{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:BAP67NBAFRTA26UQUFOHTEYRON","short_pith_number":"pith:BAP67NBA","canonical_record":{"source":{"id":"2401.05411","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.SP","submitted_at":"2023-12-26T09:14:03Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"4a9356b507749fe5b91d636041873e0222a0a269f8a7a7d7afd2c06f4c0f7ab4","abstract_canon_sha256":"4df065210a4b8156bfac1eda8902f865c6d56e72b6ec4caa3f50414ce2e3c7bf"},"schema_version":"1.0"},"canonical_sha256":"081fefb4202c660d7a90a15c7993117379954ec52453a64e1b74c8c0fbb50fa2","source":{"kind":"arxiv","id":"2401.05411","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.05411","created_at":"2026-07-05T07:32:29Z"},{"alias_kind":"arxiv_version","alias_value":"2401.05411v1","created_at":"2026-07-05T07:32:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.05411","created_at":"2026-07-05T07:32:29Z"},{"alias_kind":"pith_short_12","alias_value":"BAP67NBAFRTA","created_at":"2026-07-05T07:32:29Z"},{"alias_kind":"pith_short_16","alias_value":"BAP67NBAFRTA26UQ","created_at":"2026-07-05T07:32:29Z"},{"alias_kind":"pith_short_8","alias_value":"BAP67NBA","created_at":"2026-07-05T07:32:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:BAP67NBAFRTA26UQUFOHTEYRON","target":"record","payload":{"canonical_record":{"source":{"id":"2401.05411","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.SP","submitted_at":"2023-12-26T09:14:03Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"4a9356b507749fe5b91d636041873e0222a0a269f8a7a7d7afd2c06f4c0f7ab4","abstract_canon_sha256":"4df065210a4b8156bfac1eda8902f865c6d56e72b6ec4caa3f50414ce2e3c7bf"},"schema_version":"1.0"},"canonical_sha256":"081fefb4202c660d7a90a15c7993117379954ec52453a64e1b74c8c0fbb50fa2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:32:29.657452Z","signature_b64":"6Fgqzvph4xhZzKlSp9xQ/qR/X8vvq982GoVegvKeB/agVLRLkGtVaclvoJLTuck11tMRNKs/eN5BbPOp6o+5BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"081fefb4202c660d7a90a15c7993117379954ec52453a64e1b74c8c0fbb50fa2","last_reissued_at":"2026-07-05T07:32:29.656886Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:32:29.656886Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2401.05411","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:32:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nboNSAuQhV4r6/J+jTnEsZZkqzJr10phhHtgSoiYsOZLb6A8FEINgqMdGfrUSzGvcDVIzS0dQ7+epwBgKMByDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T06:38:39.640607Z"},"content_sha256":"aee1d5312f85e21e8abea4f9b761665f7bb9d0f90ce67f337c32e77e5f6ee289","schema_version":"1.0","event_id":"sha256:aee1d5312f85e21e8abea4f9b761665f7bb9d0f90ce67f337c32e77e5f6ee289"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:BAP67NBAFRTA26UQUFOHTEYRON","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"RawECGNet: Deep Learning Generalization for Atrial Fibrillation Detection from the Raw ECG","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.SP","authors_text":"Joachim A. Behar, Kenta Tsutsui, Leif S\\\"ornmo, Noam Ben-Moshe, Shany Biton","submitted_at":"2023-12-26T09:14:03Z","abstract_excerpt":"Introduction: Deep learning models for detecting episodes of atrial fibrillation (AF) using rhythm information in long-term, ambulatory ECG recordings have shown high performance. However, the rhythm-based approach does not take advantage of the morphological information conveyed by the different ECG waveforms, particularly the f-waves. As a result, the performance of such models may be inherently limited. Methods: To address this limitation, we have developed a deep learning model, named RawECGNet, to detect episodes of AF and atrial flutter (AFl) using the raw, single-lead ECG. We compare th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.05411","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/2401.05411/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:32:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"N8qq+0koP8BMLRcNMKO7bji1rCCwCCxZuUfwP8vXy+6qkSspwzeGf1AmC32nON2RvOhCv7xrJiw9htUJVZq7Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T06:38:39.641003Z"},"content_sha256":"ff41cee79d83c405cca6f0efef09c1a5ded558b45c767e43ddf79dd885bf2f5a","schema_version":"1.0","event_id":"sha256:ff41cee79d83c405cca6f0efef09c1a5ded558b45c767e43ddf79dd885bf2f5a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BAP67NBAFRTA26UQUFOHTEYRON/bundle.json","state_url":"https://pith.science/