WO2023112042A1 - A multiple sensors integrating system for closer assessment of attention-deficit hyperactivity disorder (adhd) of a subject. - Google Patents

Abstract

The present invention provides an integrated EEG and CCD based motion sensors based system for more objective measure of Attention-deficit Hyperactivity Disorder (ADHD). While the integrated system relies on the 5 EEG signal (spectral density of beta wave) for the assessment of attention during a given structure task (painting three segments of a circle using three different colours namely red, green and blue), the CCD sensor depicts movement pattern of the subjects engaged in a continuous performance task (CPT). The attention, movement patterns and accuracy of the completed 10 tasks were analysed by the system controller using a statistical analysis technique.

Description

TITLE: A MULTIPLE SENSORS INTEGRATING SYSTEM FOR CLOSER ASSESSMENT OF ATTENTION-DEFICIT HYPERACTIVITY DISORDER (ADHD) OF A SUBJECT.

FIELD OF THE INVENTION:

The present invention is directed to measure of Attention-deficit Hyperactivity Disorder (ADHD). More specifically, the present invention is directed to provide a system for integrating a single-probe EEG and a specially developed CCD based motion sensor for more objective measure of Attention-deficit Hyperactivity Disorder (ADHD).

BACKGROUND OF THE INVENTION:

Increasing global prevalence of attention-deficit hyperactive disorder (ADHD), which is more than 5% presently, becomes one of the most frequent disorders within child and adolescent psychiatry [1].

Although, number of publications on the alternative ways of objective diagnosis of ADHD is increasing per year, assessment and treatment continue to present a challenge for most of the clinicians globally. Presence of a professional, such as a child psychiatrist, a pediatrician, or other health care professionals with adequate training and expertise in diagnosing ADHD is considered to be inevitable during the process of diagnosis of ADHD [2-4]. In contrary to the conventional expectation that after repeated years of extensive research, nosological system-defined ADHD criteria, including the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5) and the International Classification of Diseases, editions 10 and 11 (ICD- 10/11), the assessment of ADHD have become broader and ambiguous, thereafter, encompassing wider age groups of the subjects, and placing more emphasis on the expert's proficiency which makes the process of diagnosis more subjective [5,6]. ADHD is identified by the heterogeneity of indications, which, by recurrent and inconsistent comorbidities and an overlap with other disorders, may take contradictory forms. The afore-mentioned facts indicate the need of a reliable alternative [7,8]. Inclusion of objective and quantitative diagnostic procedures (intelligence and neuropsychological tests), neuroimaging, or neurophysiological measures are not recommended in the recent National Institute for Health and Care Excellence (NICE) guidelines in routine ADHD assessment [9,10]. Although, their use as additional tools at the emergence of academic problems, coexisting abnormalities in electroencephalography (EEG), or unrecognized neurological conditions is suggested in the current guidelines. Unavailability of such reliable additional tool is the biggest concern. Psychometric tests, in spite of being subjective and qualitative, are thus considered to be the only gold standard for the validation routine ADHD assessment.

There is significant discourse in contemporary literature in connection to the EEG technique to assess symptoms of ADHD (after FDA approval) [11]. The technique is based on a biomarker theta-beta ratio, proposed to be effective in diagnosing ADHD from multi-centric clinical trial [12]. In earlier works, a subject's movement under continuous performance task (CPT) was monitored by an infrared sensor and correlated with the subject's ADHD score. While this method has been appreciated in a commentary published in nature [13- 15], its implementation in practice has been sparse.

IN 201721033233 discloses a system to diagnose the children who have ADHD or any subgroup disorder of ADHD. Here multiple sensors are appended to the group of children who are detected with hyperactivity to detect where there is any symptom for the same. The motion based sensor will be including inertia sensors, cameras and other which can detect the change in movement the data fetched will be validated in the server system, and a result will be obtained To elaborate further, the result the data from EEG will be collected and analyzed and the collaborative results will be diagnosed to get reliable detection whether one subgroup of ADHD exists among any of the group of children. However, in this approach only a subgroup disorder of ADHD can be diagnosed. This device cannot detect the presence of ADHD as the way it is in the subject under test.

It is thus there has been a need for developing a new system for correctly measuring ADHD which will address limitations of above state of art techniques for measuring ADHD and provide ADHD result which can plays a significant role in the quantitative score. References:

[1 ] D. Renate, B. Silvia, B. Daniel, G. Edna, B. Gregor, and W. Susanne, "ADHD: Current Concepts and Treatments in Children and Adolescents, " Neuropediatrics.

