U.S. patent application number 10/729425 was filed with the patent office on 2004-11-04 for system and method for determining muscle dysfunction.
Invention is credited to Appel, Gerald David, Clark, Harry Wellington, Day, Mary Kathleen.
Application Number | 20040220490 10/729425 |
Document ID | / |
Family ID | 23938770 |
Filed Date | 2004-11-04 |
United States Patent
Application |
20040220490 |
Kind Code |
A1 |
Appel, Gerald David ; et
al. |
November 4, 2004 |
System and method for determining muscle dysfunction
Abstract
A system and method for determining back muscle dysfunction
comprises data collection and data analysis elements. The system
collects electrical muscle activity measurements by applying a
plurality of electrodes in a pattern across a patient's or test
subject's back, measuring the electrical activity at each of the
electrodes and storing the measurements. One or more normative or
examined patient databases comprising measurements and results from
a number of individuals are used for comparison against the
patient's measurements. A patient's back muscle activity is
characterized by collecting electrical muscle activity measurements
for the patient and comparing an analysis of the patient's
electrical muscle activity to the results of the normative or
previously examined group.
Inventors: |
Appel, Gerald David; (Los
Angeles, CA) ; Clark, Harry Wellington; (Los Angeles,
CA) ; Day, Mary Kathleen; (Santa Monica, CA) |
Correspondence
Address: |
PILLSBURY WINTHROP LLP
ATTENTION: DOCKETING DEPARTMENT
11682 EL CAMINO REAL, SUITE 200
SAN DIEGO
CA
92130
US
|
Family ID: |
23938770 |
Appl. No.: |
10/729425 |
Filed: |
December 5, 2003 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10729425 |
Dec 5, 2003 |
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09901992 |
Jul 10, 2001 |
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09901992 |
Jul 10, 2001 |
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09488208 |
Jan 19, 2000 |
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6280395 |
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Current U.S.
Class: |
600/546 ;
600/587; 600/595 |
Current CPC
Class: |
A61B 5/316 20210101;
A61B 5/389 20210101 |
Class at
Publication: |
600/546 ;
600/587; 600/595 |
International
Class: |
A61B 005/04 |
Claims
What is claimed is:
1. A method for determining muscle dysfunction of a subject, the
method comprising the steps of: (a) selecting a plurality of sites
on the subject for sensing muscle electrical activity; (b)
calculating adipose thickness factors for the plurality of sites;
(c) making electrical activity measurements for the plurality of
sites; and (d) analyzing the electrical activity measurements and
determining thereby analysis values for a plurality of muscles,
each of the plurality of muscles corresponding to a respective one
of the plurality of sites, and in determining the analysis values,
factoring the adipose thickness factors into the electrical
activity measurements.
2. The method of claim 1, the adipose thickness factors being
determined by applying results of obtained measurements from a
sampling of individuals, the results relating adipose thickness to
general characteristics measured for the individuals, at least one
general characteristic of the subject corresponding to at least one
of the general characteristics measured for the individuals.
3. The method of claim 2, the results being represented in a set of
coefficients that are applied to a formula, each coefficient
relating to one of the at least one general characteristic of the
subject and to one site of the plurality of sites on the
subject.
4. The method of claim 3, the formula having a
form:Adipose=B.sub.0+B.sub.- 1X.sub.1+ . . .
+B.sub.nX.sub.n,wherein B.sub.0 through B.sub.n comprise the set of
coefficients for a given site, X.sub.1 through X.sub.n represent
values for a different one of the at least one general
characteristic of the subject, and n represents the number of the
at least one general characteristic.
5. The method of claim 3, wherein the coefficients are
regression-based coefficients.
6. The method of claim 3, the at least one general characteristic
of the subject being a gender, a height, a weight, a Body Mass
Index, a body type, a waist circumference, a chest circumference, a
wrist circumference, or a light transmissiveness of skin.
7. The method of claim 1, the adipose thickness factors being
determined by applying a formula that includes a set of
coefficients, each coefficient relating to one of the plurality of
sites on the subject.
8. A method for determining muscle dysfunction of a subject, the
method comprising the steps of: (a) selecting a plurality of sites
on the subject for sensing muscle electrical activity; (b) making
electrical activity measurements for the plurality of sites; and
(c) performing an analysis of the electrical activity measurements,
the analysis comprising steps of determining from the electrical
activity measurements analysis values for each of a plurality of
muscles and determining from the analysis values a degree of
departure from a normal condition, wherein the degree of departure
for the analysis values is normalized with respect to the plurality
of muscles.
9. The method of claim 8, wherein the normal condition is an ideal
normal condition.
10. The method of claim 8, the analysis further comprising a step
of mapping the degree of departure for each of the plurality of
muscles.
11. The method of claim 8, the step of making the electrical
activity measurements at a plurality of sites being performed
during specific periods in the execution of a set of motor tasks,
and the degree of departure being determined by selectively
integrating the analysis values across the set of motor tasks.
12. The method of claim 8, the electrical activity measurements
relating to a performance of a motor task, and the analysis further
comprising a step of determining a set of relationships for each of
the analysis values, each relationship in the set for an analysis
value relating the analysis value to one of the other analysis
values as a pair, and the degree of departure being determined by
selectively integrating across the set of relationships.
13. The method of claim 12, each relationship including a weighting
factor that reflects a biomechanical significance in the execution
of a motor task correlating the muscles associated with the pair of
analysis values.
14. The method of claim 12, each relationship including a weighting
factor that reflects a biomechanical significance that correlates
the motor task with the one of the plurality of muscles associated
with the analysis value.
15. The method of claim 12, each relationship including a factor
that reflects a systematic variability in measurement of electrical
activity.
16. The method of claim 8, wherein the degree of departure
comprises a continuous measure.
17. The method of claim 8, further comprising a step of calculating
adipose thickness factors for the plurality of sites, such that
determining the analysis values comprises factoring the adipose
thickness factors into the electrical activity measurements.
18. A system for determining muscle dysfunction of a subject, the
system comprising: (a) a plurality of electrical activity sensors
for measuring electrical activity at a respective plurality of
sites on the subject; and (b) a processor for determining adipose
thickness factors based on at least one general characteristic of
the subject for the plurality of sites on the subject, and for
analyzing the electrical activity and determining therefrom
analysis values for a plurality of muscles, each of the plurality
of muscles corresponding to a respective one of the plurality of
sites, wherein determining analysis values comprises factoring the
adipose thickness factors into the measured electrical
activity.
19. The system of claim 18, the processor determining the adipose
thickness factors by applying results of obtained measurements from
a sampling of individuals, the results relating adipose thickness
to general characteristics measured for the individuals, the at
least one general characteristic of the subject corresponding to at
least one of the general characteristics measured for the
individuals.
20. The system of claim 19, the processor determining the adipose
thickness factors by representing the results in a set of
coefficients that are applied to a formula, each coefficient
relating to one of the at least one general characteristic of the
subject and to one site of the plurality of sites on the
subject.
21. The system of claim 20, the formula having a
form:Adipose=B.sub.0+B.su- b.1X.sub.1+ . . .
+B.sub.nX.sub.n,wherein B.sub.0 through B.sub.n comprise the set of
coefficients for a given site, X.sub.1 through X.sub.n comprise
values for a different one of the at least one general
characteristic of the subject, and n is the number of the at least
one general characteristic.
22. The system of claim 20, wherein the coefficients are
regression-based coefficients.
23. The system of claim 20, the at least one general characteristic
of the subject being a gender, a height, a weight, a Body Mass
Index, a body type, a waist circumference, a chest circumference, a
wrist circumference, or a light transmissiveness of skin.
24. The system of claim 18, the processor determining the adipose
thickness factors by applying a formula that includes a set of
coefficients, each coefficient relating to one of the plurality of
sites on the subject.
25. A system for determining muscle dysfunction of a subject, the
system comprising: (a) a plurality of electrical activity sensors
for making electrical activity measurements for a respective
plurality of sites on the subject; and (b) a processor for
analyzing the electrical activity measurements and determining
therefrom analysis values for each of a plurality of muscles, and
determining for each of the plurality of muscles a degree of
departure from a normal condition by normalizing the analysis
values with respect to the plurality of muscles.
26. The system of claim 25, wherein the normal condition is an
ideal normal condition.
27. The system of claim 25, the processor mapping the degree of
departure for each of the plurality of muscles.
28. The system of claim 25, the plurality of electrical activity
sensors making electrical activity measurements during specific
periods in the execution of a set of motor tasks, and the processor
determining the degree of departure by selectively integrating the
analysis values across the set of motor tasks.
