Improved ADHD Diagnosis Using EEG Connectivity and Deep Learning through Combining Pearson Correlation Coefficient and Phase-Locking Value.

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Neuroinformatics Pub Date : 2024-10-18 DOI:10.1007/s12021-024-09685-3
Elham Ahmadi Moghadam, Farhad Abedinzadeh Torghabeh, Seyyed Abed Hosseini, Mohammad Hossein Moattar
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Abstract

Attention Deficit Hyperactivity Disorder (ADHD) is a widespread neurobehavioral disorder affecting children and adolescents, requiring early detection for effective treatment. EEG connectivity measures can reveal the interdependencies between EEG recordings, highlighting brain network patterns and functional behavior that improve diagnostic accuracy. This study introduces a novel ADHD diagnostic method by combining linear and nonlinear brain connectivity maps with an attention-based convolutional neural network (Att-CNN). Pearson Correlation Coefficient (PCC) and Phase-Locking Value (PLV) are used to create fused connectivity maps (FCMs) from various EEG frequency subbands, which are then inputted into the Att-CNN. The attention module is strategically placed after the latest convolutional layer in the CNN. The performance of different optimizers (Adam and SGD) and learning rates are assessed. The suggested model obtained 98.88%, 98.41%, 98.19%, and 98.30% for accuracy, precision, recall, and F1 Score, respectively, using the SGD optimizer in the FCM of the theta band with a learning rate of 1e-1. With the use of FCM, Att-CNN, and advanced optimizers, the proposed technique has the potential to produce trustworthy instruments for the early diagnosis of ADHD, greatly enhancing both patient outcomes and diagnostic accuracy.

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通过结合皮尔逊相关系数和锁相值,利用脑电图连接性和深度学习改进多动症诊断。
注意力缺陷多动障碍(ADHD)是一种广泛影响儿童和青少年的神经行为障碍,需要及早发现才能有效治疗。脑电连接测量可以揭示脑电记录之间的相互依存关系,突出大脑网络模式和功能行为,从而提高诊断的准确性。本研究通过将线性和非线性脑连接图与基于注意力的卷积神经网络(Att-CNN)相结合,介绍了一种新型多动症诊断方法。利用皮尔逊相关系数(PCC)和锁相值(PLV)从不同的脑电图频率子带创建融合连接图(FCM),然后将其输入 Att-CNN。注意力模块被战略性地置于 CNN 最新卷积层之后。对不同优化器(Adam 和 SGD)的性能和学习率进行了评估。在θ波段的 FCM 中使用 SGD 优化器,学习率为 1e-1,建议模型的准确率、精确率、召回率和 F1 分数分别达到 98.88%、98.41%、98.19% 和 98.30%。通过使用 FCM、Att-CNN 和高级优化器,所提出的技术有望为多动症的早期诊断提供值得信赖的工具,从而大大提高患者的治疗效果和诊断准确性。
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来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
期刊最新文献
Anatomic Interpretability in Neuroimage Deep Learning: Saliency Approaches for Typical Aging and Traumatic Brain Injury. Interdisciplinary and Collaborative Training in Neuroscience: Insights from the Human Brain Project Education Programme. Improved ADHD Diagnosis Using EEG Connectivity and Deep Learning through Combining Pearson Correlation Coefficient and Phase-Locking Value. A Deep Learning-based Pipeline for Segmenting the Cerebral Cortex Laminar Structure in Histology Images. Bridging the Gap: How Neuroinformatics is Preparing the Next Generation of Neuroscience Researchers.
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