{"as_of":"2026-08-10T06:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b1d65cb362973d1148eb2a997a3ab0ac4bae0fa44b49b8b95905601db57ea1fd","coverage":[{"denominator":202,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-26T06:29:47.375464Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2606.23864/citation-record","integrity":"/paper/2606.23864/integrity","json":"/paper/2606.23864/citation-record.json","paper":"/paper/2606.23864"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":null,"venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:49ca9e7d803ed817da1fb1a18c05dd157501d7a844e3f320fd3e11440aa82274","observation_id":"aee31c24-0f01-4817-8b87-312a8b5db0dd","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Machine learning in biosignal analysis from wearable devices,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:3eb444521fc7446fdcad28bca4fa89465c29539141cf6f20b46fa0463a6f8bd9","observation_id":"84861e10-aa4f-461d-9af8-e586b46a668c","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"A novel method for measuring the timing of heart sound components through digital phonocardiography,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:76da4a6456b58951df6feb5af2b968468494e4088aaae11a59ef5b95c28619f4","observation_id":"8341af72-7e03-44bb-a14a-b90e6695115a","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Analysis of ecg and pcg time delay around auscultation sites","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:054e189ed2e36d0c1f0df6827995dba8a72323539820c22ae3477b77bfae0ae5","observation_id":"44497ad2-f79e-4e57-aeaa-d2dd374a3232","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Deep generative models for physiological signals: A systematic literature review,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:5849aa184f65fdecdb964b175565aa7f7f2889494a53ae0167bac1f9b25aa3f5","observation_id":"269bcb96-b66f-42da-a425-455a436d641a","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Sig- nal acquisition of brain–computer interfaces: A medical-engineering crossover perspective review,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:8a18d907b9276833178fdda6eae97b59b4e4b876c1e3706a5744bdbcb4cdff0d","observation_id":"f7fad842-5065-4bbc-9a6d-e23f31d6a96d","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"A survey of few-shot learning for biomedical time series,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:c836d0878848aab2ceb36777c25e36f05bf78d37d2e684fc177a4c563a4b29f5","observation_id":"eec28e5a-b6a1-423d-88d9-15958b84f4d2","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"The impact of inconsistent human annotations on ai driven clinical decision making,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:bc86bf12754015550bbacd58cc5e686604ba3216d75b3b30d7c97c6921ee7a3f","observation_id":"1051d6de-611f-47d2-a2b7-51a18d286446","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"The future of digital health with federated learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:efe5eb09843229e1745e894f5abdfbe615b4d1a00a5cf1e92df7a2d9c8869049","observation_id":"ad1b9026-fc77-425b-882a-813e911e17f2","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Mitigating data quality challenges in ambulatory wrist-worn wearable monitoring through analytical and practical approaches,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:a9d510749780fb67f21f186fa27d595238994afefd5f35b4f91e63714fb2578d","observation_id":"fb4d7e64-c53e-4d6c-a336-12ebf77990c1","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"A review on multi- sensor data fusion for wearable health monitoring,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:cd099d4c73bac3c27b5c2877dff926d3ad9027d729852244146ec618892ffbe7","observation_id":"440b839a-080e-492c-87c6-c891c4589a77","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Time synchronization of multimodal physiological signals through alignment of common signal types and its technical considerations in digital health,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:4604bae520495cfd08b5cae59f2931366ac7653df13ad2f001ab69e2b0119b93","observation_id":"557abb4c-39f0-46a8-a597-c65e785c0f7b","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1406.2661","last_updated":"2014-06-10T18:58:17Z","snapshot_observed_at":"2026-07-06T03:46:00.791740Z","submitted_at":"2014-06-10T18:58:17Z","title":"Generative Adversarial Networks","version":1},"cited_work":{"arxiv_id":"1406.2661","doi":"10.48550/arxiv.1406.2661","metadata_source":"pith","pith_arxiv_id":"1406.2661","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Generative Adversarial