[2] S. Pliszka and A. W. G. o. Q. Issues, "Practice parameter for the assessment and treatment of children and adolescents with attention- deficit/hyperactivity disorder," Journal of the American Academy of Child & Adolescent Psychiatry, vol. 46, pp. 894-921, 2007.

[3] S. C. o. Q. I. Subcommittee on Attention-Deficit/Hyperactivity Disorder and Management, "ADHD: clinical practice guideline for the diagnosis, evaluation, and treatment of attention-deficit/hyperactivity disorder in children and adolescents," ed: Am Acad Pediatrics, 2011.

[4] W. H. Organization, "ICD-11 for mortality and morbidity statistics (2018)," 2018.

[5] J. Kaufman, B. Birmaher, D. Brent, U. Rao, C. Flynn, P. Moreci, et al., "Schedule for affective disorders and schizophrenia for school-age children-present and lifetime version (K-SADS-PL): initial reliability and validity data," Journal of the American Academy of Child & Adolescent Psychiatry, vol. 36, pp. 980-988, 1997.

[6] J. Kaufman, B. Birmaher, D. Axelson, F. Perepletchikova, D. Brent, and N. Ryan, "K-SADS-PL DSM-5," Pittsburgh: Western Psychiatric Institute and Clinic, 2016.

[7] F. Edition, "Diagnostic and statistical manual of mental disorders, " Am Psychiatric Assoc, vol. 21, 2013.

[8] W. H. Organization, International statistical classification of diseases and related health problems: Tabular list vol. 1: World Health Organization, 2004.

[9] T. J. Layton, M. L. Barnett, T. R. Hicks, and A. B. Jena, "Attention deficit-hyperactivity disorder and month of school enrollment, " New England Journal of Medicine, vol. 379, pp. 2122-2130, 2018.

[10] N. G. C. UK, "Attention deficit hyperactivity disorder: diagnosis and management," 2018.

[11] E. Dolgin, "FDA clearance paves way for computerized ADHD monitoring," ed: Nature Publishing Group, 2014.

[12] W. J. Ray and H. W. Cole, "EEG alpha activity reflects attentional demands, and beta activity reflects emotional and cognitive processes, " Science, vol. 228, pp. 750-752, 1985.

[13] S. M. Snyder, T. A. Rugino, M. Hornig, and M. A. Stein, "Integration of an EEG biomarker with a clinician's ADHD evaluation," Brain and behavior, vol. 5, p. e00330, 2015.

[14] M. Arns, S. K. Loo, M. B. Sterman, H. Heinrich, J. Kuntsi, P. Asherson, et al., "Editorial Perspective: how should child psychologists and psychiatrists interpret FDA device approval? Caveat emptor," ed: Wiley Online Library, 2016.

[15] M. A. Stein, S. M. Snyder, T. A. Rugino, and M. Hornig, "Commentary: Objective aids for the assessment of ADHD-further clarification of what FDA approval for marketing means and why NEBA might help clinicians. A response to Arns et al. (2016)," Journal of Child Psychology and Psychiatry, vol. 57, pp. 770-771, 2016. OBJECT OF THE INVENTION:

It is thus the basic object of the present invention is to develop a system for integrating multiple sensors for closer assessment of Attention-deficit Hyperactivity Disorder (ADHD) of a subject, especially pediatric subject.

Another object of the present invention is to develop a system for integrating EEG and CCD based motion sensors for closer assessment of Attention-deficit Hyperactivity Disorder (ADHD) of a subject, especially pediatric subject.

Another object of the present invention is to develop a system for closer assessment of Attention-deficit Hyperactivity Disorder (ADHD) of a subject, especially pediatric subject which would be adapted to continuously tracks the movement position pattern and subsequent analysis of percentage of occurrence of the subject under test as opposed to the prior art which tracks the motion of the subject.

SUMMARY OF THE INVENTION:

The present invention discloses an integrated system aimed at a closer assessment of ADHD. This system constitutes a single-probe EEG and CCD based motion sensors wherein the EEG signal is meant to study the attention span of a subject while performing a designated structure task, while the CCD sensors, on the other hand, tracks the subjects' pattern of movement while being involved in a continuous performance task. The data collected by the system, the MAHD, is essential in narrowing down possibilities and determining whether these symptoms are better explained by another condition.

