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.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This patent application claims the benefit under 35 USC §120 and is a continuation 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 
     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&#39;s peripheral temperature variability (TV) indicative of ADHD. 
     BACKGROUND OF THE INVENTION 
     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. 
     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 under achievement, 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. 
     Diagnosis of ADHD involves a process of elimination using written and verbal assessment insttuments. 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: 
     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. 
     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). 
     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). 
     4. U.S. Pat. No. 6,097,980, issued Aug. 1, 2000, inventor Monastra et al, discloses a quantitative electroencephalographic process assessing ADHD. 
     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. 
     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. 
     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. 
     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. 
     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. 
     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. 
     11. 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. 
     12. U.S. Pat. 6,117,075 Barnea discloses a method of measuring the depth of anesthesia by detecting the suppression of peripheral temperature variability. 
     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). 
     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. 
     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 
     According to the present invention, there is provided a solution to the problems and fulfillment of the needs discussed above. 
     According to a feature of the present invention, there is provided 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 said pre-selected parameter has a value indicative of ADHD; and 
     determining the proper dosage of a medication to treat ADHD based upon said determined value of said pre-selected parameter. 
     ADVANTAGEOUS EFFECT OF THE INVENTION 
     The invention has the following advantages. 
     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 
     FIG. 1 is a diagrammatic view illustrating use of an embodiment of the present invention. 
     FIG. 2 is a perspective view showing in greater detail the embodiment of FIG.  1 . 
     FIGS. 3 a  and  3   b  are block diagrams of a system incorporating the present invention. 
     FIGS. 4,  5  and  6  are graphical views useful in explaining the present invention. 
     FIG. 7 is a diagram of an example of using the threshold θ g  and the patient&#39;s computed aggregation statistic Θ m  to diagnose the presence or absence of ADHD and determine the suggested drug dosage. 
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     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. In general, as an individual&#39;s stress level increases the peripheral vasculature constricts and often the person&#39;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&#39;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&#39;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. 
     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 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 2 ), salivary IGA, electroencephalography (EEG), peripheral blood flow (measured via photoplethismography—PPG), and peripheral blood flow variability (PPG). 
     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. 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. 
     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&#39;s  10  other hand and includes groove  34  and temperature sensor  36 . 
     As shown in FIG. 3 a , 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 θ g    49 , and threshold comparison decision block  50 . The method of determining dosage is further expanded in FIG. 3 b.    
     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 Δt creating a list of n temperature samples, which are stored in storage  42 . 
     Now referring to FIG. 3 a , in block  43 , the n samples are divided into z windows of m samples, each group corresponding to a given time window of width Δt (˜32-64 sec) equally spaced in time (˜50 sec) across the entire data collection time interval Δt. The data from each window is then passed through a Fast Fourier Transform (FFT) algorithm  44  producing 2 m−1  data points spaced equally in frequency space for each window. The values are complex numbers having form 
     
       
           FFT ( f   m ) =A ( f   m ) +B ( f   m ) i   
       
     
     where i is the {square root over (−1)}. The Phase Θ(f m ) is then found from the equation                  Φ   l                     (     f   m     )       =       Tan     -   1                       (       B                   (     f   m     )         A                   (     f   m     )         )               (   1.0   )                                
     and the Magnitude M(f m ) from 
     
       
           M   l ( f   m )= {square root over (B( f   m ) 2   +A ( f   m ) 2 )}   (1.1) 
       