Networks","venue":"stat.ML","work_id":"ad1c2a45-7ac7-45e3-9ffa-c83ca5f20ab9","year":2014},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"cited_paper":"/paper/1406.2661","citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:aab02a60b986d5172907c177e3f6d2074292c96c45d820605efb2d253f834f26","observation_id":"d8e8fa75-1594-42a4-997b-9330eaedc47f","resolution":{"observed_at":"2026-07-04T12:39:49.129379Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-07-16T23:20:52.986178+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-16T23:20:52.986178+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Auto-encoding variational bayes,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:550f36e2b18a5d7815bea0727e64d5a0b8124c409f46daa8b66f45efd5c320c0","observation_id":"f4719607-df43-461b-afaf-036ef3a1189a","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Denoising diffusion probabilistic models,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:c2869967185a9a8ee69df8b9ea4937e19147b30b6411e78c6c458b93203dbdc4","observation_id":"63bdef8f-c328-473f-a3c5-e7067c6fdeb0","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Wavenet: A generative model for raw audio,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:d6b0652bfc011ecc0c3618b5dbf11c9931426f0a2f4ca63dd507d9de6abd0e3f","observation_id":"f8b4a30a-3a17-4294-b6e2-b35c9931160d","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Generative ai models in time-varying biomedical data: Scoping review,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:45e98354ec69a8af16cef4a388e1c26a5943edcb24fa6d24d60cd055c48f09cd","observation_id":"a70933e1-df19-4678-8dbb-da4efaac4969","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"A review on generative ai models for synthetic medical text, time series, and longitudinal data,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:a27976111e478b09d0536e2dbe95df592d5634d33ab89828752d2c5ba637fc9a","observation_id":"c1f49109-ccb5-4467-9a18-17717375b60a","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Generative adversarial networks in electrocardiogram synthesis: Recent developments and challenges,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:8485a36115185aaad4ab923e00fd25d0a62f89f285100073f77312152ea2973f","observation_id":"411698c5-c7f5-4ad2-bbfb-5ef51098fdc8","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Synthetic ecg signals generation: A scoping review,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:89e879a3ac8c44456a1d418613bb24f045bc50b91ba682111d2b691a1e83deae","observation_id":"cf8e16f9-3d1c-44b4-8da7-b1270226d9a5","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2606.02448","last_updated":"2026-06-01T16:19:22Z","snapshot_observed_at":"2026-08-04T13:12:21.982995Z","submitted_at":"2026-06-01T16:19:22Z","title":"Diffusion-Based Heart Sound Generation: Evaluation with Physiological Signal Metrics, Classifiers, and Expert Listening","version":1},"cited_work":{"arxiv_id":"2606.02448","doi":null,"metadata_source":"pith","pith_arxiv_id":"2606.02448","snapshot_observed_at":"2026-07-04T12:39:49.125131Z","title":"Diffusion-Based Heart Sound Generation: Evaluation with Physiological Signal Metrics, Classifiers, and Expert Listening","venue":"eess.SP","work_id":"3f7ff3a0-dc14-4202-a96b-fb02b3d6f2ef","year":2026},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"cited_paper":"/paper/2606.02448","citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:80eca144a9a41d0b96093cc9fe73584a1215b5c9e1ea809ec8baa13a44779263","observation_id":"dc346f74-dc4f-4972-8b73-563c100b4373","resolution":{"observed_at":"2026-07-04T12:39:49.126620Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Domain-adversarial pretrained encoder for ecg-based chagas disease screening,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:d71530bf905421d7450e4f62177ec09df75743edcf188c1cc0cf211c277f3cf4","observation_id":"31637971-bd96-4d6f-b16e-b1ddc37cc9bf","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Paroxysmal atrial fibrillation detection by combined recurrent neural network and feature extraction on ecg signals,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:bc071faa7dbbd58998d70532b5fc5fe37188fabc04909f142b463f609d324e30","observation_id":"6d2e6845-1a8e-43b6-b0ef-46777ead3b5b","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Cardiogan: Attentive generative adversarial network with dual discriminators for synthesis of ecg from ppg,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:05b788495cdc2d4bc0c767e9f1f6cf8747f4a3e8b9f9d08ba1e48aecb9c4a8d0","observation_id":"f73906a4-9f5c-40b6-b9f4-cc8370234e62","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"The impact of the mit-bih arrhythmia database,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:7e1d09f0635eb4c1f268a8a42b3e51e35a6a0cc1916420c1037e4750425c0cba","observation_id":"0bf1b151-61f1-44f0-ad44-0c402425e7d3","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"PTB-XL, a large publicly available electrocardiography dataset,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:e5363c51351e44fff1143117681da19376c96a87da289721ebe36cea19c83aef","observation_id":"80bc41e5-fe19-4326-a4c8-ad4ae325fad1","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"A