Thus according to the basic aspect of the present invention there is provided a system for objective measurement of Attention-deficit Hyperactivity Disorder (ADHD) of a subject comprising an EEG signal sensor to track attention span of the subject while performing a designated structure task involving delta and beta EEG waves from the subject co-relatable to relaxation and attention /cognitive load conditions; CCD based motion sensors with cooperative optical arrangements to continuously track the subjects' movement pattern while being involved in a continuous performance task; and a cooperative controller unit adapted for corelation of results of said EEG signal sensor and CCD motion sensor for desired objective measurement based assessment of the ADHD of the subject.

In a preferred embodiment, present system comprisese means for tracking variations of attention (%) levels with its frequency of occurrences, occurrences of deviations from shortest path under CPT and its movement pattern/curves; full width at half maxima generator generating based on said patterns/curves as signature of spread of a curve generated ; said controller unit is adapted to indicate based on level of the FWHM noted the desired measurement based assessment of the ADHD of the subject.

In a preferred embodiment of the present system, the EEG signal sensor includes a microchip, an embedded firmware, reference and ground electrodes for placing on frontal lobe of forehead of the subjects to collect the EEG signal from the subject's brain, in order to gauge and determine the attention and the relaxation parameters; wherein said EEG signal sensor is configured to generate a single EEG channel signal from difference between electrical potentials obtained from the active and reference electrodes and common mode rejection whereby the obtained EEG channel signal is then amplified and band-pass-filtered. In a preferred embodiment of the present system, the EEG signal sensor is configured to detect absolute power for delta and beta EEG waves by reading electric activities of brain in its states of attention and relaxation, wherein the EEG electrodes are attached onto the earlobes and the forehead, and caps serve as a voltmeter at microvolt level with the aid of the electrodes and help to obtain brain signals from neural activities and each activity state captured in an individual's brain activity is assigned a score between 0 and 100 for attention and meditation values; said CCD based motion sensor includes a CCD based optical arrangement to capture optical spectrum of in experimental condition to track the movement pattern of the subject under test.

In a preferred embodiment of the present system, the controller unit is configured to analyze and correlate absolute power for delta and beta EEG waves with attention/cognition and relaxation load conditions including eyes open and closed conditions.

In a preferred embodiment of the present system, the CCD based motion sensors is configured to track movement of the subject while performing the CPT and determine shortest path movement of the subject for performing the CPT by the CCD based optical arrangement to capture the optical spectrum of the experimental condition.

In a preferred embodiment of the present system, the controller unit is configured to analyze the movement pattern by statistical analysis of the acquired data by an programmed algorithm which integrates the acquired EEG signals with movement pattern analysis of the subject under test (CPT) including noting variation of attention (%) levels with its frequency of occurrence under the CPT and simultaneously, recording occurrence of deviations from shortest path under CPT from the movement pattern and thereby fitting the obtained data with the GaussAmp equation such as to produce a derived output parameter from the fitted equations namely full width at half maxima (FWHM) of the fitted curves, which is a signature of the spread of the curve, whereby higher FWHM in the attention pattern means enhanced spread of attention parameters which in its turn, indicates lesser focus of the subject under test and a higher FWHM in both cases, signify non- compliance of instructions or forced compliance under repeated instruction mode.

In a preferred embodiment, the present system includes imaging means to capture completed structure task images, whereby the controller is configured to evaluate degree of inappropriateness of the task by RGB analysis method including image processing wherein a region of interest (ROI) is selected to obtain an intensity profile followed by RGB analysis and individual colour intensity monitoring, whereby the value obtained from the RGB analysis is corroborated with the EEG signal data and movement pattern analysis data to give the final quantitative score.

In a preferred embodiment of the present system, the controller is configured to integrate the acquired EEG signals with movement pattern analysis of the subject under test (CPT) by involving fitting the data in GaussAmp equation:

Where, y₀ = offset, x_c = center, w = width, A = amplitude; wherein the derived output parameter from the fitted equation namely full width at half maxima (FWHM) is obtained from 2w of the fitted curves and the output parameter FWHM is a signature of spread of the curve, which corresponds to greater area coverage of the subject under test and higher FWHM in the attention pattern corresponds to enhanced spread of attention parameters which in its turn, indicates lesser focus of the subject under test.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS:

Fig. 1A and IB shows work flow of the presently developed system. Fig. 2 shows (a) detailed arrangement of the experimental set-up using an developed EEG sensor while (b) and (c) respectively show the absolute power for delta and beta EEG waves obtained which are further correlated with attention/cognition and relaxation load conditions.

