{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/sleep-staging/papers/2","list_of":"/task/sleep-staging","task":"Sleep Staging","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":2,"pages_in_order":2,"rows_per_page":100,"rows":[101,116],"of":116,"counts":{"archive_papers_tagged":116,"with_a_code_link":46,"where_syntology_ran_a_sample":8,"not_listed_spam_title":0,"listed":116,"listed_where_code_ran":8,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":5,"every_run_a_failure_of_syntologys_instrument":3,"listed_with_a_run_with_no_instrument_failure":5,"listed_every_run_a_failure_of_syntologys_instrument":3,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/sleep-staging","prev":"/task/sleep-staging","next":null,"papers":[{"url":null,"slug":"importance-weighting-with-a-adversarial","title":"Importance Weighting with a Adversarial Network for Large-Scale Sleep Staging","date":"2020-06-12","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-automatic-sleep-stage","title":"End-to-End Automatic Sleep Stage Classification Using Spectral-Temporal Sleep Features","date":"2020-05-04","arxiv_id":"2005.05437","repositories_listed":0,"syntology":null},{"url":null,"slug":"personalized-automatic-sleep-staging-with","title":"Personalized Automatic Sleep Staging with Single-Night Data: a Pilot Study with KL-Divergence Regularization","date":"2020-04-23","arxiv_id":"2004.11349","repositories_listed":0,"syntology":null},{"url":"/paper/sleeper-interpretable-sleep-staging-via","slug":"sleeper-interpretable-sleep-staging-via","title":"SLEEPER: interpretable Sleep staging via Prototypes from Expert Rules","date":"2019-10-14","arxiv_id":"1910.06100","repositories_listed":0,"syntology":null},{"url":null,"slug":"embedded-deep-learning-for-sleep-staging","title":"Embedded Deep Learning for Sleep Staging","date":"2019-06-18","arxiv_id":"1906.09905","repositories_listed":0,"syntology":null},{"url":null,"slug":"dealing-with-the-database-variability-problem","title":"Addressing database variability in learning from medical data: an ensemble-based approach using convolutional neural networks and a case of study applied to automatic sleep scoring","date":"2019-06-16","arxiv_id":"1906.06666","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-dreem-headband-as-an-alternative-to","title":"The Dreem Headband as an Alternative to Polysomnography for EEG Signal Acquisition and Sleep Staging","date":"2019-06-10","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-a-flexible-deep-learning-method-for","title":"Towards a Flexible Deep Learning Method for Automatic Detection of Clinically Relevant Multi-Modal Events in the Polysomnogram","date":"2019-05-16","arxiv_id":"1905.08059","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-transfer-learning-for-single-channel","title":"Deep Transfer Learning for Single-Channel Automatic Sleep Staging with Channel Mismatch","date":"2019-04-11","arxiv_id":"1904.05945","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-unifying-bayesian-approach-for-preterm","title":"A unifying Bayesian approach for preterm brain-age prediction that models EEG sleep transitions over age","date":"2018-09-19","arxiv_id":"1809.07102","repositories_listed":0,"syntology":null},{"url":null,"slug":"lstm-knowledge-transfer-for-hrv-based-sleep","title":"LSTM knowledge transfer for HRV-based sleep staging","date":"2018-09-12","arxiv_id":"1809.06221","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-structured-learning-approach-with-neural","title":"A Structured Learning Approach with Neural Conditional Random Fields for Sleep Staging","date":"2018-07-23","arxiv_id":"1807.09119","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-learning-approach-with-an-attention","title":"A Deep Learning Approach with an Attention Mechanism for Automatic Sleep Stage Classification","date":"2018-05-14","arxiv_id":"1805.05036","repositories_listed":0,"syntology":null},{"url":null,"slug":"sleep-stage-classification-based-on-multi","title":"Sleep Stage Classification Based on Multi-level Feature Learning and Recurrent Neural Networks via Wearable Device","date":"2017-11-02","arxiv_id":"1711.00629","repositories_listed":0,"syntology":null},{"url":null,"slug":"sleepnet-automated-sleep-staging-system-via","title":"SLEEPNET: Automated Sleep Staging System via Deep Learning","date":"2017-07-26","arxiv_id":"1707.08262","repositories_listed":0,"syntology":null},{"url":null,"slug":"tractable-fully-bayesian-inference-via-convex","title":"Tractable Fully Bayesian Inference via Convex Optimization and Optimal Transport Theory","date":"2015-09-29","arxiv_id":"1509.08582","repositories_listed":0,"syntology":null}],"record_sha256":"29304232bfa84cc22244f3c002143d6e45bfb09c0c58b5206ed95600e3bab59a","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}