NEUROTRANSMITTER IMBALANCE DETECTION SYSTEM AND METHOD OF DETECTING A NEUROTRANSMITTER IMBALANCE

Abstract

A neurotransmitter imbalance detection system (SYS) is disclosed, said system (SYS) comprising at least one eye movement sensor (SEN) for sensing movement of a closed eye (EYE), a frequency analysis arrangement (FAA), and wherein said eye movement sensor (SEN) is configured to output at least one eye movement signal (EMS) representing movement of the closed eye (EYE) and to communicate said at least one eye movement signal (EMS) to said frequency analysis arrangement (FAA), wherein said frequency analysis arrangement (FAA) is configured to receive said at least one eye movement signal (EMS) and process said at least one eye movement signal (EMS) by frequency analysis to determine a frequency distribution. Also, methods for identifying eye movement patterns, for detecting a neurotransmitter imbalance, for identifying a psychiatric disorder, and for treating a psychiatric disorder are disclosed.

Claims

1. A neurotransmitter imbalance detection system, said system comprising at least one eye movement sensor for sensing movement of a closed eye, a frequency analyzer comprising a processor, and wherein said eye movement sensor is configured to output at least one eye movement signal representing movement of the closed eye and to communicate said at least one eye movement signal to said frequency analyzer, wherein said frequency analyzer is configured to receive said at least one eye movement signal, wherein said processor is configured to process said at least one eye movement signal by frequency analysis to determine a frequency distribution, output a frequency density indication signal correlating with a frequency density within a predefined frequency range, and to determine if said frequency density indication signal exceeds a predetermined threshold.

2. The neurotransmitter imbalance detection system according to claim 1, wherein the frequency analyzer is further configured to output a frequency content signal representing a frequency content within a predefined frequency range.

3. The neurotransmitter imbalance detection system according to claim 1, wherein the system further comprises an oscillation analysis arrangement, the oscillation analysis arrangement, the oscillation analyzer being configure to receive said at least one eye movement signal, and process said at least one eye movement signal.

4. The neurotransmitter imbalance detection system according to claim 3, wherein the oscillation analyzer is configured to process said at least one eye movement signal to determine the occurrence rate of oscillations exceeding a predetermined threshold.

5. The neurotransmitter imbalance detection system according to claim 3, wherein the oscillation analyzer is configured to process said at least one eye movement signal by to determine a representative amplitude of eye movements associated with an oscillation of the eye movement signal exceeding a predetermined threshold.

6. The neurotransmitter imbalance detection system according to claim 3, wherein the oscillation analyzer is configured to process said at least one eye movement signal by to determine the occurrence rate of zero crossings of the eye movement signal.

7. The neurotransmitter imbalance detection system according to claim 3, wherein the oscillation analyzer is configured to process said at least one eye movement signal by to determine the total sum of the eye movement signal.

8. The neurotransmitter imbalance detection system according to claim 1, wherein the system is further configured to determine the maximum value of the frequency distribution.

9. The neurotransmitter imbalance detection system (SYS) according to claim 1, wherein the system is further configured to determine the maximum value of the frequency distribution within the predefined frequency range.

10. The neurotransmitter imbalance detection system according to claim 1, wherein said frequency analyzer is further configured to compare said frequency content signal with a normal range representation and an abnormal range representation.

11. The neurotransmitter imbalance detection system according to claim 1, wherein said frequency analyzer is further configured to determine if the relative content in said predefined frequency range exceeds a predetermined threshold.

12.-16. (canceled)

17. The neurotransmitter imbalance detection system according to claim 1, wherein the neurotransmitter imbalance detection system further comprises a display arranged to display a representation of the frequency content signal.

18. (canceled)

19. The neurotransmitter imbalance detection system according to claim 1, wherein the predefined frequency range comprises at least the range from 0.1 to 3 Hz.

20. The neurotransmitter imbalance detection system according to claim 1, wherein the frequency analysis is performed by a Fast Fourier Transformation (FFT) based algorithm.

21.-23. (canceled)

24. The neurotransmitter imbalance detection system according to claim 1, wherein the system further comprising an analysis unit configured to identify the number of eye movement events within a predefined time range.

25. The neurotransmitter imbalance detection system according to claim 1, wherein the eye movement events fulfill one or more selection criteria.

26.-38. (canceled)

39. The neurotransmitter imbalance detection system according to claim 1, wherein the neurotransmitter imbalance is dopamine/noradrenaline and/or is associated with Attention Deficit Hyperactivity Disorder (ADHD).

40. (canceled)

41. The neurotransmitter imbalance detection system according to claim 1, wherein the system is configured to output an evaluation value indicating a degree of positive response to a medication, the evaluation value being based on at least a first eye movement signal recorded prior to intake of the medication and a second eye movement signal recorded after intake of the medication.

42. (canceled)

43. The neurotransmitter imbalance detection system according to claim 1, wherein said frequency analyzer is implemented in a computer system for automatic processing of eye movement signals, wherein said computer system comprises a processor.

44.-48. (canceled)

49. A method for detecting a neurotransmitter imbalance, the method comprising the steps of sensing movement of a closed eye by means of an eye movement sensor, outputting at least one eye movement signal representing movement of the closed eye, processing said at least one eye movement signal by frequency analysis to determine a frequency distribution, and outputting a frequency content signal representing the frequency content within a predefined frequency range.

50.-57. (canceled)

Description

FIGURES

[0166] The invention will now be described with reference to the figures where

[0167] FIGS. 1A-1B illustrate neurotransmitter imbalance detection systems according to embodiments of the invention,

[0168] FIG. 2A-2C illustrate eye movements sensors according to an embodiment of the invention,

[0169] FIG. 3 illustrates an eye movement signal according to an embodiment of the invention,

[0170] FIG. 4A-4F illustrate frequency analyzed eye movement signals of 5 minutes according to embodiments of the invention,

[0171] FIG. 5A-5D illustrate frequency analyzed eye movement signals of 60 seconds according to embodiments of the invention,

[0172] FIG. 6A-6D illustrate frequency analyzed eye movement signals before and after treatment according to embodiments of the invention, and

[0173] FIG. 7A-B illustrate a computer system arranged to implement the neurotransmitter imbalance detection system according to embodiments of the invention.

DETAILED DESCRIPTION

[0174] Referring to FIG. 1A, a neurotransmitter imbalance detection system SYS according to an embodiment of the invention is illustrated.

[0175] The neurotransmitter imbalance detection system SYS comprises at least one eye movement sensor SEN and a frequency analysis arrangement FAA. The eye movement sensor SEN is adapted to sense movement of a closed eye EYE. During operation, the eye movement sensor SEN is fitted one the subject so as to sense eye movements in the subject, while the subject has closed eyes EYE. The subject is instructed to keep awake during the test. Various types of eye movement sensors SEN are usable within the embodiment of FIG. 1A, e.g. the eye movement sensors SEN illustrated on FIG. 4.

