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Attention Deficit Hyperactivity Disorder (ADHD) is a frequent learning disorder affecting about 5%–8% of the student population globally. Currently, the traditional methods for ADHD diagnosis are not fully specified, due to difficulties in identifying the particular factors that cause this disorder. In this paper, we present a novel system for diagnosing ADHD, which does not need special equipment. Instead, it is based on the application of machine learning (ML), using data gathered from gameplay sessions of a serious game named “ADHD360”, developed for this purpose. Participants were recruited with particular criteria in order to generate data for the study. The benefits of our approach include less subjectivity in the decision process, cost-efficiency and easier accessibility than the typical procedure. To this end, special data preprocessing steps and ML techniques were applied. Our models achieved up to 85.7% F1-score performance metric in predicting correctly a user’s label (ADHD or not) from his/her gameplay session in ADHD360. Our method also proved to be efficient using only a small amount of data for the training procedure. The results of our systems are very promising, indicating notable ability of the tool to distinguish players that probably suffer from ADHD than those who do not.

Attention deficit hyperactivity disorder (ADHD) is a mental health disorder that is very common among children and may last into their adulthood. It is known that ADHD affects the attention of patients due to problems with short-term memory. Therefore, analysis of attention and memory of these patients should come into consideration. In this study, the complexity and memory of Electroencephalogram (EEG) signals are analyzed to investigate the reduction in attention and memory of patients with ADHD compared to normal subjects while playing a serious game. To achieve this, the fractal dimension and sample entropy of EEG signals are analyzed to evaluate the alterations in the complexity of EEG signals. Moreover, the Hurst exponent of EEG signals for ADHD and non-ADHD subjects is calculated to discuss the memory of EEG signals. The results showed a smaller fractal dimension and sample entropy of EEG signals for patients with ADHD that reflects their lower attention. Besides, the Hurst exponent of EEG signals for these patients reflects their lower memory than normal subjects. Therefore, it can be concluded that the reductions of attention and memory in ADHD subjects are mapped on the reduction of complexity and memory of their EEG signals.

Based on published research on the combined cognitive behavioral therapy (CBT) versus drug therapy alone in children with attention deficit/hyperactivity disorder (ADHD), we systematically reviewed and analyzed the efficacy of two treatment methods in children with ADHD. The literature as at the end of 10 July 2017 in multiple databases was systematically searched. Standardized mean differences (SMD) and 95% confidence intervals (CIs) were calculated. The results indicated that combined CBT therapy was efficacious in reducing symptoms of ADHD (SDM $-$0.48, 95% CI $-$0.80 to $-$0.17). The executive function scores were not improved more in the combined CBT (SMD $-$0.34; 95% CI $-$0.71 to 0.03). This study suggests that combined CBT seems more efficacious in some domains affecting children with ADHD, but further evaluation is needed.