{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JHJF7HHQCMDAHHZHSWXOBHMBBV","short_pith_number":"pith:JHJF7HHQ","schema_version":"1.0","canonical_sha256":"49d25f9cf01306039f2795aee09d810d58962a81a1b36719c202319580af19d2","source":{"kind":"arxiv","id":"2412.12139","version":1},"attestation_state":"computed","paper":{"title":"ECGtizer: a fully automated digitizing and signal recovery pipeline for electrocardiograms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.SP","authors_text":"Ahmad Fall, Alex Lence, Edi Prifti, Federica Granese, Jean-Daniel Zucker, Joe-Elie Salem, Samuel David Cohen","submitted_at":"2024-12-09T10:19:02Z","abstract_excerpt":"Electrocardiograms (ECGs) are essential for diagnosing cardiac pathologies, yet traditional paper-based ECG storage poses significant challenges for automated analysis. This study introduces ECGtizer, an open-source, fully automated tool designed to digitize paper ECGs and recover signals lost during storage. ECGtizer facilitates automated analyses using modern AI methods. It employs automated lead detection, three pixel-based signal extraction algorithms, and a deep learning-based signal reconstruction module. We evaluated ECGtizer on two datasets: a real-life cohort from the COVID-19 pandemi"},"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":"2412.12139","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2024-12-09T10:19:02Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"6c82e76ac046e81325f146bd0a896b39d54e6d35b3f24f51c0f7de441d4189e8","abstract_canon_sha256":"589b56224eb9b70ef27d733f916b29afbca53f50ce4e296b43f84dae803b9c45"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:50:10.438876Z","signature_b64":"GnwCUvWILI6lYn85NmJmor4/MMJQSK/YbUxDoniRwS5KY+1aEZF660bk3TWqcRj2OiUiF5YhuGXuTAxlZ4pVDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"49d25f9cf01306039f2795aee09d810d58962a81a1b36719c202319580af19d2","last_reissued_at":"2026-07-05T09:50:10.438363Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:50:10.438363Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ECGtizer: a fully automated digitizing and signal recovery pipeline for electrocardiograms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.SP","authors_text":"Ahmad Fall, Alex Lence, Edi Prifti, Federica Granese, Jean-Daniel Zucker, Joe-Elie Salem, Samuel David Cohen","submitted_at":"2024-12-09T10:19:02Z","abstract_excerpt":"Electrocardiograms (ECGs) are essential for diagnosing cardiac pathologies, yet traditional paper-based ECG storage poses significant challenges for automated analysis. This study introduces ECGtizer, an open-source, fully automated tool designed to digitize paper ECGs and recover signals lost during storage. ECGtizer facilitates automated analyses using modern AI methods. It employs automated lead detection, three pixel-based signal extraction algorithms, and a deep learning-based signal reconstruction module. We evaluated ECGtizer on two datasets: a real-life cohort from the COVID-19 pandemi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.12139","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/2412.12139/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":"2412.12139","created_at":"2026-07-05T09:50:10.438425+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.12139v1","created_at":"2026-07-05T09:50:10.438425+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.12139","created_at":"2026-07-05T09:50:10.438425+00:00"},{"alias_kind":"pith_short_12","alias_value":"JHJF7HHQCMDA","created_at":"2026-07-05T09:50:10.438425+00:00"},{"alias_kind":"pith_short_16","alias_value":"JHJF7HHQCMDAHHZH","created_at":"2026-07-05T09:50:10.438425+00:00"},{"alias_kind":"pith_short_8","alias_value":"JHJF7HHQ","created_at":"2026-07-05T09:50:10.438425+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.07683","citing_title":"ECGLight: Compute-Light Framework For Paper ECG Digitization and Myocardial Infarction Screening","ref_index":23,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JHJF7HHQCMDAHHZHSWXOBHMBBV","json":"https://pith.science/pith/JHJF7HHQCMDAHHZHSWXOBHMBBV.json","graph_json":"https://pith.science/api/pith-number/JHJF7HHQCMDAHHZHSWXOBHMBBV/graph.json","events_json":"https://pith.science/api/pith-number/JHJF7HHQCMDAHHZHSWXOBHMBBV/events.json","paper":"https://pith.science/paper/JHJF7HHQ"},"agent_actions":{"view_html":"https://pith.science/pith/JHJF7HHQCMDAHHZHSWXOBHMBBV","download_json":"https://pith.science/pith/JHJF7HHQCMDAHHZHSWXOBHMBBV.json","view_paper":"https://pith.science/paper/JHJF7HHQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.12139&json=true","fetch_graph":"https://pith.science/api/pith-number/JHJF7HHQCMDAHHZHSWXOBHMBBV/graph.json","fetch_events":"https://pith.science/api/pith-number/JHJF7HHQCMDAHHZHSWXOBHMBBV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JHJF7HHQCMDAHHZHSWXOBHMBBV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JHJF7HHQCMDAHHZHSWXOBHMBBV/action/storage_attestation","attest_author":"https://pith.science/pith/JHJF7HHQCMDAHHZHSWXOBHMBBV/action/author_attestation","sign_citation":"https://pith.science/pith/JHJF7HHQCMDAHHZHSWXOBHMBBV/action/citation_signature","submit_replication":"https://pith.science/pith/JHJF7HHQCMDAHHZHSWXOBHMBBV/action/replication_record"}},"created_at":"2026-07-05T09:50:10.438425+00:00","updated_at":"2026-07-05T09:50:10.438425+00:00"}