This article explores the detection of Attention Deficit Hyperactivity Disorder (ADHD), a neurobehavioral disorder, from electroencephalography (EEG) signals. Due to the unstable behavior of EEG signals caused by complex neuronal activity in the brain, frequency analysis methods are required to extract the hidden patterns. In this study, the feature extraction was performed with the Multitaper and Multivariate Variational Mode Decomposition methods. Then, these features were analyzed with the neighborhood component analysis and the features that contribute effectively to the classification were selected. The deep learning model including the convolution, pooling, and bidirectional long short term cell and fully connected layer was trained with the selected features. The trained model could effectively classify the subjects with ADHD with a deep learning model, support vector machines and linear discriminant analysis. The proposed approach was validated with an ADHD open access dataset (doi:10.21227/rzfh-zn36). Experimental results showed that the proposed approach can innovatively classify ADHD subjects from Control group effectively. The proposed method was able to classify 1210 test samples in 0.1 seconds with an accuracy of 95.54%. The proposed method is promising for distinguishing subjects with attention deficit hyperactivity disorder effectively.