{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T22:39:09Z","timestamp":1783550349781,"version":"3.55.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T00:00:00Z","timestamp":1712188800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T00:00:00Z","timestamp":1712188800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"DOI":"10.1038\/s41746-024-01086-9","type":"journal-article","created":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T05:02:08Z","timestamp":1712206928000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Challenges and opportunities of deep learning for wearable-based objective sleep assessment"],"prefix":"10.1038","volume":"7","author":[{"given":"Bing","family":"Zhai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Greg J.","family":"Elder","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4049-9291","authenticated-orcid":false,"given":"Alan","family":"Godfrey","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,4,4]]},"reference":[{"key":"1086_CR1","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.jsmc.2020.02.005","volume":"15","author":"BJ Murray","year":"2020","unstructured":"Murray, B. J. Subjective and objective assessment of hypersomno- lence. Sleep Medicine Clinics 15, 167\u2013176 (2020).","journal-title":"Sleep Medicine Clinics"},{"key":"1086_CR2","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1109\/TNSRE.2019.2896659","volume":"27","author":"H Phan","year":"2019","unstructured":"Phan, H., Andreotti, F., Cooray, N., Ch\u00b4en, O. Y. & De Vos, M. Seqsleepnet: end-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging. IEEE Trans. Neural Syst. Rehab. Eng. 27, 400\u2013410 (2019).","journal-title":"IEEE Trans. Neural Syst. Rehab. Eng."},{"key":"1086_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3397325","volume":"4","author":"B Zhai","year":"2020","unstructured":"Zhai, B., Perez-Pozuelo, I., Clifton, E. A. D., Palotti, J. & Guan, Y. Making sense of sleep: multimodal sleep stage classification in a large, diverse population using movement and cardiac sensing. Proc. ACM Interactive, Mobile, Wearable Ubiquitous Technol. 4, 1\u201333 (2020).","journal-title":"Proc. ACM Interactive, Mobile, Wearable Ubiquitous Technol."},{"key":"1086_CR4","doi-asserted-by":"publisher","first-page":"2027","DOI":"10.1088\/0967-3334\/36\/10\/2027","volume":"36","author":"P Fonseca","year":"2015","unstructured":"Fonseca, P. et al. Sleep stage classification with ecg and respi- ratory effort. Physiol. Measur. 36, 2027 (2015).","journal-title":"Physiol. Measur."},{"key":"1086_CR5","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-021-00440-5","volume":"4","author":"M Perslev","year":"2021","unstructured":"Perslev, M. et al. U-sleep: resilient high-frequency sleep staging. NPJ Digit. Med. 4, 72 (2021).","journal-title":"NPJ Digit. Med."},{"key":"1086_CR6","first-page":"877","volume":"38","author":"R Xiaoli Chen","year":"2015","unstructured":"Xiaoli Chen, R. et al. Racial\/ethnic differences in sleep disturbances: the multi-ethnic study of atherosclerosis (mesa). Sleep 38, 877\u2013888 (2015).","journal-title":"Sleep"},{"key":"1086_CR7","first-page":"1077","volume":"20","author":"SF Quan","year":"1997","unstructured":"Quan, S. F. et al. The sleep heart health study: design, rationale, and methods. Sleep 20, 1077\u20131085 (1997).","journal-title":"Sleep"},{"key":"1086_CR8","doi-asserted-by":"publisher","first-page":"205520762311812","DOI":"10.1177\/20552076231181239","volume":"9","author":"J Graeber","year":"2023","unstructured":"Graeber, J. et al. Technology acceptance of digital devices for home use: Qualitative results of a mixed methods study. Digital Health 9, 20552076231181239 (2023).","journal-title":"Digital Health"},{"key":"1086_CR9","doi-asserted-by":"publisher","first-page":"zsaa097","DOI":"10.1093\/sleep\/zsaa097","volume":"43","author":"PJ Arnal","year":"2020","unstructured":"Arnal, P. J. et al. The dreem headband com- pared to polysomnography for electroencephalographic signal acquisition and sleep staging. Sleep 43, zsaa097 (2020).","journal-title":"Sleep"},{"key":"1086_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3130924","volume":"1","author":"C-Y Hsu","year":"2017","unstructured":"Hsu, C.-Y. et al. Zero-effort in-home sleep and insomnia monitoring using radio signals. Proceedings of the ACM on Interactive, Mobile, Wear- Able Ubiquitous Technol. 1, 1\u201318 (2017).","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wear- Able Ubiquitous Technol."