U.S. patent application number 12/077077 was filed with the patent office on 2009-01-29 for methods for the assessment of neuromuscular function by f-wave latency.
Invention is credited to Shai N. Gozani, Matthew A. Neimark.
Application Number | 20090030337 12/077077 |
Document ID | / |
Family ID | 25389937 |
Filed Date | 2009-01-29 |
United States Patent
Application |
20090030337 |
Kind Code |
A1 |
Gozani; Shai N. ; et
al. |
January 29, 2009 |
Methods for the assessment of neuromuscular function by F-wave
latency
Abstract
Methods are provided for the assessment of neuromuscular
function by F-wave latency. Stimuli are applied to a nerve that
traverses a wrist or an ankle joint of an individual. Stimulation
of the nerve causes a muscle innervated by that nerve to respond,
thereby generating a myoelectric potential. One component of the
myoelectric potential is the F-wave component. The F-wave latency
between application of the stimulus and the detection of the
myoelectric potential is used to provide an assessment of a
neuromuscular function of the nerve and/or muscle.
Inventors: |
Gozani; Shai N.; (Brookline,
MA) ; Neimark; Matthew A.; (Somerville, MA) |
Correspondence
Address: |
Mark J. Pandiscio
470 Totten Pond Road
Waltham
MA
02451
US
|
Family ID: |
25389937 |
Appl. No.: |
12/077077 |
Filed: |
March 14, 2008 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10780118 |
Feb 17, 2004 |
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12077077 |
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10137504 |
Apr 30, 2002 |
6692444 |
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10780118 |
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09625502 |
Jul 26, 2000 |
6379313 |
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10137504 |
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09270550 |
Mar 16, 1999 |
6132386 |
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09625502 |
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09022990 |
Feb 12, 1998 |
5976094 |
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09270550 |
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08886861 |
Jul 1, 1997 |
5851191 |
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09022990 |
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Current U.S.
Class: |
600/554 |
Current CPC
Class: |
A61B 5/6825 20130101;
A61B 5/7217 20130101; A61B 5/389 20210101; A61B 5/1106 20130101;
A61B 5/05 20130101; A61B 5/4041 20130101; A61B 5/6824 20130101;
A61B 5/7239 20130101; A61B 2505/05 20130101 |
Class at
Publication: |
600/554 |
International
Class: |
A61B 5/05 20060101
A61B005/05 |
Claims
1. A method of assessing a physiological function detectable in an
arm and a hand of an individual, comprising the steps of: (a)
applying a stimulus proximal to a wrist of an individual, whereby
application of said stimulus stimulates a nerve that traverses said
wrist and thereby generates an impulse that is conducted by said
nerve; (b) detecting a myoelectric potential proximal to said wrist
of said individual, whereby said myoelectric potential is generated
by a muscle in said hand of said individual in response to said
impulse, said muscle being in communication with said nerve and
said impulse being conducted to said muscle after propagation of
said impulse through a spinal cord of said individual; (c)
processing said stimulus and said myoelectric potential; and (d)
correlating said processing results to a physiological function of
a peripheral nervous system of said individual.
2. The method of claim 1, further comprising the step of removing a
trend from a baseline of said myoelectric potential.
3. The method of claim 2, wherein said removing step comprises the
steps of: (a) determining a straight line fit of said myoelectric
potential; and (b) subtracting said straight line from said
myoelectric potential.
4. The method of claim 2, wherein said removing step comprises the
steps of: (a) detecting a plurality of myoelectric potentials; (b)
averaging said plurality of myoelectric potentials; and (c)
subtracting said average from each of said plurality of myoelectric
potentials.
5. The method of claim 4, further comprising the steps of: (a)
determining a first derivative for each of said myoelectric
potentials, thereby to obtain a plurality of first derivatives; (b)
determining a mean of said plurality of first derivatives; (c)
determining a statistical distribution of said plurality of first
derivatives; and (d) removing from said plurality of myoelectric
potentials that are averaged in step (b) of claim 4 any segment of
a myoelectric potential of said plurality of myoelectric potentials
that has a first derivative removed by a predetermined factor from
said mean of said derivatives.
6. The method of claim 1, further comprising the step of filtering
said myoelectric potential.
7. The method of claim 6, wherein said filtering step comprises
digitally filtering said myoelectric potential.
8. The method of claim 1, wherein said physiological function
comprises a F-wave latency between application of said stimulus and
detection of said myoelectric potential and wherein said processing
step further comprises the step of determining said F-wave latency
and said correlating step further comprises the step of producing
an indicia of said F-wave latency.
9. The method of claim 8, wherein said step of determining a F-wave
latency comprises the steps of: (a) detecting an F-wave response
signal in said myoelectric potential; (b) determining a maximum
peak of said F-wave response signal; (c) identifying a first
minimum peak and a second minimum peak of said F-wave response
signal, both of said first and second minimum peaks being adjacent
said maximum peak of said F-wave response signal; (e) determining
an amplitude of said maximum peak of said F-wave response signal to
one of said first and second minimum peaks of said F-wave response
signal; (f) determining a noise dependent threshold; (g) comparing
said amplitude to said noise dependent threshold; and (h)
determining a F-wave latency when said amplitude is greater than or
equal to said noise dependent threshold.
10. The method of claim 9, wherein said step of determining a
maximum peak of said F-wave response signal comprises determining a
portion of said myoelectric potential for which a first derivative
of said myoelectric potential is equal to zero.
11. The method of claim 9, wherein said step of identifying first
and second minimum peaks of said F-wave response signal comprises
determining a portion of said myoelectric potential for which a
first derivative of said myoelectric potential is equal to
zero.
12. The method of claim 9, wherein said step of determining a noise
dependent threshold comprises the steps of: (a) determining a level
of noise after detecting said myoelectric potential; and (b)
multiplying said level of noise by a predetermined factor.
13. The method of claim 9, wherein said step of determining a noise
dependent threshold, comprises the steps of: (a) determining a
level of noise before detecting said myoelectric potential; and (b)
multiplying said level of noise by a predetermined factor.
14. The method of claim 9, wherein said step of determining a
F-wave latency comprises the step of identifying an inflection of
said myoelectric potential, said inflection preceding said maximum
peak of said F-wave response signal.
15. The method of claim 14, wherein said inflection comprises a
point on said myoelectric potential having a first derivative less
than or equal to zero.
16. The method of claim 14, wherein said inflection comprises a
minimum peak of a first derivative of said myoelectric
potential.
17. The method of claim 9, further comprising the step of
processing atypical waveform shapes in said F-wave response
signal.
18. The method of claim 17, wherein said step of processing
atypical waveform shapes comprises the steps of: (a) determining a
location of a minimum peak of said F-wave response signal; (b)
inverting said F-wave response signal; and (c) assigning a maximum
peak of said inverted F-wave response signal to said location of
said minimum peak of said F-wave response signal.
19. The method of claim 9, further comprising the step of
confirming said F-wave latency.
20. The method of claim 19, wherein said step of confirming said
F-wave latency comprises the steps of: (a) determining a first
derivative of said myoelectric potential at a plurality of points
within a first time period preceding said F-wave latency, thereby
obtaining a plurality of first derivatives within said first time
period; (b) averaging said plurality of first derivatives within
said first time period; and (c) comparing said average with a
maximum peak and a minimum peak of said F-wave response signal in a
second time period following said F-wave latency.
21. The method of claim 19, wherein said step of confirming said
F-wave latency comprises the steps of: (a) identifying a maximum or
minimum peak in said myoelectric potential at a point of said
myoelectric potential that precedes said F-wave latency; and (b)
comparing said identified peak to said maximum peak of said F-wave
response signal.
22. The method of claim 8, further comprising the step of
indicating said F-wave latency in response to said indicia.
23. The method of claim 8, further comprising the step of producing
a signal indicative of a peripheral nervous system disorder in
response to said F-wave latency.
24. The method of claim 23, further comprising the step of
indicating a peripheral nervous system disorder in response to said
signal.
25. The method of claim 8, further comprising the steps of
measuring a skin temperature of said arm of said individual and
modifying said F-wave latency in response thereto.
26. The method of claim 8, further comprising the steps of
determining a height of said individual and modifying said F-wave
latency in response thereto.
27. The method of claim 8, further comprising the steps of
determining an age of said individual and modifying said F-wave
latency in response thereto.
28. A method of assessing a physiological function of a leg and a
foot of an individual, comprising the steps of: (a) applying a
stimulus proximal to an ankle joint of an individual, whereby
application of said stimulus stimulates a nerve that traverses said
ankle joint and thereby generates an impulse that is conducted by
said nerve; (b) detecting a myoelectric potential proximal to said
ankle joint of said individual, whereby said myoelectric potential
is generated by a muscle in said foot of said individual in
response to said impulse, said muscle being in communication with
said nerve and said impulse being conducted to said muscle after
propagation of said impulse through a spinal cord of said
individual; (c) processing said stimulus and said myoelectric
potential; and (d) correlating said processing results to a
physiological function of a peripheral nervous system of said
individual.
29. The method of claim 28, further comprising the step of removing
a trend from a baseline of said myoelectric potential.
30. The method of claim 29, wherein said removing step comprises
the steps of: (a) determining a straight line fit of said
myoelectric potential; and (b) subtracting said straight line from
said myoelectric potential.
31. The method of claim 29, wherein said removing step comprises
the steps of: (a) detecting a plurality of myoelectric potentials;
(b) averaging said plurality of myoelectric potentials; and (c)
subtracting said average from each of said plurality of myoelectric
potentials.
32. The method of claim 31, further comprising the steps of: (a)
determining a first derivative for each of said myoelectric
potentials, thereby to obtain a plurality of first derivatives; (b)
determining a mean of said plurality of first derivatives; (c)
determining a statistical distribution of said plurality of first
derivatives; and (d) removing from said plurality of myoelectric
potentials that are averaged in step (b) of claim 31 any segment of
a myoelectric potential of said plurality of myoelectric potentials
that has a first derivative removed by a predetermined factor from
said mean of said derivatives.
33. The method of claim 28, further comprising the step of
filtering said myoelectric potential.
34. The method of claim 33, wherein said filtering step comprises
digitally filtering said myoelectric potential.
35. The method of claim 28, wherein said physiological function
comprises a F-wave latency between application of said stimulus and
detection of said myoelectric potential and wherein said processing
step further comprises the step of determining said F-wave latency
and said correlating step further comprises the step of producing
an indicia of said F-wave latency.
36. The method of claim 35, wherein said step of determining a
F-wave latency comprises the steps of: (a) detecting an F-wave
response signal in said myoelectric potential; (b) determining a
maximum peak of said F-wave response signal; (c) identifying a
first minimum peak and a second minimum peak of said F-wave
response signal, both of said first and second minimum peaks being
adjacent said maximum peak of said F-wave response signal; (e)
determining an amplitude of said maximum peak of said F-wave
response signal to one of said first and second minimum peaks of
said F-wave response signal; (f) determining a noise dependent
threshold; (g) comparing said amplitude to said noise dependent
threshold; and (h) determining a F-wave latency when said amplitude
is greater than or equal to said noise dependent threshold.
37. The method of claim 36, wherein said step of determining a
maximum peak of said F-wave response signal comprises determining a
portion of said myoelectric potential for which a first derivative
of said myoelectric potential is equal to zero.
38. The method of claim 36, wherein said step of identifying first
and second minimum peaks of said F-wave response signal comprises
determining a portion of said myoelectric potential for which a
first derivative of said myoelectric potential is equal to
zero.
39. The method of claim 36, wherein said step of determining a
noise dependent threshold comprises the steps of: (a) determining a
level of noise after detecting said myoelectric potential; and (b)
multiplying said level of noise by a predetermined factor.
