SSC-SleepNet: A Siamese-Based Automatic Sleep Staging Model With Improved N1 Sleep Detection: A Siamese-Based Automatic Sleep Staging Model with Improved N1 Sleep Detection
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| Titel: | SSC-SleepNet: A Siamese-Based Automatic Sleep Staging Model With Improved N1 Sleep Detection: A Siamese-Based Automatic Sleep Staging Model with Improved N1 Sleep Detection |
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| Autoren: | Songlu Lin, Zhihong Wang, Hans van Gorp, Mengzhu Xu, Merel van Gilst, Sebastiaan Overeem, Jean-Paul Linnartz, Pedro Fonseca, Xi Long |
| Quelle: | IEEE Journal of Biomedical and Health Informatics. 29:6830-6843 |
| Verlagsinformationen: | Institute of Electrical and Electronics Engineers (IEEE), 2025. |
| Publikationsjahr: | 2025 |
| Schlagwörter: | Male, Adult, N1 sleep, Neural Networks, adaptive loss function, Electroencephalography/methods, Polysomnography/methods, pseudo-Siamese network, Computer, Young Adult, Sleep staging, Deep Learning, Computer-Assisted, Signal Processing, Humans, Female, Sleep Stages/physiology, electroencephalography, Algorithms |
| Beschreibung: | Automatic sleep staging from single-channel electroencephalography (EEG) using artificial intelligence (AI) is emerging as an alternative to costly and time-consuming manual scoring using multi-channel polysomnography. However, current AI methods, mainly deep learning models such as convolutional neural network (CNN) and long short-term memory (LSTM), struggle to detect the N1 sleep stage, which is challenging due to its rarity and ambiguous nature compared to other stages. Here we propose SSC-SleepNet, an automatic sleep staging algorithm aimed at improving the learning of N1 sleep. SSC-SleepNet employs a pseudo-Siamese neural network architecture owing to its capability in one- or few-shot learning with contrastive loss. Which we selected due to its strong capability in one- or few-shot learning with a contrastive loss function. SSC-SleepNet consists of two branches of neural networks: a squeeze-and-excitation residual network branch and a CNN-LSTM branch. These two branches are used to generate latent features of the EEG epoch. The adaptive loss function of SSC-SleepNet uses a weighing factor to combine weighted cross-entropy loss and focal loss to specifically address the class imbalance issue inherent in sleep staging. The proposed new loss function dynamically assigns a higher penalty to misclassified N1 sleep stages, which can improve the model's learning capability for this minority class. Four datasets were used for sleep staging experiments. In the Sleep-EDF-SC, Sleep-EDF-X, Sleep Heart Health Study, and Haaglanden Medisch Centrum datasets, SSC-SleepNet achieved macro F1-scores of 84.5%, 89.6%, 89.5%, and 85.4% for all sleep stages, and N1 sleep stage F1-scores of 60.2%, 58.3%, 57.8%, and 55.2%, respectively. Our proposed deep learning model outperformed the most existing models in automatic sleep staging using single-channel EEG signals. In particular, N1 detection performance has been markedly improved compared to the state-of-art models. |
| Publikationsart: | Article |
| ISSN: | 2168-2208 2168-2194 |
| DOI: | 10.1109/jbhi.2025.3572886 |
| Zugangs-URL: | https://pubmed.ncbi.nlm.nih.gov/40408218 |
| Rights: | IEEE Copyright |
| Dokumentencode: | edsair.doi.dedup.....3ed7889513f4bcd662d98331b64cc066 |
| Datenbank: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstract: | Automatic sleep staging from single-channel electroencephalography (EEG) using artificial intelligence (AI) is emerging as an alternative to costly and time-consuming manual scoring using multi-channel polysomnography. However, current AI methods, mainly deep learning models such as convolutional neural network (CNN) and long short-term memory (LSTM), struggle to detect the N1 sleep stage, which is challenging due to its rarity and ambiguous nature compared to other stages. Here we propose SSC-SleepNet, an automatic sleep staging algorithm aimed at improving the learning of N1 sleep. SSC-SleepNet employs a pseudo-Siamese neural network architecture owing to its capability in one- or few-shot learning with contrastive loss. Which we selected due to its strong capability in one- or few-shot learning with a contrastive loss function. SSC-SleepNet consists of two branches of neural networks: a squeeze-and-excitation residual network branch and a CNN-LSTM branch. These two branches are used to generate latent features of the EEG epoch. The adaptive loss function of SSC-SleepNet uses a weighing factor to combine weighted cross-entropy loss and focal loss to specifically address the class imbalance issue inherent in sleep staging. The proposed new loss function dynamically assigns a higher penalty to misclassified N1 sleep stages, which can improve the model's learning capability for this minority class. Four datasets were used for sleep staging experiments. In the Sleep-EDF-SC, Sleep-EDF-X, Sleep Heart Health Study, and Haaglanden Medisch Centrum datasets, SSC-SleepNet achieved macro F1-scores of 84.5%, 89.6%, 89.5%, and 85.4% for all sleep stages, and N1 sleep stage F1-scores of 60.2%, 58.3%, 57.8%, and 55.2%, respectively. Our proposed deep learning model outperformed the most existing models in automatic sleep staging using single-channel EEG signals. In particular, N1 detection performance has been markedly improved compared to the state-of-art models. |
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| ISSN: | 21682208 21682194 |
| DOI: | 10.1109/jbhi.2025.3572886 |