A multifaceted approach for obstructive sleep apnea classification from ECG signal using deep learning.

Varghese AJ, Gatsonis AN, Agraz M, Oommen V, Parulkar A, Chu A, Karniadakis GE

Open source

DOI
10.1038/s44323-026-00101-4
Published
2026 Aug 5
Container
Npj biological timing and sleep
Publisher
Not recorded
Open access
yes

Credibility signals

limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.

Show all credibility signals

Cite this work

BibTeX

@article{allodium:10.1038/s44323-026-00101-4,
  title = {A multifaceted approach for obstructive sleep apnea classification from ECG signal using deep learning.},
  author = {Varghese AJ and Gatsonis AN and Agraz M and Oommen V and Parulkar A and Chu A and Karniadakis GE},
  year = {2026},
  journal = {Npj biological timing and sleep},
  doi = {10.1038/s44323-026-00101-4},
  url = {https://doi.org/10.1038/s44323-026-00101-4}
}

RIS

TY  - JOUR
TI  - A multifaceted approach for obstructive sleep apnea classification from ECG signal using deep learning.
AU  - Varghese AJ
AU  - Gatsonis AN
AU  - Agraz M
AU  - Oommen V
AU  - Parulkar A
AU  - Chu A
AU  - Karniadakis GE
PY  - 2026
JO  - Npj biological timing and sleep
DO  - 10.1038/s44323-026-00101-4
UR  - https://doi.org/10.1038/s44323-026-00101-4
ER  - 

APA

AJ, V., AN, G., M, A., V, O., A, P., A, C., & GE, K. (2026). A multifaceted approach for obstructive sleep apnea classification from ECG signal using deep learning.. Npj biological timing and sleep. https://doi.org/10.1038/s44323-026-00101-4

Source records