A Study Of Childhood ADHD Recognition Based On Convolutional Neural Networks And Generative Adversarial Networks

Attention deficit/hyperactivity disorder(ADHD),with core symptoms of impulsivity,hyperactivity and inattention,is one of the most common neuropsychiatric disorders in children.People with ADHD are unable to regulate their behavior and focus on their schoolwork and daily tasks.Early diagnosis of ADHD is one of the most important challenges in its management and treatment,and the earlier the disorder is diagnosed,the greater the likelihood of treatment and recovery.The goal of this study is to develop a tool to assist physicians in the diagnosis of ADHD,based on Electroenceph-alogram(EEG)to identify children with ADHD from healthy children.The main studies are as follows:(1)TS-CNN based EEG signal classification task for ADHD.Since the traditional ADHD diagnosis process is time-consuming and subjective,this study proposes a TS-CNN model to assist physicians in diagnosing ADHD based on the characteristics of ADHD EEG signals.TS-CNN is a Convolutional Neural Networks(CNN)containing temporal and spatial convolutional blocks that can extract temporal and spatial features of EEG signals to ensure that the EEG signals of ADHD and normal children can be discriminated in terms of both temporal and spatial features.The classification accuracy,robustness and stability of the TS-CNN model were all validated,as it achieved a 94.31% correct classification rate on the full EEG signal dataset,outperforming the other two comparison models.This result even provides an important method and a new idea to assist doctors in diagnosing ADHD.(2)TS-GAN based EEG signal augmentation task.One of the main challenges of EEG signal analysis in ADHD is the scarcity of data,and a data-intensive network such as the TS-CNN classification model requires a large number of EEG signals to train the network.Therefore,this study proposes a TS-GAN model based on Generative Adversarial Networks(GAN)construction,and performs EEG signal expansion to increase the amount of data in the training set for the classification task and thus improve the classification performance.Both the generator and discriminator networks in the TS-GAN model contain spatio-temporal convolutional blocks that can extract the spatio-temporal features of the EEG signal to ensure the learning capability of the network,which in turn allows the TS-GAN network to generate a generated EEG signal that is similar to the original real EEG signal.Various evaluation metrics showed that the generated EEG signal was similar to the original EEG signal and the classification rate was improved by 1%～4%,indicating that the EEG signal scarcity problem was solved by data augmentation experiments and the effectiveness of the TS-GAN model proposed in this study.This result contributes to the early diagnosis of ADHD,early recovery and ultimately to personalised medicine based on synthetic data.