     
     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. 
     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. 
     Raw Data 
     The raw data T k,1 (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. 
     Referring again to FIG. 3 a:    
     Windows 
     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. 
     In block  46  the range of magnitude variation during a window is calculated using equation (1.2) below where f max  and f min  are the frequencies where the Magnitude is the greatest and the least respectively (note the dc component at frequency zero is excluded). 
       M   range   =[M ( f   max ) −M ( f   min )]  (1.2) 
     In a further embodiment of this method, other statistics from a Fourier Transform, calculated from the quantities denoted above as A (f m ), B (f m ), θ(f m ), and M (f 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.). 
     Aggregation of Samples 
     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 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.              &lt;     M     k   ,   l       &gt;=         ∑     j   =   1     z                     [       M                     (     f   max     )     j       -     M                     (     f   min     )     j         ]       z             (   2.0   )                                
     And the corresponding variance is:              &lt;     Var     k   ,   l       &gt;=         ∑     j   =   1     z                       {         [       M                     (     f   max     )       j   ,   l         -     M                     (     f   min     )       j   ,   l           ]     -     &lt;     M     k   ,   l       &gt;     }     2         z   -   1               (   2.1   )                                
     Combining these session means and variances over both hands and all the sessions s that a subject attended gives an aggregated mean μ and aggregated variance.              μ   =           ∑     k   =   1     s                     ∑     l   =   1     2                  &lt;     M     k   ,   l       &gt;       2      s               (   2.2   )               &lt;   var   &gt;=         ∑     k   =   1     s                       ∑     l   =   1     2                     var     k   ,   l             2      s               (   2.3   )                                
     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&#39;s results. 
     Diagnostic Indicators 
     Referring again to FIG. 3 a , the normalized group diagnostic threshold indicator θ 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 θ 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. 
     When the subject&#39;s measured aggregation statistic Θ m  (from equation 2.2 or 2.3) block  47  is less than the group threshold θ g -block  50 , the test indicates the subject has ADHD. When the measured aggregation Θ m  statistic is greater than the predetermined threshold θ g , the test indicates the subject does not have ADHD-block  50  and no medication is required-block  51 . The same threshold θ g  may be used for all subjects or θ g  may have a value that is different for different groups based on gender or age. 
     Determination of Proper Dosage 
     Now referring to FIG. 3 b , based upon the computed value of the aggregation statistic Θ m -block  60  and the predetermined threshold value θ 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×(θ g −Θ m −1)+100×gender 
       
     
     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 θ g =10 and Θ m =8, and the patient is male, the example formula would call for a dosage of 100×(10−8−1)+100×1=200 milligrams of the drug. 
     Medication Effectiveness Indicator 
     If the prescribed medication is effective in correcting the ADHD, then the measured physiologic diagnostic indicator Θ m  (as defined by equation 2.2 or 2.3) would be expected to come within the normal range and exceed θ g  during the time the patient is medicated. 
     Thus, to determine if the dosage is effective, the patient will be re-tested according to the following procedure as illustrated in FIG. 3 b . The subject will take the prescribed dosage of the medication and then wait a certain period of time-block  70 . The subject&#39;s peripheral temperature will be measured and Θ 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&#39;s half-life in the body. Next, compare the newly computed Θ m  value to threshold θ g -block  74 . If value of Θ m  moves to the non-ADHD region (above threshold θ g ), it is concluded that the medication and dosage are appropriate-block  78 . If value of Θ m  remains in the ADHD region (below threshold θ 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&#39;s Θ m  value, when re-tested, is in the non-ADHD region (above threshold θ g ). 
     Because a patient&#39;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 Θ m  exceeds θ g  by a large amount, the dosage is decreased, while if Θ m  exceeds θ g  by a small amount, then the proper dosage has been found. 
     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 
       10  subject 
       12  chair 
       13  table 
       14  screen 
       16  fingertip 
       17  digit groove 
       18  analyzer module 
       20  earphones 
       22  sensor 
       24  on/off switch 
       26  display 
       27  cable 
       28  sensor module 
       29  cable 
       30  battery 
       32  external low voltage power supply port 
       34  groove 
       36  temperature sensor 
       41  temperature sampling circuit 
       42  data storage 
       43  window blocking 
       44  Fourier transform 
       45  Magnitude calculation 
       46  Mrange calculation 
       47  aggregation block 
       48  Threshold comparison block 
       49  previously determined threshold θ g  block 
       50  decision block 
       51  no medication required block  51   
       60  Computed value of aggregation statistic Θ m    
       62  Threshold value θ g    
       64  Demographics 
       66  Mathematical formula to determine initial dosage 
       68  Initial Dosage 
       70  Ingest drug and wait 
       72  Re-test step 
       74  Compare new Θ m  to threshold θ g    
       76  Increase dosage 
       78  Proper dosage