large-scale multi-label 12-lead electrocardiogram database with standardized diagnostic statements,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:aaa54450a7615375a3a32b7a9d1ccd7ff90523bda9169c2d4f0dcf761821f5f6","observation_id":"9c06af6f-7d33-4258-962d-e054f8e261e5","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Photoplethysmography and its application in clinical physi- ological measurement,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:a56ed4ddd409f97f34a693423433f16ecb2fd7339d042874562e2f1cfa022675","observation_id":"86ebe5c2-190c-42b3-9558-52b03c0d6661","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Photoplethysmogram analysis and applications: An integrative review,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:2fbef9de346484c6b1d234c3ff3ff94d5d7c173f52f4a2fe81032ec7131689ad","observation_id":"4a51b2d6-1c0e-4542-a908-849d0f8eba5c","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"A review on wearable photoplethysmography sensors and their potential future applications in health care,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:7400139988dcc1bfb6298874c5455b212ee7a73d1b5c6a981df6878b19ef51ad","observation_id":"85fdefc3-c7f3-486e-879a-360c5c90deef","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Wearable photoplethysmography for cardiovascular monitoring,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:3726c682c56c5046dc9f8f70526b3ce77b5bf8b6fbe6b52b8e252154c52e7835","observation_id":"94f6a4a8-6a4b-49bd-8f36-765c2b3b874b","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Photoplethysmography in wearable de- vices: A comprehensive review of technological advances, current challenges, and future directions,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:8a4f7f0eb1c65407fcba3f0a34d0c266bb15007a05c0d31d49fb1dca0697934e","observation_id":"1fe85f2c-7eaa-488e-a56c-843e502aff67","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Photoplethysmographic sensors: Potential and limitations,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:51cba0c81f215063769f287eff6f02e17dbc255ea60340ed50ccbcf5fa67c0c9","observation_id":"00541cfd-c93d-4800-a3fc-48e647bfa224","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Reliable wrist PPG monitoring by mitigating poor skin sensor con- tact,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:5cd78c5e4ca3d15eb7ce15cdcb4b41815185534b8cbd2de13a94fd53cb420c49","observation_id":"c1f5ffd1-acf8-47f7-8fbe-443311b9fd92","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Establishing best practices in photoplethysmography signal acquisition and processing,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:c2e83c430defdcd600dc9ad0e1b1a9505beab5bbfd24f2cabcb1afc44c73c585","observation_id":"6171ef7d-36c5-4dc9-b3aa-4237fe3d412d","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Fhrgan: Generative adversarial networks for synthetic fetal heart rate signal generation in low-resource settings,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:ba452fd06e6bf8ef801c110239648d180fcc2860654a722ca3f6159f6c67a508","observation_id":"411d754e-a07e-44c6-864e-ca9a04785ba6","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Figo con- sensus guidelines on intrapartum fetal monitoring: Cardiotocography,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:eb552e832024d2c45bb57dd68b0a6d24a53bebf288ac69ec11a9f8756f59bac2","observation_id":"7ceb789e-0a1e-4e04-a0a3-26667653ee84","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Parametric modelling of cardiac system multiple mea- surement signals: An open-source computer framework for perfor- mance evaluation of ecg, pcg and abp event detectors,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:8da6a7bb1a0ae3a2ed9c315fefba0b97d63a8854e774e3e9825130d2186a8862","observation_id":"c3b8e40b-8812-48f0-b824-9f05f8747fc5","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"The effect of signal duration on the classification of heart sounds: A deep learning approach,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:949258c02381469ede1061c026c6bb6f6578ccb3933815b3c6854fc3b5279569","observation_id":"1a45ac4a-b347-4a53-85f1-4335e81f31c4","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Hierarchical multi- scale convolutional network for murmurs detection on pcg signals,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:3980f6d838941fbf857937dc2405bfcd3764e134e8c105de84a2d0c8a767a8ce","observation_id":"f26f40e8-c715-44fc-a9cf-58b3efa4a0ba","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Time-frequency distributions of heart sound signals: A comparative study using convolutional neural networks,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:e3ae002de53d2c5f11df56f8c6d72a4c807d67aea3c39595f9f4e0fcc4c94654","observation_id":"d22b179e-35a7-46c8-9f59-7792cf3ce117","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Signal