[0176] The frequency analysis arrangement FAA may for example be a dedicated processing unit connected to the eye movement sensor SEN, or a personal computer to which the eye movement sensor SEN is connected. In any case, the frequency analysis arrangement FAA must be configured to perform frequency analyses.

[0177] As seen in FIG. 1A, the eye movement sensor SEN is configured to output at least one eye movement signal EMS, which is then communicated to the frequency analysis arrangement FAA. The at least one eye movement signal EMS represents movement of the closed eye EYE, typically such that a constant or near-constant level signal corresponds to no or only insignificant eye movements, whereas large deviations from the constant level signifies larger eye movements.

[0178] The frequency analysis arrangement FAA is configured to receive said at least one eye movement signal EMS and thereafter process the at least one eye movement signal EMS by frequency analysis to determine a frequency distribution, i.e. a frequency distribution of the eye movement signal EMS. Various methods of frequency analysis may be employed, e.g. a Fast Fourier Transformation (FFT) based algorithm.

[0179] Finally, the frequency analysis arrangement FAA outputs a frequency content signal FCS representing the frequency content within a predefined frequency range. The output may be presented to an operator in a wide range of different manners, e.g. as a diagram showing frequency components within at least the predefined frequency range, or as a sum of frequency components within the predefined frequency range. The output may in some embodiments be displayed on a dedicated screen, whereas in other embodiments it may be transmitted to the operator via the internet, e.g. to a computer program linked to the eye movement sensor SEN or even transmitted to the operator via by a digital message, such as an email.

[0180] In some further embodiments, the frequency analysis arrangement FAA is arranged to operate in accordance with FIG. 1A, with the exception that as an alternative to the frequency content signal FCS or in addition thereto, frequency analysis arrangement FAA is configured to output a different signal of diagnostic relevance.

[0181] Referring to FIG. 1B, a neurotransmitter imbalance detection system SYS according to an embodiment of the invention is illustrated. This embodiment is similar to the embodiment of FIG. 1A, whereas in the embodiment FIG. 1B, the frequency analysis arrangement FAA is cloud based, i.e. provided at a remote location, and where the eye movement signal EMS is transmitted e.g. via the internet. The eye movement sensor SEN may then be connected directly to the internet, either by cabled connection or by wireless connection, or may be connected to an internet connected device, such as a personal computer, facilitating the connection to the cloud-based frequency analysis arrangement FAA.

[0182] Non-limiting examples of important parameters usable within the scope of the invention include, according to embodiments of the invention, the following [0183] analysis of data in time domain, for example to obtain total sum of all values of the eye movement signal, average amplitude of oscillations of the eye movement signal, average frequency of the eye movement signal, occurrence rate of zero crossings of the eye movement signal, etc., [0184] frequency analysis, for example to obtain frequency content in certain frequency ranges, e.g. dominating frequency ranges measured by e.g. amplitude size, [0185] occurrence rate of large eye movements, e.g. eye movements correlated with an eye movement signal exceeding a certain threshold, which large eye movements may be indicative of too high activity in the brain, [0186] latency time, corresponding the longest period within which a person, having closed eyes, can abstain from moving the eyes, and which period is indicative of whether the person in question is in a balanced state of mind, [0187] velocity of large eye movements, giving a measure of how fast and how powerful the eye muscles are activated, [0188] the individual eye movements, and whether they are slow eye movements or rapid eye movements, and whether they are smooth and uniform eye movements, and the degree of variations between individual the eye movements.

[0189] Referring to FIGS. 2A-C, recording to eye movement signals EMS1, EMS2 by means of two eye movement sensors SEN1, SEN2 is illustrated in accordance with an embodiment of the invention.

[0190] In FIG. 2A, the cornea COR is positioned centrally looking, whereas the cornea COR is facing towards the left in FIG. 2B and right in FIG. 2C.

[0191] The eye movement sensors SEN1, SEN2 may for example be motion sensors, such as a gyroscope-based sensor or an accelerometer based sensor. When comparing the eye movement signals EMS1, EMS2 from the sensors, a measure for the movement of the eye may be obtained. In this embodiment, the measure for the movement may be obtained by subtracting the two eye movement signals EMS1, EMS2 to obtain a differential signal. This differential signal may signify movement along a lateral direction defined by an axis between the two eye movement sensors SEN1, SEN2.

[0192] In an alternative embodiment, only a single eye movement sensor SEN may be employed. In such embodiments, the eye movement signal EMS may not necessarily allow to distinguish between eye movements if different directions. Nevertheless, the eye movement signal EMS may still contain information about the frequency, speed, and intensity of the eye movements.

[0193] Still in further alternative embodiments, the system SYS may have three or more eye movement sensors SEN. Having an increased number of eye movement sensors SEN may help to increase resolution of eye movements to form a more accurate measure of the eye movements. Also, it may help to distinguish e.g. lateral eye movements from vertical eye movements.

[0194] Returning to FIG. 2A-C, it can be observed that when the cornea COR moves towards the left (FIG. 2B), the eye movement sensor SEN1 is affected and moves away from the eye, whereas the eye movement sensor SEN2 is relatively unaffected. Similarly, when the eye moves towards the right (FIG. 2), the opposite can be observed, i.e. the sensor SEN1 is affected whereas the sensor SEN2 is relatively unaffected.

[0195] When the eye movement is only slight, i.e. the cornea moves only a few degrees in angular position, only one of the sensors SEN1, SEN2 may typically be affected. Also, the time between the oscillations in the signal EMS1 to a corresponding oscillation in the signal EMS2 signifies the speed with which the eye EYE moves. E.g. longer times suggests a slow movement. Furthermore, when e.g. an accelerometer-based eye movement sensor, a high-speed eye movement results in a faster acceleration of the sensor SEN1, SEN2, again resulting in a higher signal.

[0196] FIG. 7A illustrates the schematics of a computer system COM capable of implementing the neurotransmitter imbalance detection system according to embodiments of the invention. The computer system COM shown in FIG. 7A is a stand-alone computer system, however in other embodiments of the invention, the computer system COM may be a distributed computer system, such as a cloud-based computer system in which the computer system shown in FIG. 7A represents a node of the cloud-based computer system. The computer system COM comprises a processor CPU coupled to a bus. Also coupled to the bus are a memory RAM, a storage device STOR, a first input device ID1 such as a keyboard, a graphics adapter GRAP, a second input device ID2 such as a pointing device, and a network adapter NET. A display DISP is coupled to the graphics adapter GRAP. The processor CPU may be any general-purpose processor capable of executing computer implemented instructions. The storage device STOR may be any device capable of holding large amounts of data, like a hard drive, solid state drive, compact disk-read-only memory (CD-ROM), DVD, Blu-Ray, or some form of removable storage device. The memory RAM is arranged to hold instructions and data to be used by the processor. The second input device ID2 may be a computer mouse, track-ball, light pen, touch-sensitive display, or other type of pointing device. The first input device ID1 may be a QWERTY keyboard and may be a physical computer keyboard or a keyboard implemented in a touch-sensitive display, such as the same touch-sensitive display implementing the pointing device. The input devices may be used by a user of the computer system, such as an operator, for automatic execution of commands specified by the user. The network adapter NET couples the computer system COM to a network, such as a distributed network, e.g. the internet. The network adapter may also establish a connection between the computer system COM and external devices by means of wireless connection protocols such as WiFi, Bluetooth and Zigbee.

