difference (S134). The estimation unit 112 estimates that the
first amount of change is the same as the second amount of change
if the first value of difference is the same as the second value of
difference (S135). The estimation unit 112 estimates that the first
amount of change is less than the second amount of change if the
first value of difference of the brainwave data is less than the
second value of difference of the brainwave data (S136).
[0187] [Effect]
[0188] In view of the above, the pain estimation apparatus 110
according to this embodiment can estimate the relative magnitude of
pain upon measurement of a brainwave a plurality of times from
amplitudes of a plurality of brainwave data based on linearity in
the relationship between the amplitude of a brainwave and pain.
Existence of linearity between the amplitude of a brainwave and
pain is a phenomenon elucidated by the inventor. The magnitude of
pain can be estimated without using the magnitude of pain declared
by the object being estimated by utilizing the linearity in the
relationship between the amplitude of a brainwave and pain, so that
pain of the object being estimated can be objectively and
accurately estimated. Furthermore, brainwave data does not need to
be collected in advance from the object being estimated or the
like, such that the magnitude of pain can be more readily
estimated.
[0189] The pain estimation apparatus 110 according to this
embodiment can also estimate which of first pain corresponding to
first brainwave data and second pain corresponding to second
brainwave data is greater by comparing the amplitude of the first
brainwave data with the amplitude of the second brainwave data. For
example, the magnitude of pain before and after therapy can
therefore be compared to evaluate a therapeutic effect by measuring
brainwave data before and after therapy.
[0190] The pain estimation apparatus 110 according to this
embodiment can also estimate the relative amount of change in pain
by comparing a pair of values of difference in amplitudes of two
sets of brainwave data. For example, a change in pain from first
therapy can therefore be compared with a change in pain from second
therapy, to relatively evaluate therapeutic effects of the first
and second therapy.
Embodiment 2 of Estimation by Linearity
[0191] Embodiment 2 is now explained. This embodiment uses the
upper limit value and lower limit value of an amplitude of a
brainwave of an object being estimated when inflicted with
stimulation to estimate a magnitude of pain corresponding to
object's brainwave data. Embodiment 2 is explained hereinafter with
reference to FIGS. 11 to 14 primarily with respect to the
differences from Embodiment 1.
[0192] [Configuration of Pain Estimation System]
[0193] FIG. 11 is a block diagram depicting a functional
configuration of the pain estimation system 200 according to
embodiment 2. In FIG. 11, a function block that is similar in FIG.
9 is assigned the same symbol and explanation is appropriately
omitted.
[0194] The pain estimation system 200 according to this embodiment
comprises a pain estimation apparatus 210, a brainwave meter 120,
and a stimulation apparatus 230.
[0195] The pain estimation apparatus 210 comprises a measurement
unit 211, an estimation unit 212, and an identification unit 213.
The pain estimation apparatus 210 is materialized, for example,
with a computer comprising a processor and a memory. In such a
case, the pain estimation apparatus 210 makes the processor
function as the measurement unit 211, the estimation unit 212, and
the identification unit 213 when a program stored in the memory is
executed by the processor. The pain estimation apparatus 210 can
also be materialized by, for example, a dedicated electrical
circuit. A dedicated electrical circuit can be a single integrated
circuit or a plurality of electrical circuits.
[0196] The measurement unit 211 obtains brainwave data
corresponding to stimulation of each magnitude by measuring a
brainwave from an object being estimated 99 sequentially inflicted
with stimulation of a plurality of magnitudes via the brainwave
meter 120. The brainwave data is used for the process of
identifying the upper limit value and the lower limit value of
brainwave amplitudes discussed below.
[0197] The measurement unit 211 further obtains object's brainwave
data by measuring a brainwave from the object being estimated 99.
The object's brainwave data is used for the process of estimating
pain. In other words, the pain estimation apparatus 210 estimates a
value of the magnitude of pain of the object being estimated 99
when measuring a brainwave of the object's brainwave data.
[0198] The estimation unit 212 estimates a value of the magnitude
of pain corresponding to the object's brainwave data based on a
relative magnitude of an amplitude value of the object's brainwave
data to the upper limit value and the lower limit value of the
brainwave amplitude of the object being estimated 99 identified by
the identification unit 213. Specifically, the estimation unit 212
estimates the value of the magnitude of pain corresponding to the
object's brainwave data using a ratio of a value of difference
between an amplitude value of the object's brainwave data and the
lower limit value of the brainwave amplitude to a value of
difference between the upper limit value and the lower limit value
of the brainwave amplitude.
[0199] The identification unit 213 identifies the upper limit value
and the lower limit value of the brainwave amplitude of the object
being estimated 99 based on the brainwave data obtained by the
measurement unit 211. For example, the identification unit 213
identifies the maximum value and the minimum value of amplitudes of
a plurality of brainwave data corresponding to stimulation of a
plurality of magnitudes as the upper limit value and the lower
limit value. For example, the identification unit 213 can also
identify the upper limit value and the lower limit value of the
brainwave amplitude by analyzing a plurality of brainwave data.
Specifically, the identification unit 213 can fit the amplitudes of
a plurality of brainwave data for a plurality of magnitudes of
stimulation to a sigmoid curve to identify the upper limit value
and the lower limit value of the brainwave amplitudes.
[0200] The stimulation apparatus 230 inflicts stimulation of a
plurality of magnitudes to the object being estimated 99.
Specifically, the stimulation apparatus 230 sequentially inflicts a
plurality of stimulations to the object being estimated 99 while
changing the amount of stimulation. Stimulation is, for example,
electrical stimulation, thermal stimulation, or the like.
[0201] [Processing of Pain Estimation System]
[0202] The processing of the pain estimation system 200 configured
in the above manner is now explained. The processing of the pain
estimation system 200 includes an identification process for
identifying the upper limit value and the lower limit value of a
brainwave amplitude and an estimation process for estimating the
magnitude of pain corresponding to the object's brainwave data
using the identified upper limit value and the lower limit
value.
[0203] FIG. 12 is a flow chart depicting an identification process
in the pain estimation system 200 according to embodiment 2.
[0204] First, one amount of stimulation that has not been selected
is selected from a plurality of amounts of stimulation in the
stimulation apparatus 230 (S211). For example, one amount of
stimulation that has not been selected is selected from electrical
stimulation amounts of 20 .mu.A, 40 .mu.A, 60 .mu.A, 80 .mu.A, and
100 .mu.A.
[0205] Next, the stimulation apparatus 230 inflicts the object
being estimated 99 with stimulation in the selected amount (S212).
When stimulation is inflicted in step S212, the brainwave meter 120
measures a brainwave from the object being estimated 99, and the
measurement unit 211 obtains the brainwave data (S213).
[0206] If all of the plurality of amounts of stimulation have been
selected (Yes in S214), the identification unit 213 identifies the
upper limit value and the lower limit value of brainwave amplitudes
of the object being estimated 99 based on brainwave data
corresponding to each amount of stimulation (S215). If, on the
other hand, one of the plurality of amounts of stimulation has not
been selected (No in S214), the procedure returns to S211.
[0207] In the above manner, a brainwave from the object being
estimated 99 sequentially inflicted with stimulation of a plurality
of amounts is measured to identify the upper limit value and the
lower limit value of a brainwave amplitude in the object being
estimated 99 based on the brainwave data corresponding to the
plurality of amounts of stimulation.
[0208] Next, the estimation process is explained. FIG. 13 is a flow
chart depicting an estimation process in the pain estimation system
200 according to embodiment 2. FIG. 14 is a graph for explaining
the estimation process in embodiment 2. In FIG. 14, the vertical
axis indicates the magnitude of pain and the horizontal axis
indicates the magnitude of a brainwave amplitude.
[0209] As depicted in FIG. 13, the brainwave meter 120 first
measures a brainwave from the object being estimated 99, and the
measurement unit 211 obtains object's brainwave data (S221). In
other words, a brainwave is measured when it is desirable to
estimate pain of the object being estimated 99.
[0210] Next, the estimation unit 212 estimates the magnitude of
pain in the object being estimated 99 based on the object's
brainwave data (S222). Specifically, the estimation unit 212
estimates the magnitude of pain corresponding to the object's
brainwave data based on the relative magnitude of an amplitude
value of the object's brainwave data to the upper limit value and
the lower limit value of the brainwave amplitude of the object
being estimated 99 identified in step S215 of FIG. 12.
[0211] For example, as depicted in FIG. 14, the estimation unit 212
estimates the value of the magnitude of pain Px when the amplitude
value of object's brainwave data is Ax and the upper limit value
and the lower limit value of the brainwave amplitude is Amax and
Amin by the following equation.
