Abstract:
A method of determining whether an individual has Attention Deficit Disorder (ADD) 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 ADD.

Description:
FIELD OF THE INVENTION 
     This invention relates in general to a technique for diagnosing Attention Deficit Disorder (ADD) and more particularly to a technique for measuring an individual&#39;s peripheral temperature to determine values indicative of ADD. 
     BACKGROUND OF THE INVENTION 
     ADD (with and without hyperactivity) 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 ADD. Moreover, in the general population, 9.2% of males and 2.9% of females are found to have behavior consistent with ADD. Upwards of 10 million adults may be affected. 
     ADD is a difficult disorder to diagnose. The core symptoms of ADD in children include inattention, hyperactivity, and impulsivity. ADD children may experience significant functional problems, such as school difficulties, academic underachievement, poor relationships with family and peers, and low self-esteem. Adults with ADD often have a history of losing jobs, impulsive actions, substance abuse, and broken marriages. ADD often goes undiagnosed if not caught at an early age and affects many adults who may not be aware of the condition. ADD has many look-alike causes (family situations, motivations) and co-morbid conditions (depression, anxiety, learning disabilities). 
     Diagnosis of ADD involves a process of elimination using written and verbal tests. However, there is no one objective, independent valid test for ADD. Various objective techniques have been proposed but have not yet attained acceptance. These include: 
     1. The eye problem called convergence insufficiency was found to be three times more common in children with ADD 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, Massachusetts to diagnose ADD 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 ADD are described by Lubar ( Biofeedback and Self-Regulation , Vol. 16, No. 3, 1991, Pages 201-225). 
     4. U.S. Pat. No. 5,913,310, issued Jun. 22, 1999, inventor Brown, discloses a video game for the diagnosis and treatment of ADD. 
     5. 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 ADD. 
     6. 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 ADD. 
     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 ADD. (See: Biofeedback and Self-Regulation, Vol. 20, No. 4, 1995). 
     There is thus a need for a simple, inexpensive, and reliable technique for assisting in the diagnosis of ADD. 
     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 of determining whether an individual has Attention Deficit Disorder (ADD) 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 ADD. 
     ADVANTAGEOUS EFFECT OF THE INVENTION 
     The invention has the following advantages. 
     1. A technique for diagnosing ADD is provided which is simple, inexpensive and reliable. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     FIG. 1 is a diagrammatic view illustrating an embodiment of the present invention. 
     FIG. 2 is a block diagram of a system incorporating the present invention. 
     FIGS. 3-5 are graphical views useful in explaining the present invention. 
     FIG. 6 is a diagrammatic view of another embodiment of the present invention. 
     FIG. 7 is a histogram of phase noise. 
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     According to the invention, it has been found that a signature of ADD is hidden in fluctuation of the temperature of the skin as measured at the extremities such as at a fingertip. 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 ADD. 
     As shown in FIG. 1, a subject  10  is sitting on a chair  12  watching a screen  14 . The subject is at rest in an inactive state. The temperature of a fingertip  16  of subject  10  is measured by a sensor  18 . The temperature readings are supplied to module  20 . 
     As shown in FIG. 2, module  20  includes temperature sampling circuit  22 , data storage  24 , data processor  26  and output  28  such as a display. 
     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  22  at a time interval Δt creating a list of N temperature samples which are stored in storage  24 . The N samples are divided into groups 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 in processor  26  producing 2 m−1  data points spaced equally in frequency space. The values are complex numbers having form 
     