[0197] The computer system COM comprises is arranged to establish a data communication DATA between the computer system COM and external devices such as sensors, e.g. one or more eye movement sensors SEN (not shown). The data communication DATA may be a wireless data communication implemented by the use of the network adapter, such as a Bluetooth connection, or a wired data communication implemented by a wired connection to the computer system COM, such as by a wired Universal serial bus (USB) connection. For example, the data communication DATA may facilitate transmittal of eye movement signals EMS from eye movement sensors SEN to the computer system COM.

[0198] The computer system COM may be arranged to connect with external sensors such as an eye movement sensor SEN, a breathing sensor, a pulse sensor, a skin surface tension sensor, a tension sensor, such as a piezoelectric sensor, a movement sensor, such as an accelerometer or a gyroscope-based sensor configured to detect movement, a strain gauge sensor, an electrooculographic sensor, a laser sensor, a magnetic sensor, and a liquid sensor.

[0199] The computer system COM may represent a desktop computer, a laptop computer or any other electronic device including means of processing such as a tablet computer, a smartphone and a smartwatch in which the input devices ID1-ID2 may be the same touch screen as the display DISP.

[0200] The computer system COM may be arranged to implement a frequency analysis arrangement FAA according to embodiments of the invention.

[0201] The computer system COM may be further arranged to implement an oscillation analysis arrangement according to embodiments of the invention.

[0202] FIG. 7B illustrates a computer implemented neurotransmitter imbalance detection system SYS comprising a frequency analysis arrangement FAA according to embodiments of the invention. The figure illustrates that the frequency analysis arrangement FAA is implemented in a computer system COM, such as the computer system shown in FIG. 7A. The computer system COM is arranged to receive an eye movement signal EMS, representing movement of the closed eye (EYE) provided by an eye movement sensor SEN according to various embodiments of the invention, and output a frequency content signal FCS. The output may be displayed to an operator on a display (not shown) of the computer system COM, or it may be transmitted to the operator via the internet, e.g. to a computer implemented program linked to the eye movement sensor SEN or even transmitted to the operator via by a digital message, such as an email. Such wireless transmission may be facilitated by the network adapter of the computer system COM.

[0203] The steps of processing eye movement signals EMS by frequency analysis to determine a frequency distribution is performed automatically by means of the frequency analysis arrangement FAA being implemented in a computer system COM. In this sense, the steps of processing eye movement signals is carried out by the processor of the computer system COM. In the case where the computer system COM is a distributed computer system, such as a cloud-based computer system, the steps of processing eye movement signals may be performed by one or nodes (processors) of the distributed computer system.

FIGURE REFERENCES

[0204] SYS. Neurotransmitter imbalance detection system [0205] SEN. Eye movement sensor [0206] EYE. Eye [0207] EMS. Eye movement signal [0208] FAA. Frequency analysis arrangement [0209] FCS. Frequency content signal [0210] COR. Cornea [0211] COM. Computer system [0212] CPU. Processor [0213] DATA. Data communication [0214] DISP. Display [0215] GRAP. Graphics adapter [0216] NET. Network adapter [0217] RAM. Memory [0218] STOR. Storage [0219] ID1-ID2. Input device

EXAMPLES

Example 1—Number of Eye Movements

[0220] Strain gauge sensors (SleepSense Limb Movement Sensors) from SleepSense were used as eye movement sensors. These where connected to a Digital Brain Electric Activity Mapping KT88 EEG apparatus as a recording and frequency analysis arrangement.

[0221] The subjects were instructed to keep their eyes closed, but to refrain from falling asleep.

[0222] The signal from the sensors where filtered by one of two different filters; a 2.5 Hz low pass filter with 24 dB reduction.

[0223] A signal was recorded over a period of 5 minutes. 1 minute of signal is illustrated in FIG. 3.

[0224] A large eye movement number was established as the number of times the signals exceeded 50 μV, or moved below −50 μV. This signifies the number of large eye movements.

[0225] As can be seen from FIG. 3, under these criteria, a number of eye movements are present during the beginning of the signal, whereas the rest of the signal which does not cross +/−50 μV is not considered to contain eye movements under the above criteria.

TABLE-US-00001 TABLE 1 Number of eye movements Table 1: Number of eye movements in 60 seconds intervals. The number of eye movements were identified as the number of times the eye movement signal (similar to that of FIG. 3) exceeded +/− 50 μV. 1-60 61-120 121-180 181-240 241-300 Patient ID s s s s s 36 9 0 2 1 2 38 0 0 0 0 0 53 29 0 3 2 0 54 24 1 1 2 1 27 38 22 25 30 41 31 92 61 55 68 54

[0226] As can be seen from table 1, patients 27 and 31 displayed a significantly larger number of eye movements, especially in the latter four time intervals, i.e. after the first minute.

[0227] Patients 27 and 31 have been diagnosed with the psychiatric disorder ADHD, whereas patients 36, 38, 53, and 54 are healthy persons with no diagnosed psychiatric disorder. Patients 27 and 31 are unmedicated.

[0228] Thus, it can be seen that the number of eye movements shows significant difference between healthy persons and persons diagnosed with ADHD, which shows that the number of eye movements can be used to distinguish between persons having a normal dopamine/noradrenaline neurotransmitter balance and being healthy on one hand and persons with a dopamine/noradrenaline neurotransmitter imbalance having ADHD.

Example 2—Latency Period

[0229] Using eye movement signals recorded as explained in example 1, the latency period was identified as follows.

[0230] The latency period is herein defined as the period of time of at least 10 seconds without eye movements.

[0231] This way of defining the latency time intends to account for situations, where the person is not actually trying to keep his/her eye still.

[0232] Since the 5 minutes (300 seconds) signals where used, latency periods of 300 seconds where maximum, corresponding to no movements of eyes.

[0233] Additionally, similar eye movement signals where recorded, with the difference that a 1.0 Hz low pass filter with 24 dB reduction where used instead of that in example 1.

[0234] The results are shown in table 2.

TABLE-US-00002 TABLE 2 Latency period Table 2: Latency period, being the first period of at least 10 seconds without eye movements. Shown for two different lowpass filters. Latency period (seconds) Patient ID Lowpass 2.5 Hz Lowpass 1.0 Hz 36 141 293 38 300 300 53 30 300 54 53 300 27 16 113 31 9 14

[0235] As can be seen from table 2, the latency time shows a clear difference between persons with ADHD (27, 31), and healthy persons (36, 38, 53, 54). Patients 27 and 31 are unmedicated.

[0236] Thus, it can be seen that the latency time shows significant difference between healthy persons and persons diagnosed with ADHD, which shows that the latency time can be used to distinguish between persons having a normal dopamine/noradrenaline neurotransmitter balance and being healthy on one hand and persons with a dopamine/noradrenaline neurotransmitter imbalance having ADHD on the other hand.