Px=(Ax-Amin)/(Amax-Amin)
[0212] In other words, the estimation unit 212 estimates the
magnitude of pain corresponding to the object's brainwave data
using a ratio of a value of difference between the amplitude value
of the object's brainwave data and the lower limit value of the
brainwave amplitude to the value of difference between the upper
limit value and the lower limit value of the brainwave amplitude in
the object being estimated 99.
[0213] [Effect]
[0214] In the above manner, the pain estimation apparatus 210
according to this embodiment can estimate the value of magnitude of
pain corresponding to object's brainwave data based on the relative
magnitude of an amplitude value of the object's brainwave data to
the upper limit value and the lower limit value of the brainwave
amplitude of the object being estimated to quantify the magnitude
of pain. Further, neither identification of the upper limit value
and the lower limit value nor estimation of a value of the
magnitude of pain needs to use the magnitude of pain declared by an
object being estimated, so that pain of the object being estimated
can be objectively estimated.
[0215] Further, the pain estimation apparatus 210 according to this
embodiment can estimate a value of the magnitude of pain using a
ratio of a value of difference between the amplitude value of
object's brainwave data and the lower limit value to the value of
difference between the upper limit value and the lower limit value.
Therefore, a value of the magnitude of pain can be more readily
estimated.
Other Embodiments
[0216] The pain estimation apparatuses according to one or more
embodiments of the invention have been explained based on the
embodiments above, but the present invention is not limited to such
embodiments. Various modifications applied to the present
embodiments and embodiments constructed by combining constituent
elements in different embodiments conceivable to those skilled in
the art without departing from the intent of the inventions are
also encompassed within the scope of the one or more embodiments of
the invention. Linearity can be a value other than amplitude such
as a frequency or wavelet processing value, as long as the value is
a characteristic amount of brainwaves. Linearity of a modulation
range is found not only in the characteristic amount of brainwaves,
but also in subjective evaluation. Therefore, it is understood that
the portion of the present specification explained with amplitudes
as an example is similarly applicable for other characteristic
amounts of brainwaves (e.g., frequencies which are characteristic
amount of brainwaves, wavelet processing value which is an analysis
value thereof, and the like).
[0217] For example, each of the above embodiments used a
peak-to-peak value as the amplitude value of brainwave data, but
amplitude values are not limited thereto. For example, a simple
peak value can be used as the amplitude value.
[0218] Embodiment 2 has set the range of values of magnitude of
pain so that the value Pmax of the magnitude of pain corresponding
to the upper limit value Amax of a brainwave amplitude is 1, and
the value Pmin of the magnitude of pain corresponding to the lower
limit value Amin of the brainwave amplitude is 0, but this is not a
limiting example. For example, the magnitude of pain can be
represented by 0 to 100. In such a case, the estimation unit 212
can estimate the value Px of magnitude of pain by the following
equation.
Px=Pmax.times.(Ax-Amin)/(Amax-Amin)
[0219] Embodiment 2 explains curve fitting as an example of
identifying the upper limit value and the lower limit value of a
brainwave amplitude by analyzing a plurality of brainwave data, but
this is not a limiting example. For example, the upper limit value
of a brainwave amplitude can be identified using a learning model
for estimating a brainwave amplitude for large stimulation from a
brainwave amplitude corresponding to small stimulation. In such a
case, large stimulation does not need to be inflicted upon an
object being estimated, so that physical burden on the object being
estimated can be alleviated. Further, a predetermined value can be
used as the upper limit value of a brainwave amplitude. The
predetermined value is for example 50 .mu.V, which can be
experimentally or empirically determined.
[0220] Stimulation inflicted upon the object being estimated 99 by
the stimulation apparatus 230 is not limited to electrical
stimulation and thermal stimulation. Any type of stimulation can be
inflicted as long as the magnitude of pain sensed by the object
being estimated 99 changes in accordance with the magnitude of
stimulation.
[0221] Some or all of the constituent elements of the pain
estimation apparatus in each of the above embodiments can be
comprised of a single system LSI (Large Scale Integration). For
example, the pain estimation apparatus 110 can be comprised of
system LSI having the measurement unit 111 and the estimation unit
112.
[0222] System LSI is ultra-multifunctional LSI manufactured by
integrating a plurality of constituents on a single chip, or
specifically a computer system comprised of a microprocessor, ROM
(Read Only Memory), RAM (Random Access Memory), and the like. A
computer program is stored in a ROM. The system LSI accomplishes
its function by the microprocessor operating in accordance with the
computer program.
[0223] The term system LSI was used herein, but the term IC, LSI,
super LSI, and ultra LSI can also be used depending on the
difference in the degree of integration. The approach for forming
an integrated circuit is not limited to LSI, but can be
materialized with a dedicated circuit or universal processor. After
the manufacture of LSI, a programmable FPGA (Field Programmable
Gate Array) or reconfigurable process which allows reconfiguration
of the connection or setting of circuit cells inside the LSI can be
utilized.
[0224] If a technology of integrated circuits that replace LSI by
advances in semiconductor technologies or other derivative
technologies becomes available, functional blocks can obviously be
integrated using such technologies. Application of biotechnology or
the like is also a possibility.
[0225] One embodiment of the invention can be not only such a pain
estimation apparatus, but a pain estimation method using
characteristic constituent units contained in the pain estimation
apparatus as steps. Further, one embodiment of the invention can be
a computer program for having a computer execute each
characteristic step in the pain estimation method. Further, one
embodiment of the invention can be a computer readable
non-transient storage medium on which such a computer program is
recorded.
[0226] In each of the above embodiments, each constituent element
can be materialized by being set up with a dedicated hardware or by
executing software program suited to each constituent element. Each
constituent element can be materialized by a program execution unit
such as a CPU or a processor reading out and executing a software
program recorded on a storage medium such as a hard disk or
semiconductor memory. In this regard, software materialized with
the pain estimation apparatus of each of the above embodiments is a
program such as those described below.
[0227] In other words, this program makes a computer execute a pain
estimation method for estimating a magnitude of pain based on a
brainwave of an object being estimated, comprising: a measurement
step for measuring a brainwave a plurality of times from the object
being estimated to obtain a plurality of brainwaves; and an
estimation step for estimating a relative magnitude of pain upon
the measurement of a brainwave a plurality of times from an
amplitude of the plurality of brainwaves based on linearity in a
relationship between the amplitude of a brainwave and pain.
[0228] This program can also make a computer execute a pain
estimation method for estimating a magnitude of pain of an object
being estimated based on a brainwave of the object being estimated,
comprising: a first measurement step for measuring a brainwave from
the object being estimated sequentially inflicted with stimulation
of a plurality of magnitudes to obtain brainwave data corresponding
to stimulation of each magnitude; an identification step for
identifying an upper limit value and a lower limit value of a
brainwave amplitude of the object being estimated based on the
brainwave data; a second measurement step for measuring a brainwave
from the object being estimated to obtain object's brainwave data;
and an estimation step for estimating a magnitude of pain
corresponding to the object's brainwave data, based on a relative
size of a value of amplitude of the object's brainwave data to the
upper limit value and the lower limit value.
[0229] In this manner, the inventor has elucidated that brainwave
data or analysis data thereof (e.g., amplitude) and pain have a
specific relationship and explained that various embodiments can be
designed as a result of analyzing the relationship between pain
levels from evaluating a plurality of types of pain by a plurality
of methods and brainwave data or analysis data thereof (e.g.,
amplitude value). In addition, the inventor has found that it is
possible to calculate a pain classifier for estimating a magnitude
of pain by fitting to a pain function based on the specific
relationship between brainwave data or analysis data (e.g.,
amplitude) and pain.
[0230] (Pain classifier generation) In one aspect, the present
invention provides a method of generating a pain classifier for
classifying pain of an object being estimated based on a brainwave
of the object being estimated. This method comprises the steps of:
a) stimulating the object being estimated with a plurality of
levels of stimulation intensities; b) obtaining brainwave data
(also referred to as brain activity data, amount of brain activity
or the like, such as brainwave amplitude data ("EEG amplitude"),
frequency property, and the like) of the object being estimated
corresponding to the stimulation intensities; c) plotting, and
fitting to a pain function such as a linear function with a linear
approximation of the modulation range or a more comprehensive
sigmoid function pattern encompassing the same, the stimulation
intensities or a subjective pain sensation level corresponding to
the stimulation intensities and the brainwave data to obtain a pain
function specific to the object being estimated; and d) identifying
a pain classifier for separating a pain level to at least two
(strong, moderate, and week and the like is also possible) based on
the specific pain function when a regression coefficient for
fitting to the specific pain function is equal to or greater than a
predetermined value.
