U.S. patent application number 10/856126 was filed with the patent office on 2005-02-17 for method for determining attention deficit hyperactivity disorder (adhd) medication dosage and for monitoring the effects of (adhd) medication.
Invention is credited to Blazey, Richard N., Miller, Paige, Patton, David L..
Application Number | 20050038354 10/856126 |
Document ID | / |
Family ID | 25345259 |
Filed Date | 2005-02-17 |
United States Patent
Application |
20050038354 |
Kind Code |
A1 |
Miller, Paige ; et
al. |
February 17, 2005 |
Method for determining attention deficit hyperactivity disorder
(ADHD) medication dosage and for monitoring the effects of (ADHD)
medication
Abstract
A method for determining the appropriate dosage of a medication
to treat Attention Deficit Hyperactivity Disorder (ADHD) in an
individual who has ADHD comprising: sampling the peripheral skin
temperature of a human subject during a predetermined time interval
when the subject is in an inactive state to provide sampled
peripheral skin temperature data; analyzing the sampled peripheral
skin temperature data for a pre-selected parameter to determine
whether the pre-selected parameter has a value indicative of ADHD;
and determining the proper dosage of a medication to treat ADHD
based upon the determined value of the pre-selected parameter. At a
time subsequent to administering the dosage, it is determined if a
previously administered dosage of a medication is effective at
removing the effects of ADHD as measured by this pre-selected
parameter.
Inventors: |
Miller, Paige; (Rochester,
NY) ; Patton, David L.; (Webster, NY) ;
Blazey, Richard N.; (Penfield, NY) |
Correspondence
Address: |
CLARK & ELBING LLP
101 FEDERAL STREET
BOSTON
MA
02110
US
|
Family ID: |
25345259 |
Appl. No.: |
10/856126 |
Filed: |
May 28, 2004 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10856126 |
May 28, 2004 |
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10301401 |
Nov 21, 2002 |
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6743182 |
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10301401 |
Nov 21, 2002 |
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09865329 |
May 25, 2001 |
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6520921 |
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Current U.S.
Class: |
600/549 ;
424/9.2 |
Current CPC
Class: |
A61B 5/0002 20130101;
A61B 5/7257 20130101; A61B 3/113 20130101; A61B 5/01 20130101; A61B
5/726 20130101; A61B 5/168 20130101; G16H 15/00 20180101 |
Class at
Publication: |
600/549 ;
424/009.2 |
International
Class: |
A61B 005/00; A61K
049/00 |
Claims
1-19. Cancelled.
20. A method of diagnosing a disorder comprising: sampling the
peripheral skin temperature of a human subject during a
predetermined time interval when the subject is in an inactive
state to provide sampled peripheral skin temperature data; and
analyzing the sampled peripheral skin temperature data for a
pre-selected parameter, to determine whether said pre-selected
parameter has a value indicative of a disorder characterized by the
symptoms of inattention, hyperactivity, and impulsivity.
21. The method of claim 20 wherein said data is processed with a
fourier transform algorithm to produce frequency and phase
data.
22. The method of claim 20 wherein said data is processed with a
wavelet transform algorithm to produce frequency and phase
data.
23. An apparatus for diagnosing a disorder comprising: a device for
sampling the peripheral skin temperature of a human subject during
a predetermined time interval when the subject is in an inactive
state to provide sampled peripheral skin temperature data; and an
analyzer for analyzing the sampled peripheral skin temperature data
for a pre-selected parameter to determine whether said pre-selected
parameter has a value indicative of a disorder characterized by the
symptoms of inattention, hyperactivity, and impulsivity.
24. The method of claim 23 wherein said data is processed with a
fourier transform algorithm to produce frequency and phase
data.
25. The method of claim 23 wherein said data is processed with a
wavelet transform algorithm to produce frequency and phase
data.