40. The method of claim 36, wherein said step of determining a
noise dependent threshold, comprises the steps of: (a) determining
a level of noise before detecting said myoelectric potential; and
(b) multiplying said level of noise by a predetermined factor.
41. The method of claim 36, wherein said step of determining a
F-wave latency comprises the step of identifying an inflection of
said myoelectric potential, said inflection preceding said maximum
peak of said F-wave response signal.
42. The method of claim 41, wherein said inflection comprises a
point on said myoelectric potential having a first derivative less
than or equal to zero.
43. The method of claim 41, wherein said inflection comprises a
minimum peak of a first derivative of said myoelectric
potential.
44. The method of claim 36, further comprising the step of
processing atypical waveform shapes in said F-wave response
signal.
45. The method of claim 44, wherein said step of processing
atypical waveform shapes comprises the steps of: (a) determining a
location of a minimum peak of said F-wave response signal; (b)
inverting said F-wave response signal; and (c) assigning a maximum
peak of said inverted F-wave response signal to said location of
said minimum peak of said F-wave response signal.
46. The method of claim 36, further comprising the step of
confirming said F-wave latency.
47. The method of claim 46, wherein said step of confirming said
F-wave latency comprises the steps of: (a) determining a first
derivative of said myoelectric potential at a plurality of points
within a first time period preceding said F-wave latency, thereby
obtaining a plurality of first derivatives within said first time
period; (b) averaging said plurality of first derivatives within
said first time period; and (c) comparing said average with a
maximum peak and a minimum peak of said F-wave response signal in a
second time period following said F-wave latency.
48. The method of claim 46, wherein said step of confirming said
F-wave latency comprises the steps of: (a) identifying a maximum or
minimum peak in said myoelectric potential at a point of said
myoelectric potential that precedes said F-wave latency; and (b)
comparing said identified peak to said maximum peak of said F-wave
response signal.
49. The method of claim 35, further comprising the step of
indicating said F-wave latency in response to said indicia.
50. The method of claim 35, further comprising the step of
producing a signal indicative of the presence or absence of
diabetic neuropathy in response to said F-wave latency.
51. The method of claim 50, further comprising the step of
indicating the presence or absence of diabetic neuropathy in
response to said signal.
52. The method of claim 35, further comprising the steps of
measuring a skin temperature of said leg of said individual and
modifying said F-wave latency in response thereto.
53. The method of claim 35, further comprising the steps of
determining a height of said individual and modifying said F-wave
latency in response thereto.
54. The method of claim 35, further comprising the steps of
determining an age of said individual and modifying said F-wave
latency in response thereto.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The present application is a continuation-in-part of U.S.
Ser. No. 09/022,990, filed Feb. 12, 1998, which is a divisional of
U.S. Ser. No. 08/886,861, filed Jul. 1, 1997 and now U.S. Pat. No.
5,851,191, both of which are hereby incorporated by reference.
FIELD OF THE INVENTION
[0002] The invention relates to apparatus and methods for
assessment of neuromuscular function. More specifically, the
invention relates to apparatus and methods for diagnosing
peripheral nerve and muscle pathologies based on assessments of
neuromuscular function.
BACKGROUND OF THE INVENTION
[0003] There are many clinical and non-clinical situations that
call for rapid, reliable and low-cost assessments of neuromuscular
function. Reliable and automated devices are needed to monitor
neuromuscular function in surgical and intensive care settings. For
example, muscle relaxants significantly improve surgical procedures
and post-operative care by regulating the efficacy of nerve to
muscle coupling through a process called neuromuscular blockade.
They are, however, difficult to use in a safe and effective manner
because of the wide variation and lack of predictability of patient
responses to them. In another setting, an easy to use and reliable
indicator would be beneficial in assessing potential contamination
exposure situations by chemical agents. These agents disrupt
neuromuscular function and effectively cause neuromuscular
blockage, putting soldiers and civilians at risk.
[0004] The most common causes of neuromuscular disruption are,
however, related to pathologies of the peripheral nerves and
muscles. Neuromuscular disorders, such as, for example, Carpal
Tunnel Syndrome (CTS), diabetic neuropathy, and toxic neuropathy,
are very common and well known to the general public. Detection of
such disorders involves determining the speed with which a nerve
that is believed to be affected transmits a signal. One way to make
such a determination involves stimulating a nerve that innervates a
muscle, and then determining a delay between the onset of the
stimulation and the muscle's response. The muscle response
typically has two components, namely the M-wave component and the
F-wave component. Detection and analysis of either of these two
components of the muscle response provides information on the
presence or absence of a neuromuscular pathology. Despite their
extensive impact on individuals and the health care system,
however, detection and monitoring of such neuromuscular pathologies
remains expensive, complicated, and highly underutilized.
[0005] CTS is one of the most common forms of neuromuscular
disease. The disease is thought to arise from compression of the
median nerve as it traverses the wrist. CTS often causes discomfort
or loss of sensation in the hand, and, in severe cases, a nearly
complete inability to use one's hands. Highly repetitive wrist
movements, as well as certain medical conditions, such as, for
example, diabetes, rheumatoid arthritis, thyroid disease, and
pregnancy, are thought to be factors that contribute to the onset
of CTS. In 1995, the US National Center for Health Statistics
estimated that there were over 1.89 million cases of CTS in the
United States alone.
[0006] Effective prevention of CTS and other nervous system
pathologies requires early detection and subsequent action.
Unfortunately, the state of CTS diagnosis is rather poor. Even
experienced physicians find it difficult to diagnose and stage the
severity of CTS based on symptoms alone. The only objective way to
detect CTS is to measure the transmission of neural signals across
the wrist. The gold standard approach is a formal nerve conduction
study by a clinical neurologist, but this clinical procedure has a
number of important disadvantages. First, it is a time consuming
process that requires the services of a medical expert, such as a
neurologist. Second, the procedure is very costly (e.g.,
$600-$1000). Furthermore, it is not available in environments where
early detection could significantly decrease the rate of CTS, such
as the workplace where a significant number of causes of CTS
appear. As a result of these disadvantages, formal
electrophysiological evaluation of suspected CTS is used relatively
infrequently, which decreases the likelihood of early detection and
prevention.
[0007] The prior art reveals a number of attempts to simplify the
assessment of neuromuscular function, such as in diagnosing CTS,
and to make such diagnostic measurements available to non-experts.
Rosier (U.S. Pat. No. 4,807,643) describes a portable device for
measuring nerve conduction velocity in patients. This instrument
has, however, several very important disadvantages. First, it
requires placement of two sets of electrodes: one set at the
stimulation site and one set at the detection site. Consequently, a
skilled operator with a fairly sophisticated knowledge of nerve and
muscle anatomy must ensure correct application of the device.
Inappropriate placement of one or both of the electrode sets can
lead to significant diagnostic errors. Second, the Rosier apparatus
suffers from the disadvantage that it is not automated. In
particular, it demands that the user of the device establish the
magnitude of the electrical stimulus, as well as a response
detection threshold. These parameters are difficult to determine a
priori, and their rapid and correct establishment requires an
advanced understanding of both neurophysiology and the detailed
electronic operation of the apparatus.
[0008] Spitz, et al. (U.S. Pat. No. 5,215,100) and Lemmen (U.S.
Pat. No. 5,327,902) have also attempted to enhance the earlier
prior art. Specifically, they proposed systems that measure nerve
conduction parameters between the arm or forearm and the hand, such
as would be required for diagnosing CTS. In both cases, however,
electrode supporting structures or fixtures were proposed that
would substantially fix the positions at which the stimulation
electrodes contact the arm and the detection electrodes contact the
hand. Furthermore, these systems suffer, from several important
disadvantages. First, both systems are rather large and bulky,
because they include a supporting fixture for the arm and hand of
an adult. This severely limits their portability and increases
their cost. Second, these devices still require highly trained
operators who can make the appropriate adjustments on the apparatus
so as to insure electrode contact with the proper anatomical sites
on the arm and hand. A third disadvantage of both systems is that
they continue to demand multiple operator decisions regarding
stimulation and detection parameters. Finally, these prior art
systems suffer from the disadvantage that they do not automatically
implement the diagnostic procedure and indicate the results in a
simple and readily interpretable form.
[0009] There remains a need, therefore, for apparatus and methods
for assessing neuromuscular function that are less time consuming,
less expensive, and more available to a wider range of the general
public (i.e., are more portable and easy to use). Such apparatus
and methods are needed to provide more widespread early detection
and prevention of neuromuscular pathologies, such as CTS, diabetic
neuropathy, and toxic neuropathy. The present invention addresses
these needs.
SUMMARY OF THE INVENTION
[0010] In accordance with the invention, apparatus and methods are
provided for the substantially automated, rapid, and efficient
assessment of neuromuscular function without the involvement of
highly trained personnel. Assessment of neuromuscular function
occurs by stimulating a nerve, then measuring the response of a
muscle innervated by that nerve. The muscle response is detected by
measuring the myoelectric potential generated by the muscle in
response to the stimulus. One indication of the physiological state
of the nerve is provided by the delay between application of a
stimulus and detection of a muscular response. If the nerve is
damaged, conduction of the signal via the nerve to the muscle, and,
hence, detection of the muscle's response, will be slower than in a
healthy nerve. An abnormally high delay between stimulus
application and detection of muscle response indicates, therefore,
impaired neuromuscular function.
[0011] Other indications of a physiological function of a nerve are
provided by the F-wave latency between application of a stimulus
and detection of a myoelectric response and by the conduction
velocity of the nerve. F-wave latencies account for the time that
is required for the impulse generated by the nerve as a result of
the stimulus to propagate through the spinal cord of the individual
before being conducted to the muscle. A conduction velocity is
determined by stimulating the nerve at least two different
locations, measuring the delays as a result of these stimulations,
calculating the difference between the delays, determining the
distance between the at least two stimulation locations, and then
dividing the distance by the difference between the delays.
[0012] In apparatus and methods of the invention, both the
application of stimulus and the detection of responses is carried
out entirely at a position that is immediately proximal to the
wrist of an individual (i.e., the wrist crease). In an alternative
embodiment of the invention, both the application of stimulus and
the detection of responses is carried out entirely at a position
that is at or proximal to the ankle joint. These anatomical
locations are familiar and easy to locate, thus ensuring correct
placement of the apparatus at the assessment site by non-experts
while still maintaining the accuracy of results. This ease of use
increases the availability and decreases the cost of diagnosing
pathologies such as Carpal Tunnel Syndrome (CTS) and diabetic
neuropathy, respectively.
[0013] Apparatus and methods of the invention assess neuromuscular
function in the arm of an individual by using a stimulator to apply
a stimulus to a nerve that traverses the wrist of the individual.
The stimulator is adapted for applying the stimulus to the nerve at
a position which is proximal to the wrist of the individual. The
stimulus may be, for example, an electrical stimulus or a magnetic
stimulus. Other types of stimuli may be used. A detector, adapted
for detecting the myoelectric potential generated by a muscle in
response to the stimulus, detects the response of the muscle to the
stimulus at a site that is also proximal to the wrist of the
individual. A controller then evaluates the physiological function
of the nerve by, for example, determining a delay between
application of stimulus and detection of myoelectric potential. The
delay is then correlated to the presence or absence of a
neuromuscular pathology, such as, for example, CTS.
[0014] In another embodiment, apparatus and methods of the
invention assess neuromuscular function in the leg and foot of an
individual by using a stimulator to apply a stimulus to a nerve
that traverses the ankle joint of the individual. The stimulator is
adapted for applying the stimulus to the nerve at one or more
positions which are proximal to the ankle joint of the individual.
