DeepArousal-Net: A Multi-Block Recurrent Deep Learning Model for Proactive Forecasting of Non-Apneic Arousals From Multichannel PSG
By anticipating arousals, DeepArousal-Net opens new possibilities for the development of innovative interventions and personalized sleep management strategies, potentially leading to improved sleep quality and overall well-being.
Authors
- Massimiliano de Zambotti (ORCID: https://orcid.org/0000-0002-0057-5977)
- Zahra Yousefi (ORCID: https://orcid.org/0000-0001-6856-9621)
- Mohamad Forouzanfar (ORCID: https://orcid.org/0000-0001-8849-0144)
- Fiona. C. Bakar
Institutions
- SRI International (US)
- Xuzhou Construction Machinery Group (China) (CN)
Publication Details
- Journal
- IEEE Transactions on Biomedical Engineering
- Published
- 2025-08-18
- DOI
- https://doi.org/10.1109/tbme.2025.3596900
- Citations
- 1
- Primary Topic
- Obstructive Sleep Apnea Research
- Type
- article
- Field-Weighted Citation Impact
- 1.16
Funders
- Natural Sciences and Engineering Research Council of Canada