[0231] Alternatively, the present invention provides an apparatus
for generating a pain classifier for classifying pain of an object
being estimated based on a brainwave of the object being estimated.
This apparatus comprises A) a stimulation unit for stimulating the
object being estimated with a plurality of levels of stimulation
intensities; B) a brainwave data obtaining unit for obtaining
brainwave data (e.g., amplitude data) of the object being estimated
corresponding to the stimulation intensities; and C) a pain
classifier generation unit for plotting, and fitting to a pain
function such as a linear function with a linear approximation of
the modulation range or a more comprehensive sigmoid function
pattern encompassing the same, the stimulation intensities or a
subjective pain sensation level corresponding to the stimulation
intensities and the brainwave data to obtain a pain function
specific to the object being estimated, and identifying a pain
classifier for separating a pain level to at least two based on the
specific pain function. Typically, step a) is performed at the A)
stimulation unit, step b) is performed at the B) brainwave data
obtaining unit, and step c) and step d) are performed at the C)
pain classifier generation unit.
[0232] In the present invention, pain can be "estimated" or
"determined" by "classification". It is understood that if pain can
be found to be strong or weak by "pain classification", an
operation can be administered so that strong stimulation is not
inflicted, and an effect of objectively finding a therapeutic
effect of an analgesic or the like is attained. If "strong
stimulation" can be estimated from "weak stimulation", and "whether
not weak pain is felt" can be estimated as "increase in frequency
of appearance of an amount characteristic of deviation=pain is
intensifying" if the variation range of characteristic amount of
brain activity associated with weak pain can identified. Since
there is no label of what level of pain a patient feels as strong
in actual settings, it is preferable to provide reference
stimulation from weak pain to about the middle of a modulation
point to identify a pattern of change in brain activity. Based
thereon, pain can be estimated from brain activity of the patient
to determine the status of pain. If a "variation range of the
characteristic amount of brain activity associated with weak pain"
can be found, it can be estimated that "non-weak pain is felt" when
the frequency of deviating from the range increases.
[0233] A schematic diagram is used hereinafter to explain the
approach for generating a pain classifier (FIG. 23).
[0234] In the step (S100) for stimulating the object being
estimated with a plurality of levels of stimulation intensities of
step a), the object being estimated is stimulated with a plurality
of levels (intensity of magnitude) of stimulation (e.g., cold
temperature stimulation, electrical stimulation, or the like). The
number of stimulation intensities can be a number required for
fitting to a pain function, e.g., at least three is generally
required. Even if the number is 1 or 2, fitting to a pain function
can be possible by combining this with information obtained in
advance. Thus, this number is not necessarily required. Meanwhile,
for new fitting, it can be advantageous to stimulate with generally
at least 3, preferably 4, 5, 6, or more levels of stimulation. In
this regard, burden on an object being estimated should be
minimized, so that a number of stimulation intensities that is
highly invasive to the object being estimated (in other words,
intensity that is unbearable to a subject) is preferably at a
minimum or zero. Meanwhile, highly invasive stimulation to an
object being estimated can be necessary for more accurate fitting,
so that a minimum number can be included in accordance with the
objective. For example, the number of such highly invasive levels
of stimulation can be at least 1, at least 2, or at least 3, or 4
or more if tolerable by the object being estimated.
[0235] Step b) is a step of obtaining brainwave data (also referred
to as brain activity data, amount of brain activity or the like,
such as amplitude data ("EEG amplitude"), frequency property, or
the like) of the object being estimated corresponding to the
stimulation intensity (S200). Such brainwave data can be obtained
using any method that is well known in the art. Brainwave data can
be obtained by measuring an electrical signal of a brainwave and
described in terms of potential (can be described by .mu.V or the
like) as amplitude data or the like. The frequency property is
described in terms of power spectrum density or the like.
[0236] In a preferred embodiment, the present invention is
preferably practiced 1) with minimum number of electrodes (about
2), 2) by avoiding scalp with hair as much as possible, and 3) by a
simple method that can record even while sleeping to obtain
brainwave data, but the number of electrodes can be increased as
needed (e.g., 3, 4, 5, or the like)
[0237] Step c) is a step for plotting, and fitting to a pain
function (linear function or sigmoid curve), the stimulation
intensities and the brainwave data to obtain a pain function
specific to the object being estimated (S300) This step creates a
plotted diagram using stimulation intensities used in step a) and
brainwave data obtained in step b) which is fitted to a pain
function. Fitting to a pain function can be performed using any
approach that is known in the art. In addition to linear functions,
specific examples of such a fitting function include, but are not
limited to, Boltsmann function, double Boltsmann function, Hill
function, logistic dose response, sigmoid Richards function,
sigmoid Weibull function, and the like. Among them, standard
logistic functions are called sigmoid functions, and standard
functions or modified forms are common and preferred.
[0238] Step d) is a step for identifying a pain classifier for
separating a pain level to at least two (or a pain level to 2, 3 or
more quantitative or qualitative levels) based on the pain function
when a regression coefficient for fitting to the pain function is
equal to or greater than a predetermined value as needed (S400). A
pain classifier can be identified and determined based on an
inflection point (median value or the like) of a pain function, but
this is not a limiting example. A pain classifier can be calibrated
so that the classification of a pain level is maximized as needed.
For example, brainwave data corresponding to an inflection point of
a pain function can be temporarily determined as a pain classifier.
This pain classifier can be calibrated so that the original
brainwave data and stimulation intensity corresponding thereto or
subjective pain sensation level of an object corresponding to the
stimulation intensity is actually evaluated to keep outliers low or
preferably to minimize outliers. Such a pain classifier can be
applied to calculate or classify pain levels and utilized in
determining therapeutic effects.
[0239] If the same subject is subjected to estimation, the process
can include a step of keeping or updating a classifier by using the
previous classifier data.
[0240] In the apparatus for generating a pain classifier of the
invention, A) a stimulation unit for stimulating the object being
estimated at a plurality of levels of stimulation intensities is
configured to perform step a), i.e., has means or function capable
of providing a plurality of stimulation intensities. In addition,
the stimulation unit is configured to be able to inflict such
stimulation to an object.
[0241] B) a brainwave data obtaining unit for obtaining brainwave
data (e.g., amplitude data) or analysis data thereof of the object
being estimated corresponding to the stimulation intensities is
configured to obtain brainwave data or analysis data thereof of an
object being estimated. A brainwave data obtaining unit can have
other functions (e.g., step e) in a classification apparatus) in
addition to performing step b).
[0242] C) a pain classifier generation unit for plotting, and
fitting to a pain function such as a linear function with a linear
approximation of the modulation range or a more comprehensive
sigmoid function pattern encompassing the same, the stimulation
intensities or a subjective pain sensation level corresponding to
the stimulation intensities and the brainwave data to obtain a pain
function specific to the object being estimated, and identifying a
pain classifier for separating a pain level to at least two based
on the specific pain function can have a function of fitting to a
calculated specific pain function and generating a pain classifier.
Generally, C) a pain classifier generation unit performs step c)
and step d). These two functions can be materialized in separate
apparatuses, devices, CPU, terminals or the like, or as one unit.
In general, a CPU or a calculating apparatus is configured so that
a program for materializing such calculation is integrated or
capable of being integrated.
[0243] FIG. 24 describes a schematic diagram of the apparatus of
the invention. Since this embodiment is a pain classifier
measurement apparatus therein, 1000 to 3000 are involved. The
stimulation unit 1000 corresponds to A), where a value of
stimulation is communicated to a brainwave data obtaining unit 2000
and a pain classifier generation unit 3000. The brainwave data
obtaining unit 2000 is configured (2500) to comprise or to be
linked to a brainwave meter that is or can be linked to a subject
(1500) so that brainwave data or analysis data thereof from
stimulation emanated from a reference stimulation unit to the
subject (1500) can be obtained.
[0244] FIG. 25 is a block diagram depicting the functional
configuration of a pain estimation or pain classification or pain
classifier generation system 5100 in one embodiment (it should be
noted that some parts of the configurational diagrams are optional
configurational units that can be omitted). The system 5100
comprises a brainwave measurement unit 5200, which internally
comprises, or externally connects to, a brainwave recording sensor
5250 and optionally a brainwave amplification unit 5270, and
processes pain signals and determines/estimates pain at a pain
determination/estimation apparatus unit 5300. The pain
determination/estimation apparatus unit 5300 processes a brainwave
signal at a brainwave signal processing unit 5400, (if necessary
extracts a characteristic amount of brainwave at a brainwave
characteristic amount extraction unit 5500), estimates/determines
pain at a pain determination/estimation unit 5600, and (optionally)
visualizes pain with a pain level visualization unit 5800. The
system also comprises a stimulation apparatus unit 5900 inside or
outside. The stimulation apparatus unit 5900 comprises a reference
stimulation presentation apparatus unit (terminal) 5920,
contributing to creation of a pain classification instrument for a
patient. The stimulation apparatus unit can optionally comprise a
reference stimulation level visualization unit 5960 comprising a
reference stimulation generation unit 5940.