A detector, adapted for detecting the myoelectric potential
generated by a muscle in response to the stimulus, detects the
response of the muscle to the stimulus at a site that is also
proximal to the ankle joint of the individual. A controller then
evaluates the physiological function of the nerve by, for example,
determining a conduction velocity between two stimulation sites
proximal to the ankle joint. The conduction velocity is then
correlated to the presence or absence of a neuromuscular pathology,
such as, for example, diabetic neuropathy.
[0015] In a preferred embodiment, the stimulator and the detector
are both in electrical communication with electrodes adapted for
placement on the arm of an individual proximal to the wrist. In an
alternative embodiment, the electrodes are adapted for placement on
the leg of an individual proximal to the ankle joint. The
controller may also be in electrical communication with a reference
electrode and a temperature sensor. An apparatus of the invention
may further comprise a communications port for establishing
communication between the apparatus and an external device, such
as, for example, a personal computer, a printer, a modem, or the
Internet.
[0016] In another embodiment, an apparatus of the invention further
comprises an indicator. The indicator is in electrical
communication with the controller and is adapted for indicating the
physiological function evaluated by the controller in response to
the stimulus applied and myoelectric potential detected. The
indicator may comprise a light emitting diode or a liquid crystal
display. In a particularly preferred embodiment, the indicator is
adapted for indicating the presence or absence of CTS. In other
embodiments, the indicator is adapted for indicating other
physiological functions of a peripheral nervous system of an
individual, such as F-wave latencies or diabetic neuropathies, for
example.
[0017] An apparatus of the invention may be further embodied in an
electrode configuration contained in an electrode housing for
releasably securing to the wrist of an individual. The electrode
housing contains an attachment mechanism, such as, for example, a
non-irritating adhesive material, for securing to the arm of the
individual and may be disposable. The electrode housing preferably
has a connector for electrical communication with an apparatus
comprising a stimulator, a detector, and a processor, as described
above.
[0018] The electrode housing comprises stimulation and detection
electrodes. The stimulation and detection electrodes are sized and
shaped in the housing so that they contact an anterior aspect of an
arm of the individual proximal to the wrist, when the housing is
secured to the wrist of the individual. The electrode configuration
may further contain a temperature sensor and/or a reference
electrode.
[0019] In a preferred embodiment, the electrode configuration
comprises a second stimulation electrode and a second detection
electrode. The two stimulation electrodes are positioned
substantially in the center of the electrode housing and are
arranged so that they are positioned at opposite ends of the
housing. The two stimulation electrodes are preferably arranged so
that, when the housing is placed on the anterior aspect of an arm
of a user, one of the stimulation electrodes is located immediately
proximal to the wrist and the other at a location more proximal
from the wrist. The two detection electrodes are also located at
opposite ends of the housing, but they are positioned such that,
when placed on the anterior aspect of an arm of a user, one
detection electrode is located on the medial, and the other on the
lateral, side of the wrist.
[0020] In another embodiment of the invention a neuromuscular
electrode is provided. A neuromuscular electrode for the assessment
of a physiological function of a peripheral nerve and/or a muscle
in communication with that nerve includes a stimulation site, a
detection site, and a data memory. The stimulation site is adapted
for producing a stimulus and for applying that stimulus to a nerve
of an individual. The detection site may be in a fixed relationship
with respect to the stimulation site and is adapted for detecting a
bioelectric potential. The bioelectric potential is generated by a
muscle or nerve in communication with the stimulated nerve in
response to the stimulus. The bioelectric potential may be a
myoelectric potential generated by a muscle in communication with
the stimulated nerve. The data memory is adapted for storing a
signal representative of a characteristic of the neuromuscular
electrode. A neuromuscular electrode of the invention is used to
evaluate a physiological function of the nerve and/or the muscle in
response to the stimulus, the bioelectric potential, and the
characteristic.
[0021] A characteristic of the neuromuscular electrode may include
the height of the patient that is associated with the size of the
neuromuscular electrode, a serial number of the neuromuscular
electrode, an indication that the neuromuscular electrode has been
used on an individual, or an indication that the neuromuscular
electrode has not been used on an individual. The neuromuscular
electrode may come in sizes, such as small, medium, or large, for
example. For each size, a height of an individual may be included
in the data memory of the neuromuscular electrode. This height is
later used to adjust determination of a physiological function
based on the height of the individual. An indication that a
neuromuscular electrode of the invention has been used on an
individual may include an electronic flag in the data memory. The
presence of said flag may indicate that the neuromuscular electrode
has been used to make physiological determinations and that it may
not be used again.
[0022] A neuromuscular electrode system includes a neuromuscular
electrode, as described above, and a controller in electrical
communication with the data memory, the stimulation site, and the
detection site for determining whether the electrode has been used
based on the signal representative of an indication of use in the
data memory. In one embodiment, the controller comprises a data
processor for processing this signal to determine if the
neuromuscular electrode has been used. The data processor and the
controller may be embodied as a single microprocessor. The
controller directs the stimulation site to stimulate the nerve if a
determination that the neuromuscular electrode has not been used is
made and processes the bioelectric potential and stimulus. The
controller then correlates the processing results to a
physiological function of the nerve and/or muscle. The
physiological function may include a delay between application of
the stimulus and detection of the bioelectric potential, a F-wave
latency between application of the stimulus and detection of the
bioelectric potential, a conduction velocity of the nerve, or an
amplitude of the bioelectric potential. The physiological function
may be modified by the controller as a function of the height of
the individual, which is encoded in the data memory, as described
above, or by the temperature of the skin of the individual, as
measured by a temperature sensor, which is also in electrical
communication with the controller.
[0023] A controller of a neuromuscular electrode system of the
invention is adapted for generating a deactivation signal upon
detection of certain specific signals and for transmitting that
deactivation signal to the data memory. Upon receiving the
deactivation signal, the signal representative of an indication of
use of the neuromuscular electrode is modified. This modification
may include the generation of an electronic flag in the data
memory.
[0024] The specific signal changes that cause the controller to
generate a deactivation signal include, but are not limited to,
detection of an impedance of skin that exceeds a predetermined
value. The controller further monitors an impedance of skin of the
individual and generates a deactivation signal upon detection of an
impedance of skin that exceeds a predetermined value. The
controller then transmits the deactivation signal to the data
memory. Another specific signal change includes a predetermined
change in the bioelectric potential. The controller monitors the
bioelectric potential and generates a deactivation signal upon
detection of a predetermined change in the bioelectric potential
and transmits that deactivation signal to the data memory. Finally,
the data memory may contain a unique serial number of the
neuromuscular electrode. The controller also compares the
bioelectric potential to at least one bioelectric potential
previously determined by the neuromuscular electrode having that
unique serial number. If the controller detects a predetermined
characteristic change between the bioelectric potential and the at
least one previously determined bioelectric potential for the
neuromuscular electrode having that unique serial number, the
controller generates a deactivation signal and transmits that
deactivation signal to the data memory. In other embodiments, the
unique serial number is used to match the physiological function
with the individual.
[0025] Methods of the invention relate to the assessment of
neuromuscular function using an apparatus of the invention. Using
an apparatus, as described above, a stimulus is applied to a nerve
that traverses the wrist of an individual proximal to the wrist.
Alternatively, a stimulus is applied proximal to a nerve that
traverses the ankle joint of an individual. A muscle innervated by
the nerve responds and thereby generates a myoelectric potential,
which is detected proximal to the wrist of the individual. The
detected response is processed by determining a first derivative of
the myoelectric potential and, preferably, a second derivative of
the myoelectric potential. In a preferred embodiment, these
derivatives are used to determine an appropriate stimulation level,
as well as to determine the delay between application of stimuli
and detection of the associated responses. In another embodiment,
additional measurements related to the delay are taken. For
example, changes in the delay induced by application of at least
two stimulus applications is determined. The delay and associated
parameters calculated from any of the measurements are then
correlated to a physiological function of the nerve and muscle.
[0026] In preferred embodiments, an apparatus of the invention is
used to indicate the presence or absence of CTS. A plurality of
stimuli are applied to a nerve passing through the carpal tunnel,
such as, for example, the median nerve. The stimuli may be
delivered one at a time at a predetermined rate or they may be
delivered in pairs at a predetermined rate. If delivered in pairs,
the application of stimuli is separated by a predetermined time
interval. In another embodiment, an apparatus of the invention is
used to indicate the presence or absence diabetic neuropathy. In
this embodiment, a plurality of stimuli are applied to a nerve
passing through the ankle joint, such as, for example, the peroneal
nerve.
[0027] A plurality of myoelectric potentials are generated by a
muscle innervated by the stimulated nerve in response to the
stimuli. Each myoelectric potential is generated in response to a
respective stimulus application. A delay for each of said stimulus
applications and detected responses is determined. Statistics such
as, for example, mean and standard deviation, are calculated for
the plurality of delays. The probable value that the individual has
CTS or diabetic neuropathy is calculated based on these statistics.
An indication of the presence or absence of CTS or diabetic
neuropathy is then given based on that value.
[0028] In other embodiments of the invention, the method may
involve further steps. For example, in one embodiment of the
invention, the method relates to calculating the difference between
delays measured in response to two stimuli delivered at short
temporal intervals, and determining the probable value that an
individual has CTS or diabetic neuropathy based on these delay
differences and calculated statistics, as described above. In
another embodiment, a level of noise is measured prior to
stimulating the nerve. In yet another embodiment, the mean and
standard deviation of the delays is adjusted relative to the skin
temperature.
[0029] An apparatus and method for the essentially automated and
accurate assessment of neuromuscular function is therefore
provided. The apparatus and methods of the invention allow for the
less costly and more readily available detection of neuromuscular
pathologies, such as, for example, CTS or diabetic neuropathy,
without the aid of a skilled professional.
[0030] The invention will be understood further upon consideration
of the following drawings, description, and claims.
DESCRIPTION OF THE DRAWINGS
[0031] FIG. 1A is an illustration of an embodiment of the apparatus
of the invention attached to the wrist of a user.
[0032] FIG. 1B is an illustration of an embodiment of the apparatus
of the invention attached to the ankle joint of a user.
[0033] FIG. 2A shows a top surface of the embodiment of the
apparatus of the invention shown in FIG. 1A.
[0034] FIG. 2B illustrates a bottom surface of the embodiment of
the apparatus of the invention shown in FIG. 1A depicting an
electrode configuration.
[0035] FIG. 3 is a block diagram of an embodiment of the apparatus
of the invention.
[0036] FIG. 4 illustrates electronic circuitry for an embodiment of
an apparatus of the invention.
[0037] FIG. 5 is a graph showing a M-wave muscle response evoked
and measured by an apparatus of the invention.
[0038] FIG. 6 is a graph showing a second derivative of a M-wave
muscle response signal evoked and measured by an apparatus of the
invention.
[0039] FIG. 7 is flow chart of an embodiment of an algorithm for
detecting carpal tunnel syndrome using an apparatus of the
invention.
[0040] FIG. 8A is a graph showing a F-wave muscle response evoked
and measured by an apparatus of the invention.
[0041] FIG. 8B is a graph showing a digitally filtered F-wave
muscle response signal evoked and measured by an apparatus of the
invention.
[0042] FIG. 8C is a graph showing a F-wave muscle response with
double peaks as evoked and measured by an apparatus of the
invention.
[0043] FIG. 9 is a flow chart of an embodiment of an algorithm for
detecting a F-wave latency using an apparatus of the invention.
