Detection of ADHD cases using CNN and classical classifiers of raw EEG

Purpose:

This study proposes a novel convolutional neural network (CNN) structure in conjunction with classical machine learning models, utilizing the raw electroencephalography (EEG) signal as the input to diagnose attention deficit hyperactivity disorder (ADHD) in children. The proposed EEG-based approach does not require transformation or artifact rejection techniques.

Methods:

In the first step, the suggested method uses raw EEG to train a CNN to diagnose ADHD. Then, the feature maps from different layers of the trained CNN are extracted and used to train some classical classifiers such as support vector machine (SVM), logistic regression (LR), random forest (RF), etc. This study benefits from an extended version of a dataset acquired from 61 participants diagnosed with ADHD and 60 individuals in control group, age 7 through 12 years old.

Results:

The initial CNN structure (without further use of feature maps) achieved an accuracy of $86.33\pm 2.64\text{\%}$ in 5-fold cross-validation scheme on training set, which is superior to results reported in previous studies. However, in order to increase the efficacy of the classifiers we used various feature representations across different CNN layers and after a rigorous evaluation of candidate classifiers, logistic regression provided an accuracy of $91.16\pm 0.03\text{\%}$ in training epochs using 5-fold cross-validation scheme and 95.83% in ADHD identification in unseen epochs, were achieved. Also, other metrics such as precision, sensitivity, F1-score and receiver of operating characteristic (ROC) were presented for better comparison of different hybrid methods.

Conclusion:

The suggested method for detection of ADHD in children shows high performance in different metrics such as accuracy, sensitivity, and specificity, which is superior to previously reported results.