statistics of heart sound recordings: A comparative study between smartphones and electronic stethoscopes,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:47c7f7d74f25d22d24de6d72938ed119acd5bfeb49f1c1e204624f03edec190e","observation_id":"e4a43867-e68d-4452-aed2-28a9fc700040","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Classifica- tion of heart sound recordings: The physionet/computing in cardiology challenge 2016,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:25aa11ea174c87339b815ae276d90856b3bd822b0c5ea3faf620d907dff8bb5b","observation_id":"7b5a2f10-6fe0-440b-8ee1-120a88829d6c","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"The CirCor DigiScope phonocardiogram dataset,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:865482bddab785c43b9c485074d27d973d696a58d825077542990fcd8e870b8b","observation_id":"a82fae1c-d660-4012-a43d-5c08b04025d3","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Virtual electroencephalogram acquisition: A review on electroencephalogram generative methods,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:81667d39492e5189b7d0efbbb46444670e2cf764622063c6b37698e946845a87","observation_id":"7b8c2353-d944-41c0-91e8-8dd44a9a0d4b","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Eeg and meg: Relevance to neuroscience,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:72a112065f19d58426dad71f1236644cfba931e07bd74712572ebe4490ec56da","observation_id":"9ff555e2-4152-4892-831c-42c141274f5f","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Recent progress in wearable brain–computer interface (BCI) devices based on electroencephalogram (EEG) for medical applications: A review,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:6181af28562b0866db25ca675f19d0a446a317b4384b4b00970288681496532e","observation_id":"f98b3682-1006-4d18-bda7-5da1ba3f3981","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Spatial and temporal resolutions of EEG: Is it really black and white? a scalp current density view,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:854e91731cf0ee6308b6587dc61b90472d9b4d5f431b2753591de8a63f7da178","observation_id":"515fb7c4-871b-4979-b020-c3d1070dca7f","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Motion artifact removal techniques for wearable eeg and ppg sensor systems,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:1349022847bc451cb3bf2c10287d2196df517907914d606359875afdb04ef17a","observation_id":"850a357f-42d8-4571-ab82-119cf0e5fab7","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Promises and limitations of human intracra- nial electroencephalography,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:8bc3c0cb5c71e4f33c0b34ffe422364da3b55fce962c5e9b984a42677fe6d1c1","observation_id":"0483bd67-3f95-4c1b-8587-98483d2068c3","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"E2sgan: Eeg-to-seeg translation with generative adversarial networks,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:1917ca2278496023b01af5b16c1d70d4ee1d4db930d9b537a28fa06b1d31a1bb","observation_id":"621ae053-fc0a-4ff4-8fad-c8e22250373f","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Magnetoencephalography for brain electrophysiology and imaging,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:0eea119d73c43dac7586d2fa4428701e39e7f6ab975bfd7d4dc42037c8f7883b","observation_id":"c1657b9b-b816-4e3f-bd89-9f242199133e","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Best practices for fNIRS publications,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:7c2ef639b2d3b35756aaa69ab9cc6a4dc27838e8fba204e2d7936df9fc8eb7cc","observation_id":"8a1de6d9-c478-4376-ab51-a64a687ad75a","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Optimizing spatial specificity and signal quality in fNIRS,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:d1ee0ebef50cb2a55644ae7ab9d714545de8c79fcc1ae91b4a956a2b41aeb8f3","observation_id":"f043b81f-9735-4be5-b48f-384f9338de47","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Virtual eeg-electrodes: Convolutional neural networks as a method for upsam- pling or restoring channels,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:99997f80ddc558b32612dda818119dc4215deca0e64f330fd3cf5701bb13e10f","observation_id":"91c7170c-e024-4993-ab4b-cd3db4c9db78","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Evaluating the impact of input noise and erp-based penalties on the physiological plausibility of eeg generation using wgan-gp,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:538028665f0facbe6bc4e2a3ae16479d6fcb4102a806acb433c5d7cc7f6ceb01","observation_id":"11502d15-1342-49a5-bd0e-da162545721e","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Surface emg in clinical assessment and neurorehabilitation: Barriers limiting its