29. The system of claim 25, the electrical activity measurements
relating to a performance of a motor task, and the processor
determining a set of relationships for each of the analysis values,
each relationship relating the analysis value to one of the other
analysis values as a pair, and the processor determining the degree
of departure by selectively integrating across the set of
relationships.
30. The system of claim 29, the processor factoring into each
relationship a weighting factor that reflects a biomechanical
significance for the performance of the motor task correlating the
muscles associated with the pair of analysis values.
31. The system of claim 29, the processor factoring into each
relationship a weighting factor that reflects a biomechanical
significance that correlates the motor task and the one of the
plurality of muscles associated with the analysis value.
32. The system of claim 29, the processor factoring into each
relationship a factor that reflects a systematic variability in
measurement of electrical activity.
33. The system of claim 25, wherein the degree of departure
comprises a continuous measure.
34. The system of claim 25, the processor further calculating
adipose thickness factors for the plurality of sites, and in
determining the analysis values, factoring the adipose thickness
factors into the electrical activity measurements.
35. A computer readable medium having stored therein one or more
sequences of instructions for analyzing for muscle dysfunction of a
subject, said one or more sequences of instructions causing one or
more processors to perform a plurality of acts, said acts
comprising: (a) calculating adipose thickness factors for a
predetermined plurality of sites on the subject; and (b) analyzing
electrical activity measurements and determining therefrom analysis
values for a plurality of muscles, each of the plurality of muscles
corresponding to a respective one of the plurality of sites, and in
determining the analysis values, factoring the adipose thickness
factors into the electrical activity measurements.
36. The computer readable medium of claim 35, the adipose thickness
factors being determined by applying results of obtained
measurements from a sampling of individuals, the results relating
adipose thickness to general characteristics measured for the
individuals, the at least one general characteristic of the subject
corresponding to at least one of the general characteristics
measured for the individuals.
37. The computer readable medium of claim 36, the results being
represented in a set of coefficients that are applied to a formula,
each coefficient relating to one of the at least one general
characteristic of the subject and to one site of the plurality of
sites on the subject.
38. The computer readable medium of claim 37, the formula having a
form:Adipose=B.sub.0+B.sub.1X.sub.1+ . . . +B.sub.nX.sub.n,wherein
B.sub.0 through B.sub.n comprise the set of coefficients for a
given site, X.sub.1 through X.sub.n represent values for a
different one of the at least one general characteristic of the
subject, and n is the number of the at least one general
characteristic.
39. The computer readable medium of claim 37, wherein the
coefficients are regression-based coefficients.
40. The computer readable medium of claim 37, the one of the set of
general characteristics of the subject being a gender, a height, a
weight, a Body Mass Index, a body type, a waist circumference, a
chest circumference, a wrist circumference, or a light
transmissiveness of skin.
41. The computer readable medium of claim 35, the adipose thickness
factors being determined by applying a formula that includes a set
of coefficients, each coefficient relating to one of the plurality
of sites on the subject.
42. A computer readable medium having stored therein one or more
sequences of instructions for analyzing for muscle dysfunction of a
subject, said one or more sequences of instructions causing one or
more processors to perform a plurality of acts, said acts
comprising: (a) calculating adipose thickness factors for a
predetermined plurality of sites on the subject; (b) determining
analysis values for each of a plurality of muscles from electrical
activity measurements for the plurality of sites; and (c)
determining from the analysis values, a degree of departure from a
normal condition, the degree of departure being normalized with
respect to the plurality of muscles.
43. The computer readable medium of claim 42, wherein the normal
condition is an ideal normal condition.
44. The computer readable medium of claim 42, wherein said acts
further comprise mapping the degree of departure each of the
plurality of muscles.
45. The computer readable medium of claim 42, wherein the
electrical activity measurements are previously made at the
plurality of sites are for specific periods in the execution of a
set of motor tasks, and the degree of departure is determined by
selectively integrating the analysis values across the set of motor
tasks.
46. The computer readable medium of claim 42, the electrical
activity measurements relating to a performance of a motor task,
and said acts further comprise determining a set of relationships
for each of the analysis values, each relationship in the set for
an analysis value relating the analysis value to one of the other
muscle analysis values as a pair, and the degree of departure being
determined by selectively integrating across the set of
relationships.
47. The computer readable medium of claim 46, each relationship
including a weighting factor that reflects a biomechanical
significance in the execution of a motor task correlating the
muscles associated with the pair of analysis values.
48. The computer readable medium of claim 46, each relationship
including a weighting factor that reflects a biomechanical
significance that correlates the motor task with the one of the
plurality of muscles associated with the analysis value.
49. The computer readable medium of claim 46, each relationship
including a factor that reflects a systematic variability in
measurement of electrical activity.
50. The computer readable medium of claim 42, wherein the degree of
departure comprises a continuous measure.
51. The computer readable medium of claim 42, wherein said acts
farther comprise calculating adipose thickness factors for the
plurality of sites, and in determining the analysis values,
factoring in the adipose thickness factors.
52. A back muscle dysfunction evaluation network for determining
muscle dysfunction of subjects comprising: (a) at least one data
collection system for making electrical activity measurements at a
respective plurality of sites on each of the subjects; (b) a data
analysis system for analyzing the electrical activity measurements
and determining therein analysis values for a plurality of muscles,
each of the plurality of muscles corresponding to a respective one
of the plurality of sites; and (c) a communications link linking
the data analysis system and the data collection system, for
transmitting the electrical activity measurements of subjects to
the data analysis system.
53. The back muscle dysfunction evaluation network of claim 52, the
data analysis system comprising a processor and sample database,
the processor using the sample database to determine the analysis
values for the plurality of muscles and to analyze the analysis
values.
54. The back muscle dysfunction evaluation network of claim 52, the
data analysis system producing a report on a degree of departure
from a normal condition for each of the plurality of muscles, and
the communications link transmitting the report to the data
collection system.
55. The back muscle dysfunction evaluation network of claim 54,
wherein the degree of departure for the analysis values is
normalized with respect to the plurality of muscles.
56. The back muscle dysfunction evaluation network of claim 52, the
data collection system making a measurement of at least one general
characteristic of each subject, the communications link
transmitting the measurement of the at least one general
characteristic to the data analysis system, and the data analysis
system determining adipose thickness factors for the plurality of
sites on the subject based on the at least one general
characteristic of each subject, and in determining analysis values,
factoring the adipose thickness factors into the electrical
activity measurements.
57. The back muscle dysfunction evaluation network of claim 56, the
at least one general characteristic of each subject being a gender,
a height, a weight, a Body Mass Index, a body type, a waist
circumference, a chest circumference, a wrist circumference, or a
light transmissiveness of skin.
58. The back muscle dysfunction evaluation network of claim 52, the
communications link is an Internet connection.
59. A system for determining muscle dysfunction of a subject, the
system comprising: (a) a means for making electrical activity
measurements for a respective plurality of sites on the subject;
and (b) a means for analyzing the electrical activity measurements
and determining therein analysis values for each of a plurality of
muscles from the electrical activity measurements, and for
determining from the analysis values for each of the plurality of
muscles a degree of departure from a normal condition by
normalizing the analysis values with respect to the plurality of
muscles.
60. A muscle dysfunction report comprising: (a) a reference to a
tested muscle; and (b) an impairment value representing a degree of
departure of the muscle from an ideal normal condition, wherein
different muscles having the same degree of departure have the same
impairment values.
61. The muscle dysfunction report of claim 60, wherein the
impairment value relates to an impairment index capable of
characterizing any degree of departure of the muscle from an ideal
normal condition.
Description
[0001] The present invention relates generally to the field of
muscle performance evaluation systems. More particularly, the
present invention relates to the field of determining back muscle
dysfunction.
BACKGROUND OF THE INVENTION
[0002] One method of analyzing the state of a muscle is to collect
measurements of electrical signals associated with the activity of
that muscle. This type of measurement is known as electromyographic
(EMG) measurement, and it may be performed using either invasive
(percutaneous) or non-invasive techniques. EMG measurements have
been used in a number of different medical applications including
the treatment and possible diagnosis of lower back pain.
[0003] While percutaneous EMG techniques have been accepted in
medicine as accurate for measuring the electrical activity of an
underlying muscle, their use is often undesirable or unacceptable.
That is, percutaneous EMG techniques require additional materials
and expertise, and they present risks not found with non-invasive
techniques.
[0004] Alternatively, evaluating muscle activity using non-invasive
or surface EMG (sEMG) measurements has attracted interest from
scientists and medical practitioners for the last 30 years with its
promise as an objective, painless muscle measurement technique.