26. A method for determining the appropriate dosage of a medication
comprising: measuring the stress of a human subject by sampling the
peripheral skin temperature of a human subject during a
predetermined time interval when the subject is in an inactive
state to provide sampled peripheral skin temperature data;
analyzing the sampled peripheral skin temperature data for a
pre-selected parameter to determine whether said pre-selected
parameter has a value indicative of a disorder characterized by the
symptoms of inattention, hyperactivity, and impulsivity; and
determining the proper dosage of a medication to treat said
disorder based upon said determined value of said pre-selected
parameter; and at a time subsequent to administering said dosage,
determining if the administered dosage of medication is actually
effective at removing the symptoms of said disorder as measured by
said pre-selected parameter.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This patent application is a Continuation-in-Part
Application of U.S. patent application, Ser. No. 09/865,329 filed
May 25, 2001, which application claims the benefit under 35 USC
.sctn. 120 of the earlier filing date of U.S. patent application
Ser. No. 09/597,610, filed Jun. 20, 2000, now U.S. Pat. No.
6,394,963.
FIELD OF THE INVENTION
[0002] This invention relates in general to a technique for
monitoring the effectiveness of medication taken to treat Attention
Deficit Hyperactivity Disorder (ADHD) and more particularly to a
technique for measuring an individual's peripheral temperature
variability (TV) indicative of ADHD.
BACKGROUND OF THE INVENTION
[0003] ADHD is the most common neurobehavioral disorder of
childhood as well as among the most prevalent health conditions
affecting school-aged children. Between 4% and 12% of school age
children (several millions) are affected. $3 billion is spent
annually on behalf of students with ADHD. Moreover, in the general
population, 9.2% of males and 2.9% of females are found to have
behavior consistent with ADHD. Upwards of 10 million adults may be
affected.
[0004] ADHD is a difficult disorder to diagnose. The core symptoms
of ADHD in children include inattention, hyperactivity, and
impulsivity. ADHD children may experience significant functional
problems, such as school difficulties, academic underachievement,
poor relationships with family and peers, and low self-esteem.
Adults with ADHD often have a history of losing jobs, impulsive
actions, substance abuse, and broken marriages. ADHD often goes
undiagnosed if not caught at an early age and affects many adults
who may not be aware of the condition. ADHD has many look-alike
causes (family situations, motivations) and co-morbid conditions
(depression, anxiety, and learning disabilities) are common.
[0005] Diagnosis of ADHD involves a process of elimination using
written and verbal assessment instruments. However, there is no one
objective, independently validated test for ADHD. Various objective
techniques have been proposed but have not yet attained widespread
acceptance. These include:
[0006] 1. The eye problem called convergence insufficiency was
found to be three times more common in children with ADHD than in
other children by University of California, San Diego
researchers.
[0007] 2. Infrared tracking to measure difficult-to-detect
movements of children during attention tests combined with
functional MRI imaging of the brain were used by psychiatrists at
McLean Hospital in Belmont, Mass to diagnose ADHD in a small group
of children (Nature Medicine, Vol. 6, No. 4, April 2000, Pages
470-473).
[0008] 3. Techniques based on EEG biofeedback for the diagnoses and
treatment of ADHD are described by Lubar (Biofeedback and
Self-Regulation, Vol. 16, No. 3, 1991, Pages 201-225).
[0009] 4. U.S. Pat. No. 6,097,980, issued Aug. 1, 2000, inventor
Monastra et al, discloses a quantitative electroencephalographic
process assessing ADHD.
[0010] 5. U.S. Pat. No. 5,913,310, issued Jun. 22, 1999, inventor
Brown, discloses a video game for the diagnosis and treatment of
ADHD.
[0011] 6. U.S. Pat. No. 5,918,603, issued Jul. 6, 1999, inventor
Brown, discloses a video game for the diagnosis and treatment of
ADHD.
[0012] 7. U.S. Pat. No. 5,940,801, issued Aug. 17, 1999, inventor
Brown, discloses a microprocessor such as a video game for the
diagnosis and treatment of ADHD.
[0013] 8. U.S. Pat. No. 5,377,100, issued Dec. 27, 1994, inventors
Pope et al., discloses a method of using a video game coupled with
brain wave detection to treat patients with ADHD.
[0014] 9. Dr. Albert Rizzo of the Integrated Media Systems Center
of the University of Southern California has used Virtual Reality
techniques for the detection and treatment of ADHD.
[0015] 10. U.S. Pat. No. 6,053,739, inventors Stewart et al.,
discloses a method of using a visual display, colored visual word
targets and colored visual response targets to administer an
attention performance test. U.S. Pat. No. 5,377,100, issued Dec.