use,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:2295fbc700eba63893a1a601374505e3576bede86decc69d879ea87f8b0141e1","observation_id":"401af038-aaa9-4075-a698-55b59c41addd","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"On the usability of intramuscular emg for prosthetic control: A fitts’ law approach,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:41e93b21ed514c49a8730d6ffc2199bc813a39f620ad9e4296c39417544ff8e1","observation_id":"10d25f34-0d49-4fee-b3ff-5329e030e7cc","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Surface electromyog- raphy as a natural human–machine interface: A review,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:370c471fc0c128b0938f9fc93f681af9b7c54c1bab0fae2f9fbe9414f99da12e","observation_id":"c617e6eb-cbba-464b-9b5e-6ae644ffa096","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Chatemg: Synthetic data generation to control a robotic hand orthosis for stroke,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:1b34a8656762fbefff384c707aa9447db330e83ef3fff3f089739d594e3f882d","observation_id":"953b22a9-30fe-43db-abba-7d6847f97ba4","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Emg-based hand gesture classifier robust to daily variation: Recursive domain adversarial neural network with data synthesis,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:f66d93cec032ad94ffc69d5782c1b694c5ac59a1764143f3e79b54ac4df387ab","observation_id":"be177371-2a01-4eb8-a827-66334c565e31","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Surface electromyography signal processing and classification techniques,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:aae17f45f0c361d1ac5bf99bc33c52d007f1aed885ad0c16085541f538a7364b","observation_id":"f9c630a4-ec06-4c2b-acbe-d83436f4318d","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"A novel semg data augmentation based on wgan-gp,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:27d7af908f07886eea827f51ba5218696b1567b9568201b2f7139dd90ce30c58","observation_id":"ca71f66f-835b-460c-9f25-51a635091c65","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Deep convolutional genera- tive adversarial network-based emg data enhancement for hand motion classification,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:979f08182287df16c782801c58b70e94e7ad322f10497a1605c3671e6359634e","observation_id":"0fe5d87b-f212-40c7-b998-43cac9955b58","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Conditional GAN based augmen- tation for predictive modeling of respiratory signals,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:e5bd6502791aeb992b73974748913aacafe63d9a2eaa539841b15d028011f027","observation_id":"64a5f8ad-8628-4193-b9ca-672076459ede","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"An end-to-end and accurate PPG-based respiratory rate estimation approach using cycle generative adversarial networks,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:84bb2a09b243c5d3f5cb2274890f97f7de40516d7463c873cb6c9d06f6e2119e","observation_id":"362dac04-d00c-4eac-873a-ce2390da43dd","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Data augmen- tation using variational autoencoders for improvement of respiratory disease classification,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:5ca78a595a9296ae8b09901db5fc4c6108e7fd77ccf0b2ea649948d7a9c9b135","observation_id":"49798085-e003-4a89-940f-9fd1f0cd3e5c","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"A conditional GAN for generating time series data for stress detection in wearable physiological sensor data,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:68851c98a656c27aa2b468652f90d3076cd9144e3a1f38a1b2b2b84cac28156a","observation_id":"174048a5-c75b-478a-8d95-a319490a67b9","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Generating synthetic health sensor data for privacy-preserving wearable stress detection,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:725af734002c5f8750a492bd33e74023c7c1030d4943b17f8624fc1e48d275b5","observation_id":"20f667e4-fa6b-4a04-9bd2-aff65cbafb89","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Electroocu- lography signal generation with conditional diffusion models for eye movement classification,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:65b11552df445e958bf687c9a97124d97175a98906952d69d6fd05cdf1ec8d60","observation_id":"262ec353-bfca-465a-944f-c40ed0468966","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Respiratory rate: The neglected vital sign,","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:74e121e793e8837c9b6d0755b72ae3aeb0cb9cfcad3b38bcb190f023491fe1a4","observation_id":"eb803dc8-f194-446b-b015-4318be0abadf","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Advances in respiratory monitoring: A comprehensive review of wearable and remote tech- nologies,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:06176d70bcc292b6a5303a97a9b8ad6296e5136927be25994b824d71430da079","observation_id":"80463aa4-4a69-4ea2-89d4-fb1cce485968","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Comparison