pith/BAP67NBAFRTA26UQUFOHTEYRON/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BAP67NBAFRTA26UQUFOHTEYRON/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-09T06:38:39Z","links":{"resolver":"https://pith.science/pith/BAP67NBAFRTA26UQUFOHTEYRON","bundle":"https://pith.science/pith/BAP67NBAFRTA26UQUFOHTEYRON/bundle.json","state":"https://pith.science/pith/BAP67NBAFRTA26UQUFOHTEYRON/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BAP67NBAFRTA26UQUFOHTEYRON/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:BAP67NBAFRTA26UQUFOHTEYRON","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"4df065210a4b8156bfac1eda8902f865c6d56e72b6ec4caa3f50414ce2e3c7bf","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.SP","submitted_at":"2023-12-26T09:14:03Z","title_canon_sha256":"4a9356b507749fe5b91d636041873e0222a0a269f8a7a7d7afd2c06f4c0f7ab4"},"schema_version":"1.0","source":{"id":"2401.05411","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.05411","created_at":"2026-07-05T07:32:29Z"},{"alias_kind":"arxiv_version","alias_value":"2401.05411v1","created_at":"2026-07-05T07:32:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.05411","created_at":"2026-07-05T07:32:29Z"},{"alias_kind":"pith_short_12","alias_value":"BAP67NBAFRTA","created_at":"2026-07-05T07:32:29Z"},{"alias_kind":"pith_short_16","alias_value":"BAP67NBAFRTA26UQ","created_at":"2026-07-05T07:32:29Z"},{"alias_kind":"pith_short_8","alias_value":"BAP67NBA","created_at":"2026-07-05T07:32:29Z"}],"graph_snapshots":[{"event_id":"sha256:ff41cee79d83c405cca6f0efef09c1a5ded558b45c767e43ddf79dd885bf2f5a","target":"graph","created_at":"2026-07-05T07:32:29Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2401.05411/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Introduction: Deep learning models for detecting episodes of atrial fibrillation (AF) using rhythm information in long-term, ambulatory ECG recordings have shown high performance. However, the rhythm-based approach does not take advantage of the morphological information conveyed by the different ECG waveforms, particularly the f-waves. As a result, the performance of such models may be inherently limited. Methods: To address this limitation, we have developed a deep learning model, named RawECGNet, to detect episodes of AF and atrial flutter (AFl) using the raw, single-lead ECG. We compare th","authors_text":"Joachim A. Behar, Kenta Tsutsui, Leif S\\\"ornmo, Noam Ben-Moshe, Shany Biton","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.SP","submitted_at":"2023-12-26T09:14:03Z","title":"RawECGNet: Deep Learning Generalization for Atrial Fibrillation Detection from the Raw ECG"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.05411","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:aee1d5312f85e21e8abea4f9b761665f7bb9d0f90ce67f337c32e77e5f6ee289","target":"record","created_at":"2026-07-05T07:32:29Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"4df065210a4b8156bfac1eda8902f865c6d56e72b6ec4caa3f50414ce2e3c7bf","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.SP","submitted_at":"2023-12-26T09:14:03Z","title_canon_sha256":"4a9356b507749fe5b91d636041873e0222a0a269f8a7a7d7afd2c06f4c0f7ab4"},"schema_version":"1.0","source":{"id":"2401.05411","kind":"arxiv","version":1}},"canonical_sha256":"081fefb4202c660d7a90a15c7993117379954ec52453a64e1b74c8c0fbb50fa2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"081fefb4202c660d7a90a15c7993117379954ec52453a64e1b74c8c0fbb50fa2","first_computed_at":"2026-07-05T07:32:29.656886Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:32:29.656886Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6Fgqzvph4xhZzKlSp9xQ/qR/X8vvq982GoVegvKeB/agVLRLkGtVaclvoJLTuck11tMRNKs/eN5BbPOp6o+5BA==","signature_status":"signed_v1","signed_at":"2026-07-05T07:32:29.657452Z","signed_message":"canonical_sha256_bytes"},"source_id":"2401.05411","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:aee1d5312f85e21e8abea4f9b761665f7bb9d0f90ce67f337c32e77e5f6ee289","sha256:ff41cee79d83c405cca6f0efef09c1a5ded558b45c767e43ddf79dd885bf2f5a"],"state_sha256":"02efc712dc66a67cfca48eff7b3039ff42e7c7c2156b63e6a6c72385fded6941"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Erf7W3rzSff2s1uz4YdQa42R7GKg00ooR5Z2PmeC0BDhhxmUZ0oRrJfQbNMu2F3iVn3Ml+7dBBFVWIDAmJnTBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-09T06:38:39.643640Z","bundle_sha256":"610c41f9b2449c578fd6b2c29e41b99078e408a7268addfe0836f59159243535"}}