[0245] Such a pain classifier generation system 5100 comprises the
brainwave measurement unit 5200 and the pain
determination/estimation apparatus unit estimation unit 5300, and
optionally comprises the stimulation apparatus unit 5900 (can
include a reference stimulation unit). The pain
determination/estimation apparatus unit 5300 is materialized, for
example, by a computer comprising a processor and a memory. In such
a case, the pain determination/estimation apparatus unit estimation
unit 5300 makes the processor function as the brainwave
amplification unit 5270 as needed, brainwave signal processing unit
5400, pain determination/estimation unit 5600 (as needed), pain
level visualization unit 5800 (as needed) or the like when a
program stored in the memory is executed by the processor. The
processor is also made to perform reference stimulation generation
and visualization as needed. The system 5100 and the apparatus unit
5300 of the invention can be materialized, for example, by a
dedicated electronic circuit. A dedicated electronic circuit can be
a single integrated circuit or a plurality of electrical circuits.
The brainwave data obtaining unit and the pain classifier
generation unit can have the same configuration as the pain
estimation apparatus.
[0246] The brainwave measurement unit 5200 obtains a plurality of
brainwave data by measuring a brainwave a plurality of times from
an object being estimated via a brainwave meter (brainwave
recording sensor 5250). An object being estimated is an organism
generating a change in a brainwave by pain, which does not need to
be limited to humans.
[0247] The pain determination/estimation unit 5600 generates a pain
classifier. A pain classifier is used for estimating or classifying
a magnitude of pain from amplitudes of a plurality of brainwave
data. In other words, the pain determination/estimation unit 5600
can generate a pain classifier for estimating or classifying pain
of an object from brainwave data.
[0248] The brainwave recording sensor 5250 measures electrical
activity generated in the brain of an object being estimated with
electrodes on the scalp. In addition, the brainwave recording
sensor 5250 outputs brainwave data, which is a result of
measurement. The brainwave data can be amplified as needed.
[0249] Next, a method or a process of an apparatus configured in
the above matter is explained. FIG. 23 is a flow chart depicting a
series of processes. In this aspect, S100 to S400 are involved. In
S400, a pain classifier (also referred to as a pain classification
instrument/pain prediction instruction) is generated.
[0250] Stimulation of a plurality of levels (magnitudes) of
stimulation intensities is inflicted on an object through a
reference stimulation unit 1000 (S100).
[0251] Next, brainwave data (brainwave amplification baseline data
such as amplitude data) is obtained (S200). Brainwave data is
obtained by the brainwave data obtaining unit 2000 in FIG. 24. In
terms of FIG. 25, a plurality of brainwave data are obtained by
measuring a brainwave a plurality of times from an object being
estimated by the brainwave measurement unit 5200 via the brainwave
meter (brainwave recording sensor 5250) and is used as the
brainwave data (e.g., amplitude data). The brainwave measurement
unit 5200 can also measure brainwaves at a plurality of times.
[0252] The pain classifier generation unit 3000 (see FIG. 24)
performs pain function fitting (S300). When pain function fitting
is performed and a regression coefficient is optionally determined
to be a suitable value, a pain classifier (pain classification
instrument/pain prediction instrument) can be generated using the
pain function (S400) After a pain classifier is generated, the
value can be calibrated as needed.
[0253] (Pain Classification/Estimation)
[0254] In another aspect, the present invention provides a method
of classifying pain of an object being estimated based on a
brainwave of the object being estimated. This method comprises the
steps of: e) obtaining brainwave data or analysis data thereof
(e.g., amplitude data) of the object being estimated; and f)
classifying a pain level of the object being estimated by fitting
the brainwave data or analysis data thereof to a predetermined pain
classifier;
[0255] wherein the pain classifier is obtained by fitting the
brainwave data or analysis data thereof of the object being
estimated to a pain function. Such a pain classifier can be
calculated by any approach described in the section of (Pain
classifier generation), but the value may be generated by another
approach or pre-generated (FIG. 23, S450).
[0256] In another aspect, the present invention provides an
apparatus for classifying pain of an object being estimated based
on a brainwave of the object being estimated. This apparatus
comprises: X) a brainwave data obtaining unit for obtaining
brainwave data or analysis data thereof (e.g., amplitude data) of
the object being estimated; and Y) a pain classification unit for
classifying a pain level of the object being estimated based on a
pain classifier or by fitting the brainwave data or analysis data
thereof to the pain classifier, wherein the pain classifier is
obtained by fitting the brainwave data of the object being
estimated to a pain function. Generally, X) a brainwave data
obtaining unit performs step e) and the Y) a pain classification
unit performs step f), but this is non-limiting.
[0257] Step e) is a step for obtaining brainwave data (e.g.,
amplitude data) of the object being estimated (S500). This step is
a step of obtaining brainwave data from an object to be measured
regardless of whether some type of stimulation is inflicted or
treated. This step can be any approach as long as it is an approach
that can obtain brainwave data. The same approach for obtaining
brainwave data used in step b) can be used, and the same approach
is generally used.
[0258] Step f) is a step for classifying the brainwave data to a
pain level of the object being estimated based on a predetermined
pain classifier (S600). A predetermined pain classifier is referred
to as a "pain classification instrument" or "pain prediction
apparatus" in relation to a pain level of an object being
estimated. For example, when brain amplitudes associated with pain
exhibit a decreasing pattern and a pain classifier classifies pain
into "strong pain" and "weak pain", pain is classified as "strong
pain" when brainwave data (e.g., amplitude data) lower than this
value is detected, and pain is classified as "weak pain" when
greater brainwave data (e.g., amplitude data) is detected. For
example, if the value of a pain classification instrument indicates
a standardized brainwave absolute amplitude of "0.7", brainwave
amplitude data recorded online is converted to an absolute value
based on existing data and standardized, and then "0.8" is
classified as sensing "weak pain", and "0.2" is classified as
sensing "strong pain"
[0259] In one embodiment, brainwave data or analysis data thereof
(e.g., amplitude data) is fitted to the pain classifier with a mean
value. Such a mean value can be 15 to 200 seconds, or a mean value
exceeding 200 seconds (e.g., 300 seconds, 500 seconds, 600 seconds,
900 seconds, 1200 seconds, or the like) when data is recorded over
several hours.
[0260] This aspect is explained based on FIG. 24. In FIG. 24, the
brainwave data obtaining unit 2000 as well as the pain
classification unit 4000 are referenced. The dotted lines indicate
procedures of creating a determination model, and solid lines
indicate the procedures of determining/estimating actual pain
levels. In such a case, as explained in the section (Pain
classifier generation), brainwave data can be obtained via a
brainwave meter from an object. In other words, the brainwave data
obtaining unit 2000 is configured (2500) to be linkable to the
object 1500, and the brainwave data obtaining unit 2000 is
configured to comprise or to be linked to a brainwave meter that is
or can be linked to the object (1500) so that brainwave data or
analysis data thereof obtained from the object (1500) can be
obtained. The pain classification unit 4000 is configured to store
a pain classifier in advance or receive data generated separately,
and optionally configured to enable reference. Such a configuration
of linking can be wired or wireless. A pain classifier that is
stored in advance is generated based on fitting a characteristic
amount to a pain function in the pain classifier generation unit
3000.
[0261] FIG. 25 is a block diagram depicting the functional
configuration of a pain estimation or pain classification or pain
classifier generation system 5100 in one embodiment. The system
5100 comprises the brainwave measurement unit 5200, which comprises
the brainwave recording sensor 5250 and optionally internally
comprises, or externally connects to, the brainwave amplification
unit 5270, and processes pain signals and determines/estimates pain
at a pain determination/estimation apparatus unit 5300. The pain
determination/estimation apparatus unit 5300 processes a brainwave
signal at the brainwave signal processing unit 5400, (optionally)
estimates/determines pain at the pain determination/estimation unit
5600, and (optionally) visualizes pain with the pain level
visualization unit 5800. The system comprises the stimulation
apparatus unit 5900 inside or outside. The stimulation apparatus
unit 5900 comprises the reference stimulation presentation
apparatus unit (terminal) 5920, contributing to creation of a
classification instrument for a patient. The stimulation apparatus
unit (optionally) comprises the reference stimulation generation
unit 5940.