between embroidered and gel electrodes on ecg-derived respiration rate,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:ee6379961d3c4649c06820de83bf369a6175754bdd4f79862163bbbb58bb6cc4","observation_id":"a1970e73-1825-4260-97b8-ef417832c222","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Estimation of the respiratory rate from localised ecg at different auscultation sites,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:c29bf7aec47ef14719238067c141627fff6cd1e1e68b7ccf363480293fae031a","observation_id":"c0a2371c-1ad6-4877-ac17-3c418412b3a4","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Detect- ing moments of stress from measurements of wearable physiological sensors,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:2974ca85a5c447a80eb4c74ef14587c7630c5c00c7de8e16d0ed982ff9324473","observation_id":"3b10c40c-0753-46dc-88ff-a54369804ce1","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Wrist-based electrodermal activity monitoring for stress detection: A scoping review,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:51fb4270dcf17d7b847931a62b82bb7619a1c0d41504af4f88d78dd2dc3503b9","observation_id":"79a9d940-5bf6-4285-9c5e-544e4295e3e7","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"The role of continuous glucose monitoring in physical activity and nutrition management: Perspectives on present and possible uses,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:16efc0f781a8abbcd69a58561d8963b163f07f43a21ad79c8590348bfcb2862f","observation_id":"3db304d7-2af2-4f06-ae25-2d15b46d9088","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Uncovering personalized glucose responses and circadian rhythms from multiple wearable biosensors with bayesian dynamical modeling,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:2ce2c627ae6a380386c66a5a2a2036a4283644246d957481903904f181e5ba44","observation_id":"c43a52e3-b179-45a6-b360-788d56420523","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"A conditional generative adversarial network for synthesis of continuous glucose monitoring signals,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:239de8467717708bcbeca65a28f8e850376623d9e3f64aa50ada5e0d7f4c9745","observation_id":"e6195482-2753-4c0a-95c8-1b9a7934aabe","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"GluGAN: Generating per- sonalized glucose time series using generative adversarial networks,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:36dba1338f4217deefc41ee77c994f44139e5aca6f72ddc771104c7e7e8e37a8","observation_id":"6d8d51f2-10f2-4c1b-97fa-3390039ac640","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Conditional synthesis of blood glucose profiles for T1D patients using deep generative models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:6e7b9a2c421de2a85785bf82804c6323ed0d34cbfc3ac46f60bb7bab7f70c49f","observation_id":"afe6936a-a41e-4023-8bda-94d32236401a","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Gen- erative adversarial network-based data augmentation for improving hypoglycemia prediction: A proof-of-concept study,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:8f3ae63b441ba314904f9c7b380bea7d868f6f869b4045fd772ef46e5650901b","observation_id":"8999bd97-8858-47dd-ae78-56bf0dd7161a","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"DeepGANnel: Synthesis of fully annotated single molecule patch-clamp data using generative adversarial networks,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:a90be142e82ce3512c2739438bde225e078779a9e033fbba3f21bcee480aba0f","observation_id":"ea187b55-428e-4a4f-a912-55ce3cc9d239","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Denoising and decoding spontaneous vagus nerve recordings with machine learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:701d75a2b477fb0c05fb7c4f38cb4c42b3315103403d6429ae85cd3b5ec61319","observation_id":"aa8d2905-d099-456a-bf75-83d00b51cf51","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Data imbalance in cardiac health diagnostics using cecg-gan,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:d16cd0bbb5664592caed39b622dc6aefcaeceb6889a564d507188f74e827a377","observation_id":"7fc1ee82-b39e-48e5-aef4-fd46474f8a9a","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Principal component conditional generative adversarial net- works for imbalanced ecg classification enhancement,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:6dfd3f41c2551be7aa0bc91b5905046532b7821511383746d9f1c145e67f3df9","observation_id":"7fef196a-a4da-4cd9-bf54-8f1332368bc5","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Synthetic ecg signal generation using generative neural networks,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:098ea14d8d7394e548cacf77bc49464983f1da48bd43b51c9a5b2d50e12d6f24","observation_id":"ef715b43-d951-45a9-a985-87823cc897a0","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"A