Artificial intelligence-based approaches for sleep-related breathing events identification using EEG and ECG signals
The findings highlight the effectiveness of combining non-linear features from ECG and EEG signals for classifying various sleep-related breathing events. A proposed machine learning model provides significantly precise classification compared to a deep learning approach, offering improved reliability in-home sleep setting.
Authors
- Nguyen Thi Hoang Trang (ORCID: https://orcid.org/0000-0002-9638-4672)
- Trần Ngọc Đăng (ORCID: https://orcid.org/0000-0002-5287-0265)
- Vinh Nguyen‐Nhu (ORCID: https://orcid.org/0000-0002-8358-902X)
- Bui Thi Hong Loan
- Duc‐Ly Vu (ORCID: https://orcid.org/0000-0002-5445-2729)
- Tran Thanh Duy Linh (ORCID: https://orcid.org/0000-0001-8762-0832)
Institutions
- University of Medicine and Pharmacy at Ho Chi Minh City (VN)
- University Medical Center HCMC (VN)
Publication Details
- Journal
- Sleep And Breathing
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1007/s11325-025-03442-9
- Citations
- 2
- Primary Topic
- Obstructive Sleep Apnea Research
- Type
- article
- Field-Weighted Citation Impact
- 2.33
Funders
- Korea International Cooperation Agency