Efficient Deep Learning Approach for Diagnosis of Attention-Deficit/Hyperactivity Disorder in Children Based on EEG Signals

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Hamid Jahani, Ali Asghar Safaei
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引用次数: 0

Abstract

Attention-deficit/hyperactivity disorder (ADHD) is a behavioral disorder in children that can persist into adulthood if not treated. Early diagnosis of this condition is crucial for effective treatment. The database includes 61 children with attention-deficit/hyperactivity disorder and 60 healthy children as a control group. To diagnose children with ADHD, features were first extracted from EEG signals. Next, a convolutional neural network model was trained, and a new residual network was introduced. The two proposed models were evaluated using tenfold cross-validation on the test data. The average accuracy and F1 score were 92.52% and 93.6%, respectively, for the convolutional model and 96.8% and 97.1% for the ResNet model on the epoch data, respectively. On the other hand, accuracy for subject-based prediction was 96.5% for the convolution model and 98.6% for the modified ResNet model. Accuracy, precision, recall, and F1 score for the proposed ResNet model are better than the convolution model proposed in previous studies and better than the proposed model in the literature. This work presents a paradigm shift in the cognitive-inspired domain by introducing a novel ResNet model for ADHD diagnosis. The model’s exceptional accuracy, exceeding conventional methods, showcases its potential as a biologically inspired tool. This opens avenues for exploring the neurological underpinnings of ADHD because the model can be used for the manifold learning of EEG signals. Analyzing the proposed network can lead to a deeper understanding of EEG, bridging the gap between artificial intelligence and cognitive neuroscience. The paper’s innovative approach has far-reaching implications, offering a concrete application of cognitive principles to improve mental health diagnostics in children. It is important to note that the data were augmented and the classification model is based on a single experiment containing a very small number of children but the results, and accuracy of classification, are based on classifying augmented data samples that compose the EEG signals of this small number of individuals. It is prudent to undertake a comprehensive investigation into the efficacy of these models across a broad cohort of subjects.

Abstract Image

基于脑电信号诊断儿童注意力缺陷/多动症的高效深度学习方法
注意力缺陷/多动症(ADHD)是一种儿童行为障碍,如果不加以治疗,可能会持续到成年。早期诊断这种疾病对有效治疗至关重要。该数据库包括 61 名患有注意力缺陷/多动症的儿童和 60 名健康儿童作为对照组。为了诊断多动症儿童,首先从脑电图信号中提取特征。接着,训练了一个卷积神经网络模型,并引入了一个新的残差网络。通过对测试数据进行十倍交叉验证,对提出的两个模型进行了评估。在历时数据上,卷积模型的平均准确率和 F1 分数分别为 92.52% 和 93.6%,ResNet 模型的平均准确率和 F1 分数分别为 96.8% 和 97.1%。另一方面,基于主题的预测准确率,卷积模型为 96.5%,修改后的 ResNet 模型为 98.6%。所提出的 ResNet 模型的准确度、精确度、召回率和 F1 分数均优于之前研究中提出的卷积模型,也优于文献中提出的模型。这项研究通过引入用于多动症诊断的新型 ResNet 模型,实现了认知启发领域的范式转变。该模型的准确性超过了传统方法,展示了其作为生物启发工具的潜力。由于该模型可用于脑电信号的流形学习,这为探索多动症的神经基础开辟了道路。分析所提出的网络可以加深对脑电图的理解,弥补人工智能和认知神经科学之间的差距。本文的创新方法具有深远的意义,它提供了认知原理的具体应用,以改善儿童的心理健康诊断。值得注意的是,数据是经过扩增的,分类模型也是基于包含极少数儿童的单一实验,但结果和分类的准确性是基于对组成这一小部分人的脑电信号的扩增数据样本进行分类得出的。为了谨慎起见,应该对这些模型在大量受试者中的有效性进行全面调查。
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来源期刊
Cognitive Computation
Cognitive Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-NEUROSCIENCES
CiteScore
9.30
自引率
3.70%
发文量
116
审稿时长
>12 weeks
期刊介绍: Cognitive Computation is an international, peer-reviewed, interdisciplinary journal that publishes cutting-edge articles describing original basic and applied work involving biologically-inspired computational accounts of all aspects of natural and artificial cognitive systems. It provides a new platform for the dissemination of research, current practices and future trends in the emerging discipline of cognitive computation that bridges the gap between life sciences, social sciences, engineering, physical and mathematical sciences, and humanities.
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