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R. S. Puri, A. G. Athanassiadis, N. Gill, S. S. Sathya, G. Rathod, A. Wahi, G. Satat, M. Majmudar, P. Shah. IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC). pp. 1870-1873. IEEE, (2016). PMID: 28268691
Between 7-18 million Americans suffer from sleep disordered breathing (SDB), including those who suffer from obstructive sleep apnea (OSA). Despite this high prevalence and burden of OSA, existing diagnostic techniques remain impractical for widespread screening. In this study, we introduce a new model for OSA screening and describe an at-home wearable sleep mask (named ARAM) that can robustly track the wearers' sleep patterns. This monitoring is achieved using select sensors that enable screening and monitoring in a form-factor that can be easily self-instrumented. Based on feedback from sleep doctors and technicians, we incorporate the most valuable sensors for OSA diagnosis, while maintaining ease-of-use and comfort for the patient. We discuss the results of preliminary field trials, where both our sleep mask and a commercially available device were worn simultaneously to evaluate our device's robustness. Based on these results, we discuss next steps for the design of the screening system, including analyses techniques that would provide more efficient screening than existing systems.