This paper is in the following e-collection/theme issue:

Clinical Information and Decision Making (1408) New Methods (945) Imaging Informatics (194) Machine Learning (1466) Sleep Apnea (26)

Published on in Vol 22, No 5 (2020): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/17252, first published .
Distinguishing Obstructive Versus Central Apneas in Infrared Video of Sleep Using Deep Learning: Validation Study

Distinguishing Obstructive Versus Central Apneas in Infrared Video of Sleep Using Deep Learning: Validation Study

Distinguishing Obstructive Versus Central Apneas in Infrared Video of Sleep Using Deep Learning: Validation Study

Authors of this article:

Sina Akbarian1, 2, 3 Author Orcid Image ;   Nasim Montazeri Ghahjaverestan1, 2 Author Orcid Image ;   Azadeh Yadollahi1, 2 Author Orcid Image ;   Babak Taati1, 2, 3, 4 Author Orcid Image
Article Authors Cited by (16) Tweetations (23) Metrics

Journals

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  9. Li Y, Wang S, Hung Y. A Vision-Based System for In-Sleep Upper-Body and Head Pose Classification. Sensors 2022;22(5):2014 View
  10. Akbarian S, Seyyed-Kalantari L, Khalvati F, Dolatabadi E. Evaluating Knowledge Transfer in the Neural Network for Medical Images. IEEE Access 2023;11:85812 View
  11. Kember A, Selvarajan R, Park E, Huang H, Zia H, Rahman F, Akbarian S, Taati B, Hobson S, Dolatabadi E, Fernandes C. Vision-based detection and quantification of maternal sleeping position in the third trimester of pregnancy in the home setting–Building the dataset and model. PLOS Digital Health 2023;2(10):e0000353 View
  12. Muñoz Rojo M, Pramono R, Devani N, Thomas M, Mandal S, Rodriguez-Villegas E. Validation of Tracheal Sound-Based Respiratory Effort Monitoring for Obstructive Sleep Apnoea Diagnosis. Journal of Clinical Medicine 2024;13(12):3628 View
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