DETAILED DESCRIPTION OF THE INVENTION
[0044] The present invention offers a detection and monitoring
system for peripheral neurological conditions, such as Carpal
Tunnel Syndrome, diabetic neuropathy, and toxic neuropathies, that
is less time consuming, less expensive, and more available to a
wider range of the general public than existing systems. One of the
most effective ways to detect peripheral neuropathies is to monitor
the response of a motor nerve to stimulation.
[0045] A motor nerve response signal typically consists of two
components, namely the M-wave component and the F-wave component.
The M-wave component is generally quantified by the distal motor
latency (DML). The DML is generally defined as the amount of time
that elapses between the start of the stimulus (i.e., time=0) and
the initial negative deflection of the M-wave component of the
muscle response signal (i.e., myoelectric potential). The F-wave
component of the muscle response signal, on the other hand, is
typically quantified by the minimum or median F-wave latency. The
F-wave latency represents the time lag between stimulation of a
motor nerve and arrival of the neurally conducted impulse at the
muscle group innervated by that nerve after antidromic propagation
of the impulse to the spinal cord, reflection of the impulse in the
anterior horn cells of the spinal cord, and then orthodromic
conduction back down the motor nerve.
[0046] F-waves differ from M-waves in a number of important ways
that impact their analysis and diagnostic use. First, F-waves are
typically 25-50 times smaller than M-waves. Second, unlike M-waves,
which are evoked by every stimulus, F-waves are probabilistic, and
may or may not be generated for a given stimulus. Also, F-waves
evoked by different but equivalent stimuli may consist of different
morphologies and have different latencies. Consequently, a
statistical characterization of the ensemble of F-wave latencies,
such as the minimum, mean, or median, is typically reported.
[0047] An illustrative embodiment of an apparatus of the invention
and its placement on the user's forearm 8 is shown in FIG. 1A. The
invention consists of two major components: a neuromuscular
electrode 1 and an electronic monitor 2. The neuromuscular
electrode 1 includes both a stimulator and a detector. The
electronic monitor 2 includes both a controller and an indicator.
In this embodiment, the neuromuscular electrode 1 and electronic
monitor 2 are physically separable with electrical connections
between the two components established by physical contact between
a connector 6, associated with the neuromuscular electrode 1 and
connector slot 7 associated with the electronic monitor 2. In
another embodiment, neuromuscular electrode 1 and electronic
monitor 2 constitute a single, physically inseparable unit. The
electronic monitor 2 contains means to actuate the diagnostic
process. Referring to the illustrative embodiment shown in FIG. 1A,
a push-button 3 is provided to initiate said diagnostic process.
The electronic monitor 2 also contains an indicator to display or
convey the results of the diagnostic process. Referring to the
illustrative embodiment shown in FIG. 1A, an indicator includes a
display 4, which includes two multi-segment light-emitting diodes
(LEDs) and which provides feedback and results. Other indicators
may be used, including, but not limited to, single and multicolor
discrete LEDs. Other types of indicators, such as, for example,
speakers, may provide auditory signals. The electronic monitor 2
also contains a communications port to connect and communicate with
external devices. Referring to the illustrative embodiment shown in
FIG. 1A, the communications port includes a jack 5 into which a
cable may be inserted. The other end of the cable is then connected
to any number of different devices, including, but not limited to,
computers and telephone lines.
[0048] The neuromuscular electrode 1 delivers electrical stimuli to
the skin surface, detects biopotentials from the skin surface and
measures additional physiological and biological parameters, such
as, for example, skin temperature. As shown in FIG. 1A, the
neuromuscular electrode 1 is placed on the anterior aspect of the
forearm 8 immediately proximal to the wrist crease 9. In another
embodiment, as shown in FIG. 1B, the neuromuscular electrode is
placed on the lateral anterior surface of the lower leg 246
proximal to the ankle joint 250. In the preferred embodiment, the
physical dimensions of the neuromuscular electrode 1 are chosen
from a predetermined set of dimensions which are optimized for the
range of wrist or ankle joint sizes found in adults. For example,
the electrodes may be configured in a small, regular, and large
size. In a preferred embodiment, the size of the neuromuscular
electrode 1 is chosen according to a size chart, which matches
patient characteristics, such as height and weight, to an
appropriate size. Additional embodiments are contemplated in which
the neuromuscular electrode 1 includes means to vary its physical
dimensions over a predetermined range, such as, for example, being
contained in an electrode housing, such as, an adjustable band or
strap. The band or strap may also be detachable.
[0049] An illustrative embodiment of the neuromuscular electrode 1
is shown in FIG. 2A. FIG. 2A shows the top surface of the
neuromuscular electrode 1 and its proper location on the user's
wrist. In one embodiment, the top surface of the neuromuscular
electrode 1 contains printed instructions 46 and/or other visual
indications 45 to help the user properly position it. FIG. 2B shows
the bottom surface of the neuromuscular electrode 1. The
illustrative configuration allows muscle activity in the thenar
muscle group 51 to be evoked and sensed when the neuromuscular
electrode 1 is positioned immediately proximal to the wrist crease
9, as shown in FIG. 2A. Two bioelectrical transduction sites, 30
and 31, hereafter referred to as the stimulation sites, are
positioned approximately midway between the lateral end 19 and
medial end 17 of the neuromuscular electrode 1. The two stimulation
sites, 30 and 31, are arranged in a distal to proximal line such
that one of the sites is near the distal end 18 of the
neuromuscular electrode 1 and one of the sites is near the proximal
end 20 of the neuromuscular electrode 1.
[0050] The stimulation sites may consist of stimulation electrodes
having delineated areas of bioelectrical signal transduction means
that convert electronic signals into electrochemical ones and vice
versa. In a preferred embodiment, these sites are composed of a
plurality of layers of different materials with substantially the
same area. A first layer is directly attached to the bottom face of
the neuromuscular electrode 1 and is preferably formed by a thin
layer of silver. A second layer is attached to first layer and
preferably consists of a silver-chloride salt. A third layer is
attached to second layer and contacts the user's skin on its
exposed surface. The third layer is preferably composed of an
electrolyte hydrogel, such as, for example, sodium chloride.
[0051] When the neuromuscular electrode 1 is properly positioned as
shown in FIG. 2A, the two stimulation sites, 30 and 31, will
overlie the median nerve 50. The nerve 50 is stimulated by passing
a low amplitude current (e.g., typically less than 10 milliamps)
through the two stimulation sites, 30 and 31. The current is
provided by an external source electrically coupled to contacts, 34
and 35, on the external connector 6. The contacts, 34 and 35, and
the stimulation sites, 30 and 31, are coupled by electrically
conductive and insulated means, 32 and 33.
[0052] Two transduction sites, 21 and 22, hereafter referred to as
the detection sites, are positioned at the extreme lateral end 19
and medial end 17 of the neuromuscular electrode 1 near its
proximal end 18. In a preferred embodiment, the detection sites, 21
and 22, consist of detection electrodes comprised of delineated
areas of bioelectrical signal transduction means that convert
electronic signals into electrochemical ones and vice versa. In a
preferred embodiment, these sites are constructed in a
substantially similar manner to the stimulation sites, 30 and
31.
[0053] In operation, contraction of the thenar muscles 51, as shown
in FIG. 2A, will generate a myoelectric potential and create a
bioelectrical potential difference between the lateral 21 and
medial 22 detection sites due to the relative proximity of the
lateral detection site 21 to the thenar muscles 51. This potential
difference may be measured as a small (e.g., typically less than
0.5 mV) differential voltage between contacts, 25 and 26, on the
external connector 6. The contacts, 25 and 26, and the detection
sites, 21 and 22, are coupled by electrically conductive and
insulated means, 23 and 24. The measurement of the differential
voltage signal is enhanced by the availability of a reference
potential, which is provided by transduction site 27, hereafter
referred to as the reference site, or reference electrode. This
site is positioned along the medial end 17 of the neuromuscular
electrode 1 towards its proximal end 20. The position of the
reference site 27 is, however, not critical and has relatively
little effect on the function of the invention. In a preferred
embodiment, the reference site 27 is constructed in a substantially
similar manner to the stimulation sites, 30 and 31, and detection
sites, 21 and 22. The reference potential is made available at a
contact 29 on the external connector 6, which is coupled to the
reference site 27 by electrically conductive and insulated means
28.
[0054] In an alternative embodiment, shown in FIG. 1B, the
neuromuscular electrode 1 is adapted for placement on the leg 246
of an individual. At this location, the two stimulation sites, 30
and 31, overlie the peroneal nerve 254 and deliver a stimulus to
it. Contraction of the extensor digitorum brevis (EDB) muscle 252
of the foot 248, resulting from the stimulation, generates a
myoelectric potential between the lateral 21 and medial 22
detection sites due to the differential distance between the
detection sites and the EDB muscle 252. It is often advantageous to
compare the response of the peroneal nerve 254 evoked by
stimulation at multiple sites proximal to the ankle joint 250.
Thus, in another embodiment of the invention, the neuromuscular
electrode 1 is adapted for stimulation at multiple sites proximal
to the ankle joint 250, such as, for example, at the ankle joint
250 and just below the knee 256. In all cases, however, the evoked
myoelectric potential is detected by detection electrodes, such as
21 and 22, at or proximal to the ankle joint 250.
[0055] The neuromuscular electrode 1 also preferably contains a
temperature sensor 36, such as, for example, a DS1820 (Dallas
Semiconductor, Dallas, Tex.) or a thermistor. The temperature
sensitive part of the sensor 36 contacts the users skin directly or
indirectly through an intermediary material that efficiently
conducts heat. The temperature sensor 36 can be placed at any
available location within the area of the neuromuscular electrode
1. The temperature sensor 36 is powered and transmits temperature
information to electronic monitor 2 through two or more contacts,
39 and 40, on the external connector 6. The contacts, 39 and 40,
and the temperature sensor 36 are coupled by electrically
conductive and insulated means, 37 and 38.
[0056] The neuromuscular electrode 1 contains an electrochemical
gel that is not intended for multiple applications to a test
subject. In particular, once the neuromuscular electrode 1 has been
applied to the subject and removed, its operational characteristics
may be compromised by the physical distortion and contamination
associated with application and removal from the skin. The primary
characteristic which may be affected is the critically important
electrode-to-skin impedance. Another reason for not reusing the
neuromuscular electrode 1 is the potential for spreading infection
from one person to another. Thus, it is clearly desirable that the
neuromuscular electrode 1 is disposable and non-reusable.
Consequently, it is important to ensure that the neuromuscular
electrode 1 cannot be reused.
[0057] Another embodiment of the invention therefore includes a
neuromuscular electrode 1 having a data memory for storing a signal
representative of a characteristic of the neuromuscular electrode.
In a preferred embodiment, this data memory is integrated into the
temperature sensor 36, such as the DS1820 (Dallas Semiconductor,
Dallas, Tex.), which contains a universally unique 64 bit number in
ROM and several bytes of non-volatile EEPROM. The characteristics
of the neuromuscular electrode may include the size of the
neuromuscular electrode, the height of the individual associated
with the size of the neuromuscular electrode, the serial number of
the neuromuscular electrode, an indication that the neuromuscular
electrode has been used on an individual, or an indication that the
neuromuscular electrode has not been used on an individual.
[0058] The serial number is provided by the 64 bit ROM, and the
other characteristics of a height or size of the neuromuscular are
provided by programming one or more bits of the EEPROM during
manufacturing of the neuromuscular electrodes 1. The characteristic
of an indication that the neuromuscular electrode has been used on
an individual is provided by reading and writing one or more bits
of EEPROM during regular use. For example, in a preferred
embodiment, two of the bits within the EEPROM are used to encode
the size of the neuromuscular electrode 1. In particular, the small
size is encoded as 00, the medium size as 01, and the large size as
10. Furthermore, in a preferred embodiment, one of the bits within
the EEPROM is used to inactivate the neuromuscular electrode 1
after use. In particular, an activated neuromuscular electrode 1 is
encoded as a 0 and an inactivated one is encoded as a 1.
