InsightSleepNet: the interpretable and uncertainty-aware deep learning network for sleep staging using continuous Photoplethysmography.

"our source codes used for this study are available from the github repository ( https://github com/borumnam/insightsleepnet/blob/main/insightsleepnet ipynb )."

"Declarations Ethics approval and consent to participateIn this study, a total of three polysomnography datasets were used (MESA, CFS, CAP) and this study was reviewed and approved by the Hanyang University Institutional Review Board (#HYUIRB-202309-009), and the requirement for informed consent was waived by the institution. All methods were carried out in accordance with relevant guidelines and regulations. Consent for publicationNot applicable. Competing interestsThe authors declare no competing interests. Competing interests The authors declare no competing interests."

"Funding This work was supported by (1) ‘Smart HealthCare Program’ funded by the Korean National Police Agency (KNPA, Korea). [Project Name: Development of wearable system for acquiring lifelog data and customized healthcare service for police officers/ Project Number: 220222 M04] (2) the Bio & Medical Technology Development Program of the NRF funded by the Korean government, MSIT (2021M3E5D2A01022397). Medical review, review and editing of manuscripts. All authors read and approved the final manuscript."