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

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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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Data Availability

The utilized dataset can be accessed from the IEEE DataPort repository at the following address: dx.doi.org/https://doi.org/10.21227/rzfh-zn36.

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Acknowledgements

The EEG data utilized in this study were acquired and archived under the supervision of Prof. Ali Motie Nasrabadi and his colleagues. The authors would like to sincerely thank him and his esteemed research team for generously sharing their valuable dataset.

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The authors declare that no funds, grants, or other forms of support were received during the preparation of this manuscript.

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Contributions

EAM: Conceptualization, Writing-Original draft preparation, Reviewing, and Editing. FAT: Conceptualization, Methodology, Software, Writing- Original draft preparation, and Visualization. SAH: Supervision, Investigation, Reviewing, and Editing. MHM: Supervision, Investigation, Writing- Reviewing and Editing, and Validation.

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Correspondence to Seyyed Abed Hosseini or Mohammad Hossein Moattar.

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The authors declare no competing interests.

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During the preparation of this study, the authors used ChatGPT and Grammarly to improve readability and language. After using these tools, the authors reviewed and edited the content as needed and take full responsibility for the publication's content.

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Ahmadi Moghadam, E., Abedinzadeh Torghabeh, F., Hosseini, S.A. et al. Improved ADHD Diagnosis Using EEG Connectivity and Deep Learning through Combining Pearson Correlation Coefficient and Phase-Locking Value. Neuroinform 22, 521–537 (2024). https://doi.org/10.1007/s12021-024-09685-3

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  • DOI: https://doi.org/10.1007/s12021-024-09685-3

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