[0059] In another embodiment of the neuromuscular electrode 1, the
serial number is printed on one or more removable labels attached,
for example, to the top surface of the neuromuscular electrode
1.
[0060] Additional configurations and arrangements of transduction
sites and sensors have been contemplated and should be considered
within the scope of the present invention. One such configuration
utilizes a single pair of transduction sites for both stimulation
and detection through electronic multiplexing.
[0061] The electronic monitor 2 has a number of functions. The
monitor 2 detects, amplifies, processes and stores bioelectrical
potentials, such as those generated by nerve or muscle activity. It
also generates stimuli, such as steps of electrical current, with
sufficient magnitude to trigger impulses in nerves or muscles. In
addition, it communicates with the user and with external
instruments, such as, for example, a personal computer. Finally,
the electronic monitor 2 includes a controller to process data and
control the intensity and duration of stimulus applications.
[0062] An illustrative block diagram of the electronic monitor 2 of
FIG. 1A is shown in FIG. 3. Differential amplifier 60 amplifies the
voltage difference between the input terminals and generates a
voltage that is proportional to that voltage difference. When the
electronic monitor 2 and neuromuscular electrode 1 of FIG. 1A are
connected by physical contact between connectors, 6 and 7, the
differential amplifier 60 of FIG. 3 is electrically coupled to
detection sites, 21 and 22, and reference site 27. Since the
bioelectrical signals from the body surface typically have a source
impedance between about 5 K.OMEGA. to about 50 K.OMEGA. and contain
large common mode signals, the differential amplifier 60 must have
a high input impedance, a good common mode rejection ratio and a
low leakage current. These requirements are preferably met by an
instrumentation amplifier, such as, for example, the INA111
(Burr-Brown, Tuscon, Ariz.) or the AD621 (Analog Devices, Norwood,
Mass.).
[0063] The differential amplifier 60 is electrically coupled to a
signal conditioning unit 61 that prepares the signal for
analog-to-digital conversion and subsequent processing. The signal
conditioning unit 61 preferably removes DC offsets, amplifies,
low-pass filters, performs variable gain amplification, and creates
a DC bias. Variable gain amplification is controlled by controller
63 using gain control line 61A. The output of the signal
conditioning unit 61 is electrically coupled to one or more
analog-to-digital converters on the controller 63.
[0064] Temperature sensor interface electronics 62 power the
temperature sensor and convert temperature related signals into a
form interpretable by controller 63. Stimulator 64 generates an
electrical impulse with either or both of the magnitude and
duration of the impulse being determined by signals from controller
63.
[0065] The stimulator 64 is preferably embodied by a circuit which
gates the discharge of a capacitor charged to a high voltage (e.g.,
100 volts). The capacitance value (e.g., 1 .mu.F is chosen so that
the discharge time constant (e.g., several seconds) is much longer
than the typical impulse duration (e.g., less than 1 millisecond).
The voltage across the capacitor is established by internal
charging means, such as, for example, a DC-DC converter. In another
embodiment, it is established by external charging means. In the
later case, the stimulator 64 is capable of generating a finite
number of electrical impulses before it has to be recharged by the
external charging means.
[0066] Actuating means 65 are electrically coupled to processor 63
and preferably embodied by one or more push button switches.
Indicator 66 is also electrically coupled to controller 63 and
preferably embodied in a single, or multi-segment, LED. Finally,
external interface 67 is electrically coupled to controller 63 and
preferably embodied as a standard RS-232 serial interface. The
controller 63 performs analog-to-digital conversion, senses and
controls I/O lines, and processes, analyzes and stores acquired
data. The controller 63 is preferably embodied as a single,
integrated, low-cost embedded microcontroller. However, in other
embodiments, the controller 63 is configured with multiple
components, such as, for example, a microprocessor and external
components that perform analog-to-digital conversion and other
necessary functions.
[0067] FIG. 4 shows a schematic diagram of the circuitry of one
embodiment of the electronic monitor 2 of FIG. 1A. The illustrative
circuit of FIG. 4 includes a detection sub-circuit, a stimulation
sub-circuit and a control and processing sub-circuit. The detection
stage is based on amplifier U1, a type INA111 (Burr-Brown, Tucson,
Ariz.) instrumentation amplifier. Each of a pair of inputs of
amplifier U1, 100 and 101, is electrically coupled to one of the
detector sites, 21 and 22, of FIG. 2B. In addition, amplifier U1
has a reference pin 102 at which it receives a reference potential
through electrical coupling to reference site 27 of FIG. 2B. U1 is
a monolithic instrumentation amplifier and requires one external
component, a resistor, R7, to establish its amplification gain,
which is preferably a factor of 10. Amplifier U1 is powered by a
two sided symmetrical power supply providing +Vc 110 and -Vc 111
(e.g., 6 volts), as well as a ground 112. In a preferred
embodiment, +Vc 110, -Vc 111, and the ground 112 are provided by
two batteries, B1 and B2, connected in series, as shown in FIG. 4.
The output of amplifier U1 is coupled through a high pass filter
formed by capacitor C1 and resistor R1 to the input of a
non-inverting amplifier formed by operational amplifier U2a. The
high pass filter removes any DC offset in the output of amplifier
U1.
[0068] In a preferred embodiment, capacitor C1 and resistor R1 are
chosen for a high pass corner frequency of about 2 Hz. The gain of
the non-inverting amplifier is established by resistors R2 and R10
and is preferably set to a gain of 500. The gain of U2a can be made
variable by converting R2, R10, or both R2 and R10 into digital
potentiometers under the control of microcontroller U4. The output
of first operational amplifier U2a is coupled to input of second
operational amplifier U2b by a low pass filter formed by resistor
R3 and capacitor C2. The low pass filter removes high frequency
noise from the signal. In a preferred embodiment, resistor R3 and
capacitor C2 are chosen for a low pass corner frequency of about 3
KHz. The second operational amplifier U2b is configured simply as
an impedance buffer. The output of amplifier U2b is coupled to an
analog-to-digital conversion pin on microcontroller U4 by a DC
biasing circuit consisting of capacitor C4, along with resistors R8
and R9. The purpose of the DC biasing circuit is to insure that all
signals vary from ground 112 to +Vc 110, since the
analog-to-digital conversion electronics of microcontroller U4
operate only on positive voltages. The detection stage also has a
combination communication and power line 116, for interfacing to a
"one-wire" temperature sensor 36 of FIG. 2B, connected to an I/O
pin on microcontroller U4.
[0069] The stimulation sub-circuit of the apparatus is based on
energy storage capacitor C3, which is a high capacitance (e.g., 1
.mu.F or greater) and high voltage (e.g., greater than 100 volts)
capacitor. In one embodiment of the apparatus, capacitor C3 is
charged to greater than 100 volts by an external charging means
105. Capacitor C3 charging is accomplished by charging means 105,
which passes electrical current between terminals 107 and 106,
which are temporarily electrically coupled to capacitor C3
terminals 109 and 108 during the charging period. Once capacitor C3
is charged, charging means 105 is removed. Electrical stimulation
of nerve and muscle is accomplished by discharging capacitor C3
through leads 103 and 104, which are electrically coupled to
stimulation sites, 30 and 31. Control of stimulation duration is
provided by a power MOSFET transistor Q1, which gates discharge
according to a digital signal from microcontroller U4. Resistor R4
protects transistor Q1 by limiting the current that flows through
it.
[0070] The control and processing stages of the apparatus are based
on microcontroller U4, which is preferably a type PIC12C71
(MicroChip, Chandler, Ariz.) microcontroller. U4 provides
processing and storage capabilities, analog-to-digital conversion
and input/output control. In addition to the aforementioned
connections to detection and stimulation subcircuits,
microcontroller U4 detects depression of switch S1, which is
connected to an I/O pin and controls light emitting diode LED1,
which is also connected to an I/O pin. Resistor R6 limits current
into the I/O pin when switch S1 is depressed and resistor R5 limits
current through the light-emitting diode LED1. In addition, serial
communication 115 to external devices is provided by the remaining
available I/O pin. Control and processing algorithms are stored in
microcontroller U4 and executed automatically upon application of
power. Other electronic circuitry may be used to perform the
processes described above and is considered to be within the scope
of the invention. One skilled in the art knows how to design
electronic circuitry to perform the functions outlined above.
[0071] A major object of the present invention is to serve as a
detection system for CTS. Conventional detection of CTS is based on
an analysis of certain features of the evoked M-wave muscle
response, typically the distal motor latency (DML). Referring to
FIG. 1A, the DML represents the time lag between stimulation of the
median nerve 50 immediately proximal to the wrist crease 9 and
arrival of the neurally conducted impulse at the thenar muscle
group 51 after direct orthodromic conduction through the wrist
(i.e., after traversing the Carpal Tunnel). Thus, the DML
quantifies nerve conduction in the distal most segment of the
median nerve. One of the most common and consistent indications of
CTS is an increase in the DML. Although there is no single
definition for the DML, it is generally defined as the amount of
time that elapses between the start of the stimulus (i.e., time=0)
and the occurrence of a consistent feature on the muscle response.
A typical M-wave muscle response 120, evoked and acquired using an
apparatus of the invention, is shown in FIG. 5. The vertical scale
121 indicates the amplitude of the muscle response in millivolts as
measured between detection sites 21 and 22. The horizontal scale
122 indicates the elapsed time from the onset of the stimulation
pulse (i.e., stimulus occurred at time=0). The large signal
transients 123 that occur in the first 2 milliseconds represent
stimulus associated artifacts and are unrelated to activity in the
thenar muscles 51. An evoked muscle response 120 may be
characterized by many parameters including, but not limited to, a
time to onset 124, a time to peak 125, a peak amplitude 126, a peak
to peak amplitude 127 and a peak to peak width 128. In the
illustrative example of FIG. 5, the time to onset 124 is about 3.7
milliseconds, and the time to peak 125 is about 5.8
milliseconds.
[0072] Because detection of the thenar muscle 51 response occurs at
a significant distance from its physiological site of origin, the
intervening tissue acts as a low pass filter. This results in
amplitude attenuation and temporal spreading of the detected
waveform as compared to measurements taken directly over the thenar
muscles 51. The decrease in amplitude results in a reduction in the
signal-to-noise ratio of the detected M-wave 120 response. The
temporal spreading obscures sharp characteristic features of the
M-wave response 120. Taken together these two low-pass related
effects make a consistent and accurate identification of muscle
response features, such as the time to onset 124 or the time to
peak 125, difficult and highly variable, especially in the presence
of various noise sources (e.g., extraneous muscle activity such as
would be caused by a muscle twitch in an arm muscle).
[0073] In a preferred embodiment, analysis of the M-wave muscle
response 120 is significantly enhanced by preprocessing it prior to
determination of its characteristic features. One such
preprocessing step is to take the second derivative of the M-wave
muscle response 120 as shown in FIG. 6A. The advantageous nature of
this preprocessing step is evident from the fact that the second
derivative 130 (solid line) has a peak 131 near the onset 124 of
the M-wave muscle response 120. Consequently, it is possible to
accurately and consistently obtain a latency estimate 133 by simply
detecting the presence of this peak 131. By contrast, a direct
estimation of the time to onset 124 from the M-wave muscle response
120 requires establishment of an arbitrary voltage threshold which
may vary significantly among different individuals.