[0005] Measurements of surface electrical activity, or any other
clinical measurement, must meet several objectives and criteria
relating to reliability in order to be considered useful for
providing diagnostic or evaluative information. For example, the
electrical activity signal measured should be objectively defined
and reproducible. The information obtained should meet a need that
is best met by making surface EMG measurements. Further, the
information should be usable and easily interpreted by the level of
skill of practitioners for which it is intended. Finally, the
process should be cost-effective and have universal application as
either an assessment or therapeutic system or both.
[0006] To meet these objectives, the evaluation system should
reliably differentiate between healthy, normal, pain-free subjects
and subjects with muscle disorders. The evaluation system should
also report results with an extremely high level of statistical
certainty.
[0007] Of the many possible applications for surface EMG
measurement, back function evaluation is one of the most suitable.
A relatively large percentage of the population experiences back
pain that could be attributed to soft tissue damage, i.e., muscle
dysfunction. Traditional evaluation techniques have not been
effective at objectively determining muscle dysfunction responsible
for such pain.
[0008] Typical clinical evaluation techniques have relied upon
subjective evaluations by the patient to determine the nature of
the dysfunction. That is, the patient is usually asked to perform
certain motions, and depending upon the patient's ability to
perform these motions within subjective pain parameters, a
diagnosis is made.
[0009] Further, from an economic standpoint, a large percentage of
insurance claims are made by individuals claiming to have muscle
back pain. Because of the subjective nature of the testing, these
claims usually cannot be objectively verified. Accordingly, there
is a large potential for fraudulent claims being filed at a
substantial cost to insurance companies, and ultimately, the
consuming public.
[0010] A muscle assessment system should be a capable of making
significant comparisons of any given patient to a normative group.
Because of the comparative nature of the assessment process, the
importance of having an evaluation system capable of producing
reproducible data becomes paramount.
[0011] In the past, studies that have attempted to achieve
reproducibility, or to minimize the variation in data, used the
maximum voluntary contraction (MVC) method of normalization. This
technique requires high levels of muscle activation, causing the
engagement of fast-twitch motor units not ordinarily activated in
normal movements. That is, these studies compare the measured
muscle activity during evaluation to an MVC.
[0012] In normal muscle, the slow-twitch motor units produce most
of their fused tension before fast-twitch motor units begin to add
to muscle force. The addition of fast-twitch motor units in MVC
causes a disproportionate increase in the sEMG. The inclusion of
fast-twitch motor units, which are seldom used in everyday
functioning, occurs with the MVC condition and influences the
anatomical distribution and force-voltage relationship of EMG data.
Moreover, MVC runs the risk of exacerbating pain and doing further
damage to dysfunctional muscles.
[0013] Clinical use of sEMG has failed to produce a sufficiently
objective evaluation of muscle health. In much of the literature
relating to back muscle evaluation, equivalence is sought between
EMG resting levels and painful muscles or back pain in general.
However, static resting measurements are greatly influenced by
small postural adjustment that cannot be adequately controlled.
Accordingly, the postural and instrumental error can become so
large so as to obscure useful information.
[0014] Accordingly, a need exists for a system and method that
correctly characterizes muscle dysfunction with a high degree of
reproducibility. Further, such a system and method should allow for
normalization of data using normally activated muscle values.
[0015] U.S. Pat. No. 5,502,208, entitled "Method for Determining
Muscle Dysfunction," issued to Toomin et al. ("the '208 patent")
and incorporated herein by reference, discloses a method and system
that seeks to achieve the above objectives. However, the method and
system disclosed in the '208 patent features several
disadvantages.
[0016] The method disclosed in the '208 patent employs a process
that discretely quantifies all of the data elements under analysis.
One disadvantage of this method is that it cannot account for data
that falls short of subjectively predetermined cutoff points,
regardless of the proximity to those cutoff points or the
consistency of the data that falls beneath a cutoff point.
[0017] For example, the method disclosed in the '208 patent would
be indifferent to the following hypothetical case: Out of 100 data
elements total, if 50 of them have achieved between 80% and 90% of
the predetermined cutoff point, but only ten of them exceeded the
cutoff point, then the former 50 data elements would simply be
labeled "normal," and discarded from further analysis. Those 50
data elements would not contribute to the final result, despite
their close proximity to the cutoff point and their significant
number, i.e., their consistency. The system would then use only ten
out of 100 elements to make its determination.
[0018] The method and system disclosed in the '208 patent is also
vulnerable to measurement error. By using discrete quantification,
this method allows for opportunities for measured data to fall on
one side of the cutoff points on one occasion, and to fall on the
other side of the cutoff points if measured on a different
occasion, potentially yielding very different results for each
occasion.
[0019] Moreover, the method and apparatus disclosed in the '208
patent presents its ultimate finding in a broad classification
system, wherein each muscle under diagnosis is assigned one of a
handful of categories in this classification system: "normal,"
"symptomatic," "dysfunctional," etc. These terms are only defined
as being relative to one another. For example, "symptomatic" is
considered more severe than "normal," and "dysfunctional" is more
severe than "symptomatic." However, this classification system
presents no true or absolute indication of the degree of departure
from an ideal or absolute normal condition of the muscle under
evaluation.
[0020] Another disadvantage of the apparatus disclosed in the '208
patent is that it uses adipose corrections provided from a table,
the contents of which depend upon an adipose tissue measuring
device that has since been found to be somewhat unreliable.
[0021] Another further disadvantage of the method and apparatus
disclosed in the '208 patent is that it relies heavily on an
assumed normal or Gaussian distribution of data within the
normative database. This method is therefore susceptible to error
arising from departures from a normal distribution in the actual
data collected and analyzed.
SUMMARY OF THE INVENTION
[0022] The principal object of the present invention is to provide
an improved method and system for determining back muscle
dysfunction.
[0023] Another object of the present invention is to provide a
method and system for determining back muscle dysfunction that
employs a method of continuous quantification of all data elements
that are collected.
[0024] Another object of the present invention is to provide a
method and system for determining back muscle dysfunction that
presents the results of the diagnostic procedure using a numerical
impairment index for each muscle evaluated, whereby the numerical
impairment index falls along a continuum of deviation from an ideal
normal state for that muscle.
[0025] Another object of the present invention is to provide a
method and system for determining back muscle dysfunction that
employs regression-based formulas to determine adipose corrections
necessary for each patient, based on various anatomical
measurements of that patient.
[0026] Another object of the present invention is to provide a
method and apparatus for determining back muscle dysfunction that
does not presume the existence of a normal or Gaussian distribution
for collected data.
[0027] Additional objects and advantages of the invention will be
set forth in part in the description that follows, and in part will
be evident from the description, or may be learned by the practice
of the invention. These and other objects and advantages of the
invention may be realized and obtained by means of the
instrumentalities, procedures, and combinations particularly
pointed out in the appended claims.
[0028] To achieve the objects in accordance with the purpose of the
invention, as embodied and described herein, a preferred method for
determining back muscle dysfunction comprises the steps of: (1)
selecting a set of sites on the subject for sensing muscle
electrical activity, (2) making electrical activity
measurements-for the set of sites; and (3) performing an analysis
of the electrical activity measurements, the analysis including
determining from the electrical activity measurements analysis
values for each of a set of muscles and determining from the
analysis values a degree of departure from a normal condition,
where the degree of departure for the analysis values is normalized
with respect to the plurality of muscles.
BRIEF DESCRIPTION OF THE DRAWINGS
[0029] The accompanying drawings, which are incorporated in and
constitute a part of the specification, illustrate one embodiment
of the invention and, together with the description, serve to
explain the principles of the invention.
[0030] FIG. 1A is an illustration of a preferred embodiment of a
back muscle dysfunction system of the present invention comprising
a data collection subsystem and a data analysis subsystem.
[0031] FIG. 1B is an illustration of a preferred embodiment of a
data collection subsystem in the back muscle dysfunction system of
the present invention.
[0032] FIG. 1C is an illustration of a preferred embodiment of a
data analysis subsystem in the back muscle dysfunction system of
the present invention.
[0033] FIG. 2 is an illustration of a preferred embodiment of a
communication network implementing a back muscle dysfunction system
of the present invention where a data analysis subsystem as
depicted in FIG. 1C is networked to a plurality of data collection
subsystems as depicted in FIG. 1B.
[0034] FIG. 3 is a flow chart illustrating basic steps in a
preferred method of the present invention for performing back
muscle dysfunction analysis.
[0035] FIG. 4 illustrates the pattern of electrode placements
according to a preferred embodiment of the invention.
[0036] FIGS. 5-13 illustrate motions performed in accordance with a
preferred embodiment of the present invention.
[0037] FIG. 14 is a flow chart detailing preferred sub-steps for
performing the data analysis step depicted in FIG. 3.