       
         FFT( f   n )= A ( f   n )+ B ( f   n )i 
       
     
     where i is the {square root over (−1+L )}. The Phase Φ(f n ) is then found from the equation                Φ        (     f   n     )       =       Tan     -   1            (       B        (     f   n     )         A        (     f   n     )         )               (   .00   )               and                 the                 Magnitude                   M        (     f   n     )                     from                             M        (     f   n     )       =           B        (     f   n     )       2     +       A        (     f   n     )       2                 (   0.0   )                                
     FIGS. 3 and 4 respectively graphically illustrate the phase transform for a normal subject and a person diagnosed with ADD and a normal subject. The magnitude spectrum undergoes dramatic changes essentially changing from a hyperbolic curve to a flat response and simultaneously the phase exhibits a burst of noise we call phase noise. FIG. 7 shows histograms of the phase noise data taken from subjects with diagnosed ADD and normal subjects. We measure the phase noise during a time window Δt. The data in FIG. 7 is a histogram of the standard deviation σ of the phase noise during 10 of these windows spaced equally across the 10 minute duration of the experimental period. Subjects with a diagnosis of ADD generally show significantly more phase noise than the normal subjects as evidenced by the fact that there are many more samples at high values of the σ we use as a phase noise metric than for the normal subjects. 
     The following is another feature of the present invention: 
     Raw Data 
     The raw data T i,k (t) is the temperature taken at a fingertip during the 10-minute baseline period, which preceded each session of the VIBE project. The sessions were taken over a period of weeks or months. Some subjects had as few as 2 sessions and some as many as 5 sessions k is used to represent the session. 
     Windows 
     The data for each session were divided into a series of windows prior to performing the Fourier Transform operation. Call the window width w. In the data reported in FIG. 5, 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. 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. The range of magnitude variation during a window is given 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). 
     Session Mean and Standard Deviation 
     The mean magnitude range for subject i during session k is found from equation 1.0. where m is the number of windows in the session.                〈     M     i   ,   k       〉     =         ∑     j   =   1     m                     [         M        (     f   max     )       j     -       M        (     f   min     )       j       ]       m             (   1.0   )                                
     And the corresponding standard deviation is:                〈     s     i   ,   k       〉     =           ∑     j   =   1     m                       {       [         M        (     f   max     )       j     -       M        (     f   min     )       j       ]     -     〈     M     i   ,   k       〉       }     2         m   -   1                 (   1.1   )                                
     Combining these session means and standard deviations over all the sessions n that a subject attended gives the ensemble mean μ i  and ensemble standard deviation. σ i                 μ   i     =         ∑     k   =   1     n                     〈     M     i   ,   k       〉       n             (   1.2   )                                
     and correspondingly the ensemble standard deviation is                〈     σ   i     〉     =         ∑     k   =   1     n                     s     i   ,   k         n             (   1.3   )                                
     Chart 
     FIG. 5 is a chart comparing all the subject data in a data base. For each subject the curve shows the ensemble mean μ i  given from equation 1.2 and the I standard deviation limits defined by equations 1.4 and 1.5. 
     Diagnosis 
     Diagnosis is made from the chart by setting a threshold level for one of the parameters. Below that limit, the subject is diagnosed with ADD above the limit, the subject is called normal. In the chart the limit is set at a value of μ i  of 3.0. which yields one false negative (subject with &gt;3.0 who says he has ADD) and two false positives (subjects who are less than 3.0 and do not report a diagnosis of ADD). 
     FIG. 6 illustrates a schematic view of a subject and apparatus of another embodiment of the present invention. Shown is a subject  110 , viewing a screen  120 , wearing a set of earphones  130  connected via a wire  140  to a sound generating device  150 . The subject&#39;s  110  skin temperature is monitored via a finger temperature sensor  160  connected via a wire  170  to a control and recording device  180 . The earphone  130  maybe 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 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 
                 human subject 
               
               
                 12 
                 chair 
               
               
                 14 
                 screen 
               
               
                 16 
                 fingertip 
               
               
                 18 
                 sensor 
               
               
                 20 
                 module 
               
               
                 22 
                 temperature sampling circuit 
               
               
                 24 
                 data storage 
               
               
                 26 
                 data processor 
               
               
                 28 
                 output 
               
               
                 110 
                 human subject 
               
               
                 120 
                 viewing screen 
               
               
                 130 
                 earphones 
               
               
                 140 
                 wire 
               
               
                 150 
                 sound generating device 
               
               
                 160 
                 finger temperature sensor 
               
               
                 170 
                 wire 
               
               
                 180 
                 recording device