few-shot learning-based eeg and stage transition sequence generator for improving sleep staging performance,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:389969da555d3b26f4d4b02a3e973986f911c64eb34b0698efa95af14384d865","observation_id":"000e88dd-1d6b-4d22-96dd-4aa9bffbdd14","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Multichannel high noise level ecg denoising based on adversarial deep learning approach,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:7d9eed2fe88859d756d370e267f173de54de82cab3ff4e922f2f5f17472daf72","observation_id":"ff92f2f7-9191-4c07-8700-39f5064357fb","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Eeg channel reconstruction using convolutional neural networks in limited bcis: A proposed method for neuromarketing applications,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:79cd77c5033a48715d9da3c5e3a28a45cfda2b42cd566614158e6262c90ac453","observation_id":"8b10c1e0-9d87-48a9-a500-0722e545240e","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Filling missing values on wearable-sensory time series data,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:faa423988cc8419b2869dd59eb17d5a91b6d57a87edb1232ceabd3cd8a3d2978","observation_id":"7fb89811-c60a-4f1e-a0b8-d3dfb05c2338","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Reducing noise, artifacts and interference in single-channel emg signals: A review,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:9ba6984dea77e97de165e53392f2c0f2bd4900d0c46303904e56fbb85f9f5a78","observation_id":"f3b02be8-330d-4793-9a11-855d10ee1091","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Region-disentangled diffusion model for high-fidelity ppg-to-ecg translation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:dbd68c0cf95f4df974a545a87935d57c14bd99712575c3aa606157df0f12bf72","observation_id":"5e5522d6-83ae-420e-83b4-cb8b9b4ab9eb","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Crossl: Cross-modal self-supervised learning for time- series through latent masking,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:afe7a03c0ce5c2112076b0f7a159eb201054010459241c6c9ea864c89fa8d079","observation_id":"12440429-adba-400e-b23d-b1c8f918b3f5","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Synthetic ecg signals generation: A scoping review,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:2b5a31b2eee83f2cb148eb2f877628fac071b4c1641cbbe36a5a04135f366c24","observation_id":"0a3d516d-d395-46a7-bdb3-bc85f0f35345","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Ecgan: Self-supervised generative adver- sarial network for electrocardiography,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:c2dcb043f255a3b0a70138e0e9f3d346fccb028ec443634b20a4692ace272f5e","observation_id":"5850555c-d04f-40de-9223-3079c69841d9","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Transdiffecg: Semantically controllable ecg synthesis via transformer-based diffusion modeling,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:30396f248f09133a4d777d2498b4a2d70fdf6d2b0a1b789460b7384d55ed2cd8","observation_id":"7f5815a4-c0c4-4329-84fc-6d5686d6ad04","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Plethaugment: Gan-based ppg augmentation for medical diagnosis in low-resource settings,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:fb29358886c9f5aa370d606f347ebe91f15cdb03b4f24df661a48d4c6cc73bb0","observation_id":"55c3fb0c-4ba5-4e5d-95f6-c8648e54b539","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Biosignal data augmentation based on generative adversarial networks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:6830d366e21eef2c4dc66e1beb18d98bcaaf0eb570621bd1df7f12494421451d","observation_id":"054b95ce-f614-4604-bea3-41aaf7262589","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T06:29:47.375464Z","title":"Sgecg: A stargan- based framework for intelligent ecg generation and augmentation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-06-26T06:29:47.375464Z"},"links":{"citing_paper":"/paper/2606.23864"},"observation_digest":"sha256:73acfc5a3e1bf96adfeb1cb1634f9fe2bfce34e9849a49e441707acc452446e6","observation_id":"51292d59-1c0f-4da5-9b67-010a3a5b220d","resolution":{"observed_at":"2026-06-26T06:29:47.375464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.23864","last_updated":"2026-06-22T19:03:45Z","latest_version":1,"primary_category":"eess.SP","snapshot_observed_at":"2026-08-03T17:45:33.397556Z","submitted_at":"2026-06-22T19:03:45Z","title":"Generative Modeling for Physiological Signals"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":98,"verified_exact":2,"verified_fuzzy":0},"total_outbound_references":202},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 100 of 202 outbound references and 0 inbound Pith citation observations for arXiv:2606.23864."}