[0038] FIGS. 15A-15C depict a flow chart detailing preferred
sub-steps for the step of FIG. 14 of determining a muscle's degree
of departure from an ideal normal condition.
[0039] FIG. 16 is a diagram of an exemplary evaluation report for a
patient experiencing back muscle dysfunction.
DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0040] Reference will now be made to one or more present preferred
embodiments of the invention, examples of which are illustrated in
association with the accompanying drawings.
[0041] A preferred embodiment of the system and method for
determining back muscle dysfunction is practiced using the back
muscle dysfunction (BMD) system 8 illustrated by way of example in
FIG. 1A. While the preferred embodiments of the BMD system 8 and
method for muscle dysfunction analysis are described in reference
to back muscle dysfunction, it is understood that the present
system is also applicable to other muscles and muscle groups, such
as, for example, leg or abdominal muscles. The basic elements of
the BMD system 8 preferably include a data collection subsystem 10
and a data analysis subsystem 11. The BMD system 8 is preferably
implemented by separating the functional elements of collection and
analysis because of a practical preference to allow for data
collection at one location and data analysis at another location.
However, the BMD system 8 is optionally implemented in one location
wherein elements of the BMD system 8 that aid in the performance of
data collection also perform data analysis. Thus, the communication
link depicted in FIG. 1A between the two subsystems 10,11 is
optionally unnecessary or is internal to the processing
architecture of the BMD system 8.
[0042] FIG. 1B details preferred elements of the data collection
subsystem 10. The data collection subsystem 10 includes a set of
electrodes 12, a collector 14, and a collection subsystem data
controller 16. The electrodes 12 are electromyographic surface
electrodes, which contact the surface of an individual's skin. Each
of the electrodes 12 includes two contacts across which electric
potential is measured. The electrodes 12 detect electrical activity
changes, i.e., voltage changes between the two contacts of each
electrode. Typically, the detected magnitude of electrical activity
is in the range of 0-500 microvolts.
[0043] The measurement of electric potential changes (electrical
activity) on the surface of an individual's skin generated by
underlying muscles (sEMG) is performed at each of the plurality of
electrodes 12. While sEMG is preferably used to obtain the
electrical activity measurements in the preferred data collection
subsystem 10, muscle activity is equivalently measured by
alternative techniques to sEMG without departing from the spirit
and scope of the present invention. Moreover, alternative electrode
configurations are also contemplated which correspond to
equivalents known in the art. For example, single-contact type
electrodes are alternatively used which measure electric potentials
with reference to a single common ground, or reference voltage.
[0044] The collector 14 preferably includes a signal encoder 60 and
a computer I/O interface 40. Voltage detected by the electrodes 12
is transmitted to the signal encoder 60. Prior to transmission to
the signal encoder 60, detected analog electrical signals are
amplified at the electrodes 12. Preferably, the individual
on-electrode amplifier circuitry 32 amplifies the signals detected
from the electrodes 12 into a desired voltage range. In a preferred
embodiment, the voltage range is zero to four volts. Once the
signals from the electrodes 12 are amplified, the signal encoder 60
preferably receives as input amplified analog electrical signals
from multiple channels. The signal encoder 60 then preferably
digitizes the analog signals, transforms the electrical signals
into light signals, and yields as output to the computer I/O
interface 40 a digitally encoded light signal along a single
optical conduit. The signal encoder 60 also supplies power to
operate the on-electrode amplifier circuitry 32. An additional
contact is preferably applied in order to cancel common mode
variations for the signals detected by the electrodes 12. That is,
a common mode drive electrode (not shown) is attached to the
patient's back.
[0045] To perform these functions, the signal encoder 60 preferably
includes a multiplexer 34, an analog-to-digital converter 36, and
an optical isolator 38. The amplified analog signals are fed to the
multiplexer 34, which selects each of the signals in turn. Each
selected signal is then transmitted to the analog-to-digital
converter 36. The analog-to-digital converter 36 then converts the
received analog signal into a digital signal.
[0046] The electrodes 12 and signal encoder 60 are optically
isolated from the rest of the data collection subsystem 10 to
protect any patient with whom the electrodes 12 are in contact.
Thus, there exists no direct electrical connection between the
electrodes 12 and the output signal from the collector 14. In the
preferred embodiment, the optical isolator 38 is located after the
analog-to-digital converter 36. In another preferred embodiment,
the optical isolator 38 is realized via a fiber optic cable, which
transmits the digital signal from the analog-to-digital converter
36 to the data controller 16 via the computer I/O interface 40. In
addition to potentially protecting a patient from shock, the
transmission of data using an optical signal though the optical
isolator 38 is immune to contamination from environmental
electrical or RF interference. Alternatively, instead of the
optical isolator 38, another device for electrically insulating the
patient from the rest of the data collection subsystem 10 is
implemented including using an infrared, radio or another wireless
mechanism for communicating measurement data.
[0047] Preferably, however, the optical isolator 38 is used and is
an integrated component within the signal encoder 60. Within the
optical isolator 38, the input signal activates a light-emitting
diode, LED (not shown), which transmits a light signal containing
information on the magnitude of the electrical potential to a fiber
optic cable. The fiber optic cable, in turn, carries the light
signal out of the signal encoder 60 to a photo detector. The photo
detector (not shown) converts the light signal containing the
magnitude information into an electrical signal.
[0048] In a preferred embodiment, the output of the optical
isolator 38 is transmitted to a computer I/O interface 40.
Contemporary examples of such computer I/O interfaces are Universal
Serial Bus, PCMCIA card, PCI card, SCSI and FireWire. The computer
I/O interface 40 transmits data between the signal encoder 60 and
the data controller 16.
[0049] The data controller 16 preferably includes a processor 22
and computer media storage 24. Preferably, the output of the
optical isolator 38 is transmitted to a computer I/O interface 40
which transmits digital signals between the collector 14 and the
processor 22. The processor 22 uses a software-implemented
multiplexer to select the received signals from the computer I/O
interface 40. Preferably, the software multiplexer is a program
executed by the processor 22 to select given inputs at
predetermined times, so that the signal is time-division
multiplexed. Thus, the multiplexer 34 selects a corresponding
output of the amplifier circuits. 32, and then transmits groups of
signals in sequence through the analog-to-digital converter 36, the
optical isolator 38 and the computer I/O interface 40. The software
multiplexer then selects these signals in sequence. For the full
set of data from all electrodes 12 in the pattern to be read, the
sequences of signals are transmitted to the software multiplexer,
whereby each sequence corresponds to the outputs from the
electrodes 12. The rate at which the data is stored and sampled is
such that no substantial change occurs in the muscle between sample
times. Preferably, the data is received and processed in parallel
such that sampling rates need not be a consideration in the
subsequent analysis. Accordingly, the sEMG measurements from the
pattern of electrodes 12 are grouped such that each group has
measurements that have been taken at substantially the same
time.
[0050] Preferably, the collection subsystem data controller 16
further includes a video display 18, a keyboard 20, a graphics
program 26, a data reduction program 28, a sound generator 30, and
a speaker 31. Via the keyboard 20, a user controls the operation of
the data collection subsystem 10 by issuing commands that are
processed by the processor to begin and end the receipt and storage
of data. The digital electronic activity signals received by the
processor 22 are optionally displayed on the video display 18, and
stored on the computer media storage 24. The graphic program 26
uses the data received through the processor 22 to preferably
generate a graphical display of the variation in electrical
signals. The data reduction program 28 preferably compresses data
to be stored on the computer media storage 24. The sound generator
30 and speaker 31 enable the output of audio cues to aid an
operator of the data collection subsystem 10 to timely instruct a
patient regarding the performance of the various motor tasks
required by the patient in a preferred method of determining back
muscle dysfunction.
[0051] The collection subsystem data controller 16 may be a
computer like that manufactured by IBM or Apple with a monitor such
as, for example, a cathode ray tube (CRT) or liquid crystal display
(LCD). A computer executing software is preferably used for the
data controller 16 because of the utility and flexibility in
programming and modifying the software, displaying results, and
running other peripheral applications. Alternatively, the
collection subsystem data controller 16 may be implemented using
any type of processor or processors that may analyze electrical
measurements of muscle activity as described herein. Thus, as used
throughout, the term "processor" refers to a wide variety of
computational devices or means including, for example, using
multiple processors that perform different processing tasks or have
the same tasks distributed between processors. The processor(s) may
be general purpose CPUs or special purpose processors such as are
often conventionally used in digital signal processing systems.
Further, multiple processors may be implemented in a server-client
or other network configuration, as a pipeline array of processors,
etc. Further, some or all of the processing is alternatively
implemented with hard-wired circuitry such as an ASIC, FPGA or
other logic device. In conjunction with the term "processor," the
term "computer media storage" refers to any storage medium that is
accessible to a processor that meets the memory storage needs for
analyzing electrical measurements of muscle activity.