},{"key":"1086_CR11","doi-asserted-by":"publisher","first-page":"13900","DOI":"10.1109\/JIOT.2021.3068798","volume":"8","author":"B Yu","year":"2021","unstructured":"Yu, B. et al. Wifi-sleep: sleep stage monitoring using commodity wi-fi devices. IEEE Internet Things J. 8, 13900\u201313913 (2021).","journal-title":"IEEE Internet Things J."},{"key":"1086_CR12","unstructured":"Goodfellow, I., Bengio, Y. & Courville, A. Deep learning. (MIT Press, 2016)."},{"key":"1086_CR13","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-023-00802-1","volume":"6","author":"MR Patterson","year":"2023","unstructured":"Patterson, M. R. et al. 40 years of actigra- phy in sleep medicine and current state of the art algorithms. NPJ Digital Med. 6, 51 (2023).","journal-title":"NPJ Digital Med."},{"key":"1086_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3494961","volume":"5","author":"B Zhai","year":"2021","unstructured":"Zhai, B., Guan, Y., Catt, M. & Pl\u00a8otz, T. Ubi-sleepnet: Ad- vanced multimodal fusion techniques for three-stage sleep classification using ubiquitous sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technol. 5, 1\u201333 (2021).","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technol."},{"key":"1086_CR15","first-page":"27730","volume":"35","author":"L Ouyang","year":"2022","unstructured":"Ouyang, L. et al. Training language models to follow instructions with human feedback. Adv. Neural Inform. Process. Syst. 35, 27730\u201327744 (2022).","journal-title":"Adv. Neural Inform. Process. Syst."},{"key":"1086_CR16","doi-asserted-by":"publisher","first-page":"04TR01","DOI":"10.1088\/1361-6579\/ac6049","volume":"43","author":"H Phan","year":"2022","unstructured":"Phan, H. & Mikkelsen, K. Automatic sleep staging of eeg signals: recent development, challenges, and future directions. Physiol. Measur. 43, 04TR01 (2022).","journal-title":"Physiol. Measur."},{"key":"1086_CR17","doi-asserted-by":"publisher","first-page":"051001","DOI":"10.1088\/1741-2552\/ab260c","volume":"16","author":"Y Roy","year":"2019","unstructured":"Roy, Y. et al. Deep learning-based electroen- cephalography analysis: a systematic review. J. Neural Eng. 16, 051001 (2019).","journal-title":"J. Neural Eng."},{"key":"1086_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fphys.2013.00294","volume":"4","author":"E Tobaldini","year":"2013","unstructured":"Tobaldini, E. et al. Heart rate variability in normal and patho- logical sleep. Front. Physiol. 4, 1\u201311 (2013).","journal-title":"Front. Physiol."},{"key":"1086_CR19","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-0244-4","volume":"3","author":"I Perez-Pozuelo","year":"2020","unstructured":"Perez-Pozuelo, I. et al. The future of sleep health: a data-driven revolution in sleep science and medicine. NPJ Digit. Med. 3, 42 (2020).","journal-title":"NPJ Digit. Med."},{"key":"1086_CR20","doi-asserted-by":"publisher","first-page":"721919","DOI":"10.3389\/fdgth.2021.721919","volume":"3","author":"MR Lujan","year":"2021","unstructured":"Lujan, M. R., Perez-Pozuelo, I. & Grandner, M. A. Past, present, and future of multisensory wearable technology to monitor sleep and circadian rhythms. Front. Dig. Health 3, 721919 (2021).","journal-title":"Front. Dig. Health"},{"key":"1086_CR21","doi-asserted-by":"crossref","unstructured":"Kwon, S., Kim, H. & Yeo, W-H. Recent advances in wearable sensors and portable electronics for sleep monitoring. Iscience, 24 (2021).","DOI":"10.1016\/j.isci.2021.102461"},{"key":"1086_CR22","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1046\/j.1365-2869.2001.00263.x","volume":"10","author":"J Trinder","year":"2001","unstructured":"Trinder, J. et al. Autonomic activity during human sleep as a function of time and sleep stage. J. Sleep Res. 10, 253\u2013264 (2001).","journal-title":"J. Sleep Res."},{"key":"1086_CR23","doi-asserted-by":"publisher","first-page":"1918","DOI":"10.1161\/01.CIR.91.7.1918","volume":"91","author":"E Vanoli","year":"1995","unstructured":"Vanoli, E. et al. Heart rate variability during specific sleep stages. Circulation 91, 1918\u20131922 (1995).","journal-title":"Circulation"},{"key":"1086_CR24","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.smrv.2011.02.005","volume":"16","author":"PK Stein","year":"2012","unstructured":"Stein, P. K. & Pu, Y. Heart rate variability, sleep and sleep disorders. Sleep Med. Rev. 16, 47\u201366 (2012).","journal-title":"Sleep Med. Rev."