[0074] In a preferred embodiment, the sharp peak 131 in the second
derivative 130 of FIG. 6 is obtained by first smoothing the muscle
response 120, such as by, for example, convolving it with a
normalized Gaussian waveform with a predetermined standard
deviation. Subsequently, the first derivative is calculated by
estimating the instantaneous slope for each data point in the
muscle response 120. The second derivative is then calculated by
estimating the instantaneous slope for each data point in the just
computed first derivative. In order to conserve dynamic memory
resources, the first and second derivatives 130 can be sequentially
calculated for small sections of the muscle response 120 and the
values discarded if they do not indicate the presence of a peak 131
in the second derivative 130.
[0075] Once the peaks 131 in the second derivative 130 have been
identified, the largest positive peak within a defined time window
136 is selected. This time window 136 is defined as occurring
between two time limits, 134 and 135. In a preferred embodiment,
the lower time limit 134 is predetermined and reflects the amount
of time required for artifacts 123 associated with the stimulus to
decay to an amplitude that is significantly less than the amplitude
of the actual signal evoked from the muscle 120. The lower time
limit 134 is preferably about 2.5 milliseconds. Other lower time
limits may, however, be used. In addition, it is possible to
dynamically establish the lower time limit 134 by analyzing the
amplitude decay of the stimulus associated artifact 123. The upper
time limit 135 is determined dynamically. In a preferred
embodiment, the upper time limit 135 is set to reflect the time
during which the first derivative of the evoked muscle response 120
is positive. In other words, it reflects the period of time during
which the evoked muscle response 120 is increasing. By establishing
the upper time limit 135 in this fashion, large peaks 132 in the
second derivative of the response 130, which occur in the latter
portion of the response, are ignored and, therefore, do not result
in incorrect estimates of the latency 133.
[0076] In accordance with a preferred embodiment of the present
invention, FIG. 7 shows an illustrative algorithm for detecting CTS
using an apparatus of the invention in an entirely automated
fashion. The algorithm commences in process step 140 by activation
of actuating means 65, such as, for example, by depression of a
START switch S1. If the actuation means have been activated, the
algorithm continues with process step 142. Otherwise process step
140 is continuously executed until the actuating means are
activated. In process step 142, the root-mean-square (RMS) value of
the noise is obtained in the absence of any electrical stimulation
and compared against a predetermined threshold, n.sub.max. If the
noise RMS is less than n.sub.max, the algorithm continues with
process step 146. However, if the noise RMS is greater than
n.sub.max, the algorithm proceeds to process step 144, in which
indicator 66 is used to indicate a problem with the noise level to
the user. Subsequently, the algorithm returns to process step 140
and waits for reactivation of the START switch S1.
[0077] In process step 146, the magnitude of stimuli to be used in
diagnosing CTS is determined. In a preferred process, this
parameter is determined automatically without user involvement.
This is accomplished by gradually increasing the stimulation
duration in predetermined increments (e.g., 25 microseconds) until
the evoked muscle response 120 meets one or more predetermined
criteria. As an illustrative example, the stimulation duration is
increased until the peak of the first derivative of the evoked
muscle response 120 exceeds a predetermined threshold (e.g., 0.1
mV/ms). If the proper stimulation duration is obtained, the
algorithm proceeds from process step 148 to process step 152. If a
proper stimulation magnitude is not obtained, (i.e., predetermined
threshold not exceeded) the algorithm proceeds to process step 150,
in which indicator 66 is used to indicate a problem with the
determination of stimulation magnitude to the user. Subsequently,
the algorithm returns to process step 140 and waits for
reactivation of the START switch.
[0078] Upon determination of the proper stimulation magnitude, the
algorithm proceeds with process step 152. In this step, the median
nerve 50 is stimulated at a predetermined rate (e.g., 2 Hz) for a
predetermined duration (e.g., 2 seconds). Each thenar muscle
response 120 is analyzed, as previously described, to estimate the
distal motor latency (DML) as the first major peak 133 of the
second derivative 130 of the muscle response 120. Furthermore, the
plurality of DML estimates are combined to obtain a mean DML (m)
and a standard deviation (s) about this mean. The algorithm then
proceeds to process step 153 in which m and s are adjusted for
variations in skin temperature. In particular, the following two
adjustment equations are applied:
m.sub.corrected=m.sub.uncorrected+k.sub.1T+k.sub.2 (A)
s.sub.corrected=s.sub.uncorrected+k.sub.1T+k.sub.2 (B)
The corrected mean DML (m.sub.corrected) and standard deviation
(s.sub.corrected) represent the expected values at room temperature
(i.e., 25.degree. C. or 298.degree. K). The skin temperature, as
measured by the temperature sensor 36, is represented by the
variable T. The values of constants k.sub.1 and k.sub.2 are
determined by a temperature calibration process. In this process,
multiple measurements of the mean DML are obtained at a variety of
temperatures spanning the expected range of temperatures over which
the invention is normally used (e.g., 25.degree. C. to 40.degree.
C.). Subsequently, a linear regression is performed between the
temperatures and the mean DML measurements. The constants k.sub.1
and k.sub.2 are determined directly from the regression
coefficients.
[0079] The algorithm then continues with process step 154, in which
the standard deviation of the DML measurements, s, is compared
against a predetermined threshold, s.sub.min. If s is larger or
equal to s.sub.min, process step 156 is executed. Process step 156
evaluates the number of times m and s have been determined. If
these values have been calculated only once, the algorithm returns
to process step 146, where determination of the proper stimulation
level and all subsequent processing is repeated. If m and s have
been determined twice, however, process step 158 is executed,
resulting in indication of a diagnostic error to the user through
indicator 66. Subsequently, the algorithm returns to process step
140 and waits for reactivation of the START switch S1.
[0080] If in process step 154 it is determined that s is less than
s.sub.min, the algorithm proceeds with process step 160. In this
step, the mean of the DML estimates, m, is compared against a first
predetermined latency threshold, t.sub.normal. If m is less than
t.sub.normal, the algorithm proceeds to process step 162, in which
a normal (i.e., user does not have CTS) test result is indicated to
user through indicator 66. Subsequently, the algorithm returns to
process step 140 and waits for reactivation of the START switch S1.
If m is greater or equal to t.sub.normal, the algorithm proceeds
with process step 164, in which the mean distal motor latency, m,
is compared against a second predetermined latency value,
t.sub.CTS. If m is greater than t.sub.CTS, the algorithm proceeds
to process step 166, in which an abnormal (i.e., user has CTS) test
result is indicated to user through indicator 66. Subsequently, the
algorithm returns to process step 140 and waits for reactivation of
the START switch S1.
[0081] If neither of the two previous inequalities is true, the
algorithm continues with process step 168. In this step, the median
nerve 150 is stimulated by pairs of electrical stimuli spaced apart
at a predetermined temporal interval (e.g., 3 milliseconds). For
each evoked muscle response 120, the difference between the DML
estimated from the first and second stimuli is determined.
Furthermore, the plurality of DML difference estimates are combined
to obtain a mean DML difference (m') and a standard deviation (s')
about this mean. Upon measurement of these two parameters, the
algorithm proceeds to process step 170 in which the mean DML
difference, m' is compared against a predetermined threshold,
t.sub.shift. If m' is greater than t.sub.shift, process step 166 is
executed, in which an abnormal test result is indicated to the
user, as described above. If this inequality does not hold, then an
unknown test result is indicated to user in process step 172.
Subsequently, the algorithm returns to process step 140 and waits
for activation of the START switch S1.
[0082] Another object of the present invention is to serve as a
detection system for diabetic neuropathy. Conventional detection of
diabetic neuropathy is based on an analysis of certain features of
the evoked muscle response, such as the distal motor latency (DML)
and the motor nerve conduction velocity (MNCV). Referring to FIG.
1B, the peroneal nerve DML represents the time lag between
stimulation of the peroneal nerve 254 proximal to the ankle joint
250 and arrival of the neurally conducted impulse at the EDB muscle
252. The peroneal nerve MNCV is calculated by dividing the distance
258 between two stimulation points proximal to the ankle joint,
such as 251 and 256, by the difference between the time lag evoked
by stimulation of the peroneal nerve 254 at the first location 251,
and the time lag evoked by stimulation of the peroneal nerve 254 at
the second location 256. One of the most common and consistent
indications of diabetic neuropathy is an increase in the peroneal
nerve DML and/or a decrease in the peroneal nerve MNCV. Methods
similar to those described above may be used to detect delays and
conduction velocities associated with stimulation and detection
proximal to the ankle joint 250.
[0083] Another major object of the present invention is to detect
systemic neuropathies by determining an F-wave latency of a muscle
response. The F-wave latency is typically defined as the median
interval between the time of administering a stimulus to a motor
nerve (i.e., time=0) and the onset of a myoelectric response in a
muscle innervated by the nerve following antidromic activation of
motor neurons in the spinal cord. Referring again to FIG. 1A, the
F-wave latency represents the time lag between stimulation of the
median nerve 50 immediately proximal to the wrist crease 9 and
arrival of the neurally conducted impulse at the thenar muscle
group 51 after antidromic propagation of the impulse to the spinal
cord, reflection of the impulse in the anterior horn cells of the
spinal cord, and then orthodromic conduction back down the median
nerve. Thus, the F-wave latency quantifies nerve conduction over
the entire course of the median nerve and includes the brachial
plexus and the spinal cord.
[0084] It is important to note that, by electrodiagnostic
convention, negative deflections are plotted above the horizontal
axis and positive deflections are plotted below the horizontal
axis. An F-wave latency is generally defined as the amount of time
that elapses between the start of the stimulus (i.e., time=0) and
the initial positive or negative deflection of the F-wave
component.
[0085] A typical F-wave muscle response 174, evoked and acquired
using an apparatus of the invention, is shown in FIG. 8A. The
vertical scale 176 indicates the amplitude of the response in
microvolts as measured between detection sites 21 and 22. The
horizontal scale 178 indicates the elapsed time from onset of the
stimulus pulse (i.e., stimulus occurred at time=0). The F-wave
response is primarily characterized by the time to initial
deflection 180. However, the peak-to-peak amplitude 182 is
occasionally used as well. In the illustrative example of FIG. 8A,
the time to initial deflection 180 is 28 milliseconds, and the
peak-to-peak amplitude 182 is about 60 microvolts.
[0086] Referring again to FIG. 8A, a F-wave response 174 is
analyzed to yield the time to the initial deflection 180, typically
referred to as the F-wave latency. F-wave latencies may be
determined either by stimulation and detection proximal to the
wrist or by stimulation and detection proximal to the ankle joint.
These F-wave latencies are then correlated to the presence or
absence of CTS or to the presence or absence of diabetic
neuropathy, respectively.
[0087] A F-wave latency is determined first by detecting an F-wave
response signal, which is a component of the myoelectric potential.
This F-wave response signal is then analyzed to determine the
F-wave latency. The analysis includes the steps of removing a trend
from the baseline of the myoelectric potential, filtering the
myoelectric potential, determining a maximum peak of the F-wave
response signal, identifying a first minimum peak and second
minimum peak adjacent the maximum peak of the F-wave response
signal, determining the amplitude between the maximum peak of the
F-wave response signal and one of the two minimum peaks of the
F-wave response signal, determining a noise dependent threshold,
and comparing this noise dependent threshold to the amplitude
between the maximum peak of the F-wave response signal and one of
the two minimum peaks of the F-wave response signal. If this
amplitude is greater than or equal to the noise dependent
threshold, a F-wave latency is determined.
[0088] The myoelectric potential and the F-wave response signal 174
are generally contaminated by a significant trend in the baseline.