[0052] FIG. 1C depicts a preferred embodiment of the data analysis
subsystem 11 for analyzing the patient data collected and stored by
the data collection subsystem 10. The data analysis subsystem 11
preferably comprises a processor 53, a normative database 54 stored
on computer media storage, and a database/patient comparison
program that is similarly stored in computer media storage and
executed by the processor 53. The database processor 53 executing
the comparison program 55 is optionally a processor of known
design, such a personal computer or a mainframe system, any other
form of processor as described in reference to the processor 22, or
even the same processor as processor 22, as noted above. In
preferred embodiment, the normative database 54 is a database of
normal activity and may be one of many such databases in the data
analysis subsystem 11 that is used for comparison purposes.
Preferably, the normative database 54 contains data regarding a
population of individuals whose measurements are considered healthy
and which constitute a healthy population of individuals.
Additional databases having data from a sufficient number of
individuals who have been diagnosed with muscle dysfunction may be
established as examined patient databases. Further, sub-databases
of the normative database 54 or the examined patient databases
according to a variety of classification schemes, may be defined to
enable more specific comparison studies with specific patients.
Functionally, the data analysis subsystem 11 receives as input
patient data from the data collection subsystem 10 and performs a
comparative analysis using the normative database 54, and
preferably, one or more examined patient databases.
[0053] In a preferred embodiment, the database comparison program
55 reads data from the computer media storage 24 and compares the
patient's data with the normative database 54 or a sub-database of
the normative database 54 to produce a document report addressing
the condition of the patient. Data on a patient from the computer
media storage 24 is input into the database processor 53. The raw
data and calculations from the computer media storage 24 are used
by the database comparison program 55 to quantify back muscle
dysfunction for a patient. Accordingly, the electrical muscle
activity measurements collected for the patient and stored in the
computer media storage 24 are used to calculate electrical muscle
activity ratio values and other analysis values that are then
compared by the database comparison program 55 to the sample values
of the normative database 54. Preferably, in quantifying back
muscle dysfunction, the processor 53 determines a measure of back
muscle dysfunction in response to the comparison of the patient
ratios. Preferably, the database comparison program 55 returns a
number of conclusions on which a diagnostic evaluation may be
based.
[0054] Preferably, the conclusions of the database comparison
program 55 are based on a comparison of electrical activity
measurements. However, the conclusions may additionally be based
upon other factors. These other factors include relaxation time and
change in relaxation time between activity and rest periods.
Another indication of muscle health may be determined by the
spectral characteristics of the measured signal. The adipose tissue
correction factor is also preferably considered in making the
comparison as discussed above.
[0055] FIG. 2 illustrates a preferred embodiment of the BMD system
8 implemented as a muscle dysfunction evaluation network where a
centralized data analysis subsystem 11 as depicted in FIG. 1C is
networked to a plurality of data collection subsystems 10 as
depicted in FIG. 1B. FIG. 2 depicts ten data collection subsystems
10 with a single data analysis subsystem 11 purely as an example of
a possible network configuration of the BMD system 8. It is
understood that a BMD system 8 implemented as a network is not
inherently limited in terms of the number of connected subsystems
10, 11. Specifically, in the embodiment shown in FIG. 2, BMD data
is collected at one location and analyzed at another location. The
data may be provided from one of the data collection subsystems 10
to the data analysis subsystem 11 by any available means. However,
the communications for the BMD system 8 are preferably implemented
using Internet Communication or switched telephone line services,
preferably using 56 Kbps modems or ISDN interfaces. The BMD system
8 is also optionally adapted to high-speed access over available
high-speed links, such as T1, T3, ADSL, telephone lines, cable
modems or other means of high-speed access. The communications are
alternatively implemented using available wireless communicating
means, including satellite or terrestrial systems. In an Internet
communication configuration, data collection subsystems 10 securely
communicate with the data analysis subsystem 8 via an Internet web
page that preferably requires login and password entry. Preferably,
a mechanism is provided for Internet transmission of collected data
from the data collection subsystems 10. Further, an alternative
mechanism allows users associated with the data collection
subsystems 10 to retrieve analyzed data, preferably in the form of
analysis reports. The retrieval process preferably allows the users
to have the reports securely downloaded, emailed, faxed or in any
other manner transmitted to the data collection subsystem 10, to a
fax machine, or to any other data output computer or terminal that
can display, produce or otherwise output analysis reports. The
retrieval process further allows the reports to be directed using
traditional mail.
[0056] If the network is wholly implemented in a local area, such
as within a clinic or hospital, as a local network such as an
intranet, client-server system, or other similarly-sized network,
the communications are preferably implemented using systems and
protocols that are used for such communications such as Ethernet,
TCP/IP, parallel port, serial port, etc. A wireless communication
system for communicating the data is optionally implemented,
preferably using infrared, RF, one of the ISM (Industrial,
Scientific and Medical) bands or other frequencies. In one
preferred embodiment, multiple data analysis subsystems in a
plurality of local clinics or hospitals communicate with a
centralized data analysis subsystem over a wide area network. In
this configuration, the databases in the local data analysis
subsystems 11 periodically update and are updated by the
centralized database subsystem. To the users of the data collection
subsystems 10, the network of data analysis subsystems 11
preferably operates as a virtual single analysis subsystem.
[0057] FIG. 3 illustrates basic steps of a preferred method of
determining back muscle dysfunction. In the first step 132, the
method for determining back muscle dysfunction is initiated or
initialized. The processor 22 initializes the data collection
subsystem 10 for data collection. As a second step 134, anatomical
measurements are taken to determine sensor site placement and
adipose correction. In the next step 136, the electrodes 12 are
applied in a pattern across an individual's back and measurements
of electrical activity preferably as a set of measurements from the
pattern of electrodes are collected. The set of measurements
includes a predetermined number of values, corresponding
respectively to measurements of electrical activity made at
substantially the same time from each of the plurality of
electrodes 12 in the pattern.
[0058] FIG. 4 is an anatomical diagram of the muscles of the back
illustrating the pattern of electrode placements on the back
according to the preferred embodiment. Electrodes 12 are
illustrated by blackened ovals. The white center-portion in each of
the ovals is where the electrode is centered. The electrodes 12 are
named in accordance with the corresponding muscles over which they
are located. The left side of the pattern of electrodes is
illustrated in FIG. 3 and includes a cervical paraspinal electrode
121, an upper trapezius electrode 122, a middle trapezius electrode
123, a thoracic paraspinal electrode 125, a latissimus dorsi
electrode 126, an obliquus externus electrode 127, and a lumbar
paraspinal electrode 128. A matching number of electrodes are
present on the right side in equivalent positions. Thus, preferably
fourteen electrodes are placed on the back of the individual or
patient whose sEMG signals are being measured. Alternatively,
additional or fewer electrodes may be applied to a patient's back,
or a completely separate muscle set of muscle sites may be
tested.
[0059] The electrodes in the preferred embodiment may be applied
individually as may be the case with the illustrated electrodes 12
or may, in an alternate embodiment, be mounted in an electrode
jacket, not shown. The electrode jacket is worn by the individual
and has electrodes similar to the electrodes 12 mounted therein in
the desired pattern. The electrodes of the electrode jacket make
contact in the appropriate locations when the individual wears the
jacket.
[0060] Adipose tissue can affect the transmission of electrical
activity from the underlying muscle. The adipose tissue attenuates
the signal between the underlying muscle and the corresponding
electrode on the surface of the skin. A correction factor can be
computed for a given adipose tissue thickness that can be applied
to every measurement from the corresponding electrode. This
correction factor can be derived from regression-based formulas
using various anatomical measurements of the patient.
[0061] The measurement of electrical activity over a muscle can be
indicative of the health of that muscle. More particularly,
depending upon how and when the measurement is taken, a significant
amount of information may be obtained regarding the health of a
muscle. Muscle dysfunction is often indicated by a relatively low
electrical output (hypoactivity), a relatively high electrical
output (hyperactivity), changes in the electrical output's spectral
characteristics, or changes in the output during the relaxation
time after activity. With respect to the electrical output's
spectral characteristics, if the electrical activity measured at
the muscle has frequent variations in amplitude, the signal is said
to be a rough signal. This "roughness" in the detected electrical
signal is typically an indication of muscle dysfunction.
[0062] If a muscle is damaged, the sEMG may indicate muscle
substitution. Muscle substitution occurs when another set of
muscles is used to compensate for the lack of functionality of the
muscle being evaluated, due to the damage to that muscle.