Example 3—Frequency Analysis of 5 Minutes

[0237] Using eye movement signals recorded as explained in example 1, frequency analysis was performed to determine a frequency distribution. A Fast Fourier Transformation (FFT) based algorithm was used, with a 100 mHz resolution.

[0238] Results are shown in FIGS. 4A-4F, for the persons 36, 38, 53, 54, 27, and 31, respectively.

[0239] As can be seen, a higher frequency content for ADHD-diagnosed persons 27, 31 were observed, compared to any of healthy control subjects 36, 38, 53, 54, 27.

[0240] Thus, it can be seen that the frequency content shows significant difference between healthy persons and persons diagnosed with ADHD, which shows that the frequency content can be used to distinguish between persons having a normal dopamine/noradrenaline neurotransmitter balance and being healthy on one hand and persons with a dopamine/noradrenaline neurotransmitter imbalance having ADHD on the other hand.

Example 4—Frequency Analysis of 10 Seconds

[0241] Using eye movement signals recorded as explained in example 1, frequency analysis was performed to determine a frequency distribution. However, only a 10 seconds period corresponding to the first 10 seconds of the latency time of example 2 was used. A Fast Fourier Transformation (FFT) based algorithm was used, with a 100 mHz resolution.

[0242] Results are shown in FIG. 5A-5D for person 53 (FIG. 5A), person 27 (FIG. 5B), person 54 (FIG. 5C), and person 31 (FIG. 5D).

[0243] As can be seen, a significantly higher frequency content is shown for persons with ADHD reaching much higher maximum values in the FFT diagram. Also, a frequency shift towards higher frequencies can be observed for persons with ADHD.

[0244] Thus, it can be seen that the frequency content shows significant difference between healthy persons and persons diagnosed with ADHD, which shows that the frequency content can be used to distinguish between persons having a normal dopamine/noradrenaline neurotransmitter balance and being healthy on one hand and persons with a dopamine/noradrenaline neurotransmitter imbalance having ADHD on the other hand.

[0245] Particularly, examples 3-4 illustrate that the selection of the time interval for the signal to frequency analyze may be important. Also, selecting a shorter signal time (e.g. 10 minutes instead of 5 minutes) surprisingly does not decrease accuracy, but perhaps even makes it easier to distinguish persons with ADHD from persons without ADHD.

Example 5—Measurements Before/after Treatment

[0246] Using eye movement signals recorded as explained in example 1, frequency analysis was performed to determine a frequency distribution. A Fast Fourier Transformation (FFT) based algorithm was used, with a 100 mHz resolution.

[0247] In example 5, signals were recorded on a patient with ADHD.

[0248] On FIG. 6A is the result when the patient was in an initial, unmedicated state. A second measurement was made after administering suitable ADHD-medication to the patient (one tablet of 10 mg of methylphenidate), this is shown in FIG. 6B.

[0249] Also, measurements were made as described in example 4, where FIG. 6C shows the result before medication, and FIG. 6D shows the result after the medication.

[0250] As can be seen when comparing FIGS. 6A and 6B, the frequency content is lowered within the order of a reduction to 50%.

[0251] Similarly, FIGS. 6C-6D show a significant reduction in frequency components, with a reduction of the maximum value within the order of a reduction to 20%.

[0252] Thus, it can be seen that the frequency content shows significant difference between medicated and unmedicated persons diagnosed with ADHD, which shows that the frequency content can be used to distinguish between persons having ADHD with an unmedicated dopamine/noradrenaline neurotransmitter imbalance on one hand and persons having ADHD with a medicated dopamine/noradrenaline neurotransmitter imbalance on the other hand.

[0253] Furthermore, the number of eye movements was analyzed as in example 1, before and after medication. The results are shown in table 3.

TABLE-US-00003 TABLE 3 Eye movements before/after medication Table 3: Number of eye movements in 60 seconds intervals. The number of eye movements were identified as the number of times the eye movement signal (similar to that of FIG. 3) exceeded +/− 50 μV. 1-60 s 61-120 s 121-180 s 181-240 s 241-300 s Before 54 15 23 16 14 medication After 60 0 2 1 2 medication

[0254] As can be seen from table 3, the number of eye movements were significantly reduced, especially in the last four time intervals, i.e. after the first minute.

[0255] Thus, it can be seen that the number of eye movements shows significant difference between medicated and unmedicated persons diagnosed with ADHD, which shows that the number of eye movements can be used to distinguish between persons having ADHD with an unmedicated dopamine/noradrenaline neurotransmitter imbalance on one hand and persons having ADHD with a medicated dopamine/noradrenaline neurotransmitter imbalance on the other hand.

Example 6

[0256] A further study was performed using a group consisting of 10 control subjects (i.e. healthy subjects), 7 subjects diagnosed with attention deficit hyperactivity disorder (ADHD), and 8 subjects diagnosed with attention deficit disorder (ADD).

[0257] Eye movement signals were recorded using the methods described in example 1.

[0258] 6.1 Occurrence Rate of Large Eye Movements

[0259] First, a grouping of the subjects was performed according to the occurrence rate of large eye movements. In this context, a large eye movement is defined as giving an eye movement signal exceeding a certain predefined threshold. The groups were as follows:

[0260] A1: No pauses between temporally adjacent large eye movements of more than 5 seconds.

[0261] A2: At least one pause between temporally adjacent large eye movements above 5 seconds, but none above 20 seconds.

[0262] A3: At least one pause between temporally adjacent large eye movements above 20 seconds.

[0263] The grouping of the subjects into A1, A2, and A3 is shown in table 4.

TABLE-US-00004 TABLE 4 Table 4. Shows distribution of subjects between the three groups A1, A2, and A3, and the diagnostic groups (i.e. control, AHDH, ADD). Control ADHD ADD A1 140 148 141 175 172 177 179 187 A2 137 143 154 173 157 146 A3 138 167 170 139 155 142 152 150 184 151 189

[0264] As can be seen from table 4, ADHD subjects tend to have shorter pauses between larger eye movements compared to control subjects, whereas ADD subjects tend to have longer pauses between large eye movements compared to control subjects.

[0265] 6.2 Amplitudes of Eye Movement Signal

[0266] The eye movement signals were analyzed to determine a representative amplitude. In this example, the sixth highest amplitude of the eye movement signal was used as representative amplitude. By not choosing the very largest, it is avoided that a single very high value among other much lower values disturbs the analysis.

[0267] Representative values are shown in table 5.