27, 1994, inventors Patton et al., discloses a system and of
managing the psychological state of an individual using images.
U.S. Pat. No. 6,117,075 Barnea discloses a method of measuring the
depth of anesthesia by detecting the suppression of peripheral
temperature variability.
[0016] There are several clinical biofeedback and physiologic
monitoring systems (e.g. Multi Trace, Bio Integrator). These
systems are used by professional clinicians. Although skin
temperature spectral characteristics have been shown to indicate
stress-related changes of peripheral vasomotor activity in normal
subjects, there has been no disclosure of use of variations in
skin-temperature response to assist in diagnosing ADHD. (See:
Biofeedback and Self-Regulation, Vol. 20, No. 4, 1995).
[0017] As discussed above, the primary method for diagnosing ADHD
is the use of a bank of written and verbal assessment instruments
designed to assess the children for behavioral indicators of
criteria established by American Medical Association (AMA) as
described in the Diagnostic and Statistics manual--IV (DSM-IV).
Psychiatrists, psychologists, school psychologists and other
licensed practitioner administer these assessment instruments. In
some cases those individuals who meet DSM-IV criteria for ADHD
diagnosis are prescribed a drug such as Ritalin. Behavioral
observations of the patient while on Ritalin are conducted to
assess the impact of prescribed medication. However, clearly
established criteria for evaluating the impact of specific
medications e.g., Ritalin and specific dosages are lacking. It
would be advantageous for physicians to have access to clearly
established physiologic criteria, which could be measured, to
determine if a specific medication at a specific dosage effectively
addressed the underlying physiologic parameter, which was
indicative of ADHD.
[0018] There is thus a need for a simple, inexpensive, and reliable
technique for determining the effectiveness of the medication and
appropriate dosage taken to counteract ADHD by an individual who
has ADHD.
SUMMARY OF THE INVENTION
[0019] According to the present invention, there is provided a
solution to the problems and fulfillment of the needs discussed
above.
[0020] According to a feature of the present invention, there is
provided a a method for determining the appropriate dosage of a
medication to treat Attention Deficit Hyperactivity Disorder (ADHD)
in an individual who has ADHD comprising:
[0021] measuring the stress of a human subject by sampling the
peripheral skin temperature of a human subject during a
predetermined time interval when the subject is in an inactive
state to provide sampled peripheral skin temperature data;
[0022] analyzing the sampled peripheral skin temperature data for a
pre-selected parameter to determine whether said pre-selected
parameter has a value indicative of ADHD; and
[0023] determining the proper dosage of a medication to treat ADHD
based upon said determined value of said pre-selected parameter;
and
[0024] at a time subsequent to administering said dosage,
determining if the administered dosage of medication is actually
effective at removing the effects of ADHD as measured by said
pre-selected parameter.
[0025] Advantageous Effect of the Invention
[0026] The invention has the following advantages.
[0027] 1. A device and technique for determining the effectiveness
of the medication and appropriate dosage taken to counteract ADHD
by an individual whom has ADHD which is simple, inexpensive and
reliable.
BRIEF DESCRIPTION OF THE DRAWINGS
[0028] FIG. 1 is a diagrammatic view illustrating use of an
embodiment of the present invention.
[0029] FIG. 2 is a perspective view showing in greater detail the
embodiment of FIG. 1.
[0030] FIGS. 3a and 3b are block diagrams of a system incorporating
the present invention.
[0031] FIGS. 4, 5 and 6 are graphical views useful in explaining
the present invention.
[0032] FIG. 7 is a diagram of an example of using the threshold
.theta..sub.g and the patient's computed aggregation statistic
.THETA..sub.m to diagnose the presence or absence of ADHD and
determine the suggested drug dosage.
[0033] FIGS. 8, 9 and 10 are graphical illustrations respectively
showing mean Mrange values and Mrange variance for both medicated
and non-medicated sessions.
