Electroencephalogram-based Unified Approach for Multiple Neurodevelopmental Disorders Detection in Children Using Successive Multivariate Variational Mode Decomposition

Early age identification and prompt intervention play a crucial role in mitigating the severity of neurodevelopmental disorders in children. Traditional diagnostic approaches can be lengthy, but there is growing research potential in using electroencephalogram (EEG) signals to detect attention deficit hyperactivity disorder (ADHD) and intellectual developmental disorder (IDD). By recording the electrical activity of the brain, EEG has emerged as a promising technique for the early identification of these disorders. This research proposes a novel integrated method for identifying multiple neurodevelopmental disorders from the EEG signals of children. The approach combines successive multivariate variational mode decomposition (SMVMD) for analyzing multi-component non-stationary signals and a machine learning (ML)-based classifier, addressing the issue of inconsistent numbers of extracted features by introducing an energy-based feature integration approach. By integrating enhanced features from SMVMD with a K-nearest Neighbor (KNN) classifier, the unified approach successfully detects two separate neurodevelopmental disorders from normal subjects. The proposed method demonstrates perfect classification scores in detecting IDD under three different scenarios and achieves 99.17% accuracy in classifying ADHD subjects from normal subjects. Evaluation against different ML-based classifiers confirms the effectiveness of the proposed feature extraction algorithm and highlights its superior performance compared to recent methods published on similar datasets.