TMP19: A Novel Ternary Motif Pattern-Based ADHD Detection Model Using EEG Signals

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental condition worldwide. In this research, we used an ADHD electroencephalography (EEG) dataset containing more than 4000 EEG signals. Moreover, these EEGs are noisy signals. A new hand-modeled EEG classification model has been proposed to separate healthy versus ADHD individuals using the EEG signals. In this model, a new ternary motif pattern (TMP) has been incorporated. We have mimicked deep learning networks to create this hand-modeled classification method. The Tunable Q Wavelet Transform (TQWT) has been utilized to generate wavelet subbands. We applied the proposed TMP and statistics to construct informative features from both raw EEG signals and wavelet bands by generating TQWT. Herein, features have been generated by 18 subbands and the original EEG signal. Thus, this model is named TMP19. The most informative features have been chosen by deploying neighborhood component analysis (NCA), and the selected features have been classified using the k-nearest neighbor (kNN) classifier. The used ADHD EEG dataset has 14 channels. Thus, these three phases-(i) feature extraction with TQWT, TMP, and statistics; (ii) feature selection by deploying NCA; and (iii) classification with kNN-have been applied to each channel. Iterative hard majority voting (IHMV) has been applied to obtain a higher and more general classification response. Our model attained 95.57% and 77.93% classification accuracies by deploying 10-fold and leave one subject out (LOSO) cross-validations, respectively.

Automated summary

In this research, a hand-modeled classification model using a new ternary motif pattern (TMP) has been proposed for separating healthy versus ADHD individuals based on noisy EEG signals. The model utilizes the Tunable Q Wavelet Transform (TQWT) for feature extraction and applies neighborhood component analysis (NCA) and k-nearest neighbor (kNN) classifier for feature selection and classification. The model achieved high classification accuracies of 95.57% and 77.93% using 10-fold and leave one subject out (LOSO) cross-validations, respectively.