Accordingly, muscle substitution can be a measurable indication of
damage, as well as an indication of the nature and location of the
damage.
[0063] In the preferred embodiment, the individual performs a
predetermined set of motor tasks during collection of EMG
measurements. These motor tasks are carried out via consecutive
periods of rest, dynamic muscle engagement, and static muscle
engagement. The audio cues from the speaker 31 aid in the
instruction of when the patient should begin a motor task, should
remain static, or rest. Rest is defined as having the patient stand
in a relaxed position with arms at the sides. Dynamic muscle
engagement is defined as the process of transitioning from the rest
position to the particular static pose required for the specific
motor task being performed. Static muscle engagement is defined as
having the patient maintain the static pose of the specific motor
task for a specific period. As an example, one complete EMG
measurement session for a motor task consists of a period of rest,
immediately followed by a period of dynamic muscle engagement, with
that immediately followed by a period of static muscle engagement.
The length of the rest period should equal the combined length of
the dynamic and static muscle engagement periods. This sequence of
three phases of muscle engagement constitutes one complete EMG
measurement session. This one complete EMG measurement session is
itself repeated several times for each motor task in the process of
completing acquisition of EMG data sufficient for analysis by the
data analysis subsystem 11.
[0064] In the preferred embodiment, only the EMG data collected
during the periods of static muscle engagement are used. In an
alternate embodiment, EMG data collected during the rest and
dynamic periods as well as the static period are used in the data
analysis conducted.
[0065] Electrical activity measurements are made at specific
periods within the movements. Preferably, measurements are made
during the range of motion and at the endpoints of the motion. This
dynamic measurement of muscles during a motor task and the
measurement of muscles under tension give a more accurate picture
of the muscle action. Relaxed measurements are subject to small
postural variations that are hard to correct.
[0066] FIGS. 5-13 illustrate the movements used during the dynamic
measurements. FIG. 5 illustrates the right arm overhead movement,
whereby the individual extends the right arm overhead, and the left
leg backward. FIG. 6 illustrates the left arm overhead movement,
whereby the individual extends the left arm overhead, and the right
leg backward. FIG. 7 illustrates the arms overhead movement,
whereby the individual extends both arms overhead. FIG. 8
illustrates the forward arm flexion movement, whereby the
individual extends both arms forward to a 90-degree angle from the
body, with the palms facing downward. FIG. 9 illustrates the arm
abduction movement, whereby the individual extends both arms from
the sides to a 90-degree angle from the body, with the palms of the
hand facing down. FIG. 10 illustrates the shoulder shrug movement,
whereby the individual shrugs both shoulders upward. FIG. 11
illustrates the forward bow movement, whereby the individual bows
45-degrees forward at the waist, with both arms to the sides. FIG.
12 illustrates the left trunk rotation movement, whereby the
individual rotates to the left with a maximum range of motion at
the hips and head. FIG. 13 illustrates the right trunk rotation
movement, whereby the individual rotates to the right with a
maximum range of motion at the hips and head.
[0067] In a preferred embodiment, there are 7.2-second motion
periods and 7.2-second rest periods, and the full pattern of the
electrodes 12 is sampled about 2000 times per second. As a next
step 138 in the preferred method, these measurements are stored in
the processor 22 in computer media storage 24 in the manner
described above. Alternatively, other sampling rates and groups of
measurements are used.
[0068] The computer media storage 24 preferably only contains raw
EMG activity data. However, the preferred embodiment ultimately
employs ratios of EMG activity data in its analysis. This ratio
technique eliminates much of the variability inherent in using sEMG
to measure muscle activity.
[0069] In accordance with a preferred embodiment, the next step 140
is to analyze the data using a previously compiled database of sets
of measurements from a plurality of individuals by making
diagnostic comparisons of data. In the previously performed process
of compiling the database of measurements, electrical muscle
activity measurements for asymptomatic individuals are collected in
a number sufficient to develop a sample representative of a
population. Preferably, a group of individuals is selected for
collection of electrical muscle activity measurements. The
individuals can be considered a normative set of individuals to
which patients can be compared.
[0070] In a final basic step 142 of the preferred method of
analyzing for muscle dysfunction, the diagnostic conclusions
generated by the data comparison program 55 are output in a
document report 58. The document report 58 preferably provides
patient identifying data and reports diagnostic conclusions and
helps determine a desirable therapeutic treatment.
[0071] In preferred embodiment of the system and method of
analyzing back muscle dysfunction, the data analysis step 140
depicted in FIG. 3 is performed as a series of sub-steps described
below and illustrated in FIGS. 14 and 15A-C. Preferably, the data
for patient analysis and the compilation of the normative database
54 are concerned primarily with the periods in which the patients
are statically engaged in motor tasks. First, a sub-step 146 of
averaging the data collected during each respective motor task is
performed. Thus, the sample measurements taken during the periods
of static engagement for a particular muscle and a particular motor
task are averaged.
[0072] In the next sub-step 148, the averaged electrical activity
data measurements for each muscle are adjusted for the adipose
thickness underlying the corresponding electrode. The correction
factor to the averaged measurements is preferably computed by
processor 53 using regression-based formulas to determine "true"
EMG values for each muscle based on the EMG measurements made at
the skin's surface. In one preferred embodiment, the attenuation of
the EMG signals due to adipose thickness are accounted for
according to the equation:
EMG.apprxeq.sEMG*antilog(B*Adipose)
[0073] where EMG is a regression-based estimate of the EMG at a
muscle, sEMG is the EMG measured at the skin's surface, Adipose is
a factor relating to the adipose thickness underlying the
electrode, and B is a regression coefficient that has one of two
values depending on the gender of the patient. Notice that as
Adipose approaches zero, sEMG approaches EMG. Alternatively, other
relationships between Adipose, sEMG, and EMG are used. For example,
in one alternative embodiment, an inverse relationship is used:
EMG=Adipose*sEMG/B.
[0074] In another alternative embodiment, an inverse square
relationship is applied to determine the estimated EMG at the
muscle:
EMG=Adipose.sup.2*sEMG/B.
[0075] Ideally, to determine the Adipose values that are inserted
into one of the above adipose attenuation formulas, the adipose
thickness measurements for each patient at each muscle are measured
directly. These measurements may be obtained via caliper
measurements of the skin fold or by using ultrasound. Ultrasound
devices potentially provide the most precise adipose measurements.
Preferably, the ultrasound device for performing such measurements
is designed to be inexpensive and easy to use such that patient
measurements of adipose are practical. Further, the ultrasound
device has post-processing to provide as output actual adipose
measurements rather than requiring medical personnel to
subjectively estimate adipose thickness from an ultrasound
image.
[0076] In an alternative preferred embodiment, rather than
requiring medical personnel to make adipose measurements for each
tested patient, regression-based formulas are used to estimate the
adipose correction factors. In one embodiment, the regression
formula has the following form:
Adipose.sub.i=B.sub.0+B.sub.1*Height.sub.i+B.sub.2*Weight.sub.i
(1).
[0077] In equation (1) above, Adipose is the adipose correction
factor, Height is the height of the patient in specified units,
Weight in the weight of the patient in specified units, and (i) is
a patient or observation number index. B.sub.0, B.sub.1, and
B.sub.2 are regression coefficients that vary depending on gender
and the bilateral muscle group at which an adipose value is
desired. B.sub.0 preferably is a coefficient relating specifically
to gender, B.sub.1 is a coefficient relating to height, and B.sub.2
is a coefficient relating to weight. Given preferably seven
examined bilateral muscle groups and two genders, preferably 14
sets of coefficients are established to enable the regression-based
adipose thickness estimation. By using such a formula, only
efficiently measured anatomical measurements for each patient need
be determined in the field, rather direct measurements of adipose
that require more time and/or complex measurement devices.
[0078] In another regression-based embodiment, the following more
generic formula is used:
Adipose.sub.i=B.sub.0+B.sub.1X.sub.1i+ . . . +B.sub.nX.sub.ni
(2).
[0079] In equation (2) above, X.sub.1i, X.sub.2i . . . X.sub.ni are
measurement values such as Height and Weight that were specified in
equation (1). However, equation (2) enables other types of
anatomical measurements to factor into the determination of
Adipose, up to (n) types. Such other types of measurements may
include, but are not limited to Body Mass Index (BMI), body type,
such as muscular, obese and slim, waist circumference, chest
circumference, wrist circumference, and light transmissiveness of
skin/adipose tissue. In equation (2) as in equation (1), B.sub.0,
B.sub.1, . . . B.sub.n are previously established adipose thickness
coefficients that relate to the types of measurements they
modify.