Fig. 3 shows (a) experimental arrangement for determining the presence of hyperactivity in the paediatric subject, (b) live location of the subject while performing the OPT, as tracked by the developed CCD based integrated motion sensor in the MAHD device and (c) the diversion of the subject from the shortest path between the two tables which is indicative of the hyperactive nature of the subject under test.

Fig. 4 shows (a) level of attention (%) with its frequency of occurrence under the CPT (colouring task) (b) depicts movement, that is, occurrence of deviations from shortest path under CPT (balls separation task).

Fig. 5 shows (a) and (b) the completed colouring tasks (CPT) by two different subjects under test, (c) the histogram plots of the evaluated worksheets. Degree of inappropriateness of the task for different colours is reflected in the y-axis amplitude.

Fig. 6 shows the typical report generated by the developed MAHD device for normative subjects who were critically evaluated by expert clinical psychologist.

Fig. 7 shows the typical report generated by the developed MAHD device for a subject reported to have ADHD by an expert clinician's evaluation.

Fig. 8 shows different parameters measured using MAHD show statistically significant differences between the normal and ADHD population studied. Violins depict kernel density estimation of the underlying data distribution with the width of each violin scaled by the number of observations at that Y- value. Three lines (from the bottom to the top) in each violin plot show the location of the lower quartile (25th), the median and the upper quartile (75th), respectively. The shaded area indicates the probability distribution of the variable. Individual data points are represented as colored circles. The difference between two groups were estimated using non-parametric, unpaired, two-tailed t-test after Welch s correction. *P<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

DETAILED DESCRIPTION OF THE INVENTION WITH REFERENCE TO THE ACCOMPANYING DRAWINGS:

As stated hereinbefore, the present invention discloses an integrated system aimed at a closer assessment of ADHD. This system includes a single-probe EEG and a specially developed CCD based motion sensors. The EEG signal is meant to study the attention span of a subject while performing a designated structure task. The CCD sensors, on the other hand, track the subjects' pattern of movement while being involved in a continuous performance task. The data is collected by a cooperative system controller unit.

To start the process, an EEG signal from a single-channel dry sensor is placed on the subject (3-5 years old, pre-schoolers). It was studied that three colours used to paint three sections of a circle indicated the absolute power for delta and beta EEG waves are in tandem with the conditions of relaxation and attention/cognitive load condition. During physical movement of the subject, who has been engaged in a continuous performance task (CPT) i.e., segregation of the different coloured balls from one table to another, the pattern of the activity is tracked by a CCD-based motion sensor. An advancement involving developed programmed product has been embodied in the controller unit for the statistical analysis of the sensor data for the purpose of deriving a scale for the objective assessment of ADHD.

In present work, the assessment of the attention as well as the movement pattern of the subjects are performed during given structure tasks (painting three segments of a circle using three different colours namely red, green and blue), the CCD sensor depicts movement pattern of the subjects engaged in a continuous performance task (CPT) (comprising of segregation of the different coloured balls from one table to another).

The present system is an integrated platform to detect and measure ADHD in children. This completely innovative approach has unique combination of developed hardware as well as the programmed software used to analyse the data that is collected by the device. The positive corelation between the results obtained from the present system and the test results given by the conventional psychometric tests taken by medical practitioners, depicts the capability of the present system.

The Fig. 2a schematically describes the detailed arrangement of the experimental set-up using the developed EEG signal sensor, which includes a single-channel dry sensor (TGAM Module, Neurosky/ Brainwave Starter Kit) and a developed acquisition/control programmed software to collect the absolute power for delta and beta EEG waves from the subjects. It collects the absolute power for delta and beta EEG waves from the subjects and then correlates the afore-mentioned waves with relaxation and attention/cognitive load conditions. The EEG signal sensor is configured to detect absolute power for delta and beta EEG waves by reading electric activities of brain in its states of attention and relaxation, wherein the EEG electrodes are attached onto the earlobes and the forehead, and caps serve as a voltmeter at microvolt level with the aid of the electrodes and help to obtain brain signals from neural activities and each activity state captured in an individual's brain activity is assigned a score between 0 and 100 for attention and meditation values .