[0262] In this manner, the pain classifier generation system 5100
comprises the brainwave measurement unit 5200 and the pain
determination/estimation apparatus unit 5300. The pain
determination/estimation apparatus unit 5300 is materialized, for
example, by a computer comprising a processor and a memory. In such
a case, the pain determination/estimation apparatus estimation unit
5300 makes the processor function as the brainwave amplification
unit 5270 as needed, brainwave signal processing unit 5400, pain
determination/estimation unit 5600 (as needed), pain level
visualization unit 5800 (as needed) or the like when a program
stored in the memory is executed by the processor. The processor
can also be made to perform reference stimulation generation and
visualization as needed. The system 5100 and the apparatus unit
5300 of the invention can be materialized, for example, by a
dedicated electronic circuit. A dedicated electronic circuit can be
a single integrated circuit or a plurality of electrical circuits.
The brainwave data measurement unit and the pain classifier
generation unit 3000 (see FIG. 24) can have the same configuration
as the pain estimation apparatus or configured to be an external
unit.
[0263] The brainwave measurement unit 5200 obtains a plurality of
brainwave data by measuring a brainwave a plurality of times from
an object being estimated via a brainwave meter (brainwave
recording sensor 5250). An object being estimated is an organism
generating a change in a brainwave by pain (e.g., animals such as
mammals such as primates), which does not need to be limited to
humans.
[0264] The pain determination/estimation unit 5600 estimates or
classifies a magnitude of pain from amplitudes of a plurality of
brainwave data based on a pain classifier created by the pain
classifier generation unit 3000 (see FIG. 24). In other words, the
pain determination/estimation unit 5600 estimates or classifies
pain of an object from brainwave data based on a pain
classifier.
[0265] The brainwave recording sensor 5250 measures electrical
activity generated in the brain of an object being estimated with
electrodes on the scalp. In addition, the brainwave recording
sensor 5250 outputs brainwave data, which is a result of
measurement. The brainwave data can be amplified as needed.
[0266] Next, a method or a process of an apparatus configured in
the above manner is explained. FIG. 23 is a flow chart depicting a
series of processes. In this aspect, S400 to S600 can be involved.
These are steps after a pain classifier (also referred to as a pain
classification instrument/pain prediction instrument) is generated
in S400. Alternatively, this is when a pain classifier is
separately available (when such a value is previously obtained and
stored or the like), and such a case starts from S450.
[0267] This pain classifier can be stored in advance in the pain
classification unit 4000 after the creation thereof, or the pain
classification unit 4000 can be configured to be able to receive
value data. Alternatively, when the pain classifier generation unit
3000 is attached, the value can be stored in the generation unit
and a storage medium can be provided separately. This value can
also be received by communication.
[0268] Next, brainwave data is obtained from an object (S500). The
same technique explained in S200 can be used to obtain such
brainwave data to employ the same embodiment. However, it is not
necessarily to use the same apparatus or device as S200, which can
be the same or different.
[0269] Next, brainwave data (e.g., amplitude data) obtained in S500
is fitted to a pain classifier to classify pain corresponding to
the brainwave data (S600). Such pain classification can be
configured to display or speak a certain phrase (strong pain, weak
pain, or the like) when a predetermined value is output. The actual
value and pain classifier can be displayed together to allow a user
(clinician) to review the values.
[0270] (Pain Classifier Generation and
Classification/Estimation)
[0271] In another aspect, the present invention relates to a method
and apparatus for performing both pain classifier generation
(determination model creation) and classification/estimation (model
application).
[0272] Thus, in this aspect, the present invention provides a
method of classifying/estimating pain of an object being estimated
based on a brainwave of the object being estimated. This method
comprises the steps of: a) stimulating the object being estimated
with a plurality of levels of stimulation intensities; b) obtaining
brainwave data (e.g., amplitude data) of the object being estimated
corresponding to the stimulation intensities; c) plotting, and
fitting to a pain function such as a linear function with a linear
approximation of the modulation range or a more comprehensive
sigmoid function encompassing the same, the stimulation intensities
or a subjective pain sensation level corresponding to the
stimulation intensities and the brainwave data to obtain a pain
function specific to the object being estimated; d) identifying a
pain classifier for separating a pain level to at least two based
on the specific pain function when a regression coefficient for
fitting to the specific pain function is equal to or greater than a
predetermined value; e) obtaining the brainwave data (e.g.,
amplitude data) of the object being estimated; and f) classifying a
pain level of the object being estimated by fitting the brainwave
data to a pain classifier. Each step is explained in the sections
of (Pain classifier generation) and (Pain
classification/estimation) Each step can be carried out by any
embodiment or combination of the described matters therein.
[0273] In another aspect, the present invention provides an
apparatus for classifying pain of an object being estimated based
on a brainwave of the object being estimated. The apparatus
comprises: A) a stimulation unit for stimulating the object being
estimated with a plurality of levels of stimulation intensities; B)
a brainwave data obtaining unit for obtaining brainwave data (e.g.,
amplitude data) of the object being estimated (obtains brainwave
data corresponding to the stimulation intensities and actual
brainwave data); C) a pain classifier generation unit for plotting,
and fitting to a pain function, the stimulation intensities or a
subjective pain sensation level corresponding to the stimulation
intensities and the brainwave data to obtain a pain function
specific to the object being estimated, and identifying a pain
classifier for separating a pain level to at least two based on the
specific pain function (linear function or sigmoid curve);
[0274] and D) a pain classification unit for classifying a pain
level of the object being estimated by fitting the brainwave data
to a pain classifier or based on the pain classifier.
[0275] Each constituent unit is explained in the sections of "Pain
classifier generation) and (Pain classification/estimation). Each
step can be carried out by any embodiment or combination of the
described matters therein.
[0276] An exemplary embodiment is explained based on FIG. 26.
[0277] FIG. 26 is a block diagram depicting the functional
configuration of a system 5150 of the invention. In FIG. 26,
functional blocks similar to FIG. 25 are assigned the same symbol,
and explanation is appropriately omitted.
[0278] The system 5150 in this aspect comprises the pain
determination/estimation apparatus unit 5300, the brainwave meter
5220 in the measurement unit 5200 (unlike the brainwave recording
sensor 5250, this unit combines a sensor and an amplification
unit), and the stimulation apparatus unit 5900 (can comprise a
reference stimulation unit). The stimulation unit can have the same
function as the stimulation unit 1000, can be a separate apparatus
from the pain determination/estimation apparatus unit 5300 or can
be integrated as a part thereof. The pain classifier generation
unit 3000 is a unit for generating a pain classifier by obtaining a
brainwave characteristic amount from the characteristic amount
extraction unit 5500 and pain level information from the
stimulation apparatus unit 5900 (can comprise a reference
stimulation unit) and fitting to a sigmoid function, which is one
form of a pain function. The pain classifier generation unit
transmits a classifier to the estimation unit 5600.
[0279] The pain determination/estimation apparatus unit 5300
comprises an optionally integrated measurement unit 5200,
estimation unit 5600, identification unit 5650, and optionally the
visualization unit 5800. The pain determination/estimation
apparatus unit 5300 is materialized, for example, by a computer
comprising a processor and a memory. In such a case, the pain
determination/estimation apparatus unit 5300 makes a processor
function as at least 1, 2, 3, or all of, as needed, the measurement
unit 5200, estimation unit 5600, visualization unit 5800, and when
installed inside, the classifier generation unit 3000 when a
program stored in the memory is executed by the processor. The pain
determination/estimation apparatus unit 5300 can also be
materialized by, for example, a dedicated electronic circuit. A
dedicated electrical circuit can be a single integrated circuit or
a plurality of electrical circuits.
[0280] The measurement unit 5200 measures a brainwave from an
object being estimated 5099 sequentially inflicted with stimulation
of a plurality of magnitudes to obtain brainwave data corresponding
to stimulation of each magnitude via the brainwave meter 5220. The
brainwave data is used in a process of fitting to a sigmoid
function, which is one form of a pain function, and identifying a
pain classifier.
[0281] Furthermore, the measurement unit 5200 obtains object's
brainwave data by measuring a brainwave from the object being
estimated 5099. The object's brainwave data is used in creating a
pain classifier and in estimation process of a pain level. In other
words, the pain determination/estimation apparatus unit 5300
estimates or classifies a value of magnitude of pain of the object
being estimated 5099 based on a pain classifier when a brainwave
for object's brainwave data is measured.
[0282] The determination/estimation unit 5600 estimates or
classifies a value of magnitude of pain corresponding to object's
brainwave data based on a pain classifier, based on the object's
brainwave data or analysis data thereof (e.g., amplitude value) of
the object being estimated 5099 identified by the classifier
generation unit 3000. A classifier obtained by pain function
(herein, an example includes a sigmoid function) fitting of the
invention is introduced in the classifier generation unit.
