Paper
WatchSleepNet: A Novel Model and Pretraining Approach for Advancing Sleep Staging with Smartwatches
Proceedings of Machine Learning Research (CHIL / Health, Inference, and Learning) vol. 287
Open code
TL;DR
Open-source 3-stage (Wake/NREM/REM) sequence-to-sequence model (ResNet + TCN + BiLSTM + self-attention) using IBI as a shared representation across PSG ECG and wrist PPG. Pretrains on large PSG IBI (SHHS/MESA lineage) and fine-tunes on Empatica E4 wrist PPG (DREAMT). Reported REM F1 0.642 ± 0.072 and Cohen’s κ 0.684 ± 0.051, with public model/code release.
- Form factor: wrist
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Tasks & signals
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Cite this paper
APA
Wang, W. K., Chen, B., Yang, J., Jeong, H., Hershkovich, L., Islam, S. M. M., Liu, M., Roghanizad, A. R., Shandhi, M. M. H., Spector, A. R., & Dunn, J. (n.d.). WatchSleepNet: A Novel Model and Pretraining Approach for Advancing Sleep Staging with Smartwatches. Proceedings of Machine Learning Research vol. 287.
BibTeX
@inproceedings{wangwatchsleepnet,
title = {{WatchSleepNet: A Novel Model and Pretraining Approach for Advancing Sleep Staging with Smartwatches}},
author = {Wang, Will Ke and Chen, Bill and Yang, Jiamu and Jeong, Hayoung and Hershkovich, Leeor and Islam, Shekh Md Mahmudul and Liu, Mengde and Roghanizad, Ali R. and Shandhi, Md Mobashir Hasan and Spector, Andrew R. and Dunn, Jessilyn},
booktitle = {Proceedings of Machine Learning Research vol. 287}
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