The developed CCD-based motion sensor includes a sensor (Sony IMX219) with an attached lens and acquisition/control programmed software is used to track the movement pattern of the paediatric subject who is involved in a continuous performance task (CPT). The developed programmed software in the LabVIEW platform (National Instruments, USA) and embodied in the system controller is used for the statistical analysis for deriving a scale for the objective assessment of ADHD by considering both the EEG wave signals and the movement pattern of the subject in test. The quantitative scale has also been compared thoroughly with clinicians ADHD evaluation. The control of the system including interfacing and loT strategy is also executed by the developed programmed software.

Working Principle:

The developed EEG signal sensor integrates a microchip, an embedded firmware, a 10 mm active material reference and ground electrodes. It records EEG signals using dry-sensor technology. A single EEG channel signal is derived from the difference between the electrical potentials obtained from the active and reference electrodes and common mode rejection. The obtained EEG channel signal is then amplified 8000 times. The signals are band-pass-filtered (1-40 Hz) and sampled at 128 Hz. Thus, from the EEG signal obtained from the single-channel dry sensor placed on frontal lobe of the forehead of the paediatric subjects, absolute power for delta and beta EEG waves are correlated with relaxation and attention/cognitive load conditions. In spite of the relaxation condition of the subject hinting towards hyperactivity condition, specially developed CCD-based motion sensor is used during physical displacement of the subject engaged in a given precise task. The present advancement includes developed programmed software operations to perform the statistical analysis to derive a scale for the quantitative assessment of ADHD.

ADHD is a condition which combines multiple neurological problems which includes hyper kinetics as well as lack of attention in paediatric human subjects. The present data from the EEG is collected wirelessly via Bluetooth which ensures no perturbation to the subject under test. This data is collected corroboration with the present movement sensor, which performs the statistical analysis, which in turn is related to the subject's neurological condition.

The working flowchart of the present system is shown Fig la and lb.

Study Design:

Study Population and Ethical Considerations

The study was conducted following the guidelines approved by the Institutional Ethics Committee (Ref: CNMC 93/Psychiatry/4.9.2020, dated 04.09.2020). All studies involving human subjects were performed in accordance with the guidelines of the Declaration of Helsinki (Declaration 2014) and Indian Council for Medical Research (ICMR), Govt, of India.

Sample size of n=25 was estimated using the following formula considering the proof-of-the concept nature of the study. n = 1.96²

Where, the prevalence (p) of ADHD in Indian subpopulation of the age group was estimated to be 0.113, the confidence level was set at 95% and the desired width of the confidence interval (d) was set at 0.25. For better statistical results, the sample size n=30 has been finalized for the study.

For the study, a group of volunteers were roped in, who then submitted a written consent agreeing to take part after understanding and acknowledging the key aspects and the outcome of the study. All data and information about the subjects are kept confidential and utilized only for this study.

Sample Collection:

The paediatric subject (3-5 years old), was instructed to colour three different segments of a circle with three distinct colours, namely, red, green and blue. While the child is performing the task, the single-channel dry sensor placed on frontal lobe of its forehead obtains the EEG signal, which thereafter gets analysed by the programmed software on the system controller unit to provide the attention span of the subject. The final painting is also collected and analysed to the cleanliness and the extent of perfection of the given work. Thereafter, for the hyperactivity test, the child is again given a precise task of separating various coloured balls from one table to another while the CCD-based motion sensor of the system is used to monitor the movement pattern of the subject engaged in a continuous performance task (CPT).

The present system gives a quantitative score after analysing both the attention and the physical activity of the child. It also takes into consideration the number of times a child has been instructed to perform a particular task.

Statistical Analysis:

Data are represented as Mean±SD, unless otherwise stated. The difference between two groups were estimated using non-parametric, unpaired, two- tailed t-test after Welch's correction. P<0.05 was considered statistically significant. All statistical analysis was performed using GraphPad Prism v8.00. Results and Discussions:

As stated hereinbefore, the Fig. 2a schematically represents the experimental set-up to collect the EEG signal from the subject's brain, in order to gauge and determine the attention and the relaxation parameters. The EEG waves, thus obtained, are correlated with attention/cognition and relaxation load conditions as shown in Fig. 2b and 2c, respectively. The experiment was repeated with the subject opening (EO) and then closing its eyes (EC). It was observed that, the average relative power of beta wave had a comparatively low value in EO condition suggesting low attention level. However, in the case of EC, a rise in the concentration level was noted (Fig. 2b). The observation is consistent with the fact that in the case of EO, a subject may sense significantly higher signals than that in the case of EC which may decrease the attention increasing relaxation level. A statistical analysis of the attention as shown is found to be consistent with attention scale as assessed by an expert (Connors' Parent Rating Scale).