[0283] The classifier generation unit 3000 identifies a pain
classifier of the object being estimated 5099 based on brainwave
data obtained by the measurement unit 5200. For example, the
identification unit 5650 identifies an amplitude of a plurality of
brainwave data corresponding to stimulation of a plurality of
magnitudes. For example, the classifier generation unit 3000 can
estimate or classify based on a pain classifier by analyzing a
plurality of brainwave data or analysis data thereof. Specifically,
the classifier generation unit 3000 can identify a pain classifier
by fitting amplitudes of a plurality of brainwave data
corresponding to a plurality of stimulation magnitudes to a pain
function (herein, an example includes a sigmoid function). The
visualization unit 5800 displays a pain level obtained by the
determination/estimation unit 5600 temporally continuously or by
points on a liquid crystal display or the like for monitoring
pain.
[0284] The stimulation apparatus unit 5900 (can comprise a
reference stimulation unit) inflicts stimulation of a plurality of
magnitudes individually to the object being estimated 5099.
Specifically, the stimulation apparatus unit 5900, for example,
inflicts a plurality of magnitudes of stimulation sequentially to
the object being estimated 5099 by changing the amount of
stimulation. Stimulation is for example electrical stimulation,
thermal stimulation, and the like. The stimulation apparatus unit
5900 (can comprise a reference stimulation unit) also provides
information on pain levels to the pain classifier generation unit
3000.
[0285] Next, the process of the pain estimation/classification
system 5150 configured in the above manner is explained. The
processes of the system 5150 include an identification process for
identifying an amplitude of a brainwave based on stimulation, a
process for fitting to a pain function (herein, an example includes
a sigmoid function) for generating a pain classifier, a process for
generating a pain classifier, and a process for estimating or
classifying pain from received brainwave data based on the pain
classifier. First, the identification process is explained. FIG. 23
is referenced in this regard.
[0286] Stimulation of a plurality of levels (magnitude) of
intensities is inflicted on an object through the stimulation unit
1000 (S100).
[0287] Next, brainwave data is obtained (S200). The brainwave data
is obtained by the brainwave data obtaining unit 2000. In terms of
FIG. 25, a plurality of brainwave data of analysis data thereof are
obtained by measuring a brainwave a plurality of times from an
object being estimated by the brainwave measurement unit 5200 via
the brainwave meter (term collectively referring to a recording
sensor and an amplification unit) 5220 and are used as the
brainwave data for analysis The measurement unit 5200 can measure
brainwaves at a plurality of times.
[0288] The pain classifier generation unit 3000 performs pain
function fitting (S300). When pain function fitting is performed
and a regression coefficient is optionally determined to be a
suitable value, a pain classifier can be generated using the pain
function (S400). After a pain classifier is generated, the value
can be calibrated as needed to correct the classifier.
[0289] Such a pain classifier can be stored in the pain
classification unit 4000 or the pain classifier generation unit
3000, or a storage medium can be provided separately. This value
can also be transmitted/received by communication.
[0290] Next, brainwave data is obtained from an object (S500). The
same technique explained in S200 can be used to obtain such
brainwave data to employ the same embodiment. However, it is not
necessary to use the same apparatus or device as S200, which can be
the same or different.
[0291] Next, brainwave data or analysis data thereof (e.g.,
amplitude data) obtained in S500 is classified as pain
corresponding to the brainwave data or analysis data thereof based
on a pain classifier (S600). Such pain classification can be
configured to display or speak a certain phrase (strong pain, weak
pain, or the like) when a predetermined value is output. The actual
value and pain classifier can be displayed together to allow a user
(clinician) to review the value.
[0292] (Application of Pain Function)
[0293] The pain function used in the present invention has, when
broadly defined, a linear relationship, in other words a one-to-one
relationship, i.e., (i) first pain corresponding to first brainwave
data or analysis data thereof (including, for example, amplitude)
can be estimated to be greater than second pain corresponding to
second brainwave data or analysis data thereof (including, for
example, amplitude) if the first brainwave data is greater than the
second brainwave data, and (ii) the first pain can be estimated to
be less than the second pain if the first brainwave data or
analysis data thereof (including, for example, amplitude) is less
than the second brainwave data or analysis data thereof (including,
for example, amplitude). It has been explained that any function
that can express this can be used, and application examples thereof
include linear functions and sigmoid functions. Since the end of an
asymptote of the minimum value to the start of an asymptote of the
maximum value can be partially taken out and approximated with a
linear function, a sigmoid function can be understood as having a
broadly defined linearity as a pain function of the invention and
utilized as a pain function. In such a case, the modulation range
of a sigmoid function can be considered as having "linearity" (see
FIG. 27). FIG. 27 depicts a schematic diagram of a linearly
approximated portion of a sigmoid function. Such linearity is
significant not only as just a form of function patterns, but also
in terms of reflecting "pain sensitivity property" of an individual
due to a difference in the width of the modulation range and
amplitude of modulation. For example, some people have the
potential to exhibit slow continuity with gradual modulation, while
others have the potential to exhibit a step function pattern of
instant modulation. In this regard, identification of such a pain
function of modulation (e.g., slope of linear function) is
considered meaningful in determining pain. The linearly
approximated portion of a sigmoid function depicted in FIG. 27 is
further explained. As depicted therein, the linearly approximated
portion of a sigmoid function for unpleasantness can vary depending
on the stimulation intensity even among individuals. A pattern of
change in the pain characteristic amount of an individual can be
predicted from such a property. For example, an object with a low
slope can be left alone for a while even when a characteristic
amount of pain starts to change, but an individual with a steep
slope is envisioned to require immediate clinical attention or the
like (see FIG. 28).
Other Embodiments
[0294] The pain classifier generation technologies according to one
or more embodiments of the invention have been explained above
based on the embodiments, but the present invention is not limited
to such embodiments. Various modifications applied to the present
embodiments and embodiments constructed by combining constituent
elements in different embodiments conceivable to those skilled in
the art without departing from the intent of the inventions are
also encompassed within the scope of one or more embodiments of the
invention.
[0295] For example, each of the above embodiments used a
peak-to-peak value as the amplitude value of brainwave data, but
amplitude values are not limited thereto. For example, a simple
peak value can be used as the amplitude value.
[0296] The above embodiment of the invention for generating a pain
classifier has set the range of values of magnitude of pain so that
the value Pmax of the magnitude of pain corresponding to the upper
limit value Amax of a brainwave amplitude is 1, and the value Pmin
of the magnitude of pain corresponding to the lower limit value
Amin of the brainwave amplitude is 0, but this is not a limiting
example. For example, the magnitude of pain can be represented by 0
to 100. In such a case, the determination/estimation unit 5600 can
estimate the value Px of magnitude of pain by the following
equation.
Px=Pmax.times.(Ax-Amin)/(Amax-Amin)
[0297] Curve fitting was explained above as an example of
generating a pain classifier by analyzing a plurality of brainwave
data, but this is not a limiting example. For example, a value
corresponding to large stimulation can be identified using a
learning model for estimating a brainwave amplitude for large
stimulation from a brainwave amplitude corresponding to small
stimulation. In such a case, large stimulation does not need to be
inflicted upon an object being estimated, so that physical burden
on the object being estimated can be alleviated. Further, a
predetermined value can be used as the upper limit value of a
brainwave amplitude. The predetermined value is for example 50
.mu.V to 100 .mu.V, which can be experimentally or empirically
determined. In such normal analysis, data from about plus or minus
50 .mu.V to 100 .mu.V is eliminated as an artifact removal method.
Such artifact removal can also be performed in the present
invention as needed in pain classifier generation.
[0298] Stimulation inflicted upon the object being estimated 5099
by the stimulation apparatus 5900 (can comprise a reference
stimulation unit) is not limited to electrical stimulation and
thermal stimulation. Any type of stimulation can be inflicted as
long as the magnitude of pain sensed by the object being estimated
5099 changes in accordance with the magnitude of stimulation.
[0299] Some or all of the constituent elements of the pain
estimation apparatus in each of the above embodiments can be
comprised of a single system LSI (Large Scale Integration). For
example, the determination/estimation apparatus unit 5300 can be
comprised of system LSI optionally having the measuring unit 5200
and optionally the stimulation apparatus 5900 (can comprise a
reference stimulation unit).
[0300] System LSI is ultra-multifunctional LSI manufactured by
integrating a plurality of constituents on a single chip, or
specifically a computer system comprised of a microprocessor, ROM
(Read Only Memory), RAM (Random Access Memory) and the like. A
computer program is stored in a ROM. The system LSI accomplishes
its function by the microprocessor operating in accordance with the
computer program.