TABLE-US-00005 TABLE 5 Table 5. Representative amplitudes are shown for each subject. Subject id numbers are followed by their diagnosis, where K signifies a control subject. Representative amplitude [micro Volts] 137 K 160 138 K 180 139 K 80 140 K 240 141 K 220 142 K 180 150 K 120 151 K 80 172 K 100 173 K 60 143 ADHD 220 148 ADHD 140 175 ADHD 240 177 ADHD 260 179 ADHD 140 167 ADHD 200 187 ADHD 140 170 ADD 40 146 ADD 60 154 ADD 80 157 ADD 100 155 ADD 40 152 ADD 120 184 ADD 100 189 ADD 80

[0268] As can be seen from table 5, ADHD subjects have representative amplitudes above 140 μV. ADD subjects have representative amplitudes between 40 and 100 μV, and control subjects have representative amplitudes within 60 to 240 μV, i.e. within both the ADHD range and the ADD range.

[0269] It is believed that subjects with ADHD have excess noradrenalin relative to dopamine, and the representative amplitudes are believed to reflect this imbalance.

[0270] Subjects with ADD have a deficit of noradrenalin relative to dopamine, which result in the smaller representative amplitudes.

[0271] Since control subjects are randomly selected, it is assumed that when a subject is balanced, i.e. relaxed and not in a stressful state, the resulting representative amplitude would be found in the range between typical ADHD-values and ADD-values. However, when a person is in a stressful state, the instant noradrenalin-dopamine balance is shifted, and larger representative amplitudes result thereof.

[0272] On the other hand, when a person is tired, the noradrenalin levels decreases, leading the noradrenaline-dopamine balance to shift the other way, and the representative amplitudes to become smaller.

[0273] 6.3 Further Results

[0274] Further results obtained are given in tables 6-7.

TABLE-US-00006 TABLE 6 Table 6. “FFT Amp” signifies the maximum value of a fast Fourier transform (FFT) of the eye movement signal. “(FFT Amp){circumflex over ( )}2/Fq” signifies the squared value of FFT Amp, divided by “Fq”, corresponding to the average frequency (shown as “Av Fq” in table 7). “Crossing” refers to the occurrence rate of zero crossings of the eye movement signal. Finally, “SUM” refers to the total sum of the values constituting the eye movement signal. FFT (FFT Subject no. Diagnosis Amp Amp){circumflex over ( )}2/Fq Crossing SUM 137 Control 5.3 280 2692 391.487907 138 Control 4.4 190 2355 793.386133 139 Control 2.4 58 3049 392.388801 140 Control 11 1100 2041 1324.21210 141 Control 12 1500 1616 178.777147 142 Control 9.5 900 3916 879.771725 150 Control 4.5 250 3005 −523.718925 151 Control 2.8 78 3298 588.683299 172 Control 5.5 310 2602 615.009381 173 Control 4.6 200 2230 520.215457 143 ADHD 15 2000 1945 −11242.23941 148 ADHD 9.5 900 2152 −105.202442 175 ADHD 23 5200 1348 −14411.77996 177 ADHD 28 8000 1232 17357.999237 179 ADHD 9.5 900 1845 61.060502 167 ADHD 10.0 1000 2707 −5970.515909 187 ADHD 9.5 910 1642 1853.136187 170 ADD 2.5 60 3483 927.819303 146 ADD 3.0 90 3046 958.950178 154 ADD 7.5 550 2115 −2699.27486 157 ADD 6.0 400 2671 −744.037232 155 ADD 3.9 150 3410 1159.949341 152 ADD 5.5 320 2177 1832.41566 184 ADD 5.1 260 2654 1645.230182 189 ADD 8.8 720 2277 382.779278

TABLE-US-00007 TABLE 7 Table 7. “Av amp” signifies the average amplitude. When this value is negative, it signifies predominant oscillation in negative direction. “Av Fq” signifies average frequency of the eye movement signal. “RMS” signifies the root mean square value of the amplitudes. Subject no. Diagnosis Av amp Av Fq RMS 137 Control +0.01305 4.486667 22.426845 138 Control +0.026446 3.925000 15.965592 139 Control +0.013080 5.081667 11.100932 140 Control +0.044140 3.401667 44.548648 141 Control +0.005959 2.693333 40.616152 142 Control +0.029326 6.52667 31.440807 150 Control −0.017457 5.008333 18.865063 151 Control +0.019623 5.49667 12.738009 172 Control +0.020500 4.336667 24.681106 173 Control +0.017341 3.716667 17.622235 143 ADHD −0.37474 3.241667 55.999352 148 ADHD −0.003507 3.58667 34.155251 175 ADHD −0.48393 2.246667 89.091864 177 ADHD +0.5786 2.053333 106.048559 179 ADHD +0.002030 3.07500 33.064496 167 ADHD −0.199017 4.511667 35.943681 187 ADHD +0.061771 2.736667 33.765549 170 ADD +0.030927 5.80555 8.968474 146 ADD +0.031965 5.07667 14.518534 154 ADD −0.089976 3.52500 25.326694 157 ADD −0.024801 4.451667 24.597789 155 ADD +0.038665 5.68333 13.508409 152 ADD +0.061081 3.62833 22.452989 184 ADD 0.054841 4.42333 21.075961 189 ADD 0.012759 3.79500 28.486843

[0275] 6.4 Comparisons

[0276] Using the data obtained in the above tables 4-7, the following comparisons are made

[0277] First, using the SUM values and the Crossing values from table 6, the subjects are mapped as shown in table 8.

TABLE-US-00008 TABLE 8 Table 8. Mapping of subjects as function of SUM and Crossing values. Crossing Crossing Crossing 0-2000 2000-3000 3000- SUM < 100.000 175 ADHD 167 ADHD 150 K 143 ADHD 154 ADD 179 ADHD 157 ADD 148 ADHD SUM 100.000- 141 K 137 K 139 K 900.000 173 K 151 K 172 K 142 K 138 K 189 ADD SUM > 900.000 187 ADHD 140 K 170 ADD 177 ADHD 184 ADD 146 ADD 152 ADD 155 ADD

[0278] As can be seen from table 8, and ADD/ADHD subjects are within lower-moderate Crossing combined with low SUM, or within the high SUM range; except for the following:

[0279] 140 and 150 can be seen as fake positives, whereas 189 is not detected. It is noted that by looking at the frequency content around 10 Hz of the FFT of the eye movement signals, it was noted that 140 displayed much lower values than average ADHD subjects. Also, 189 was further diagnosed with severe eating disorder, anxiety condition and obsessive compulsion disorder (OCD).

[0280] Secondly, a comparison between average amplitude and occurrence rate of zero crossings was made, where the results are shown in table 9.

TABLE-US-00009 TABLE 9 Table 9. Mapping of subjects as function of average amplitude and Crossing values. Crossing Crossing Crossing above Average amplitude 0-2000 2000-3000 3000 10-0.03 177 ADHD 140 K 170 ADD 187 ADHD 152 ADD 146 ADD 184 ADD 155 ADD 0.005-0.03 .sup.  141 K 137 K 139 K 138 K 142 K 172 K 151 K 173 K 189 ADD −10-0.005 143 ADHD 148 ADHD 150 K 175 ADHD 167 ADHD 179 ADHD 154 ADD 157 ADD

[0281] As can be seen from table 9, similar results as for the mapping of table 8 may be obtained, most cases of ADHD/ADD are in the range of above 0.03 or below 0.005 for the average amplitude. Again, subjects 140, 150, and 189 are incorrectly identified, as for table 8.