Fig. 3a schematically shows the experimental arrangement for determining the presence of hyperactivity in the paediatric subject. The subject is given the task of separating different coloured balls from two tables separated by a certain distance. While the subject is performing the CPT, its movement is tracked by the developed CCD based integrated motion sensor in the MAHD device as shown in Fig. 3b. The digression of the subject from the shortest path between the two tables is shown in Fig. 3c which is indicative of the hyperactive nature of the subject under test. A statistical analysis of the movement pattern as shown below is found to be consistent with hyperactivity scale as assessed by an expert (Connors' Parent Rating Scale).

Statistical analysis of the acquired data was performed by the controller unit. The controller unit integrates the acquired EEG signals with movement pattern analysis of the subject under test (CPT) as described earlier. Fig 4a shows level of attention (%) with its frequency of occurrence under the CPT (colouring task). Fig. 4b depicts movement, that is, occurrence of deviations from shortest path under CPT (balls separation task). The data in Fig. 4a and 4b are fitted with the following GaussAmp equation.

Where, y₀ = offset, x_c = center, w = width, A = amplitude. The derived output parameter from the fitted equations namely full width at half maxima (FWHM) is obtained from 2w of the fitted curves. The output parameter FWHM, in present case, is a signature of the spread of the curve, which means, greater area coverage of the subject under test as shown in Fig. 3a and 3b. Additionally, higher FWHM in the attention pattern means enhanced spread of attention parameters which in its turn, indicates lesser focus of the subject under test as shown in Fig. 4a. Evidently, in present case, a higher FWHM both in Fig. 4a and 4b signifies non-compliance of instructions or forced compliance under repeated instruction mode.

Fig. 5a and 5b show the completed colouring tasks (CPT) by two different subjects under test. In the case of the first subject (Fig. 5a), the task was improperly carried out and hence the task completion weightage in the final formula should be less. Similarly, 4b shows a properly completed task by the other subject with colours not pouring out of borders and evenly distributed colour density.

The developed algorithm (Fig. 1) and associated hardware captures the worksheet completed by the subject and evaluates the same based on RGB analysis method. Individual component colours are plotted in real time and task completion evaluation is automatically generated in the output report. Fig. 5c shows the histogram plots of the evaluated worksheets and distinctive difference between the incomplete and the complete tasks is evident. Degree of inappropriateness of the task for different colours is reflected in the y-axis amplitude of the Fig. 5c. The controller unit is configured to analyze the movement pattern. The controller unit embodies the developed algorithm which integrates the acquired EEG signals with movement pattern analysis of the subject under test (CPT) including noting variation of attention (%) levels with its frequency of occurrence under the CPT and simultaneously, recording occurrence of deviations from shortest path under CPT from the movement pattern and thereby fitting the obtained data with the GaussAmp equation such as to produce a derived output parameter from the fitted equations namely full width at half maxima (FWHM) of the fitted curves, which is a signature of the spread of the curve, whereby higher FWHM in the attention pattern means enhanced spread of attention parameters which in its turn, indicates lesser focus of the subject under test and a higher FWHM in both cases, signify non-compliance of instructions or forced compliance under repeated instruction mode. The controller unit further embodies image processing technique wherein a region of interest (ROI) is selected to obtain an intensity profile followed by RGB analysis and individual colour intensity monitoring, whereby the value obtained from the RGB analysis is corroborated with the EEG signal data and movement pattern analysis data to give the final quantitative score.

Fig. 6 and 7 show the typical reports generated by the developed MAHD device for two different representative subjects who were critically evaluated by expert clinical psychologist. Fig. 6 illustrates the report for a normative subject. The average attention of this subject towards completing the colouring task is 82%, which is found to be higher than average. The work produced is neat, analysed to give the respective RGB values of 80.62, 82.96 and 92.97, respectively. The HD analysis shows the compliance of the subject to the OPT with one instruction. The expert clinician's evaluation according to Conners' Parent Rating Scale was marked at 37.

Fig. 7 shows the results of a subject reported to have ADHD whose expert clinician's evaluation according to Conners' Parent Rating Scale test score was 68, which is lying under severe category of ADHD. During the CPT, it was observed that its average attention level was as low as 27%. The output of the CPT (colouring task) was incomplete, which when analysed, gave the RGB values as 98.04, 98.32 and 106.29, respectively which are relatively higher than that of the normative subject. The HD analysis depicted its random and hyperactive movement pattern, and its incapability to stick to the shortest path, thus having a broad spectrum. This subject had to be instructed five times in order to complete the tasks.