This occurs because the F-wave response 174 is acquired at high
gain and is often superimposed on the tail end of the M-wave
response 120. Analysis of the F-wave response signal 174 is
significantly improved by first removing this trend, as described
above. In a preferred embodiment of the algorithm, detrending is
performed by determining the best straight-line fit from the
myoelectric potential and subtracting that line from the
myoelectric potential. In another embodiment, this trend is removed
by averaging a plurality of myoelectric potentials and subtracting
that average from the individual myoelectric potentials. In yet
another embodiment, the statistical distributions of first, and
possibly higher, derivatives of each of the plurality of
myoelectric potentials are determined. Those signals with regions
that are removed by a predetermined factor, such as about 2.5 to
about 4.0 standard deviations, from the distribution's mean or
other statistical center, are not included for the purposes of
averaging, as described above.
[0089] The myoelectric potential and F-wave response signal 174 are
also contaminated by low and high frequency noise, which makes
identification of the onset 180 difficult. The myoelectric
potential is, therefore, digitally filtered using a predetermined
filter. One filter that may be used is a wiener filter, a type of
optimal filter well known to those skilled in the art. The wiener
filter for use in an embodiment of the invention identifies a first
group of signals that clearly contain F-waves (based on the
expertise of a neurophysiologist) and a second group of signals
that clearly do not contain F-waves (again, based on the expertise
of a neurophysiologist). In an alternative embodiment, the
myoelectric potential is filtered by wavelet analysis. Wavelet
de-noising is a method of removing noise known to those skilled in
the art.
[0090] The filtered (and detrended) version 175 of the F-wave
response signal 174 is shown in FIG. 8B. All of the local maxima
184 and local minima 186 and 190 of the detrended and filtered
myoelectric potential and F-wave response signal 175 are
automatically identified. These extrema are preferably determined
by identifying those portions of the myoelectric potential for
which the first derivative is equal to zero.
[0091] The maximum peak 184 of the F-wave response signal and the
larger of the two minimum peaks 186 immediately adjacent (e.g.,
either preceding or succeeding) this maximum peak 184 are then
identified. The temporal location and values of these peaks serve
as points of reference for deciding whether an F-wave actually
exists in the signal and, if so, to determine the F-wave latency
180. In one embodiment, the minimum peak 186 must represent
positivity in the signal. In another embodiment, the minimum peak
186 is initially chosen such that it represents positivity in the
signal, but if an F-wave is not detected according to these
reference points, minima corresponding to negativity in the signal
are chosen.
[0092] To determine whether a viable F-wave response signal exists
in the evoked myoelectric potential, the amplitude 188 between the
maximum peak of the F-wave response signal and one of the two
minimum peaks of the F-wave response signal, is compared against a
noise dependent threshold. The noise dependent threshold is
calculated by measuring a level of noise immediately preceding or
following the acquisition of the myoelectric potential and then
multiplying this level of noise by a predetermined factor. The
predetermined factor is preferably about 3.5 to about 6.0, but
other values are possible. The amplitude 188 is compared against
this noise dependent threshold. If the amplitude 188 is greater
than or equal to the noise dependent threshold, a F-wave latency
exists. If the amplitude 188 is less than the noise dependent
threshold, a F-wave latency cannot be reliably determined.
[0093] Unlike M-wave 120 of FIG. 5, F-wave 174 can have a multitude
of waveform shapes, although most will look similar to the waveform
174 shown in FIG. 8A. Thus, to increase the sensitivity of the
determination of a F-wave latency, a number of atypical waveform
shapes that do not yield the maximum and minimum peaks of the
F-wave response signal, as described above, are detected and
processed. For example, in the preferred embodiment, it is
recognized that F-wave response signals occasionally have double
peaks, such as 196 shown in FIG. 8C. In this situation, the maximum
peak 198, may not represent an optimal reference point for
determining the F-wave latency 180 (see below for F-wave latency
determination). Thus, another local maximum 200 is chosen as a
reference point for purposes of latency determination in order to
account for this particular waveform irregularity.
[0094] In another embodiment, the reference point can be further
altered through detection of a minimum peak that is significant in
magnitude. This minimum peak preceeds the current reference point
(i.e., the maximum peak). In such an instance, the signal would be
inverted and the current reference point for the determination of
the F-wave latency would be reassigned to the temporal location of
this detected minimum peak.
[0095] Referring to FIG. 8C, the F-wave latency 197 of the evoked
response is determined when the amplitude 189 is greater than or
equal to the noise dependent threshold, as described above. The
F-wave latency 196 is identified by determining an inflection 199
in the F-wave response signal immediately preceding the reference
point 200 in the F-wave response signal. In an embodiment of the
invention, this inflection is identified as the last point
preceding the minimum peak of the F-wave response signal for which
the signal's first derivative is 0 or negative. If no such point
exists, the inflection can be identified as the last point at which
the first derivative is at its minimum.
[0096] After the F-wave latency is determined, the signal is
reanalyzed to confirm the F-wave latency by ensuring that the
F-wave latency makes sense within the context of the entire signal.
In one embodiment, this is accomplished by, for example, averaging
the absolute values of the F-wave response signal in a first
predetermined window of time preceeding the F-wave latency 197 and
comparing this value to the absolute value of the maximum and
minimum first derivative of the F-wave response signal 196 in a
second predetermined window of time following the F-wave latency
197. Another method of confirming the F-wave latency includes
determining that there are no positive or negative extrema
preceding the F-wave latency point 197 that are significant (i.e.,
greater than 50%, but preferably in the range of about 25% to about
75%) in magnitude with respect to the amplitude of the maximum peak
of the F-wave response signal 201. If either of the two
confirmation determinations described above fail, an F-wave latency
is not yielded.
[0097] Once a F-wave latency is determined, this F-wave latency is
correlated to an indicia of the latency. This indicia is indicated,
but may also be correlated to a physiological function of the nerve
and/or muscle. The physiological function may relate to a disorder
of a peripheral nervous system of the test subject, such as CTS or
diabetic neuropathy. Furthermore, F-wave latencies so determined
may be modified in response to the temperature of skin at the test
site, in response to the height of the test subject, or in response
to the age of the test subject, all as described below.
[0098] In accordance with a preferred embodiment of the present
invention, FIG. 9 shows an illustrative algorithm for measuring the
DML and F-wave latency of a peripheral nerve using an apparatus of
the invention in an entirely automated fashion. The algorithm
commences in process step 202 by activation of actuating means 65,
such as, for example, by depression of the START switch S1. If the
actuation means have been activated, the algorithm continues with
process step 204. Otherwise process step 202 is continuously
executed until the actuating means are activated. In process step
204, the mean absolute deviation of the noise is obtained in the
absence of any electrical stimulation and compared against a
predetermined threshold, n.sub.max. If the mean absolute deviation
of the noise is less than or equal to n.sub.max, the algorithm
continues with process step 208. If the mean absolute deviation of
the noise is greater than n.sub.max, the algorithm proceeds to
process step 206, in which indicator 66 indicates to the user a
problem with the noise level. Subsequently, the algorithm returns
to process step 202 and waits for reactivation of the START switch
S1.
[0099] The mean absolute deviation of the noise, .phi., is
calculated according to the following equation:
.PHI. = 1 N i = 0 N n _ - n i ##EQU00001## where ' n _ = 1 N 1 N n
i ##EQU00001.2##
[0100] The individual noise samples, n.sub.i, are acquired by the
controller 61 at a predetermined sampling frequency for a
predetermined duration of time. The sampling frequency is chosen so
that consecutive samples are unlikely to be correlated and is
between about 100 Hz and about 1000 Hz, and is preferably about 500
Hz. The sampling duration is chosen so that a stable measurement of
the noise is obtained, and is between about 100 milliseconds and
about 1000 milliseconds, and preferably about 200 milliseconds. The
mean absolute deviation of the noise is functionally similar to the
standard deviation or root mean square of the noise, but, because
it does not involve squaring and square-root operations, the mean
absolute deviation of the noise is more readily implemented in an
efficient manner in a microcontroller. The predetermined noise
threshold, n.sub.max, is generally in the range of about 1 .mu.V to
about 15 .mu.V, and is more preferably in the range of about 1
.mu.V to about 5 .mu.V.
[0101] In process step 208, the magnitude of the stimuli used in
measuring the DML and F-wave latency is determined. In a preferred
process, this parameter is determined automatically without user
involvement. If the proper stimulation duration is obtained, the
algorithm proceeds from process step 210 to process step 214. If a
proper stimulation magnitude is not obtained, the algorithm
proceeds to process step 212, in which indicator 66 indicates to
the user a problem with the determination of stimulation magnitude.
Subsequently, the algorithm returns to process step 204 and waits
for reactivation of the START switch.
[0102] Upon determination of the proper stimulation magnitude, the
algorithm proceeds with process step 214. In this step, a
stimulation counter, i, is initialized to a value of one. The
algorithm then proceeds to process step 216 in which the gain of
the signal conditioning subsystem 61 is set to a first gain value
of g.sub.mwave by the controller 63. The algorithm then continues
with process step 218, in which the nerve 50 is stimulated with the
previously determined stimulation magnitude. Immediately
thereafter, in process step 220, the evoked muscle response is
acquired by the controller 63 for a first predetermined amount of
time, t.sub.mwave.
[0103] The first gain of the signal conditioning system,
g.sub.mwave, can be predetermined or dynamically established. The
predetermined value is between about 500 and about 8000, and is
preferably about 2000, based on an empirical analysis of a many
signals. The system can also dynamically determine the gain by
incrementally increasing the gain, under control by the controller
63, until the amplified response generated by the signal
conditioning subsystem 61 saturates the analog-to-digital
acquisition circuit of the controller 63.
[0104] The value of t.sub.mwave must be sufficient to ensure that
normal and prolonged pathological M-waves are captured. In one
comprehensive study of the distal motor latency in subjects with
Carpal Tunnel Syndrome, the mean distal motor latency was found to
be 4.94.+-.1.03 milliseconds. (See Kimura, "The Carpal Tunnel
Syndrome: Localization of Conduction Abnormalities within the
Distal Segment of the median Nerve", Brain, 102:619-635 (1979)). In
this study, 99% of the subjects had a DML of 8 milliseconds or
less. Thus, t.sub.mwave is set at about 10 milliseconds to about 30
milliseconds, and is preferably about 12.8 milliseconds. This
ensures that all pathological signals will be acquired and that,
even in those severely pathological signals (e.g., DML >7
milliseconds), a sufficient portion of the M-wave will be recorded
for waveform analysis purposes (e.g., at least 5 milliseconds).
[0105] Immediately after completing process step 220, the algorithm
continues with process step 194, in which the gain of the signal
conditioning subsystem 61 is set to a second gain value of
f.sub.mwave by the controller. The algorithm then continues with
process step 226, in which the output from the signal conditioning
subsystem 61 is acquired for a predetermined amount of time,
t.sub.fwave, starting a predetermined amount of time, t.sub.offset,
after the onset of the stimulus.
[0106] The second gain of the signal conditioning system,
f.sub.mwave, is predetermined and is between about 10,000 and about
30,000, and is preferably about 15,000. This value is based on an
empirical analysis of a many signals. The values of t.sub.offset
and t.sub.fwave are predetermined according to the known values of
F-wave latencies in the literature. Normal subjects typically have
F-wave latencies of 26.6.+-.2.2 milliseconds (See, e.g.,
Electrodiagnosis in Diseases of Nerve and Muscle: Principles and
Practice, 1989, Ed. J. Kimura). Therefore, 99% of patients have a
F-wave latencies greater than 20 milliseconds. Thus, t.sub.offset
is set at this value. The value of t.sub.fwave is then set at 32
milliseconds, which will capture the majority of pathological
F-wave latencies (See, e.g., Kimura, Principles and Practice,
supra) and utilizes a reasonable amount of memory.
