# Automatic Identification of Children with ADHD from EEG Brain Waves
## 7 Discussion  
As ADHD is the most common disorder in children, early diagnosis will help to prevent future complications [23]. In this paper, we present a machine-learning approach for identifying children with ADHD using an SVM applied to a publicly available dataset (120 participants' 19-channel EEG data). After denoising, we divided the EEG signal into five sub-bands and took only four frequency bands (delta, theta, alpha, and beta) for the next process, because it is a slow (delta or theta) brain wave and due to a shortage of fast (beta) brain waves in ADHD patients. From the four sub-bands, we extracted statistical, time-domain, and frequency-domain features from each subject's data. The STD measures the variability and RMS is calculated to determine the power changes in the brain wave. Hjorth parameters indicate the complexity of the brain wave. Mobility, activity, and complexity are the most used Hjorth parameters and also the first derivatives of the signal [56]. Skewness represents the rate of asymmetric distribution of the EEG data. Kurtosis measures the distribution of observed data around the mean. It describes how \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**\% Variance**} & **Accuracy** & **Accuracy** & \multicolumn{4}{c}{**Confusion Matrix (Test)**} \\ \cline{3-7}  & **(Validation)** & **(Test)** & **TP** & **TN** & **FP** & **FN** \\ \hline
80\% & 89.3\% & 89.7\% & 650 & 828 & 90 & 79 \\ \hline
85\% & 92.7\% & 93.9\% & 681 & 866 & 59 & 41 \\ \hline
90\% & 93.4\% & 94.2\% & 665 & 886 & 75 & 21 \\ \hline
95\% & 88.5\% & 88.3\% & 560 & 895 & 180 & 12 \\ \hline
97\% & 84.8\% & 84.4\% & 493 & 897 & 247 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of the SVM classifier for different PCA variance after 10-fold cross-validation. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & **Accuracy** & **Accuracy** & \multicolumn{4}{c}{**Confusion Matrix (Test)**} \\ \cline{3-7}  & **(Validation)** & **(Test)** & **TP** & **TN** & **FP** & **FN** \\ \hline
1 & 93.4\% & 93.9\% & 990 & 1331 & 120 & 30 \\ \hline
2 & 93.1\% & 92.7\% & 969 & 1322 & 141 & 39 \\ \hline
3 & 93.0\% & 93.8\% & 992 & 1326 & 118 & 35 \\ \hline
4 & 92.7\% & 93.0\% & 959 & 1339 & 152 & 21 \\ \hline
5 & 92.3\% & 93.6\% & 982 & 1330 & 128 & 31 \\ \hline
6 & 91.3\% & 92.5\% & 953 & 1332 & 158 & 28 \\ \hline
7 & 93.3\% & 93.6\% & 987 & 1325 & 124 & 35 \\ \hline
8 & 91.6\% & 93.0\% & 976 & 1321 & 135 & 39 \\ \hline
9 & 93.1\% & 93.2\% & 981 & 1321 & 129 & 40 \\ \hline
10 & 93.17\% & 93.24\% & 994 & 1354 & 139 & 33 \\ \hline
**Mean** & & 93.2\% & & & & \\ \hline
**STD** & & 0.44 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of the SVM classifier for different runs for 90% PCA variance. often outliers occur [57]. Entropy measures the uncertainty or randomness of the brain wave [56]. PSD calculates the power distribution of EEG series in the frequency domain, and it is used to evaluate the abnormalities of the brain [58]. STD, RMS, skewness, and kurtosis are statistical and simple features. These are the simple features to characterize the brain wave. We also applied PCA of different percentages of variance to reduce dimensionality to prevent overfitting. PCA reduces the number of variables or features of a large dataset while preserving as much information as possible [59]. It makes it convenient and faster for the machine learning algorithm to analyze the dataset. We used two different cross-validation methods and split the dataset into the train-test-validation set. For different percentages of correlated features, we got different accuracy for both cross-validation methods in the SVM classifier. The highest test accuracy was 93.24% for 90% of the variance in the holdout cross-validation method and 94.2% accuracy for 90% of the variance in the k-fold cross-validation method.