An observational study in Attention Deficit Hyperactivity Disorder, Psychiatric Diagnosis and Diagnosis, Psychiatric, sponsored by Daegu Catholic University Medical Center. Completed at 1 site in Korea, Republic of. Open to participants aged 7 Years to 12 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2024-02-20.
Sponsored by Daegu Catholic University Medical Center · Observational
This study investigated quantitative electroencephalography (QEEG) subtypes as auxiliary tools to assess Attention Deficit Hyperactivity Disorder (ADHD). Patient assessed using the Korean version of the Diagnostic Interview Schedule for Children Version IV and were assigned to one of three groups: ADHD, ADHD-Not Otherwise specified (NOS), and Neurotypical (NT). The investigators measure absolute and relative EEG power in 19 channels and conducted an auditory continuous performance test. The investigators analyzed QEEG according to the frequency range: delta (1-4 Hz), theta (4-8 Hz), slow alpha (8-10 Hz), fast alpha (10-13.5 Hz), and beta (13.5-30 Hz). The subjects were then grouped by Ward's method of cluster analysis using the squared Euclidian distance to measure dissimilarities.
Participants between 7 and 12 years of age diagnosed with ADHD according to the DSM-5 criteria were included in the study. The ADHD diagnosis was based on a Korean version of the Diagnostic Interview Schedule for Children Version IV (DISC-IV), which is a structured interview tool, and these diagnoses were confirmed by multiple child and adolescent psychiatrists. If participants did not meet the ADHD diagnostic criteria of DSM-IV and DISC-IV, they were assigned to the Neurotypical (NT) group. Based on the results of the DISC-IV test, participants were assigned to the ADHD or Non-Other Specified (NOS) group. Patients who met the diagnostic criteria of ADHD in DSM-IV, but whose score did not exceed six, and had a score of more than three in the attention deficit or hyperactivity/impactivity scale of DISC-IV were included in the NOS group. Children with a history of brain damage, neurological disorders, genetic disorders, substance dependence, epilepsy, or any other mental disorder were excluded from participation. Children with an IQ of 70 or lower according to the Korean-Wechsler Intelligence Scale for Children (Fourth Edition) or who were receiving drug treatment were also excluded from this study. The EEG recordings were performed using a SynAmps2 direct-current (DC) amplifier and a 10-20 layout 64-channel Quick-Cap electrode-placement system (Neuroscan Inc., NC, USA). The EEG data were digitally recorded from 19 gold cup electrodes placed according to the international 10-20 system. The impedances were maintained below 5 kΩ, and the sampling rate was 1000 Hz. The investigators use the linked mastoid reference and two additional bipolar electrodes to measure horizontal and vertical eye movements. During the recording, each participant laid in a dimly lit, electrically shielded, sound-attenuated room. Resting EEG recordings were recorded after three minutes with the participants' eyes closed.
1,817 studies on the registry are indexed under Attention Deficit Disorder with Hyperactivity; 296 are open to participants now.
This study's enrollment of 140 is above the median of 127 across 329 observational studies indexed under Attention Deficit Disorder with Hyperactivity.
Browse Attention Deficit Disorder with Hyperactivity studies →Daegu Catholic University Medical Center is the lead sponsor of 28 studies on the registry; 1 is open to participants now.
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Patients who suspected attention-deficit/hyperactivity disorder conducted QEEG and diagnosed with DISC-IV
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
The American Psychiatric Association’s manual of mental health diagnoses. DSM-5 (2013, revised as DSM-5-TR in 2022) replaced DSM-IV, and some studies still name the older edition. “Meets DSM criteria for …” means diagnosed by that edition’s diagnostic criteria, usually by a clinician in an interview.
DSM diagnostic criteria
The American Psychiatric Association’s manual of mental health diagnoses. DSM-5 (2013, revised as DSM-5-TR in 2022) replaced DSM-IV, and some studies still name the older edition. “Meets DSM criteria for …” means diagnosed by that edition’s diagnostic criteria, usually by a clinician in an interview.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Diagnostic Test: electroencephalography absolute delta power · Diagnostic Test: electroencephalography relative delta power · Diagnostic Test: electroencephalography absolute theta power · Diagnostic Test: electroencephalography relative theta power · Diagnostic Test: electroencephalography absolute slow alpha power · Diagnostic Test: electroencephalography relative slow alpha power · Diagnostic Test: electroencephalography absolute fast alpha power · Diagnostic Test: electroencephalography relative fast alpha power · Diagnostic Test: electroencephalography absolute beta power · Diagnostic Test: electroencephalography relative beta power · Diagnostic Test: Korean ADHD rating scale · Diagnostic Test: Korean Version of Diagnostic Interview Schedule for Children Version IV
Diagnostic Test: electroencephalography absolute delta power · Diagnostic Test: electroencephalography relative delta power · Diagnostic Test: electroencephalography absolute theta power · Diagnostic Test: electroencephalography relative theta power · Diagnostic Test: electroencephalography absolute slow alpha power · Diagnostic Test: electroencephalography relative slow alpha power · Diagnostic Test: electroencephalography absolute fast alpha power · Diagnostic Test: electroencephalography relative fast alpha power · Diagnostic Test: electroencephalography absolute beta power · Diagnostic Test: electroencephalography relative beta power · Diagnostic Test: Korean ADHD rating scale · Diagnostic Test: Korean Version of Diagnostic Interview Schedule for Children Version IV
Diagnostic Test: electroencephalography absolute delta power · Diagnostic Test: electroencephalography relative delta power · Diagnostic Test: electroencephalography absolute theta power · Diagnostic Test: electroencephalography relative theta power · Diagnostic Test: electroencephalography absolute slow alpha power · Diagnostic Test: electroencephalography relative slow alpha power · Diagnostic Test: electroencephalography absolute fast alpha power · Diagnostic Test: electroencephalography relative fast alpha power · Diagnostic Test: electroencephalography absolute beta power · Diagnostic Test: electroencephalography relative beta power · Diagnostic Test: Korean ADHD rating scale · Diagnostic Test: Korean Version of Diagnostic Interview Schedule for Children Version IV
We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings
We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings
We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings
We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings
We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings
We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings
We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings
We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings
We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings
We used MATLAB 7.0.1 (Math Works, Natick, MA, USA) and the EEGLAB toolbox to pre-process and analyze the EEG recordings. First, the EEG data were down-sampled to 250 Hz. Next, the EEG data were detrended and mean-subtracted to remove the DC component. A 1-Hz high-pass filter and a 60-Hz notch filter were applied to remove eye and electrical noise. Next, independent component analysis (ICA) was performed to remove the well-defined sources of artifacts. ICA has been shown to reliably isolate artifacts caused by eye and muscle movements and heart noise (23). Finally, clinical psychiatrists and EEG experts visually inspected the corrected EEGs. For the analysis, we selected more than two minutes of artifact-free EEG readings from the three-minute recordings
The KARS is a standardized screening tool for ADHD in Korean children and rating scale completed by the parents.
The DISC-IV is a structured diagnostic tool that was developed for use in epidemiological studies in children and adolescents
QEEG topographical plots of the results of the statistical comparisons to normative values (z-scores) using Neuroguide software
The investigators measures QEEG on first outpatient clinic and conduct topography for mapping
Time frame: through study completion, an average of 1 year
Plan to share: No — Data will be shared on request for proper reason
No publications or documents are linked to this record.
Ran from Aug 2018 to primary completion in Feb 2021, 30 months.
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This study is completed, as verified in Feb 2024. You cannot join it, but the record below documents what was studied.
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Attention Deficit Disorder with Hyperactivity →
Daegu Catholic University Medical Center