DETAILED DESCRIPTION OF THE INVENTION
[0034] According to the invention, it has been found that a
signature of ADHD is hidden in fluctuation of the temperature of
the skin as measured at the extremities such as at a fingertip as a
function of variations in stress level. In general, as an
individual's stress level increases the peripheral vasculature
constricts and often the person's blood pressure increases. As the
blood vessels in the body constrict, blood flow is restricted. This
is most easily monitored in the extremities such as the fingers,
because the blood vessels in the extremities are small and very
responsive to Sympathetic Nervous System (SNS) innervations. A
direct result of decreased blood flow to the blood vessels in the
extremities is a decrease in the peripheral temperature of the
extremities. Conversely, as an individual's stress level decreases
and relaxation occurs, the blood vessels expand, allowing blood to
flow in a less restricted manner. As the blood flow to the vessels
in the extremities increases the peripheral temperature of the
extremities increases. It is suspected that when a subject with
ADHD is subjected to sensory deprivation such as being made to look
at a blank screen or an obscured image for a period of time in an
inactive state, the lack of stimulation increases and there tends
to be a shift in the subject's physiologic reactivity indicative of
an increase in their stress level. As their stress level increases
their blood vessels contract and the peripheral temperature of
their extremities decreases. Biofeedback practitioners have long
used measurement of hand temperature to help subjects manage their
physiology by controlling blood flow to the extremities. The
literature reports that reduced blood flow to the brain is
frequently found in patients with ADHD.
[0035] In addition to peripheral skin temperature and peripheral
skin temperature variability there are other known physiologic
measures which are known (or potential) indicators of stress and
therefore ADHD such as; bilateral temperature variability, heart
rate, heart rate variability, muscle tension (excessive and
chronic, measured via surface electromyography--sEMG), bilateral
muscle tension imbalance, galvanic skin response (i.e., electro
dermal response--EDR), eye saccades, blood oxygen (SpO.sub.2),
salivary IGA, electroencephalography (EEG), peripheral blood flow
(measured via photoplethismography--PPG), and peripheral blood flow
variability (PPG).
[0036] As shown in FIG. 1, a subject 10 is sitting on a chair 12 at
a table 13 watching a screen 14. The screen 14 is used to block any
visual stimulus from disturbing the subject 10. The subject 10 is
wearing a set of earphones 20. The earphones 20 can be connected to
a sound-generating device not shown. The earphones 20 can be used
to block out ambient noise or to produce a white noise intended to
reduce or eliminate the audio stimulus from the environment during
the test. The subject is at rest in an inactive state. During the
test no visual or auditory stimulus is provided to the subject. The
fingertip 16 of subject 10 is inserted into an analyzer module 18,
where the skin temperature is measured via a sensor 22 (shown in
FIG. 2). In another embodiment of the present invention, which is
not shown, the subject can wear a pair of translucent glasses,
goggles or eye mask. The glasses or goggles are used to block any
visual stimulus from the subject.
[0037] FIG. 2 shows an illustration of the analyzer module 18.
Analyzer module 18 includes a temperature sensor 22, where the
subject 10 inserts their fingertip 16 in groove 17, an on/off
switch 24, and a display 26. The analyzer module 18 can have an
internal power supply, such as a battery 30, or an external low
voltage power supply port 32 for an external low voltage power
supply (not shown), such as used for a telephone. The analyzer
module 18 can be connected to an external CPU (not shown) via a
cable 27 (such as an USB or RS 232 cable), or wireless transmitting
device such as an RF or IR link (not shown). In a further
embodiment a second temperature sensor module 28 can be connected
to the analyzer 18 via a cable 29. The second temperature sensor
module 28 can be used to sample the skin temperature of the
subject's 10 other hand and includes groove 34 and temperature
sensor 36.
[0038] As shown in FIG. 3a, module 18 includes temperature sampling
circuit 41, data storage 42, window blocking 43, Fourier transform
44, Magnitude calculation 45, Mrange calculation 46, aggregation
step block 47, Threshold comparison step block 48, previously
determined threshold .theta..sub.g block 49, and threshold
comparison decision block 50. The method of determining dosage is
further expanded in FIG. 3b.
[0039] In FIG. 1, the fingertip temperature is first recorded
during an interval when the subject 10 has been asked to sit
quietly for a period of about 10 minutes. The temperature data is
sampled by 41 at a time interval .DELTA.t creating a list of n
temperature samples, which are stored in storage 42.
