Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.14227 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910836855930880 |
|---|---|
| author | Zhao, Chenjun Niu, Xuesen Yu, Xinglin Chen, Long Lv, Na Zhou, Huiyu Zhao, Aite |
| author_facet | Zhao, Chenjun Niu, Xuesen Yu, Xinglin Chen, Long Lv, Na Zhou, Huiyu Zhao, Aite |
| contents | Sleep staging is a key method for assessing sleep quality and diagnosing sleep disorders. However, current deep learning methods face challenges: 1) postfusion techniques ignore the varying contributions of different modalities; 2) unprocessed sleep data can interfere with frequency-domain information. To tackle these issues, this paper proposes a gated multimodal temporal neural network for multidomain sleep data, including heart rate, motion, steps, EEG (Fpz-Cz, Pz-Oz), and EOG from WristHR-Motion-Sleep and SleepEDF-78. The model integrates: 1) a pre-processing module for feature alignment, missing value handling, and EEG de-trending; 2) a feature extraction module for complex sleep features in the time dimension; and 3) a dynamic fusion module for real-time modality weighting.Experiments show classification accuracies of 85.03% on SleepEDF-78 and 94.54% on WristHR-Motion-Sleep datasets. The model handles heterogeneous datasets and outperforms state-of-the-art models by 1.00%-4.00%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_14227 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SleepGMUformer: A gated multimodal temporal neural network for sleep staging Zhao, Chenjun Niu, Xuesen Yu, Xinglin Chen, Long Lv, Na Zhou, Huiyu Zhao, Aite Machine Learning Artificial Intelligence Sleep staging is a key method for assessing sleep quality and diagnosing sleep disorders. However, current deep learning methods face challenges: 1) postfusion techniques ignore the varying contributions of different modalities; 2) unprocessed sleep data can interfere with frequency-domain information. To tackle these issues, this paper proposes a gated multimodal temporal neural network for multidomain sleep data, including heart rate, motion, steps, EEG (Fpz-Cz, Pz-Oz), and EOG from WristHR-Motion-Sleep and SleepEDF-78. The model integrates: 1) a pre-processing module for feature alignment, missing value handling, and EEG de-trending; 2) a feature extraction module for complex sleep features in the time dimension; and 3) a dynamic fusion module for real-time modality weighting.Experiments show classification accuracies of 85.03% on SleepEDF-78 and 94.54% on WristHR-Motion-Sleep datasets. The model handles heterogeneous datasets and outperforms state-of-the-art models by 1.00%-4.00%. |
| title | SleepGMUformer: A gated multimodal temporal neural network for sleep staging |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2502.14227 |