[0301] The term system LSI was used herein, but the term IC, LSI,
super LSI, and ultra LSI can also be used depending on the
difference in the degree of integration. The approach for forming
an integrated circuit is not limited to LSI, but can be
materialized with a dedicated circuit or universal processor. After
the manufacture of LSI, a programmable FPGA (Field Programmable
Gate Array) or reconfigurable process which allows reconfiguration
of connection or setting of circuit cells inside the LSI can be
utilized.
[0302] If a technology of integrated circuits that replace LSI by
advances in semiconductor technologies or other derivative
technologies becomes available, functional blocks can obviously be
integrated using such technologies. Application of biotechnology or
the like is also a possibility.
[0303] One embodiment of the invention can be not only such a pain
classifier generation, pain determination/classification apparatus,
but a pain classifier generation, pain determination/classification
method using characteristic constituent units contained in the pain
estimation apparatus as steps. Further, one embodiment of the
invention can be a computer program for having a computer execute
each characteristic step in the pain classifier generation, pain
determination/classification method. Further, one embodiment of the
invention can be a computer readable non-transient storage medium
on which such a computer program is recorded.
[0304] In each of the above embodiments, each constituent element
can be materialized by being configured with a dedicated hardware
or by executing software program suited to each constituent
element. Each constituent element can be materialized by a program
execution unit such as a CPU or a processor reading out and
executing a software program recorded on a storage medium such as a
hard disk or semiconductor memory. In this regard, software
materialized with the pain estimation apparatus of each of the
above embodiments is a program such as those described below.
[0305] Specifically, this program makes a computer execute a pain
determination method for estimating a magnitude of pain based on a
brainwave of an object being estimated, comprising the steps of: a)
stimulating the object being estimated with a plurality of levels
of stimulation intensities; b) obtaining brainwave data (e.g.,
amplitude data) of the object being estimated corresponding to the
stimulation intensities; c) plotting, and fitting to a pain
function such as a linear function with a linear approximation of a
modulation range or a more comprehensive sigmoid function
encompassing the same, the stimulation intensities or a subjective
pain sensation level corresponding to the stimulation intensities
and the brainwave data to obtain a pain function specific to the
object being estimated; d) identifying a pain classifier for
separating a pain level to at least two based on the specific pain
function when a regression coefficient for fitting to the specific
pain function is equal to or greater than a predetermined value; e)
obtaining the brainwave data (e.g., amplitude data) of the object
being estimated; and f) classifying a pain level of the object
being estimated by fitting the brainwave data to the pain
classifier, and optionally converting and visualizing pain into a
suitable pain indicator.
[0306] This program also makes a computer execute a method of
generating a pain classifier based on a brainwave of an object
being estimated, comprising the steps of: a) stimulating the object
being estimated with a plurality of levels of stimulation
intensities; b) obtaining brainwave data (also referred to as brain
activity data, amount of brain activity, or the like, such as
amplitude data (EEG amplitude), frequency property, or the like) of
the object being estimated corresponding to the stimulation
intensities; c) plotting, and fitting to a pain function such as a
linear function with a linear approximation of a modulation range
or a more comprehensive sigmoid function encompassing the same, the
stimulation intensities or a subjective pain sensation level
corresponding to the stimulation intensities and the brainwave data
to obtain a pain function specific to the object being estimated;
and d) identifying a pain classifier for separating a pain level to
at least two based on the specific pain function when a regression
coefficient for fitting to the specific pain function is equal to
or greater than a predetermined value; or a method of classifying
pain of an object being estimated based on a brainwave of the
object being estimated, comprising the steps of: e) obtaining the
brainwave data of the object being estimated; and f) classifying a
pain level of the object being estimated by fitting the brainwave
data to a predetermined pain classifier.
[0307] As used herein, "or" is used when "at least one or more" of
the listed matters in the sentence can be employed. When explicitly
described herein as "within the range of two values", the range
also includes the two values themselves.
[0308] Reference literatures such as scientific literatures,
patents, and patent applications cited herein are incorporated
herein by reference to the same extent that the entirety of each
document is specifically described.
[0309] As described above, the present invention has been explained
while showing preferred embodiments to facilitate understanding.
The present invention is explained hereinafter based on Examples.
The above explanation and the following Examples are not provided
to limit the present invention, but for the sole purpose of
exemplification. Thus, the scope of the present invention is not
limited to the embodiments or the Examples specifically described
herein and is limited only by the scope of claims.
Examples
[0310] Examples are described hereinafter. The objects used in the
following Examples were handled, as needed, in compliance with the
standards of the Osaka University, and the Declaration of Helsinki
and ICH-GCP in relation to clinical studies. While the products
specifically described in the Examples were used for the reagents,
equivalent products of other manufacturers can also be used
instead.
Example 1: Generation of Pain Classifier
[0311] In this Example, a sigmoid function was used as a pain
function to generate a pain classifier. The materials and methods
are shown below.
[0312] (Materials and Methods)
[0313] (Participants)
[0314] 72 healthy adult patients in their 20s to 70s participated
in the Example. Informed consent was obtained from the participants
prior to the clinical trial. All participants self-reported as
never having undergoing a neurological and/or psychiatric illness,
or acute and/or chronic pain under clinical drug therapy
conditions. This Example was in compliance with the Declaration of
Helsinki and conducted under approval of the Osaka University
Hospital ethics committee.
[0315] (Experimental Procedure)
[0316] A temperature stimulation system (Pathway; Medoc Co., Ltd.,
Ramat Yishai, Israel) was used to inflict cold stimulation to the
inside of the forearm of the participants. The test included six
levels of temperature intensities. Under low temperature pain
conditions, six temperature levels were linearly decreased by
5.degree. C. in the range of -15.degree. C. to 10.degree. C. Each
level consisted of three stimulations with a 5 second ISI
(inter-stimulus interval). Each stimulation had 5 seconds of
increase and maintained 15 seconds of plateau with a pseudo waiting
period. The intervals between blocks were fixed at 100 seconds. The
participants continuously evaluated pain intensities in the range
of 0 to 100 (0: "no pain"; 100: "unbearable pain") on a
computerized visual analog scale (COVAS). COVAS data was
simultaneously recorded with changes in stimulation
intensities.
[0317] (Electroencephalogram (EEG) Data Record)
[0318] Commercially available Bio-Amplifier (EEG 1200: Nihon Koden)
was used to record EEG from four scalp Ag/AgCl scalp electrodes
(Fz, Cz, C3, and C4). The front-most electrode Fp1 was used for
recording EOG activity. A reference electrode was attached to both
earlobes, and an outside electrode was placed on the forehead. The
sampling rate was 1000 Hz using a bandpass filter in the range of
0.3 to 120 Hz. The impedance of all electrodes was less than 15
k.OMEGA..
[0319] (Electroencephalogram (EEG) Analysis)
[0320] Continuous EEG data was converted to 18 epoch data
comprising 3 epochs in 6 temperature levels under low temperature
pain conditions. Each epoch had a duration of 30 seconds after the
start of stimulation. EOG artifacts were decreased based on the
following regression filter.
Raw EEG=.beta..times.EOG+C
EEG estimate=raw EEG-P.times.EOG [Numeral 1]
.beta.: regression coefficient C: intercept EEG estimate: estimated
EEG
[0321] Fp1 was the closest to the left eye and affected heavily by
the eye movement, so that Fp1 data was used as EOG data. After VEOG
was diminished, a baseline correction was applied separately to
each intensity level. In other words, each epoch was corrected
using a first epoch reference value from 5 seconds before the start
of stimulation to the start of stimulation. The amplitudes
corrected with the reference value were converted to an absolute
value and averaged for each intensity level.
[0322] (Results)
[0323] One representative subject data is shown immediately below
in order to show a sigmoid function between stimulation intensity
and EEG activity.
[0324] FIG. 15 represents absolute amplitude data (one subject) of
18 epochs associated with 6 intensity levels immediately before
averaging for each level. The horizontal axis and vertical axis
indicate time and standardized absolute amplitude, respectively. By
observation, a change in amplitude in the three lower intensity
levels (levels 1 to 3) is greater than a change in amplitude in the
higher intensity levels (levels 4 to 6).
[0325] FIG. 16 shows absolute amplitudes averaged from three epochs
in each intensity level. Amplitudes are standardized with the
maximum value. The horizontal and vertical axes indicate
stimulation intensity and standardized amplitude, respectively. As
can be observed in FIG. 16 (electrode position information is
indicated by four plots), the mean amplitude more clearly shows
that the lower intensity levels (levels 1 to 3) exhibit greater EEG
amplitudes than the higher intensity levels (levels 4 to 6). This
intensity-amplitude function is not a negative linear function, but
a decreasing sigmoid function.