To further test about the distinct differences in attention level, hyperactivity index and the completeness of the color chart, the evaluation was repeated to 30 subjects among whom 10 were diagnosed with ADHD. The mean attention levels were found to be significantly different in the two populations (p<0.0001; t=7.252, df=21.90, Welch-corrected two-tailed t-test) (Fig. 8a). The hyperactivity index as measured by the FWHM described above were also significantly different for the two groups of subjects (p<0.0001; t=8.836, df=26.21, Welch-corrected two-tailed t-test) (Fig. 8b). Similarly, in RGB

5 analysis the red (p=0.0013; t=3.709, df=21.28, Welch-corrected two-tailed t-test), green (p=0.0002; t=4.620, df= 18.54, Welch-corrected two-tailed t- test) and blue (p<0.0001; t=5.618, df=20.10, Welch-corrected two-tailed t- test) were significantly different between the two populations (Fig. 8c-8e). Therefore, it can be noted that there is a significant difference in the indices 10 of a normative subject in comparison with a clinically reported ADHD subjects and that present results are co-related with the results obtained from the Conners' Parent Rating Scale.

Referring the following test results as in the table I and II for establishing essentiality and advantages of the two sensor combination of the present 15 system it can be further concluded that the corroboration of the average attention of the subject, the FWHM of the HD analysis and the different RGB values obtained from the final completed coloring task give a final result. This result can be compared with the expert clinician's evaluation according to Conners' Parent Rating Scale.

20 Table I: Co-relation of our detection with the gold standard psychometric test

A. For normal subjects, majority of MAHD results are in agreement with that of the conventional psychometric test

B. For ADHD patients, majority of MAHD results are in agreement with that of the conventional psychometric test

Table 2: Comparative analysis of the proposed system with available ADHD detection techniques

1. Attention: Attention deficiency is one of the primary symptoms of ADHD. Subjects with ADHD is shown to have trouble paying attention, controlling impulsive behaviors, or be overly active.

2. Meditation: Meditation or brain relaxation is very important in ADHD patients as it helps in focusing, planning, and impulse controlling mechanisms. It is also known to raise one's brain's level of dopamine, which is in short supply in ADHD brains.

3. Activity under CPT: Study of the brain signal or that of the movement pattern of the subject under test is done best when the subject is involved in a continuous performance task.

4. Quality of task: Analysis of the task after the completion of the study is of utter importance. This adds a parameter to the final quantitative score.

5. No. of prompting for CPT completion: The number of times the subject under test is instructed to complete a particular task is noted and the final quantitative score is modulated by the same.

6. Cross corelation between attention and quality of task: Co relating attention and the quality of the task is very important for the analysis and for providing the final result of the data. For example, the attention required for the completion of a particular CPT in almost same fashion may vary from one subject to another and incorporation of the same in the analysis is very necessary.

Different parameters measured using MAHD show statistically significant differences between the normal and ADHD population studied. Referring the graphs as in the Fig 8, violins depict kernel density estimation of the underlying data distribution with the width of each violin scaled by the number of observations at that Y-value. Three lines (from the bottom to the top) in each violin plot show the location of the lower quartile (25^th), the median and the upper quartile (75^th), respectively. The shaded area indicates the probability distribution of the variable. Individual data points are represented as colored circles. The differences between two groups were estimated using non-parametric, unpaired, two-tailed t-test after Welch s correction. *P<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

Conclusion

In the present work, the single-probe EEG and CCD based motion sensors have been integrated for the first time for more objective measure of Attention-deficit Hyperactivity Disorder (ADHD). The programmed software /algorithm inside the integrated system controller called MAHD (Measuring Attention and Hyperactive Disorder) for objective analysis of ADHD subjects (3-5 years). The results showed that in patients meeting ADHD criteria per an individual clinician, those with a relatively lower attention, and higher activity and inappropriate completion of continuous performance task (CPT) were more likely to have conditions that may account for ADHD symptoms or otherwise impact the ADHD evaluation. Integration of the analysis results from MAHD device with a clinical psychologist's evaluation is expected to improve the diagnosis in a particular case when addressing criterion E in a complex clinical population in which all patients have ADHD-like symptoms but not all have ADHD. The above experimental results support that the MAHD-based assessment may help the clinical psychologist to estimate the prognosis of the severity of ADHD.

