DICE-Net: A Novel Convolution-Transformer Architecture for Alzheimer Detection in EEG Signals

Topic: Brain imaging, connectivity and assessment

Our results show that DICE-net achieved an accuracy of 83.28% in the AD-CN problem using Leave-One-Subject-Out validation, outperforming several baseline models, and achieving good generalization performance. Our findings suggest that a convolution transformer network can effectively capture the complex features of EEG signals for the classification of AD patients versus control subjects and may be expanded to other types of dementia, such as FTD. This approach could improve the accuracy of early diagnosis and lead to the development of more effective interventions for AD.

Key findings

• Our results show that DICE-net achieved an accuracy of 83.28% in the AD-CN problem using Leave-One-Subject-Out validation, outperforming several baseline models, and achieving good generalization performance.
• Our findings suggest that a convolution transformer network can effectively capture the complex features of EEG signals for the classification of AD patients versus control subjects and may be expanded to other types of dementia, such as FTD.
• This approach could improve the accuracy of early diagnosis and lead to the development of more effective interventions for AD.

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Study details

• Effect size83.28%
• Evidence strengthLow

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• DOI: 10.1109/access.2023.3294618
• Original source →

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@article{study2023-d70e3f0a,
  author  = {Μιλτιάδους, Ανδρέας and Gionanidis, Emmanouil and Tzimourta, Katerina D. and Γιαννακέας, Νικόλαος and Tzallas, Alexandros T.},
  title   = {{DICE-Net: A Novel Convolution-Transformer Architecture for Alzheimer Detection in EEG Signals}},
  journal = {IEEE Access},
  year    = {2023},
  doi     = {10.1109/access.2023.3294618},
  url     = {https://doi.org/10.1109/access.2023.3294618}
}

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