[0040] Now referring to FIG. 3a, in block 43, the n samples are
divided into z windows of m samples, each group corresponding to a
given time window of width .DELTA.t (.about.32-64 sec) equally
spaced in time (.about.50 sec) across the entire data collection
time interval .DELTA.t. The data from each window is then passed
through a Fast Fourier Transform (FFT) algorithm 44 producing
2.sup.m-1 data points spaced equally in frequency space for each
window. The values are complex numbers having form
FFT (f.sub.m)=A(f.sub.m)+B(f.sub.m)i
[0041] where i is the {square root}{square root over (-1)}. The
Phase .PHI.(f.sub.m) is then found from the equation 1 l ( f m ) =
Tan - 1 ( B ( f m ) A ( f m ) ) ( 1.0 )
[0042] and the Magnitude M(f.sub.m) from
M.sub.l(f.sub.m)={square root}{square root over
(B(f.sub.m).sup.2+A(f.sub.- m).sup.2)} (1.1)
[0043] In the equations 1.0 and 1.1 the subscript l refers to the
fact that a separate signal is extracted for each hand so the
subscript is l for data extracted from the left-hand data and r for
data from the right hand. FIG. 4 graphically illustrates the
temperature signal during one window for a normal subject and a
person diagnosed with ADHD.
[0044] FIGS. 5 and 6 graphically illustrate the magnitude transform
for the data corresponding with a subject with ADHD and normal
subject. In FIG. 5, the magnitude spectrum undergoes dramatic
changes essentially changing from a hyperbolic curve to a flat
response for a normal subject. In FIG. 6, the magnitude range is
substantially less than shown in FIG. 5, indicating ADHD.
[0045] Raw Data
[0046] The raw data T.sub.k,l(t) is the temperature taken from hand
l at a fingertip 16 as shown in FIG. 1, during the 10-minute
session. The sessions were taken over a period of weeks. Some
subjects had as few as 2 sessions and some as many as 5 sessions. k
is used to represent the session.
[0047] Referring again to FIG. 3a:
[0048] Windows
[0049] The data for each session were divided into a series of
windows (block 43) prior to performing the Fourier Transform
operation. Call the window width w. In this analysis, the window
width was 64 seconds and there were 10 windows spaced at 50-second
intervals (the windows overlap) across the 600 sec baseline
spanning the range of 100-500 sec, other values of w can be used.
The window number in a session is referred to with the letter j.
For each window a FFT algorithm calculates the Fourier Transform
F(f). The Magnitude and Phase of this transform are defined as
given above.
[0050] In block 46 the range of magnitude variation during a window
is calculated using equation (1.2) below where f.sub.max and
f.sub.min are the frequencies where the Magnitude is the greatest
and the least respectively (note the dc component at frequency zero
is excluded).
M.sub.range=[M(f.sub.max)-M(f.sub.min)] (1.2)
[0051] In a further embodiment of this method, other statistics
from a Fourier Transform, calculated from the quantities denoted
above as A (f.sub.m), B (f.sub.m), .theta. (f.sub.m), and M
(f.sub.m) may be used. In addition to using Fourier Transforms,
this further embodiment may use statistics derived from a Wavelet
transform of data or other filtering of the data (as in Strang, G.
and Nguyen, T. (1996), Wavelets and Filter Banks,
Wellesley-Cambridge Press, Wellesley, Mass.).
[0052] Aggregation of Samples
[0053] MRange values for all windows are aggregated in block 47.
There are z windows from each hand from each session. The first
step is to choose an aggregation statistic, which can be the mean,
median, variance, or other statistic, which is an aggregate of the
computed M.sub.range values in each window for each session and
each hand. Other statistics that may be used for aggregation
include the standard deviation, range, interquartile distance,
skewness, kurtosis, Winsorized mean and variance, and robust
estimates of mean and variance. Equations below are given for
aggregating the mean and the variance. The mean magnitude range for
the left hand during session k is found from equation 2.0 where z
is the number of windows in the session. 2 M k , l = j = 1 z [ M (
f max ) j - M ( f min ) j ] z ( 2.0 )
[0054] And the corresponding variance is: 3 Var k , l = j = 1 z { [
M ( f max ) j , l - M ( f min ) j , l ] - M k , l } 2 z - 1 ( 2.1
)
[0055] Combining these session means and variances over both hands
and all the sessions s that a subject attended gives an aggregated
mean .mu. and aggregated variance. 4 = k = 1 s l = 1 2 M k , l 2 s
( 2.2 ) var = k = 1 s l = 1 2 var k , l 2 s ( 2.3 )
[0056] Further embodiments of this aggregation step include using
the data from only one hand--either the left hand, the right hand,
or the dominant hand (and if the subject is ambidextrous, the
dominant hand would be defined as the average of both hands). In
addition, future embodiments may not require averaging of several
sessions, but selecting only one session for use or using a
weighted combination of each session's results.
