Decision Support Algorithm for Diagnosis of ADHD Using Electroencephalograms

Abibullaev, Berdakh; An, Jinung

doi:10.1007/s10916-011-9742-x

Cited by 60 publications

(45 citation statements)

References 21 publications

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“…High rates of classification accuracy for both the original sample (91 %) and a subsequent validation sample (94 %) were obtained [41]. This, and other studies, using a semi-supervised feature selection to define new features of the EEG signal [42], as well as studies using graph theory and community pattern analysis of EEG-derived functional connectivity [43] may provide new avenues to identify and test EEG measures, which can both tolerate sample heterogeneity and provide maximal discrimination between individuals with and without ADHD.…”

Section: New Methods For Adhd Discriminationmentioning

confidence: 58%

Clinical Utility of EEG in Attention-Deficit/Hyperactivity Disorder: A Research Update

2012

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Summary Psychiatric research applications of electroencephalography (EEG), the earliest approach to imaging human cortical brain activity, are attracting increasing scientific and clinical interest. For more than 40 years, EEG research has attempted to characterize and quantify the neurophysiology of attention-deficit/hyperactivity disorder (ADHD), most consistently associating it with increased frontocentral theta band activity and increased theta to beta (θ/β) power ratio during rest compared to non-ADHD controls. Recent reports suggest that while these EEG measures demonstrate strong discriminant validity for ADHD, significant EEG heterogeneity also exists across ADHD-diagnosed individuals. In particular, additional studies validating the use of the θ/β power ratio measure appear to be needed before it can be used for clinical diagnosis. In recent years, the number and the scientific quality of research reports on EEG-based neurofeedback (NF) for ADHD have grown considerably, although the studies reviewed here do not yet support NF training as a first-line, stand-alone treatment modality. In particular, more research is needed comparing NF to placebo control and other effective treatments for ADHD. Currently, after a long period of relative stasis, the neurophysiological specificity of measures used in EEG research is rapidly increasing. It is likely, therefore, that new EEG studies of ADHD using higher density recordings and new measures drawn from viewing EEG as a 3-dimensional functional imaging modality, as well as intensive re-analyses of existing EEG study data, can better characterize the neurophysiological differences between and within ADHD and non-ADHD subjects, and lead to more precise diagnostic measures and effective NF approaches.

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Section: New Methods For Adhd Discriminationmentioning

confidence: 58%

Clinical Utility of EEG in Attention-Deficit/Hyperactivity Disorder: A Research Update

2012

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“…Similarly, Abibullaev and An [36] obtained a maximal accuracy of 97%, using relative theta measures recorded from nine frontal scalp electrodes. Based on these accuracy rates, we may conclude that the potential of multivariate machine learning tools in EEG-based diagnostics is intriguing but, as such studies remain sparse and the results offer no simple interpretation (also c.f.…”

Section: Neurophysiological Candidates For Biomarkers Of Adhdmentioning

confidence: 99%

Use of EEG to Diagnose ADHD

Lenartowicz¹,

Loo²

2014

Curr Psychiatry Rep

220

134

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Electroencephalography (EEG) has, historically, played a focal role in the assessment of neural function in children with attention deficit hyperactivity disorder (ADHD). We review here the most recent developments in the utility of EEG in the diagnosis of ADHD, with emphasis on the most commonly used and emerging EEG metrics and their reliability in diagnostic classification. Considering the clinical heterogeneity of ADHD and the complexity of information available from the EEG signals, we suggest that considerable benefits are to be gained from multivariate analyses and a focus towards understanding of the neural generators of EEG. We conclude that while EEG cannot currently be used as a diagnostic tool, vast developments in analytical and technological tools in its domain anticipate future progress in its utility in the clinical setting.

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“…In many ways this is a refinement from previous work that looked at specific EEG bands. Neurophysiological studies have previously shown that children with ADHD exhibit specific patterns on the electroencephalogram (EEG) and this has been utilized clinically to diagnose, treat and even predict response to treatment with medication [14], [15], [16], [17], [18]. EEG studies of children with ADHD showed the majority to exhibit abnormal patterns of resting cortical activity including increased slow-wave activity (primarily theta waves), decreased fast-wave activity (primarily beta waves) and increased beta-theta ratio [19], [20], [21].…”

Section: Discussionmentioning

confidence: 99%

A Brain-Computer Interface Based Attention Training Program for Treating Attention Deficit Hyperactivity Disorder

Lee²,

et al. 2012

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Attention deficit hyperactivity disorder (ADHD) symptoms can be difficult to treat. We previously reported that a 20-session brain-computer interface (BCI) attention training programme improved ADHD symptoms. Here, we investigated a new more intensive BCI-based attention training game system on 20 unmedicated ADHD children (16 males, 4 females) with significant inattentive symptoms (combined and inattentive ADHD subtypes). This new system monitored attention through a head band with dry EEG sensors, which was used to drive a feed forward game. The system was calibrated for each user by measuring the EEG parameters during a Stroop task. Treatment consisted of an 8-week training comprising 24 sessions followed by 3 once-monthly booster training sessions. Following intervention, both parent-rated inattentive and hyperactive-impulsive symptoms on the ADHD Rating Scale showed significant improvement. At week 8, the mean improvement was −4.6 (5.9) and −4.7 (5.6) respectively for inattentive symptoms and hyperactive-impulsive symptoms (both p<0.01). Cohen’s d effect size for inattentive symptoms was large at 0.78 at week 8 and 0.84 at week 24 (post-boosters). Further analysis showed that the change in the EEG based BCI ADHD severity measure correlated with the change ADHD Rating Scale scores. The BCI-based attention training game system is a potential new treatment for ADHD.Trial RegistrationClinicalTrials.gov NCT01344044

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Decision Support Algorithm for Diagnosis of ADHD Using Electroencephalograms

Cited by 60 publications

References 21 publications

Clinical Utility of EEG in Attention-Deficit/Hyperactivity Disorder: A Research Update

Clinical Utility of EEG in Attention-Deficit/Hyperactivity Disorder: A Research Update

Use of EEG to Diagnose ADHD

A Brain-Computer Interface Based Attention Training Program for Treating Attention Deficit Hyperactivity Disorder

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