Summary of Sad-time: a Spatiotemporal-fused Network For Depression Detection with Automated Multi-scale Depth-wise and Time-interval-related Common Feature Extractor, by Han-guang Wang et al.
SAD-TIME: a Spatiotemporal-fused network for depression detection with Automated multi-scale Depth-wise and TIME-interval-related common feature extractor
by Han-Guang Wang, Hui-Rang Hou, Li-Cheng Jin, Chen-Yang Xu, Zhong-Yi Zhang, Qing-Hao Meng
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel deep learning-based method for identifying depressive disorders is proposed, addressing limitations of current questionnaire-based diagnostic methods. The Spatiotemporal-fused network with Automated multi-scale Depth-wise and TIME-interval-related common feature extractor (SAD-TIME) incorporates automated nodes’ common features extractor, spatial sector, temporal sector, and domain adversarial learner. The CFE preserves unique information from each EEG channel, while the SpS fuses functional and distance-based connectivities. The TeS combines long short-term memory and graph transformer networks to fuse temporal information. SAD-TIME achieves 92.00% and 94.00% depression classification accuracies on two datasets in cross-subject mode. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is being developed to diagnose depression more accurately. Researchers are using deep learning to analyze brain signals from people with depression. They’ve created a special network called SAD-TIME that helps identify patterns in these signals. It looks at how different parts of the brain connect and change over time. This method might be better than current methods because it’s more objective and doesn’t rely on people’s answers. |
Keywords
» Artificial intelligence » Classification » Deep learning » Spatiotemporal » Transformer