[0057] Diagnostic Indicators
[0058] Referring again to FIG. 3a, the normalized group diagnostic
threshold indicator .theta..sub.g was established previously from
the aggregation statistics determined using data from a large group
of subjects having similar demographic characteristics-block 49,
and can vary based upon gender, age or weight. This group
diagnostic threshold .theta..sub.g is calculated statistically from
group temperature variability data using methods described in U.S.
patent application Ser. No. 09/597,610, filed Jun. 20, 2000.
[0059] When the subject's measured aggregation statistic
.THETA..sub.m (from equation 2.2 or 2.3) block 47 is less than the
group threshold .theta..sub.g-block 50, the test indicates the
subject has ADHD. When the measured aggregation .THETA..sub.m
statistic is greater than the predetermined threshold
.theta..sub.g, the test indicates the subject does not have
ADHD-block 50 and no medication is required-block 51. The same
threshold .theta..sub.g may be used for all subjects or
.theta..sub.g may have a value that is different for different
groups based on gender or age.
[0060] Determination of Proper Dosage
[0061] Now referring to FIG. 3b, based upon the computed value of
the aggregation statistic .THETA..sub.m-block 60 and the
predetermined threshold value .theta..sub.g-block 62, a
mathematical formula-block 66 is used to compute the proper
dosage-block 68 for subjects who are diagnosed as having ADHD. This
mathematical formula may also include demographic information-block
64, including gender, age and weight. An example of such a
mathematical formula is the following:
Dosage=100.times.(.theta..sub.g-.THETA..sub.m-1)+100.times.gender
[0062] where the dosage is in milligrams of a drug, and where
gender is coded as 0 if the patient is female and 1 if the patient
is male. For example, if .theta..sub.g=10 and .THETA..sub.m=8, and
the patient is male, the example formula would call for a dosage of
100.times.(10-8-1)+100.times.1=200 milligrams of the drug.
[0063] Medication Effectiveness Indicator
[0064] If the prescribed medication is effective in correcting the
ADHD, then the measured physiologic diagnostic indicator
.THETA..sub.m (as defined by equation 2.2 or 2.3) would be expected
to come within the normal range and exceed .theta..sub.g during the
time the patient is medicated.
[0065] In studies using this method, subjects who had ADHD were
tested while on medication and again while not on medication. The
diagnostic indicator .THETA..sub.m was higher on average when the
subject was medicated, and lower on average when the subject was
not medicated. This is consistent with what the hypothesis would
predict. Paired t-tests (for example, see Hildebrand, D. K. and
Ott, L. (1991), Statistical Thinking for Managers, PWS-KENT
Publishing, Boston, p. 440) showed this change in .THETA..sub.m was
statistically significant (.alpha.=0.05), indicating that the
method described was able to determine changes brought about by the
medication.
[0066] When we let .THETA..sub.m be the mean Mrange, FIG. 8 shows
the values of .THETA..sub.m for both the medicated and
non-medicated sessions. The lines connect the two data points from
each subject, and a subject identifier is given by a letter next to
the data point. When the lines slope downward, they indicate a
decrease in .THETA..sub.m when the subject was not medicated, which
is what the hypothesis predicts. We see that five of the six lines
in FIG. 8 slope downward (subjects A, B, C, D, and E). We see that
the sixth line (subject F) slopes upward but only by a small
amount. The mean Mrange shows an average change between medicated
and non-medicated sessions of 4.3, the standard deviation of this
change is 3.6 and with six subjects, the paired t-test has a p
value of 0.0337, indicating statistical significance with
.alpha.=0.05.