[0326] 14 participants strictly exhibited a decreasing sigmoid
function. Since a decrease in the start level of amplitude varies
among participants and electrodes on individual participants, the
inventor selected a suitable channel to identify four channels
covering higher amplitude levels immediately before the start of
the change and lower amplitude levels after the change. The overall
mean of amplitudes in four levels was calculated, and statistical
test was run between closely corresponding levels by a
corresponding t-test. FIG. 17 clearly shows a decreasing sigmoid
function between intensity level and amplitude, which is supported
by a statistical result of lower level pain 2 and higher level pain
1 having statistically significantly different amplitudes (t=2.886,
p=0.013). Fitting analysis indicated that this function
significantly fits the following equation.
Sigmoid function=0.9987-0.2211/(1+10{circumflex over (
)}((3.2722-x).times.39.7591))
x: EEG data standardized with maximum value
[0327] The inventor generated a pain classifier for one patient as
shown in FIG. 18 using a sigmoid fitting function. First, the
inventor calculated the mean subjective pain evaluation value (x
axis) and mean EEG amplitude (Fz: y axis) standardized with the
maximum value. The inventor then estimated a sigmoid fitting
function for these values to generate a pain classifier or a median
value. In other words, standardized EEG activity exceeding a
threshold value of pain classification (>0.8559) was labeled as
"weaker pain intensity" and standardized EEG amplitudes below a
threshold of pain classification was labeled "stronger pain
intensity".
[0328] A pain classifier based on a sigmoid function can also be
used as a pain prediction value for other pain. First, stimulation
of weak reference electrical stimulation level 3 and strong
reference electrical stimulation level 3 was inflicted three times
each on one participant. The mean absolute amplitude for each
stimulation was calculated over a duration of 5 seconds after
stimulation and standardized with the maximum amplitude in 6 levels
of data samples. Six levels of data were linearly arranged and
fitted to a sigmoid function as shown in FIG. 19. The inventor
obtained a pain classifier with a threshold value of 0.9126 based
on such a fitting function. This pain classifier was used to
predict a low temperature pain intensity level for the same
participant using a different type of low temperature simulation
data in FIG. 16A. Each stimulation was independently averaged over
a duration of 30 seconds and standardized, and then 18 mean value
data were obtained. These data were arranged linearly from the
weakest level (level 1) to the strongest level (level 6) and
classified with a pain classifier having a threshold value of
0.9126. Three higher levels were accurately predictable based on
reference pain classification or prediction value (FIG. 20).
However, a relatively large error was observed in the three lower
low temperature pain levels. In fact, use of this pain prediction
instrument resulted in 60% classification error, suggesting that
calibration of the classification threshold value was required. In
this regard, the classification threshold value was corrected by
the following method. The standardized EEG amplitude maximum value
for strong pain tests was clearly established to lower the
threshold value to 0.661 (FIG. 21). Such a linear calibration
method improved classification errors from 60% to 44%.
[0329] It was therefore demonstrated that a pain classifier can
more accurately classify and estimate pain by calibration.
Example 2
[0330] Example 2 demonstrated that a change in brain activity
exhibits a sigmoid function form via an inflection point in
accordance with a change in pain levels by using a different
analysis method.
[0331] The following analysis was conducted in this Example. The
sample size was 14.
[0332] Various types of stimulation were inflicted in accordance
with the description in Example 1 with the following notable
modifications. [0333] The mean of EEG amplitude absolute value for
30 seconds after exposure to stimulation of level 1 (for three
runs) and the standard error were found to find the threshold value
of level 1 (mean-standard error.times.2). Since Example 1 has
revealed that a monotonous decreasing pattern is exhibited by an
increase in pain levels, a baseline was determined to be increasing
pain levels when lower than the level 1 threshold value. [0334] For
each level from levels 2 to 6, the frequency of EEG amplitude
absolute value being less than the level 1 threshold value (number
of time points) was calculated. [0335] The frequency of points
below the level 1 threshold value in level 2 and other levels was
calculated for each individual, and the mean value was compared by
a t-test.
[0336] In other words, it is demonstrated that a pain level is
stronger for higher frequency of deviation from the level 1
threshold value, and the threshold value is effective as a pain
classifier for higher degree of deviation.
[0337] (Results)
[0338] While a higher level does not always mean a higher frequency
in all subjects, pain level 2 and the highest level 6 have been
proven to have a statistically significant difference as shown in
FIG. 22. This is data demonstrating that there is a difference,
i.e., gap, via an inflection point between "unbearably painful"
level 6 and other "not painful" or "weaker pain" levels due to the
present invention.
Example 3: Diversity in Linearity of the Modulation Range in
Sigmoidal Pain Function
[0339] The objective of Example 3 was to study how the linear
region of a sigmoidal pain function (modulation range) changes
among individuals due to a pattern of pain stimulation. The focus
was especially on the change in slope as in FIG. 28A and the change
in amplitude in the modulation region as in FIG. 28B.
[0340] (Method) One healthy male was inflicted with 1) three
consecutive or 10 consecutive electrical stimulation of 0.25 mA and
2) 10 consecutive electrical stimulation of 0.25 mA and 0.75 mA.
After the electrical stimulation, the subject was asked to
subjectively evaluate the unpleasantness of stimulation in levels
of 1 to 10.
[0341] (Results) As shown in FIG. 28C, when the number of
consecutive electrical stimulation of 0.25 mA was changed, the
point of reaching the maximum value of unpleasantness moved up from
12 to 10 applications of stimulation, and the maximum value of
unpleasantness also increased, so that the slope changed.
Meanwhile, when the electrical stimulation intensity was changed,
the unpleasantness score at the start of modulation doubled, so
that the slope slightly decreased. In other words, this indicates
that if the pain intensity is high from the beginning, a change is
pain sensitivity is low and difficult to detect. This is an example
showing that linearity in the modulation region in a sigmoidal pain
function is effective in identifying pain sensitivity of
individuals.
Application Example
[0342] As shown in the above Examples, a pain stimulation
presentation method (i.e., reference stimulation presentation
method from the stimulation apparatus unit 5900) can be changed and
a linearity changing pattern in the modulation region of a sigmoid
function can be identified for subjects to identify pain
sensitivity of individuals and to create and correct the
determination algorithm installed in the pain
determination/estimation apparatus unit 5300.
[0343] (Note)
[0344] As disclosed above, the present invention has been
exemplified by the use of its preferred embodiments. However, it is
understood that the scope of the present invention should be
interpreted based solely on the Claims. It is also understood that
any patent, any patent application, and any other references cited
herein should be incorporated herein by reference in the same
manner as the contents are specifically described herein. The
present application claims priority to Japanese Patent Application
No. 2016-162195 filed on Aug. 22, 2016 and Japanese Patent
Application No. 2017-133424 filed on Jul. 7, 2017 with the Japan
Patent Office. The entire content thereof is incorporated herein by
reference.
INDUSTRIAL APPLICABILITY
[0345] The present invention can be utilized as a pain estimation
apparatus for estimating a magnitude of pain based on a brainwave
of an object being estimated. The present invention can also
precisely classify pain, estimate pain without inflicting strong
pain, and finely diagnose and treat pain.
REFERENCE SIGNS LIST
[0346] 99: object being estimated [0347] 100, 200: pain estimation
system [0348] 110, 210: pain estimation apparatus [0349] 111, 211:
measurement unit [0350] 112, 212: estimation unit [0351] 120:
brainwave meter [0352] 213: identification unit [0353] 230:
stimulation apparatus [0354] 1000: reference stimulation unit
[0355] 1500: subject [0356] 2000: brainwave data obtaining unit
[0357] 2500: brainwave meter [0358] 3000: pain classifier
generation unit [0359] 4000: pain classification unit [0360] 5099:
object [0361] 5100: pain classifier generation system [0362] 5150:
pain estimation/classification system [0363] 5200: brainwave
measurement unit [0364] 5220: brainwave meter [0365] 5250:
brainwave recording sensor [0366] 5270: brainwave amplification
unit [0367] 5300: pain determination/estimation apparatus unit
[0368] 5400: brainwave signal processing unit [0369] 5500:
brainwave characteristic amount extraction unit [0370] 5600: pain
determination/estimation unit [0371] 5700: pain determination
correction unit [0372] 5800: pain level visualization unit [0373]
5900: stimulation apparatus unit [0374] 5920: reference stimulation
presentation terminal [0375] 5940: reference stimulation generation
unit [0376] 5960: reference stimulation level visualization
unit
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