{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:NF2SXPA7ZAE2CB43665F37RN56","short_pith_number":"pith:NF2SXPA7","schema_version":"1.0","canonical_sha256":"69752bbc1fc809a1079bf7ba5dfe2defbce99549fde9485efc7d53fcb301a4f9","source":{"kind":"arxiv","id":"2102.12245","version":1},"attestation_state":"computed","paper":{"title":"Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Eoin Brophy, Geraldine Boylan, Maarten De Vos, Tomas Ward","submitted_at":"2021-02-24T12:11:23Z","abstract_excerpt":"Ischemic heart disease is the highest cause of mortality globally each year. This not only puts a massive strain on the lives of those affected but also on the public healthcare systems. To understand the dynamics of the healthy and unhealthy heart doctors commonly use electrocardiogram (ECG) and blood pressure (BP) readings. These methods are often quite invasive, in particular when continuous arterial blood pressure (ABP) readings are taken and not to mention very costly. Using machine learning methods we seek to develop a framework that is capable of inferring ABP from a single optical phot"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2102.12245","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-02-24T12:11:23Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"acefafcb3deadaa9cae4089ae41528309e8ce194c0e776b4faff7b8b5b7f0dc4","abstract_canon_sha256":"47dcccb056faf6fe21c9d0335d63b97a7b90e3d68959370ae59d63a785a75ed3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:18:10.913094Z","signature_b64":"ygFzWt794x3xRtpX6GEd5yYWhtbMY0FaiKlDw85lnGpfnM9ZQmCG/4PJ6dea1Sck41ilefSCH4YBChpCC8fzBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"69752bbc1fc809a1079bf7ba5dfe2defbce99549fde9485efc7d53fcb301a4f9","last_reissued_at":"2026-07-05T02:18:10.912744Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:18:10.912744Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Eoin Brophy, Geraldine Boylan, Maarten De Vos, Tomas Ward","submitted_at":"2021-02-24T12:11:23Z","abstract_excerpt":"Ischemic heart disease is the highest cause of mortality globally each year. This not only puts a massive strain on the lives of those affected but also on the public healthcare systems. To understand the dynamics of the healthy and unhealthy heart doctors commonly use electrocardiogram (ECG) and blood pressure (BP) readings. These methods are often quite invasive, in particular when continuous arterial blood pressure (ABP) readings are taken and not to mention very costly. Using machine learning methods we seek to develop a framework that is capable of inferring ABP from a single optical phot"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.12245","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2102.12245/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2102.12245","created_at":"2026-07-05T02:18:10.912804+00:00"},{"alias_kind":"arxiv_version","alias_value":"2102.12245v1","created_at":"2026-07-05T02:18:10.912804+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.12245","created_at":"2026-07-05T02:18:10.912804+00:00"},{"alias_kind":"pith_short_12","alias_value":"NF2SXPA7ZAE2","created_at":"2026-07-05T02:18:10.912804+00:00"},{"alias_kind":"pith_short_16","alias_value":"NF2SXPA7ZAE2CB43","created_at":"2026-07-05T02:18:10.912804+00:00"},{"alias_kind":"pith_short_8","alias_value":"NF2SXPA7","created_at":"2026-07-05T02:18:10.912804+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NF2SXPA7ZAE2CB43665F37RN56","json":"https://pith.science/pith/NF2SXPA7ZAE2CB43665F37RN56.json","graph_json":"https://pith.science/api/pith-number/NF2SXPA7ZAE2CB43665F37RN56/graph.json","events_json":"https://pith.science/api/pith-number/NF2SXPA7ZAE2CB43665F37RN56/events.json","paper":"https://pith.science/paper/NF2SXPA7"},"agent_actions":{"view_html":"https://pith.science/pith/NF2SXPA7ZAE2CB43665F37RN56","download_json":"https://pith.science/pith/NF2SXPA7ZAE2CB43665F37RN56.json","view_paper":"https://pith.science/paper/NF2SXPA7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2102.12245&json=true","fetch_graph":"https://pith.science/api/pith-number/NF2SXPA7ZAE2CB43665F37RN56/graph.json","fetch_events":"https://pith.science/api/pith-number/NF2SXPA7ZAE2CB43665F37RN56/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NF2SXPA7ZAE2CB43665F37RN56/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NF2SXPA7ZAE2CB43665F37RN56/action/storage_attestation","attest_author":"https://pith.science/pith/NF2SXPA7ZAE2CB43665F37RN56/action/author_attestation","sign_citation":"https://pith.science/pith/NF2SXPA7ZAE2CB43665F37RN56/action/citation_signature","submit_replication":"https://pith.science/pith/NF2SXPA7ZAE2CB43665F37RN56/action/replication_record"}},"created_at":"2026-07-05T02:18:10.912804+00:00","updated_at":"2026-07-05T02:18:10.912804+00:00"}