},{"key":"1086_CR25","doi-asserted-by":"publisher","first-page":"1919","DOI":"10.5665\/sleep.3230","volume":"36","author":"P Boudreau","year":"2013","unstructured":"Boudreau, P., Yeh, W. H., Dumont, G. A. & Boivin, D. B. Circadian variation of heart rate variability across sleep stages. Sleep 36, 1919\u20131928 (2013).","journal-title":"Sleep"},{"key":"1086_CR26","unstructured":"Zhao, M., Yue, S., Katabi, D., Jaakkola, T. S. & Bianchi, M. T. Learning sleep stages from radio signals: A conditional adversarial architecture. In International Conference on Machine Learning, pages 4100\u20134109. (PMLR, 2017)."},{"key":"1086_CR27","doi-asserted-by":"publisher","first-page":"036044","DOI":"10.1088\/1741-2552\/ac6ca8","volume":"19","author":"ERM Heremans","year":"2022","unstructured":"Heremans, E. R. M. et al. From unsupervised to semi-supervised adversarial domain adaptation in electroencephalography-based sleep stag- ing. Journal of Neural Engineering 19, 036044 (2022).","journal-title":"Journal of Neural Engineering"},{"key":"1086_CR28","doi-asserted-by":"crossref","unstructured":"Xiao, Q. et al. Self-supervised learning for sleep stage classification with predictive and discriminative contrastive coding. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1290\u20131294 (IEEE, 2021).","DOI":"10.1109\/ICASSP39728.2021.9414752"},{"key":"1086_CR29","doi-asserted-by":"publisher","first-page":"046020","DOI":"10.1088\/1741-2552\/abca18","volume":"18","author":"H Banville","year":"2021","unstructured":"Banville, H., Chehab, O., Hyv\u00a8arinen, A., En- gemann, D.-A. & Gramfort, A. Uncovering the structure of clinical eeg signals with self-supervised learning. J. Neural Eng. 18, 046020 (2021).","journal-title":"J. Neural Eng."},{"key":"1086_CR30","unstructured":"Gidaris, S., Singh, P. & Komodakis, N. Unsupervised representation learning by predicting image rotations. arXiv preprint arXiv:1803.07728 (2018)."},{"key":"1086_CR31","unstructured":"Devlin, J., Chang, M-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)."},{"key":"1086_CR32","unstructured":"Radford, A. et al. Improving language understanding by generative pre-training. (2018)."},{"key":"1086_CR33","first-page":"9","volume":"1","author":"A Radford","year":"2019","unstructured":"Radford, A. et al. Language models are unsupervised multitask learners. OpenAI blog 1, 9 (2019).","journal-title":"OpenAI blog"},{"key":"1086_CR34","first-page":"857","volume":"35","author":"X Liu","year":"2021","unstructured":"Liu, X. et al. Self-supervised learning: generative or contrastive. IEEE Trans. Knowledge Data Eng. 35, 857\u2013876 (2021).","journal-title":"IEEE Trans. Knowledge Data Eng."},{"key":"1086_CR35","unstructured":"Chen, T., Kornblith, S., Norouzi, M. & Hinton, G. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597\u20131607 (PMLR, 2020)."},{"key":"1086_CR36","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S. & Girshick, R. Mo- mentum contrast for unsupervised visual representation learning. In Pro- ceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pages 9729\u20139738 (2020).","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"1086_CR37","doi-asserted-by":"crossref","unstructured":"Chen, X. & He, K. Exploring simple siamese representation learning. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pages 15750\u201315758 (2021).","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"1086_CR38","unstructured":"Zbontar, J., Jing, L., Misra, I., LeCun, Y. & St\u00b4ephane, D. Barlow twins: Self-supervised learning via redundancy reduction. In International Conference on Machine Learning, pages 12310\u201312320 (PMLR, 2021)."},{"key":"1086_CR39","doi-asserted-by":"crossref","unstructured":"Hang Yuan, T. et al. Self-supervised learning of accelerom- eter data provides new insights for sleep and its association with mortality. medRxiv (2023).","DOI":"10.1038\/s41746-024-01065-0"}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01086-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01086-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01086-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T17:10:28Z","timestamp":1731690628000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01086-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,4]]},"references-count":39,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["1086"],"URL":"https:\/\/doi.org\/10.1038\/s41746-024-01086-9","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,4]]},"assertion":[{"value":"12 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 April 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A.G. is a Deputy Editor of npj Digital Medicine and played no role in the internal review or decision to publish this Editorial. The remaining authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"85"}}