Claims

Claims:

1. A system for objective measurement of Attention-deficit Hyperactivity Disorder (ADHD) of a subject comprising an EEG signal sensor to track attention span of the subject while performing a designated structure task involving delta and beta EEG waves from the subject co-relatable to relaxation and attention /cognitive load conditions;

CCD based motion sensors with cooperative optical arrangements to continuously track the subjects' movement pattern while being involved in a continuous performance task; and a cooperative controller unit adapted for corelation of results of said EEG signal sensor and CCD motion sensor for desired objective measurement based assessment of the ADHD of the subject.

2. The system as claimed in claim 1 comprising means for tracking variations of attention (%) levels with its frequency of occurrences, occurrences of deviations from shortest path under CPT and its movement pattern/curves; full width at half maxima (FWHM) generator generating based on said patterns/curves as signature of spread of a curve generated ; said controller unit is adapted to indicate based on level of the FWHM noted the desired measurement-based assessment of the ADHD of the subject.

3. The system as claimed in anyone of claims 1 or 2, wherein the EEG signal sensor includes a microchip, an embedded firmware, reference and ground electrodes for placing on frontal lobe of forehead of the subjects to collect the EEG signal from the subject's brain, in order to gauge and determine the attention and the relaxation parameters;

23 wherein said EEG signal sensor is configured to generate a single EEG channel signal from difference between electrical potentials obtained from the active and reference electrodes and common mode rejection whereby the obtained EEG channel signal is then amplified and band-pass-filtered.

4. The system as claimed in anyone of claims 1 to 3, wherein the EEG signal sensor is configured to detect absolute power for delta and beta EEG waves by reading electric activities of brain in its states of attention and relaxation, wherein the EEG electrodes are attached onto the earlobes and the forehead, and caps serve as a voltmeter at microvolt level with the aid of the electrodes and help to obtain brain signals from neural activities and each activity state captured in an individual's brain activity is assigned a score between 0 and 100 for attention and meditation values; said CCD based motion sensor includes a CCD based optical arrangement to capture optical spectrum in experimental condition to track the movement pattern of the subject under test.

5. The system as claimed in anyone of claims 1 to 4, wherein the controller unit is configured to analyze and correlate absolute power for delta and beta EEG waves with attention/cognition and relaxation load conditions including eyes open and closed conditions.

6. The system as claimed in anyone of claims 1 to 5, wherein the CCD based motion sensors is configured to track movement of the subject while performing the CPT and determine shortest path movement of the subject for performing the CPT by the CCD based optical arrangement to capture the optical spectrum of the experimental condition.

7. The system as claimed in anyone of claims 1 to 6, wherein the controller unit is configured to analyze the movement pattern by statistical analysis of the acquired data by a programmed algorithm which integrates the acquired EEG signals with movement pattern analysis of the subject under test (CPT) including noting variation of attention (%) levels with its frequency of occurrence under the CPT and simultaneously, recording occurrence of deviations from shortest path under CPT from the movement pattern and thereby fitting the obtained data with the GaussAmp equation such as to produce a derived output parameter from the fitted equations namely full width at half maxima (FWHM) and maximum (centre) position of the fitted curves, which is a signature of the spread of the curve, whereby higher FWHM in the attention pattern means enhanced spread of attention parameters around the centre position which in its turn, indicates lesser focus of the subject under test and a higher FWHM in both cases, signify non- compliance of instructions or forced compliance under repeated instruction mode.

8. The system as claimed in anyone of claims 1 to 7, includes imaging means to capture completed structure task images, whereby the controller is configured to evaluate degree of inappropriateness of the task by RGB analysis method including image processing wherein a region of interest (ROI) is selected to obtain an intensity profile followed by RGB analysis and individual colour intensity monitoring, whereby the value obtained from the RGB analysis is corroborated with the EEG signal data and movement pattern analysis data to give the final quantitative score.

9. The system as claimed in anyone of claims 1 to 8, wherein the controller is configured to integrate the acquired EEG signals with movement pattern analysis of the subject under test (CPT) by involving fitting the data in GaussAmp equation:

Where, y₀ = offset, x_c = center, w = width, A = amplitude; wherein the derived output parameter from the fitted equation namely full width at half maxima (FWHM) is obtained from 2w of the fitted curves and the output parameter FWHM is a signature of spread of the curve, which corresponds to greater area coverage of the subject under test and higher FWHM in the attention pattern corresponds to enhanced spread of attention parameters which in its turn, indicates lesser focus of the subject under test.