[0282] As it can be seen from table 9, typical test subjects would give rise to lowest magnitude of average amplitudes.

[0283] Then, a comparison between average amplitude and maximum FFT amplitude was made, where the results are shown in table 10.

TABLE-US-00010 TABLE 10 Table 10. Mapping of subjects as function of average amplitude and maximum FFT amplitudes. Here, FFT amp refers to the maximum value of a fast Fourier transform (FFT) of the eye movement signal, as in table 6. Average amplitude FFT amp 0-6 FFT amp 6-12 FFT amp 12-30 10-0.03 170 ADD 140 K 177ADHD 146 ADD 187ADD 155 ADD 152 ADD 184 ADD 0.005-0.03 .sup.  137 K 141 K 138 K 142 K 139 K 151 K 172 K 173 K −10-0.005 150 K 148 ADHD 143 ADHD 179 ADHD 175 ADHD 167 ADHD 154 ADD 157 ADD

[0284] As can be seen from table 10, most subjects having ADHD/ADD can be correctly identified.

[0285] Then, a comparison between above described groups A1, A2, and A3 and the total sum of the values constituting the eye movement signal (SUM) was made, where the results are shown in table 11.

TABLE-US-00011 TABLE 11 Table 11. Mapping of subjects as function of group A1, A2, and A3 and total sum of the values constituting the eye movement signal (SUM). Group SUM < 100.000 100.000-900.000 SUM > 900.000 A1 175 ADHD 141 K 187 ADHD 179 ADHD 172 K 177 ADHD 148 ADHD 140 K A2 143 ADHD 137 K 154 ADD 173 K 157 ADD A3 150 K 138 K 184 ADD 167 ADHD 151 K 152 ADD 142 K 155 ADD 139 K 170 ADD 189 ADD 146 ADD

[0286] As can be seen from table 11, most subjects having ADHD/ADD can be correctly identified.

[0287] Then, a comparison between above described groups A1, A2, and A3 and the average amplitude of the eye movement signal was made, where the results are shown in table 12.

TABLE-US-00012 TABLE 12 Table 12. Mapping of subjects as function of group A1, A2, and A3 and the average amplitude of the eye movement signal was made. Av-amp Av-amp Av-amp −10- 10-0.03 0.005-0.03 0.005 A1 177 ADHD 141 K 175 ADHD 187 ADHD 172 K 179 ADHD 140 K 148 ADHD A2 137 K 143 ADHD 173 K 154 ADD 157 ADD A3 152 ADD 138 K 167 ADD 184 ADD 151 K 150 K 170 ADD 142 K 146 ADD 139 K 155 ADD 189 ADD

[0288] As can be seen from table 12, most subjects having ADHD/ADD can be correctly identified.

[0289] As seen from the above, ADHD/ADD subjects may be identified in a number of different ways, with a relatively high degree of certainty, comparing to other available and typically subjective diagnostic criteria.

[0290] However, ADD subjects and ADHD subjects are typically difficult to distinguish using the methods described above in example 6.

[0291] 6.5 Use of Frequency Analysis

[0292] Fast Fourier transforms (FFT) were made for eye movement signals recorded from subjects. It was observed that the FFTs of ADHD had considerably higher frequency contents within certain ranges, compared to ADD subjects.

[0293] It was also noted that when squaring the FFT values and dividing by the respective frequency, and even more significant deviation between ADHD subjects and ADD subjects was observed.

[0294] 6.6 Treatment of ADHD and ADD

[0295] Subjects with ADHD have a too high level of noradrenalin relative to dopamine. Thus, an input of dopamine could theoretically correct this imbalance.

TABLE-US-00013 TABLE 13 Table 13. Shows data of ADHD subjects before (Data A) and after (Data B) treatment with methylphenidate. FFT Amplitude refers to the maximum value of a fast Fourier transform (FFT) of the eye movement signal. Av amp” signifies the average amplitude. When this value is negative, it signifies predominant oscillation in negative direction. “Av Fq” signifies average frequency of the eye movement signal. DATA A DATA B FFT FFT Amplitude Av Amp Av Fq Amplitude Av Amp Av Fq 143/144 15 −0.37474 3.241667 7 0.0011375 4.858333 177/178 28 0.578600 2.05333 26 0.505568 2.23000 179/180 9.5 0.002030 3.07500 4.1 0.049990 3.69500 187/188 9.5 0.061771 2.73667 12 0.092632 2.55333 189/190 8.5 0.012759 3.79500 5.0 0.007571 3.946667 167/168 10 −0.199017 4.511667 9.6 0.044788 4.646667 192/193 22 0.046206 2.425000 8.0 0.000667 4.838333

[0296] Data from 7 subjects with ADHD examined before treatment. Hereafter methylphenidate was administered, and after a subjective assessment of an effect thereof, the measurement was repeated. The sensors were in place during the entire test.

[0297] It is observed that in 6 cases a fall in the amplitude occurs. At the same time, an increase in the average frequency is observed.

[0298] One case of an increase in amplitude and decrease in frequency was observed.

[0299] Subjects with ADD have a too high level of dopamine relative to noradrenalin. By adding dopamine, a worsening of the condition should therefore be expected.

TABLE-US-00014 TABLE 14 Table 14. Shows data of ADD subjects before (Data A) and after (Data B) treatment with methylphenidate. FFT Amplitude refers to the maximum value of a fast Fourier transform (FFT) of the eye movement signal. Av amp” signifies the average amplitude. When this value is negative, it signifies predominant oscillation in negative direction. “Av Fq” signifies average frequency of the eye movement signal. DATA A DATA B FFT FFT Amplitude Av Amp Av Fq Amplitude Av Amp Av Fq 146/147 3 0.031965 5.07667 6.1 0.032752 4.08333 170/171 2.5 0.030927 5.80555 2.6 0.025912 5.52333 184/185 5.0 0.054841 4.42333 8.0 0.054724 3.64000 152/153 5.5 0.061080 3.62833 4.2 0.048525 4.61833

[0300] Table 14 shows 4 subjects treated with methylphenidate, which increases the dopamine levels in the brain. In 3 out of 4 cases, an increase in the amplitude and a decrease in the average frequency was observed.

[0301] In one case, a decrease in amplitude and an increase in frequency was observed.

[0302] In most cases in table 13-14, the administration of methylphenidate gave the expected results.

[0303] It is noted that the subject in table 13 reacting opposite to other subjects may represent a case an erroneous diagnosis, i.e. a subject which should have been diagnosed with ADD. Similarly, the one case reacting opposite to the other subjects in table 14 may represent a case an erroneous diagnosis, i.e. a subject which should have been diagnosed with ADHD.

[0304] 6.7 Imbalance Detection

[0305] It is hypothesized that the ratio between the average amplitude and the average frequency is a measure for the ratio between the dopamine level and the noradrenalin level of the brain. Thus, a deviation in the ratio between the average amplitude and the average frequency would signify an imbalance in the dopamine-noradrenaline levels.

