EPSPatNet86: eight-pointed star pattern learning network for detection ADHD disorder using EEG signals

Abstract

Objective. The main objective of this work is to present a hand-modelled one-dimensional signal classification system to detect Attention-Deficit Hyperactivity Disorder (ADHD) disorder using electroencephalography (EEG) signals. Approach. A novel handcrafted feature extraction method is presented in this research. Our proposed method uses a directed graph and an eight-pointed star pattern (EPSPat). Also, tunable q wavelet transforms (TQWT), wavelet packet decomposition (WPD), statistical extractor, iterative Chi2 (IChi2) selector, and the k-nearest neighbors (kNN) classifier have been utilized to develop the EPSPat based learning model. This network uses two wavelet decomposition methods (TQWT and WPD), and 85 wavelet coefficient bands are extracted. The proposed EPSPat and statistical feature creator generate features from the 85 wavelet coefficient bands and the original EEG signal. The learning network is termed EPSPatNet86. The main purpose of the presented EPSPatNet86 is to detect abnormalities of the EEG signals. Therefore, 85 wavelet subbands have been generated to extract features. The created 86 feature vectors have been evaluated using the Chi2 selector and the kNN classifier in the loss value calculation phase. The final features vector is created by employing a minimum loss-valued eight feature vectors. The IChi2 selector selects the best feature vector, which is fed to the kNN classifier. An EEG signal dataset has been used to demonstrate the presented model’s EEG signal classification ability. We have used an ADHD EEG dataset since ADHD is a commonly seen brain-related ailment. Main results. Our developed EPSPatNet86 model can detect the ADHD EEG signals with 97.19% and 87.60% accuracy using 10-fold cross and subject-wise validations, respectively. Significance. The calculated results demonstrate that the presented EPSPatNet86 attained satisfactory EEG classification ability. Results show that we can apply our developed EPSPatNet86 model to other EEG signal datasets to detect abnormalities.

A novel approach to identify the brain regions that best classify ADHD by means of EEG and deep learning

“…When working with ADHD and EEG, techniques such as Machine Learning (ML) [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] and Deep Learning (DL) [16] , [17] , [18] are used. ML is a set of techniques comprising algorithms that automatically learn from data and develop optimized solutions.…”

Detection of ADHD from EEG signals using new hybrid decomposition and deep learning techniques

“…The proposed method is the learning network model with a directed graph and an eight-pointed star pattern (EPSPat). While the method focused on analysing the abnormalities in the EEG signals, it was claimed by that ADHD was detected with a high accuracy rate [12].…”