3DSleepNet: A Multi-Channel Bio-Signal Based Sleep Stages Classification Method Using Deep Learning
Article
Ji, Xiaopeng, Li, Yan and Wen, Peng. 2023. "3DSleepNet: A Multi-Channel Bio-Signal Based Sleep Stages Classification Method Using Deep Learning." IEEE Transactions on Neural Systems and Rehabilitation Engineering. 31, pp. 3513-3523. https://doi.org/10.1109/TNSRE.2023.3309542
| Article Title | 3DSleepNet: A Multi-Channel Bio-Signal Based Sleep Stages Classification Method Using Deep Learning |
|---|---|
| ERA Journal ID | 5044 |
| Article Category | Article |
| Authors | Ji, Xiaopeng, Li, Yan and Wen, Peng |
| Journal Title | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Journal Citation | 31, pp. 3513-3523 |
| Number of Pages | 11 |
| Year | 2023 |
| Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
| Place of Publication | United States |
| ISSN | 1534-4320 |
| 1558-0210 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/TNSRE.2023.3309542 |
| Web Address (URL) | https://ieeexplore.ieee.org/document/10233098 |
| Abstract | A novel multi-channel-based 3D convolutional neural network (3D-CNN) is proposed in this paper to classify sleep stages. Time domain features, frequency domain features, and time-frequency domain features are extracted from electroencephalography (EEG), electromyogram (EMG), and electrooculogram (EOG) channels and fed into the 3D-CNN model to classify sleep stages. Intrinsic connections among different bio-signals and different frequency bands in time series and time-frequency are learned by 3D convolutional layers, while the frequency relations are learned by 2D convolutional layers. Partial dot-product attention layers help this model find the most important channels and frequency bands in different sleep stages. A long short-term memory unit is added to learn the transition rules among neighboring epochs. Classification experiments were conducted using both ISRUC-S3 datasets and ISRUC-S1, sleep-disorder datasets. The experimental results showed that the overall accuracy achieved 0.832 and the F1-score and Cohen's kappa reached 0.814 and 0.783, respectively, on ISRUC-S3, which are a competitive classification performance with the state-of-the-art baselines. The overall accuracy, F1-score, and Cohen's kappa on ISRUC-S1 achieved 0.820, 0.797, and 0.768, respectively, which also demonstrate its generality on unhealthy subjects. Further experiments were conducted on ISRUC-S3 subset to evaluate its training time. The training time on 10 subjects from ISRUC-S3 with 8549 epochs is 4493s, which indicates its highest calculation speed compared with the existing high-performance graph convolutional networks and U2- Net architecture algorithms. |
| Keywords | Deep learning; 3D convolutional networks; sleep stages classification |
| Related Output | |
| Is part of | Deep learning based sleep stage classification |
| ANZSRC Field of Research 2020 | 400607. Signal processing |
| 461103. Deep learning | |
| Public Notes | This article is part of a UniSQ Thesis by publication. See Related Output. |
| Byline Affiliations | School of Mathematics, Physics and Computing |
| School of Engineering |
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