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

Clinical Information and Decision Making (1407) Sleep Monitoring, Sleep Quality, Sleep Disorders (183) Machine Learning (1465) Insomnia and Sleep Hygiene (167) Sleep Apnea (26) Tools, Programs and Algorithms (266) New Methods (944) Telehealth and Telemonitoring (1671)

Published on in Vol 23, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26524, first published .
Noncontact Sleep Monitoring With Infrared Video Data to Estimate Sleep Apnea Severity and Distinguish Between Positional and Nonpositional Sleep Apnea: Model Development and Experimental Validation

Noncontact Sleep Monitoring With Infrared Video Data to Estimate Sleep Apnea Severity and Distinguish Between Positional and Nonpositional Sleep Apnea: Model Development and Experimental Validation

Noncontact Sleep Monitoring With Infrared Video Data to Estimate Sleep Apnea Severity and Distinguish Between Positional and Nonpositional Sleep Apnea: Model Development and Experimental Validation

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 (9) Tweetations (6) Metrics

Journals

  1. Chan P, Tay A, Chen D, De Freitas M, Millet C, Nguyen-Duc T, Duke G, Lyall J, Nguyen J, McNeil J, Hopper I. Ambient intelligence–based monitoring of staff and patient activity in the intensive care unit. Australian Critical Care 2023;36(1):92 View
  2. Akbarian S, Nelder M, Russell C, Cawston T, Moreno L, Patel S, Allen V, Dolatabadi E. A Computer Vision Approach to Identifying Ticks Related to Lyme Disease. IEEE Journal of Translational Engineering in Health and Medicine 2022;10:1 View
  3. Wang Y, Chen C, Gu L, Zhai Y, Sun Y, Gao G, Xu Y, Pang L, Xu L. Effect of short-term mindfulness-based stress reduction on sleep quality in male patients with alcohol use disorder. Frontiers in Psychiatry 2023;14 View
  4. KARADÖL İ. OBSTRÜKTİF UYKU APNESİ TESPİTİNDE POLİSOMNOGRAFİYE ALTERNATİF YENİ YÖNTEMLER. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 2023;26(1):295 View
  5. 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
  6. Espinosa M, Ponce P, Molina A, Borja V, Torres M, Rojas M. Advancements in Home-Based Devices for Detecting Obstructive Sleep Apnea: A Comprehensive Study. Sensors 2023;23(23):9512 View
  7. Sattaratpaijit N, Thanawattano C, Leelasittikul K, Pugongchai A, Saiborisut N, Yuenyongchaiwat K, Tepwimonpetkun C, Saiphoklang N. Comparison of sleep position monitoring between NaTu sensor and video-validated polysomnography in patients with obstructive sleep apnea. Sleep and Breathing 2024;28(5):1977 View
  8. Kember A, Zia H, Elangainesan P, Hsieh M, Adijeh R, Li I, Ritchie L, Akbarian S, Taati B, Hobson S, Dolatabadi E. Transitioning sleeping position detection in late pregnancy using computer vision from controlled to real-world settings: an observational study. Scientific Reports 2024;14(1) View