[0080] To obtain meaningful values for the adipose thickness
coefficients, adipose measurement tests on a large sample of
patients are preferably performed. In performing such tests for
developing the regression-based formulas, the ultrasound or other
measurement devices that enable precise measurements of adipose may
be used. Regressions are then performed to account for the
interaction effects of the various types of anatomical measurement,
and to finally determine sets of B coefficient values.
[0081] Returning to the sub-steps of the data analysis illustrated
in FIG. 14, in the next sub-step 150, the degree of departure from
an ideal normal condition or level of dysfunction for each muscle
is determined. The ideal normal condition represents a specific
state or condition within a normal condition range. The
identification and level of the dysfunctional muscles of sub-step
150 is determined by a procedure involving comparing patient ratio
data to sample ratio data stored in a sample database, i.e. a
normative database. In effect, the procedure is a muscle pattern
recognition (MPR.TM.) procedure in which a sample set of muscular
responses are compared to a library database of such responses to
determine identifying characteristics of the sample set. The steps
depicted in FIGS. 15A-C illustrate the procedure of sub-step
150.
[0082] Referring now to FIGS. 15A-C, the MPR.TM. analysis for
determining dysfunctional muscles as represented in sub-step 150 of
FIG. 14 comprises the following steps. In the first step 164, the
total number of motor tasks (M) to be used in the MPR.TM. analysis
and the total number of muscles (c) to be evaluated are identified.
Each combination of motor tasks and muscles are kept track of
individually. In the next step 166, for each combination of muscle
and motor task, a set of EMG measurements E.sub.m,c are
established. From these measurements, each step of the MPR.TM.
analysis preferably generates one or more types of values that are
termed herein generally as "analysis values." Specifically, in
initiating the MPR.TM. analysis, an index (i) to ratio values, R,
of EMG measurements within each motor task is established. Thus,
R.sub.m,c,i is the ratio value between the EMG measurement for
muscle (c) in motor task (m), E.sub.m,c, and the EMG measurement
for muscle (i) in motor task (m), E.sub.m,i. To assure meaningful
ratio values, (i) never equals (c). In the next step 168, a
logarithmic transformation of each of the previously calculated EMG
ratio values, R.sub.m,c,i, is determined.
[0083] In the next step 170, with the EMG ratio values,
R.sub.m,c,i, now being logarithmic, for each type of ratio
R.sub.m,c,i in the normative data base 54, two predetermined ratio
thresholds are now used. For each type of ratio R.sub.m,c,i, the
first ratio threshold, R.sub.m,c,i,-, is between the minimum ratio
and the median ratio in the normative database. The second ratio
threshold, R.sub.m,c,i,+, is between the median ratio and the
maximum ratio in the normative database. Preferably, R.sub.m,c,i,-
is set at the 2.5.sup.th percentile level value in the range of
values for R.sub.m,c,i in the normative database and R.sub.m,c,i,+
is set at the 97.5.sup.th percentile level value. These thresholds
guarantee a window of ratios that comprise 95% of the normative
database. By thresholding in this manner, the analysis is
independent of any assumptions regarding the distribution of the
normative database 54. Thus, in the next step 172, a direction of
aberrance, H.sub.m,c,i for each muscle pair, (c) to (i), for each
motor task (m) is determined based on the value of the logged ratio
R.sub.m,c,i in relation to its corresponding ratio thresholds,
R.sub.m,c,i,- and R.sub.m,c,i,+. In particular, if
.vertline.R.sub.m,c,i-R.sub.m,c,i,-.vertline.<.vertline.R.sub.m,c,i-R.s-
ub.m,c,i,+.vertline.,
[0084] then H.sub.m,c,i equals Hypoactive (HYPO). Alternatively,
if
.vertline.R.sub.m,c,i-R.sub.m,c,i,-.vertline.>.vertline.R.sub.m,c,i-R.s-
ub.m,c,i,+.vertline.,
[0085] then H.sub.m,c,i equals Hyperactive (HYPER). Finally, if the
absolute values of the differences are equal, then H.sub.m,c,i is
zero, as there is no direction of aberrance.
[0086] In the next step 174, a probability of aberrance,
P.sub.m,c,i, for each logged ratio value, R.sub.m,c,i, is
determined. The probability of aberrance, P.sub.m,c,i, is a
probability measure that combines the actual aberrance of each
ratio value, R.sub.m,c,i, based on its proximity from R.sub.m,c,i,-
and R.sub.m,c,i,+ and the EMG measurement system's retest
variability. Knowledge of retest variability is required to
estimate the inherent variability introduced by the MPR.TM.
analysis as a whole. Statistics estimating the variability of the
MPR.TM. analysis in recording each ratio under the conditions of
retest are preferably previously computed from a gathered retest
data set. Using the known normative distribution and the retest
variability model, the probability of aberrance value, P.sub.m,c,i,
is computed for each ratio value by first determining the estimated
standard deviation of the ratio, s, as determined by the retest
variability model. Then, a value for a Line of Aberrance (LOA) is
defined as either R.sub.m,c,i,- or R.sub.m,c,i,+ depending upon the
ratio's direction of aberrance. Specifically,
If H.sub.m,c,i=HYPO: LOA=R.sub.m,c,i,-,
If H.sub.m,c,i=HYPER: LOA=R.sub.m,c,i,+.
[0087] Then, a z-score, z, of the LOA relative to the ratio's
modeled variability is determined and is preferably given by:
z=(LOA-R.sub.m,c,i)/s.
[0088] Then, the probability of aberrance, P.sub.m,c,i, for the
ratio is preferably given by the following formulas:
If H.sub.m,c,i=HYPO: P.sub.m,c,i=OTCDF(z),
If H.sub.m,c,i=HYPER: P.sub.m,c,i=1-OTCDF(z),
If H.sub.m,c,i.noteq.HYPO and H.sub.m,c,i.noteq.HYPER:
P.sub.m,c,i=0.
[0089] In the above equations, OTCDF(z) is the One-Tailed
Cumulative Distribution Function for the standard normal
distribution; i.e., OTCDF(z) is the probability that a standard
normal random variable is less than z. For example,
OTCDF(1.96)=0.975.
[0090] Thus, the probability of aberrance, P.sub.m,c,i, is the
probability of the patient's ratio exceeding its LOA. Further, by
example, the probability of aberrance where H.sub.m,c,i is neither
HYPO nor HYPER is exactly zero, and the probability of aberrance
where R.sub.m,c,i is equal to the LOA is exactly 50%.
[0091] In the next step 176, a weighted value, B.sub.m,c,i, for
each logged ratio value, R.sub.m,c,i, is derived. The values for
B.sub.m,c,i are weights assigned to each ratio value in each motor
task based upon the relative biomechanical significance of the
muscle relationship reflected by the muscles involved in the ratio
during the specific motor task. These weighted values are
predetermined portions of the MPR.TM. analysis, arrived at
empirically using principles of biomechanics.
[0092] In the next step 178, another weighted value, W.sub.m,c, is
determined using the predetermined bio-mechanical significance of
each motor task (m) in assessing abnormal recruitment of each
muscle (c). The values for W.sub.m,c are weights assigned to each
motor task for each muscle. These weighted values are based upon
the predictive value of the specified motor task in assessing
performance of the specified muscle. The weighted values are
pre-determined portions of the MPR.TM. analysis, arrived at
empirically using principles of biomechanics. Then, in the next
step 180, using the weighted values derived in the previous three
steps, a weighted deviation value from the ideal normal,
A.sub.m,c,i, for each logged ratio value, R.sub.m,c,i, is
determined. The value for A.sub.m,c,i for each ratio value is
preferably simply the product of the three weighted values
determined above. That is,
A.sub.m,c,i=P.sub.m,c,i*B.sub.m,c,i*W.sub.m,c.
[0093] In the next step 182, an overall deviation from normal,
A.sub.m,c, and a direction of deviation from normal, H.sub.m,c, for
each muscle (c) in each motor task (m) is determined. These
measures characterize the performance of each muscle in a given
motor task in a manner that is independent of each muscle's
relationship to other muscles in performing the given motor task.
Preferably, in determining values for A.sub.m,c and H.sub.m,c, a
hyperactive weighted deviation value, A.sub.m,c,+, is determined by
summing all values for A.sub.m,c,i for which H.sub.m,c,i has a
value of HYPER. Then, a hypoactive weighted deviation value,
A.sub.m,c,-, is determined by summing all values for A.sub.m,c,i
for which H.sub.m,c,i has a value of HYPO. If
A.sub.m,c,+>A.sub.m,c,-, then muscle (c) is considered
hyperactive in motor task (m). In this case, A.sub.m,c is set equal
to A.sub.m,c,+ and H.sub.m,c equals HYPER. Alternatively, if
A.sub.m,c,+<A.sub.m,c,-, then muscle (c) is considered
hypoactive in motor task (m). In this case, A.sub.m,c is set equal
to A.sub.m,c,- and H.sub.m,c equals HYPO.
