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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.08521 |
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| _version_ | 1866915762728337408 |
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| author | Xu, Chen-Yang Wang, Han-Guang Zhang, Lan Zhang, Yong-Hui Hou, Hui-Rang Meng, Qing-Hao |
| author_facet | Xu, Chen-Yang Wang, Han-Guang Zhang, Lan Zhang, Yong-Hui Hou, Hui-Rang Meng, Qing-Hao |
| contents | Recently, researchers have begun to experiment with deep learning-based methods for detecting major depressive disor-der (MDD) using electroencephalogram (EEG) signals in search of a more objective means of diagnosis. However, exist-ing spatiotemporal feature extraction methods only consider the functional correlation between multiple electrodes and temporal correlation of EEG signals, ignoring the spatial posi-tion connection information between electrodes and the conti-nuity between time windows, which reduces the model's fea-ture extraction capabilities. To address this issue, a Spatio-temporal fused network for MDD detection with Electrode spatial Topology and adjacent TIME-window transition in-formation (SET-TIME) is proposed in this study. SET-TIME is composed by a common feature extractor, a secondary time-correlation feature extractor, and a domain adaptation (DA) module, in which the former extractor is used to obtain the temporal and spatial features, and the latter extractor can mine the correlation between multiple time windows, and the DA module is adopted to enhance cross-subject detection ca-pability. The experimental results of 10-fold cross-validation show that the proposed SET-TIME method outperforms the state-of-the-art (SOTA) method by achieving MDD detection accuracies of 92.00% and 94.00% on the public datasets PRED+CT and MODMA, respectively. Ablation experiments demonstrate the effectiveness of the multiple modules in SET-TIME, which assist in MDD detection by exploring the intrin-sic spatiotemporal information of EEG signals. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_08521 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A spatiotemporal fused network considering electrode spatial topology and time-window transition for MDD detection Xu, Chen-Yang Wang, Han-Guang Zhang, Lan Zhang, Yong-Hui Hou, Hui-Rang Meng, Qing-Hao Machine Learning Artificial Intelligence Recently, researchers have begun to experiment with deep learning-based methods for detecting major depressive disor-der (MDD) using electroencephalogram (EEG) signals in search of a more objective means of diagnosis. However, exist-ing spatiotemporal feature extraction methods only consider the functional correlation between multiple electrodes and temporal correlation of EEG signals, ignoring the spatial posi-tion connection information between electrodes and the conti-nuity between time windows, which reduces the model's fea-ture extraction capabilities. To address this issue, a Spatio-temporal fused network for MDD detection with Electrode spatial Topology and adjacent TIME-window transition in-formation (SET-TIME) is proposed in this study. SET-TIME is composed by a common feature extractor, a secondary time-correlation feature extractor, and a domain adaptation (DA) module, in which the former extractor is used to obtain the temporal and spatial features, and the latter extractor can mine the correlation between multiple time windows, and the DA module is adopted to enhance cross-subject detection ca-pability. The experimental results of 10-fold cross-validation show that the proposed SET-TIME method outperforms the state-of-the-art (SOTA) method by achieving MDD detection accuracies of 92.00% and 94.00% on the public datasets PRED+CT and MODMA, respectively. Ablation experiments demonstrate the effectiveness of the multiple modules in SET-TIME, which assist in MDD detection by exploring the intrin-sic spatiotemporal information of EEG signals. |
| title | A spatiotemporal fused network considering electrode spatial topology and time-window transition for MDD detection |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2411.08521 |