TABLE-US-00015 TABLE 15 Table 15. Values of ratio between average amplitude and average frequency. Subject no. Diagnose Av - amp Av-Fq Av - amp/Av - F 137 Kontrol +0.01305 4.486667 345 138 Kontrol +0.026446 3.925000 150 139 Kontrol +0.013080 5.081667 390 140 Kontrol +0.044140 3.401667 77 141 Kontrol +0.005959 2.693333 448 142 Kontrol +0.029326 6.52667 225 150 Kontrol −0.017457 5.008333 −294 151 Kontrol +0.019623 5.49667 274 172 Kontrol +0.020500 4.336667 173 173 Kontrol +0.017341 3.716667 218 143 ADHD −0.37474 3.241667 −8 148 ADHD −0.003507 3.58667 −896 175 ADHD −0.48393 2.246667 −4 177 ADHD +0.5786 2.053333 3 179 ADHD +0.002030 3.07500 1537 167 ADHD −0.199017 4.511667 −22 187 ADHD +0.061771 2.736667 44 170 ADD +0.030927 5.80555 187 146 ADD +0.031965 5.07667 158 154 ADD −0.089976 3.52500 −39 157 ADD −0.024801 4.451667 −178 155 ADD +0.038665 5.68333 145 152 ADD +0.061081 3.62833 59 184 ADD 0.054841 4.42333 80 189 ADD 0.012759 3.79500 316

[0306] These values may also be seen in the mapping shown in table 16.

TABLE-US-00016 TABLE 16 Table 16. Mapping of subjects as function of average amplitude (Av amp) and the average frequency (Av Fq). Values of average amplitude shown in parenthesis Av-Fq: 0-3 Av-Fq: 3-4 Av-Fq: above 4 Av-amp 175 ADHD (−0.48) 143 ADHD (−3.7) 150K (−0.017) lower than 179 ADHD (0.002) 148 ADHD (−0.003) 167 ADHD (−0.199) 0.005 154 ADD (−0.089 157 ADD (−0.024) Av-amp 141K (0.0059) 138K (0.026) 137K (0.013) 0.005-0.03 173K (0.017) 139K (0.013) 189 ADD 142K (0.029) 151K (0.019) 172K (0.017) Av-amp 177 ADHD (0.58) 140K (0.044) 170 ADD (0.030) at least 187 ADHD (ADD) 152 ADD (ADHD) 146 ADD (0.031) 0.03 (0.061) (0.061) 155 ADD (0.038) 184 ADD (0.054)

[0307] This table shows similar results as the mapping in example 6.4.

Example 7

[0308] 7.1 Evaluation of Sensor Response

[0309] Sensor response was evaluated.

[0310] Two types of movements of the sensor was evaluated. First, movements with smaller deflection was compared with movements with larger deflections. Secondly, slower movements were compared with faster movements.

[0311] Not surprisingly, larger movements generated a larger signal (e.g. in terms of maximum amplitude) than smaller deflection movements. Similarly, faster movements generated a larger signal (e.g. in terms of maximum amplitude) than slower movements

[0312] 7.2 Signal Recording and Processing

[0313] A number of sample signals were recorded. Each sample was recorded over a period of 360 seconds. Only the middle 240 seconds are used, as the first 60 seconds and the last 60 second of the signal is discarded to avoid introducing artifacts related to the startup and termination of the test and the subject getting used to the eye sensors etc.

[0314] The signals were subjected to frequency analysis by FFT with 100 mHz solution.

[0315] 7.3 Signal Recording and Processing

[0316] Maximum amplitude of the frequency distribution (i.e. max FFT amplitude) is identified.

[0317] 20 subjects with an AHDH or ADD diagnosis were evaluated with respect to effect of startup of methylphenidate treatment. A sample was recorded for each subject in accordance with example 7.2, and a maximum FFT amplitude was outputted from the frequency analysis arrangement (maximum FFT amplitude before, MAB). Thereafter, a startup dose of methylphenidate was prescribed for each subject. After ingestion, a further sample was recorded for each subject, and a maximum FFT amplitude was outputted from the frequency analysis arrangement (maximum FFT amplitude after, MAA). The results are shown in table 17.

TABLE-US-00017 TABLE 17 Table 17. SI/D: Subject identifier/diagnosis. MAB: Maximum FFT amplitude before. MAA: Maximum FFT amplitude after. RC: relative change in FFT amplitude. Eff. M.: Evaluation of effect of methylphenidate. C/SE: Comorbidity/side effects Eff. SI/D MAB MAA RC M. C/SE Final treatment 202 34.4 45.9 33 No — Atomoxetine ADHD 177 26.7 24.8 −7 No Depression No effect of ADHD ADHD treatment 192 21.4 7.3 −66 Good — Methylphenidate ADHD 255 22.9 16.0 −30 Good — Methylphenidate ADD 248 15.9 18.2 14 No — No effect of ADHD ADHD treatment 250 15.8 12.0 −24 Good Depression Methylphenidate ADD 197 14.2 6.2 −56 Good Depression Methylphenidate ADD 212 13.1 16.7 27 No — No effect of ADD ADHD treatment 143 14.3 6.7 −53 Good Psychosis No treatment ADHD 258 11.2 11.0 −2 No Depression Atomoxetine ADD 187 9.0 10.7 19 Good Addiction No treatment ADHD 148 8.7 3.5 −60 Good — Methylphenidate ADHD 179 8.9 3.1 −65 Good — Lisdexamphetamine ADHD 210 7.6 8.7 14 No Anxiety No effect of ADHD ADHD treatment 155 3.6 4.2 17 No Depression No effect of ADD ADHD treatment 204 6.2 2.1 −66 Good — Lisdexamphetamine ADHD 189 9.0 5.5 −39 Good OCD, Lisdexamphetamine ADD Anxiety 184 4.8 7.9 65 Good — No effect of ADD ADHD treatment 152 4.58 2.0 −56 Good — Methylphenidate ADD 146 2.8 5.6 100 No Depression Atomoxetine ADD

[0318] As can be seen from table 17, some subjects showed a significant reduction in maximum FFT amplitude, whereas others showed no significant change or even increase. A reduction of the maximum FFT amplitude indicates a reduced eye activity. Comparing the evaluated effect of the methylphenidate with the change in eye activity (RC), it is seen that except for subjects 187 and 184, a complete correspondence between a good effect and a reduction in eye activity (RC) is observed.

[0319] Subjects 177 and 258 both showed a small decrease in eye activity (RC) below 10% and had not positive effect of the treatment.

[0320] Depending on the comorbidity/side effects (C/SE), it could be decided whether to continue or terminate treatment or to try an alternative drug, such as atomoxetine or lisdexamphetamine.

[0321] 7.4 Evaluation of Correlation Between Peak FFT Amplitude and Diagnosis.