[0094] In step 184, the procedure continues forward by eliminating
the particular motor task as a variable in the characterization of
each muscle and, instead, characterizing each muscle's state
independent of the motor task performed. In step 184, values for
the weighted deviation from normal, A.sub.c, and the direction of
deviation from normal, H.sub.c, are determined for each muscle.
Preferably, in a manner similar to the calculation performed above,
in determining values for A.sub.c and H.sub.c, a hyperactive muscle
deviation value, A.sub.c,+, is determined by summing all values for
A.sub.m,c for which H.sub.m,c has a value of HYPER. Then, a
hypoactive muscle deviation value, A.sub.c,- is determined by
summing all values for A.sub.m,c for which H.sub.m,c has a value of
HYPO. If A.sub.c,+>A.sub.c,-, then muscle (c) is considered
hyperactive overall. In this case, A.sub.c is set equal to
A.sub.c,+ and H.sub.c equals HYPER. Alternatively, if
A.sub.c,+<A.sub.c,-, then muscle (c) is considered hypoactive
overall. In this case, A.sub.c is set equal to A.sub.c,- and
H.sub.c equals HYPO.
[0095] In step 186, the values for A.sub.c are normalized using a
normalization function based on the statistics compiled from the
normative database to determine an Impairment Index.TM., I.sub.c,
for each muscle. I.sub.c thereby represents a scale that allows for
immediate recognition of the level of impairment of a muscle,
regardless of the type of muscle or the patient involved.
Specifically, in order to establish the extent of dysfunction
within each muscle, a common frame of reference for
muscle-to-muscle comparison is required. To realize this, each
A.sub.c value is normalized by a muscle-specific function derived
from the normative database. These muscle-specific normalization
functions are preferably derived as follows.
[0096] First, A.sub.c values are computed for every muscle for all
subjects, N, within the normative database. Then, the distribution
of each muscle's A.sub.c value within the normative database is
examined. Specifically, certain percentiles of the A.sub.c values
in the normative database population are determined for each
muscle. In the preferred embodiment, these percentile values are
determined at intervals of five, from five to 90, with the 1.sup.st
percentile also determined (i.e., the 1.sup.st, 5.sup.th,
10.sup.th, 15.sup.th, 20.sup.th, . . . , 80.sup.th 85.sup.th, and
90.sup.th percentiles). After the 90.sup.th percentile, percentiles
from 90 to 99 are preferably determined at intervals of length 1
(i.e., 90.sup.th, 91.sup.st, 92.sup.nd, . . . 98.sup.th, and
99.sup.th). The 99.sup.th percentile is then preferably established
as a so-called normative cutoff value or abnormal cutoff value,
which defines a separation point between "normal" and "abnormal."
That is, all A.sub.c values below the cutoff are considered normal,
and all above are abnormal. Thus, given a particular muscle, for 1%
of the normative database, that muscle is abnormal (i.e., a
pre-determined false positive rate).
[0097] Piece-wise linear interpolation functions (interpolants) are
then constructed using standard mathematical techniques, where the
normative database percentiles are used as the node points of each
interpolant function. The idea of the interpolant function is to
"map" the often-different A.sub.c values for each muscle to the
same basic function. A different interpolant function may be
prepared for each muscle. The interpolant functions are preferably
linear, although higher order interpolant functions such as
quadratic functions are alternatively used. Before this mapping,
all the A.sub.c values are in "different units of measure" and are
incomparable. After the mapping has converted the A.sub.c values to
an Impairment Index.TM. for each muscle, the muscles are
effectively measured in the "same units" and comparable to one
another.
[0098] The interpolant function for each muscle is preferably
determined by first establishing the 1.sup.st, 5.sup.th, 10.sup.th,
. . . , 85.sup.th, 90.sup.th, 91.sup.st, 92.sup.nd, . . ,
98.sup.th, and 99.sup.th normative database percentiles for the
A.sub.c values. The finally determined Impairment Index.TM. is
preferably approximately linear with respect to percentile. For
example, the 99.sup.th percentile may be required to map to a
specific value in the Impairment Index.TM. that would always
indicate a threshold for an abnormal muscle. In the preferred
embodiment, this specific value is labeled the "Index of the
Abnormal Cutoff" (IAC).
[0099] From there, the Impairment Index.TM., I.sub.c, corresponding
to any of the other percentiles preferably is a value that is a
fraction of the IAC value. The fraction preferably equals the
percentile's fraction of the 99.sup.th percentile. Thus, where
A.sub.c(p) is the p.sup.th normative percentile of the A.sub.c
values, the Impairment Index.TM., I.sub.c, for the p.sup.th
percentile is given by:
I.sub.c(A.sub.c(p))=IAC*A.sub.c(p)/A.sub.c(99.sup.th)
[0100] with p being each of the percentiles listed above.
[0101] The unique piece-wise linear interpolant function is created
using standard mathematical techniques. The interpolant function is
such that the values of A.sub.c at each of the node point
percentiles are mapped by the interpolant function to their
corresponding Impairment Indexes.TM.. The scheme guarantees that
the dysfunction of different muscles in different subjects can be
meaningfully compared using the Impairment Index.TM.. To determine
an interpolant function segment, S.sub.i, values, x.sub.i, are
defined to be equal to normative database values of A.sub.c for the
specific percentiles listed above, where (i) is the number of
segments from 1 to n. Thus,
x.sub.1=A.sub.c(1.sup.st), x.sub.2=A.sub.c(5.sup.th), . . . ,
x.sub.n-1=A.sub.c(98.sup.th), and x.sub.n=A.sub.c(99.sup.th).
[0102] Also, y.sub.i is defined to be I.sub.c(x.sub.i), noting that
y.sub.n=IAC. Further, x.sub.0 and y.sub.0 are preferably set to
equal to zero. The interpolant function segment, S.sub.i, is then
defined to be the line segment with the endpoints
(x.sub.i-1,y.sub.i-1) and (x.sub.i,y.sub.i), for i=1 to n. The
general equation, therefore, for each segment S.sub.i is given
by:
S.sub.i(x)=y.sub.i+(x-x.sub.i)*(y.sub.i-y.sub.i-1)/(x.sub.i-x.sub.i-1),
with x.sub.i-1.ltoreq.x.ltoreq.x.sub.i
[0103] in which
I.sub.c(A.sub.c)=S.sub.i(A.sub.c), for
0.ltoreq.x.sub.i-1.ltoreq.A.sub.c.l-
toreq.x.sub.i.ltoreq.x.sub.n.
[0104] The entire piece-wise linear interpolant is identical to the
(n) line segments S.sub.1 to S.sub.n.
[0105] For values of A.sub.c above the 99.sup.th normative database
percentile, a linear extrapolation function is determined. The
extrapolant function maps the value of A.sub.c to the line
continued from the segment between zero and the 99.sup.th
percentile. The ordinate on this line corresponding to the abscissa
represented by A.sub.c becomes the Impairment Index.TM.. The
equation for the extrapolant function, S.sub.ext(x), is given
by:
S.sub.ext(x)=x*y.sub.n/x.sub.n, with x.gtoreq.x.sub.n,
[0106] in which
I.sub.c(A.sub.c)=S.sub.ext(A.sub.c), for A.sub.c>x.sub.n.
[0107] Through the procedure described above, a separate
normalization function is created for each muscle group examined
through the muscle dysfunction analysis system 10.
[0108] In step 188, the Impairment Index.TM., I.sub.c, for each
muscle representing the overall degree of departure from normal and
the overall direction of deviation, H.sub.c, for each muscle are
displayed, recorded and/or otherwise provided in a report.
[0109] After the determination of the dysfunctional muscles, as
illustrated in FIGS. 15A-C, as a final sub-step 152 of the analysis
procedure shown in FIG. 14, the patterns of compensating
relationships for dysfunctional muscles are mapped. These patterns
are based on the muscle activity levels and the kinesiological
relationships of the muscles. The mapped patterns graphically
illustrate the muscle dysfunction and assist the physician in
selecting an appropriate course of therapy. An illustrative example
of a mapped pattern of dysfunction is shown in FIG. 16.
[0110] It will be apparent to those skilled in the art that various
modifications, variations and additions can be made in the method
for determining back muscle dysfunction of the present invention
without departing from the scope or spirit of the invention. Thus,
it is intended that the present invention cover the modifications,
variations and additions provided that they come within the scope
of the appended claims and their equivalents.
* * * * *