An Ensemble Fuzzy-Based Deep Learning Framework for Automatic Detection of Children with ADHD From EEG Signal

<span style="color: rgb(51, 51, 51); font-family: ltr-font; font-size: 14px; text-align: justify;">The goal of the research is to introduce a method for differentiating children with ADHD from those without the disorder by analyzing their EEG signals while they engage in a cognitive task. This paper presents a novel technique utilizing deep learning to construct images from EEG signals. The approach involves creating two images from EEGs, utilizing both functional connectivity and spectral attributes. These constructed images are then fed into an ensemble deep framework. Three transfer learning models, namely VGGNet, Inception V3, and Inception ResNet V2, are utilized in this task, enhanced with extra layers to catch data-specific attributes. Introducing a fresh ensemble method, we aim to consolidate the outputs generated by these models through an approach centered on minimizing the error discrepancies between observed and ground-truth values. When confronted with samples yielding multiple predictions, we initiate the process by computing four distinct distance metrics - Euclidean, Hamming, Manhattan, and Cosine - for every group relative to their best possible solutions. Subsequently, the defuzzification of these distance metrics is executed through the product rules to arrive at ultimate prediction. This model classifies each sample into either the ADHD or Normal classes. Through 10-fold cross-validation method, average values of accuracy, sensitivity, specificity, F1, and false discovery rate indices achieved by suggested technique were 99.41%, 99.33%, 99.51%, 99.42%, and 0.68%, respectively, for ADHD diagnosis. These findings prove the high accuracy of proposed framework in classifying EEG signals for ADHD diagnosis compared to previous techniques.</span>