[0322] 20 subjects diagnosed with AHDH or ADD or begin control subjects without diagnosis were investigated. A sample was recorded for each subject in accordance with example 7.2, and a peak FFT amplitude was outputted from the frequency analysis arrangement.

[0323] Also, the average frequency (Av. freq.), the RMS, and the number of zero crossings were determined and listed in table 18.

TABLE-US-00018 TABLE 18 Table 18. Value for 20 subjects. Ranked by peak FFT amplitude. Diagnosis is indicated after each subject identifier, where K indicates a control subject. SI/D: Subject identifier diagnosis. Peak FFT No. of zero SI/D amplitude Av. freq. RMS crossing 202 ADHD 34.44 1.99 120.32 935 177 ADHD 26.7 2.13 99.25 1023 255 ADD 22.93 2.32 79.39 1416 192 ADHD 21.48 2.48 81.31 1191 248 ADD 16.98 3.47 74.26 2471 250 ADD 15.81 3.25 61.12 1568 143 ADHD 14.35 3.8 52.2 1927 197 ADHD 14.26 2.88 52.67 1387 212 ADHD 13.16 2.46 54.75 1184 258 ADD 11.22 3.35 38.55 1614 141 K 10.34 3.05 32.94 1466 189 ADD 9.06 3.85 28.57 1860 187 ADHD 9.05 2.96 30.07 1421 179 ADHD 8.97 3.47 29.43 1669 148 ADHD 8.73 3.94 29.66 1894 210 ADHD 7.67 3.17 31.36 1523 204 ADD 6.29 4.43 22.61 2129 246 K 5.69 3.43 20.55 1651 172 K 5.66 4.01 22.96 1931 142 K 5.32 7.48 17.53 3593 184 ADD 4.86 4.59 19.35 2207 137 K 4.8 5.01 19.93 2407 152 ADD 4.58 4.22 16.04 2026 173 K 4.52 4.12 16.59 1982 150 K 4.27 5.92 15.76 2843 155 ADD 3.6 5.79 11.92 2879 138 K 2.95 6.23 9.95 2991 151 K 2.48 6.15 11.23 2956 146 ADD 2.32 5.14 13.93 1519 139 K 2.21 5.18 10.22 2489 Average K 4.39 5.05 17.76 2430 Average 15.87 2.82 58.10 1415 ADHD Average ADD 9.76 4.04 36.57 1968

[0324] The entries in table 18 are ranked by peak FFT amplitude. This shows a very clear tendency that all ADHD display a peak FFT amplitude above a threshold value of about 6-7.

[0325] Also, all but one of the control subjects displayed a peak FFT amplitude below the threshold value of about 6-7.

[0326] Finally, no clear threshold can be seen in view of the ADD subjects, even though the average peak FFT amplitude for ADD subjects of 9.76 were significantly higher than for control subjects with average of 4.39. However, ADHD subjects showed an even higher average peak FFT amplitude of 15.87, consistent with the clearer delimitation relative to control subjects.

[0327] Further, the average frequency is lowest for the ADHD subjects consistent with a relatively high contribution from eye movements in the 1-5 Hz range compared to a background noise from eye lid muscle behavior e.g. in the 10-20 Hz range.

[0328] Finally, ADHD subjects showed the highest average RMS-value and the lowest average number of zero crossings.

TABLE-US-00019 TABLE 19 Table 19. Value for three subjects with severe depression. Ranked by peak FFT amplitude. SI/D: Subject identifier/diagnosis. Peak FFT No. of zero SI/D amplitude Av. freq. RMS crossing 208 9.5 4.08 38.66 1470 209 13.5 2.355 47.15 847 227 27 2.79 113.83 1007

[0329] As can be seen from table 19, the three subjects all scored high on the peak FFT amplitude (samples measured in accordance with example 7.2), above threshold of e.g. 6-7, indicating that the peak FFT amplitude cannot discriminate between subjects with ADHD and severe depression.

[0330] In other words, the use of the present invention provides a tool to advance towards a diagnosis (i.e. it is diagnostically relevant) but cannot be used by itself to provide a diagnosis, as several different diseases may be the source of the deviation from normal parameters.

[0331] It is particularly believed that ADHD, ADD, stress disorder, depression, anxiety, obsessive-compulsive disorder (OCD), post-traumatic stress disorder (PTSD), and schizophrenia will score high values e.g. at peak FFT amplitude. To distinguish these disorders from each other a number of different tools may be applied. This may include conventional psychiatric diagnostic tools, but also physiological measurements such as e.g. pulse to distinguish between stress (typically faster pulse) and ADHD (typically normal level pulse).

[0332] It is believed that the above diagnoses, particularly AHDH and ADD, are results of a dopamine-noradrenaline imbalance, and that the frequency density indication signal indicating a frequency density in e.g. a 1-5 Hz range is an indication of a high eye movement activity, which again indicates a dopamine-noradrenaline imbalance.

[0333] 7.5 Evaluation of Correlation Between Peak FFT Amplitude and Diagnosis

[0334] To evaluate the relevance of the frequency range used, FFT distributions for three control subjects were compared to with FFT distributions for three subjects with depressions in the frequency range of 10-20 Hz. Samples were recorded in accordance with example 7.2

[0335] Control subjects scored peak FFT amplitude in the 10-20 Hz range of 0.17 to 0.26, whereas subjects with depression scored at least 0.44, i.e. approximately twice as high as control subjects.

[0336] It is believed that this frequency range (10-20 Hz) correlates more with muscle activity in the eye lid rather than eye movements, whereas eye movements predominantly give rise to frequency components in the 1-5 Hz range.

[0337] 7.6 Evaluation of Reproducibility

[0338] To evaluate the degree of reproducibility, an FFT distribution was recorded several times in accordance with example 7.2 for the same subject at different points of time over a total period of several days. Parameters were calculated and shown in table 20.

TABLE-US-00020 TABLE 20 Table 20. Comparable values obtained for the same subject over time. SI/D: Subject identifier/diagnosis. Peak FFT No. of zero SI/D amplitude Av. freq. RMS crossing 138 K, 1 1.6 6.50 6.05 2341 138 K, 2 1.6 7.4 6.91 2666 138 K, 3 2.0 6.85 8.72 2469 138 K, 4 7.5 5.51 23.01 1984 138 K, 5 3.7 7.11 15.35 2562 138 K, 6 2.8 5.41 11.01 1957 138 K, 7 2.8 5.85 9.55 2108 138 K, 8 6.5 2.98 24.95 1076 138 K, 9 1.5 6.07 6.20 2187 138 K, 10 2.6 7.84 6.22 2823 138 K, 11 1.4 8.04 4.72 2896 138 K, 12 2.1 7.2 6.73 2594 138 K, 13 2.4 7.79 9.19 2804 138 K, 14 2.4 5.73 8.58 2066

[0339] As can be seen from table 20, relatively consistent measurements where obtained, with some deviation. This indicates that average values taken at different points of time appear to be more accurate than individual values. E.g. while two peak FFT amplitudes exceeds 6, the average value of 2.9 is well below this threshold.