A Unified Framework for Detection of ADHD Using EEG Signals and Coherent Models

A behavioral and neuropsychological disorder that develops in young children during their early school years is called attention-deficit hyperactivity disorder (ADHD). When young children are diagnosed with ADHD, they have a tendency not to concentrate on academic and extracurricular activities. Moreover, children affected with ADHD suffer from mood swings, so it becomes quite difficult for them to establish good connections with teachers and friends. In the field of clinical research, deploying Electroencephalography (EEG) signals, a rapid and accurate diagnosis of ADHD is essential so that an effective treatment can be given to the children affected with ADHD. In this work, a unified framework is proposed for the detection of ADHD using EEG signals and some coherent models. The framework initially employs the concept of normalization of EEG signals, followed by the usage of dimensionality reduction techniques such as Local Linear Embedding (LLE), Sammon Mapping (SM) and Locally Linear Coordination (LLC). The dimensionally reduced EEG values are further clustered using four techniques such as spectral clustering, K-means clustering, Fuzzy C-means (FCM) clustering, Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), and finally, silhouette coefficient analysis is used to analyze the clustering effectiveness. The features are then extracted from the clustered values using an Improved Wavelet Transform (IWT) and then the features are selected with four efficient techniques such as the chi-squared test, Mutual Information (MI), Mahalanobis analysis and Binary Horse Herd Optimization (BHHO) techniques. Finally, the selected values are fed into classifiers for classification with the help of ten traditional machine learning classifiers. The work is tested on a publicly available ADHD dataset and the analysis shows that the best results are obtained when the LLC dimensionality reduction is utilized with FCM clustering and IWT feature extraction, BHHO feature selection, and classified with LGBA classifier reporting a high classification accuracy of 98.12%.