[0067] When .THETA..sub.m is the median Mrange, the results are
shown in FIG. 9. Again, five of the six lines (subjects A, B, C, D
and E) slope downward and one line (subject F) slopes upward. The
mean of these changes between medicated and non-medicated sessions
is 3.066, the standard deviation is 2.74 and with 6 subjects, the p
value for the paired t-test is 0.0409, again indicating statistical
significance with .alpha.=0.05.
[0068] When .THETA..sub.m is the variance of the Mrange, the
results are shown in FIG. 10. Again, four of the six lines
(subjects A, C, D and E) slope downward by quite a large amount,
and the other two lines (subject B and F) slope upward slightly.
The mean of these changes between medicated and non-medicated
sessions is 60.03, the standard deviation is 51.67 and with 6
subjects, the p value for the paired t-test is 0.0360, again
indicating statistical significance with .alpha.=0.05.
[0069] Thus, to determine if the dosage is effective, the patient
will be re-tested according to the following procedure as
illustrated in FIG. 3b. The subject will take the prescribed dosage
of the medication and then wait a certain period of time-block 70.
The subject's peripheral temperature will be measured and
.THETA..sub.m will be calculated-block 72. This time period can
range from the minimum time it takes for the drug to become
effective after ingestion, to the maximum length of time the drug
is effective after ingestion. Ideally, the test will occur at a
time period equal to the drug's half-life in the body. Next,
compare the newly computed .THETA..sub.m value to threshold
.theta..sub.g-block 74. If value of .THETA..sub.m moves to the
non-ADHD region (above threshold .theta..sub.g), it is concluded
that the medication and dosage are appropriate-block 78. If value
of .THETA..sub.m remains in the ADHD region (below threshold
.theta..sub.g), it is concluded that a larger dosage is needed
block 76. The dosage can be increased according to best medical
practices. This procedure blocks 70-78 can be repeated until
appropriate medication and dosages are determined such that the
patient's .THETA..sub.m value, when re-tested, is in the non-ADHD
region (above threshold .theta..sub.g).
[0070] Because a patient's physiology can change over time, the
effective dosage may change over time as well. Thus, the patient
needs to be monitored during the treatment period in accordance
with the best medical practices. One such monitoring scheme, which
should be followed during the entire time the patient is taking the
drug, is to periodically re-test the patient. The interval between
these periodic tests can for example, be one month to one year. The
monitoring procedure involves repeating blocks 70-78. In one
embodiment of the invention, the initial dosage found-block 78
could be replaced with an enhancement in which, if .THETA..sub.m
exceeds .theta..sub.g by a large amount, the dosage is decreased,
while if .THETA..sub.m exceeds .theta..sub.g by a small amount,
then the proper dosage has been found.
[0071] The invention has been described in detail with particular
reference to certain preferred embodiments thereof, but it will be
understood that variations and modifications can be effected within
the spirit and scope of the invention.
Parts List
[0072] 10 subject
[0073] 12 chair
[0074] 13 table
[0075] 14 screen
[0076] 16 fingertip
[0077] 17 digit groove
[0078] 18 analyzer module
[0079] 20 earphones
[0080] 22 sensor
[0081] 24 on/off switch
[0082] 26 display
[0083] 27 cable
[0084] 28 sensor module
[0085] 29 cable
[0086] 30 battery
[0087] 32 external low voltage power supply port
[0088] 34 groove
[0089] 36 temperature sensor
[0090] 41 temperature sampling circuit
[0091] 42 data storage
[0092] 43 window blocking
[0093] 44 Fourier transform
[0094] 45 magnitude calculation
[0095] 46 Mrange calculation
[0096] 47 aggregation block
[0097] 48 threshold comparison block
[0098] 49 previously determined threshold .theta..sub.g block
[0099] 50 decision block
[0100] 51 no medication required block 51
[0101] 60 computed value of aggregation statistic .THETA..sub.m
[0102] 62 threshold value .theta..sub.g
[0103] 64 demographics
[0104] 66 mathematical formula to determine initial dosage
[0105] 68 initial Dosage
[0106] 70 ingest drug and wait
[0107] 72 re-test step
[0108] 74 compare new .THETA..sub.m to threshold .theta..sub.g
[0109] 76 increase dosage
[0110] 78 proper dosage
* * * * *