[0107] Upon completion of process step 226, the algorithm
immediately proceeds to process step 228, in which the mean
absolute deviation of the signal, .phi..sub.i, is calculated in a
manner similar to that described above for the absolute deviation
of the noise. This value is used to determine the noise dependent
threshold utilized in the aforementioned algorithm for identifying
the F-wave latency 208.
[0108] Upon completing process step 228, the algorithm continues
with process step 230, in which the signal acquired during the
t.sub.mwave portion is processed to yield a DML, as described
above. The algorithm then determines the F-wave latency in process
step 232 using the approach described above. Finally, in process
step 234, the algorithm compares the stimulation counter against a
predetermined limit, max.sub.stim. If the stimulation counter, i,
is not equal to this limit then the algorithm proceeds to process
step 216, which restarts the nerve stimulation and acquisition
sequence. If the stimulation counter, i, is equal to the limit,
max.sub.stim, the algorithm proceeds to process step 238, in which
the mean DML is calculated. The algorithm then proceeds to process
step 240, in which the median F-wave latency is calculated. Both of
these calculated values (DML and F-wave latency) are corrected for
variations in skin temperature in process step 242 using the
equations described below.
dml.sub.corrected=dml.sub.raw+(T-T.sub.0)k.sub.dml
fwave.sub.corrected=fwave.sub.raw+(T-T.sub.0)k.sub.fwave
where T is the skin surface temperature as measured by the
temperature sensor 36, k.sub.dml is a temperature correction factor
for the distal motor latency derived from the neurological
literature (See, e.g., Electrodiagnosis in Diseases of Nerve and
Muscle: Principles and Practice, 1989, Ed. J. Kimura), k.sub.fwave
is a temperature correction factor for the F-wave latency derived
from the neurological literature (See, e.g., Kimura, Principles and
Practice, supra), and T.sub.0 is the desired temperature to which
the mean DML and median F-wave latency are corrected. T.sub.0 is
between about 30.degree. C. and about 34.degree. C., and is
preferably about 32.degree. C. Finally, these corrected values are
displayed in process step 244. Subsequently, the algorithm returns
to process step 202 and waits for reactivation of the START switch
S1.
[0109] An important object of the present invention is to provide
the operator with neuromuscular parameters that are accurate and
reproducible. For example, as has been described above, the DML and
the F-wave latency are corrected for variations in the skin surface
temperature as measured by the temperature sensor 36 embedded
within the neuromuscular electrode 1. Additional factors that
impact the accuracy of neuromuscular diagnostic parameters are the
height and age of the test subject. In a preferred embodiment, the
DML and F-wave latency are automatically adjusted by the controller
63 to account for the height and age according to the following
equations.
dml height_age _corrected = - 1 - 1 dml temperature_corrected + ( A
0 - A ) h dml + ( H o - H ) h dml ##EQU00002## fwave height_age
_corrected = - 1 - 1 fwave temperature_corrected + ( A 0 - A ) h
fwave + ( H o - H ) h fwavel ##EQU00002.2##
where H is the height of the test subject in centimeters, H.sub.0
is the normalized height in centimeters to which the DML and F-wave
values are corrected, A is the age of the test subject in years,
A.sub.0 is the normalized age in years to which the DML and F-wave
values are corrected, h.sub.dml and a.sub.dml are height and age
correction factors, respectively, for the distal motor latency
derived from the neurological literature (See, e.g., Stetson, et
al., "Effects of Age, Sex, and Anthropometric Factors on Nerve
Conduction Measures," Muscle & Nerve, 15:1095-1104 (1992)), and
h.sub.fwave and a.sub.fwave are height and age correction factors,
respectively, for the F-wave latency derived from the neurological
literature (See, e.g., Stetson, supra). Furthermore, other height
and age correction equations have been contemplated and should be
considered within the scope of the present invention. Additionally,
correction of the conduction velocity by equations similar to those
provided above is well known to those of ordinary skill in the
art.
[0110] In the preferred embodiment, the approximate height of the
test subject is automatically derived from the size of the
neuromuscular electrode 1 used. In particular, the height is
obtained by reading the two bits within the EEPROM in the
temperature sensor 36, which encodes the size of the neuromuscular
electrode 1, as described above, and then translating that size
into a height using Table 1.
TABLE-US-00001 TABLE 1 Height (in EEPROM Size centimeters) 00 Small
161.8 (4.028 in) 01 Medium 172 (4.308 in) 10 Large 180.9 (4.579
in)
In other embodiments, the height of the test subject can be entered
into the monitor 2 using user actuable controls 65 or the external
interface 67.
[0111] Although the illustrative algorithms described above pertain
to the detection of CTS, the apparatus of the present invention may
used to detect other forms of nerve disease and to evaluate
neuromuscular blockade. For example, the train-of-four (TOF)
protocol, which is commonly used to evaluate the degree of
neuromuscular blockade in anesthetized patients, is readily
implemented using an apparatus of the invention. In particular, a
predetermined number (usually four) of muscle responses 120 are
evoked at a predetermined rate (e.g., 2 Hz) and the amplitude 126
of each response determined. Subsequently, the ratio of the
amplitude of the last of the plurality of muscle responses to be
evoked is divided by the amplitude of the first of the plurality of
muscle responses to be evoked. This ratio is recognized as a
sensitive indicator of neuromuscular blockade.
[0112] The aforementioned algorithms are intended for illustrative
purposes only. Other algorithms may be developed which detect CTS
or diabetic neuropathy using an apparatus of the invention. For
example, parameters other than the DML, F-wave latency, and
conduction velocity may be incorporated into the diagnostic
algorithms. Illustrative parameters include: waveform features of
the evoked muscle response 120, such as, for example, the amplitude
and width. Additional illustrative parameters include waveform
features of processed forms of the evoked muscle response 120, such
as, for example, its derivatives, its Fourier transform, and other
parameters derived from statistical analyses (e.g., principal
component analysis). Furthermore, additional parameters are
obtained by comparison of any of the above parameters at different
stimulation levels.
[0113] Another algorithm of the invention is for ensuring that the
neuromuscular 1 is not reused, for the reasons enumerated above. In
this algorithm, an electronic flag (i.e., a single bit) within the
EEPROM of temperature sensor 36 is utilized. In particular, once a
test is initiated, such as by pressing the START switch S1, the
status of the electronic flag is checked. If the flag is clear
(i.e., the appropriate bit is 0), the monitor 2 proceeds with the
test. If the electronic flag is set (i.e., the appropriate bit is
1), the monitor 2 does not proceed and instead indicates, such as
with indicator 66, that the user is attempting to perform a test
with an inactivated neuromuscular electrode 1. It is important to
note that the electronic flag is always cleared during
manufacturing of the neuromuscular electrodes 1, so the
neuromuscular electrode always has a cleared (i.e., the appropriate
bit is 0) electronic flag upon first use.
[0114] In one algorithm, the neuromuscular electrode 1 must be
inactivated through setting of the electronic flag upon its removal
from the skin after use. This may be accomplished in a number of
different ways. In one embodiment, the impedance between any two
among the plurality of electrodes 21, 22, 30,31, 27 within the
neuromuscular electrode 1 is monitored at a frequent and
predetermined rate (such as for example, every second). Removal of
the neuromuscular electrode 1 from, for example, the subject's
forearm 8 is detected when the impedance exceeds a predetermined
level, which is preferably greater than 1 M.OMEGA.. In another
embodiment, the signal from the signal conditioning subsystem 61 is
continuously monitored by the controller 63. When the neuromuscular
electrode 1 is removed from the forearm 8, the inputs to the
differential amplifier 60 float causing certain detectable
characteristics of the signal to change. These characteristics
include the DC offset and the power spectrum. In yet another
embodiment, certain predetermined characteristics of the evoked
muscle response 120 are monitored and compared against previous
tests in the same neuromuscular electrode 1. The identity of the
neuromuscular electrode 1 is established by the unique serial
number, as stored in data memory of temperature sensor 36. If these
characteristics are found to change to a significant degree, the
test is halted and the electronic flag is set, thus inactivating
the neuromuscular electrode 1. A particularly effective muscle
response 120 characteristic is the polarity of the signal, which is
quantified by the amplitude (e.g., positive or negative) of the
peak 126. A switch in the polarity of the muscle response 120
indicates that neuromuscular electrode 1 has been moved from one
hand to the other.
[0115] An additional object of the present invention is to ensure
that neuromuscular diagnostic information obtained with the
disclosed apparatus and methods is correctly associated with the
test subject. In a preferred embodiment, the unique serial number
embedded within the data memory of neuromuscular electrode 1 is
read by the monitor 2 and associated with the display, such as with
indicator 66, or other output though the external interface 67, of
test results such as the DML and F-wave latency. For example, the
external interface 67 may be connected to a modem that transmits
the DML, F-wave latency and associated waveforms to a remote
information service. In a preferred embodiment, the data is tagged,
or otherwise associated, with the unique serial number 36 embedded
in the data memory of neuromuscular electrode 1 used to obtain the
data. Furthermore, in the preferred embodiment, the operator is
directed to attach the previously described labels that have the
same unique serial number printed on them to the test subject's
chart. As a result, the test subject's chart and his neuromuscular
diagnostic information, stored on the remote information service,
are robustly linked.
[0116] The disclosed invention provides a new approach to
monitoring neuromuscular physiology. Apparatus and methods are
described for the substantially automated and highly efficient
measurement of many different parameters of neuromuscular
physiology. These indicators may be used to detect CTS and other
peripheral nerve diseases, such as diabetic neuropathy, as well as
to monitor neuromuscular blockade caused by pathological,
pharmacological and chemical means. The invention possesses the
significant advantage that, unlike conventional measurements of
nerve conduction across the wrist, the disclosed invention provides
for a single integrated neuromuscular electrode that is placed
immediately proximal to the wrist (i.e., the wrist crease).
Alternatively, the neuromuscular electrode is placed at or proximal
to the ankle joint. These are very familiar anatomic locations, so
the placement operation is rapidly and easily undertaken by most
adults. Unlike apparatus and methods of the prior art, the
disclosed invention does not require placement of multiple sets of
electrodes on both sides of the wrist, which is a difficult and
error prone procedure for non-experts. An additional advantage of
the disclosed invention emerges from the fact that the integrated
neuromuscular electrode may be manufactured as a low-cost,
disposable item. Consequently, the possibility of
cross-contamination among users of the apparatus is significantly
reduced. Furthermore, the low-cost, and ease of use will promote
frequent monitoring of neuromuscular disorders, such as CTS and
diabetic neuropathy, providing the potential benefits of early
detection and regular tracking of these diseases. Another advantage
of the present invention is that the process of evoking, detecting
and processing neuromuscular signals is carried out in an entirely
automated fashion, without requiring involvement of either the user
of the apparatus or trained personnel. A further advantage of the
present invention is that the smallest and fewest electrical
stimuli consistent with an accurate diagnostic assessment are used.
As a result, discomfort to the user is minimized and, in most
cases, eliminated entirely.
[0117] While the present invention has been described in terms of
certain exemplary preferred embodiments, it will be readily
understood and appreciated by one of ordinary skill in the art that
it is not so limited, and that many additions, deletions and
modifications to the preferred embodiments may be made within the
scope of the invention as hereinafter claimed. Accordingly, the
scope of the invention is limited only by the scope of the appended
claims.
* * * * *