# Automatic Identification of Children with ADHD from EEG Brain Waves
## 2 Related Works  
ADHD was originally known as childhood hyperkinetic reaction [17]. The American Psychiatric Association (APA) did not officially recognize it as a mental disorder until the 1960s, and in the 1980s, the diagnosis was \"attention deficit disorder with or without hyperactivity\" [17]. Since then, many studies have been done to identify ADHD using fMRI and EEG. [PERSON] et al. [18] found that neural flexibility altered in children with ADHD and demonstrated the potential clinical utility of neural flexibility to identify children with ADHD, as well as to monitor treatment responses and disease severity using fMRI data. They obtained moderate accuracy of 77% for 10-fold cross-validation and 74.46% for the independent test. [PERSON] et al. [19] mentioned that the accuracy of ADHD classification ranged from 60% to 80% using neuroimaging features. According to [PERSON], circular analysis and a small sample can exaggerate high classification accuracies in neuroimaging studies of ADHD. The use fMRI shows moderate accuracy. It is also expensive, whereas EEG has more portability and freedom in data acquisition. [PERSON] et al. [20] calculated the weighted phase lag index (WPLI) for each frequency band of EEG to describe the functional EEG connectivity as a neuromarker for adult ADHD symptoms. [PERSON] et al. [21] applied a machine learning classifier on an EEG-controlled serious game to detect ADHD patients, where EEG data was monitored during the game. In this study, the participants had to play a \"FOCUS\" game and their attention levels were observed. In the game, the player had to move an avatar by focusing and using mental commands. It achieved 96% in classifying the EEG data to detect the correct attention state during gameplay and 98% in classifying the patients' EEG data. [PERSON] et al. [22] used nonlinear EEG features to classify adult normal and ADHD participants. Fifty participants underwent a continuous performance test (CPT), where they had to click the left mouse button with their index finger when any letter except for the target \"X\" was shown on the screen. Three nonlinear features--wavelet entropy, correlation dimension, and Lyapunov exponent--were extracted, and the KNN algorithm was used as a classifier. This study achieved an accuracy of around 96%. [PERSON] et al. [23] performed EEG classification on the data acquired from 30 healthy (9.85 \(\pm\) 1.77 years) and 30 ADHD (9.62 \(\pm\) 1.75 years) children during a visual attention task. Higuchi, Katz, and Petrosian fractal dimension exponents and approximate entropy nonlinear features were extracted from the signal. Overall, 92.28% and 93.65% accuracy was achieved, respectively, using the mRMR method and the DISR method using a multilayer perceptron (MLP) neural network. [PERSON] et al. [24] also used visual attention tasks to detect ADHD in 20 healthy and 29 children with ADHD aged 7-12 years using EEG nonlinear features. Lyapunov exponent, Higuchi fractal dimension, Katz fractal dimension, and Sevcik fractal dimension nonlinear features were extracted from the EEG data and showed an accuracy of 96.7% using frontal lobe electrodes with an MLP neural network. Most of the work discussed above mostly used EEG nonlinear features and neural networks for classification. For our model development, we selected this SVM classifier for its simplicity and effectiveness in high-dimension spaces. The number of participants in these reported studies did not exceed 49, while our focus was to find a sample with more variability in the dataset, with a balance between ADHD and healthy subjects. In addition, the experimental setting and the data collection procedure are important for reproducing these studies and establishing a standard for child ADHD detection. The selection of the dataset for this study (described in Section 3) was made considering these factors.