{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:P67CLM4O2RYFG4C3CQZ7VKAOAY","short_pith_number":"pith:P67CLM4O","schema_version":"1.0","canonical_sha256":"7fbe25b38ed47053705b1433faa80e06134a89b50f188d487dcdd038ecb7ad7f","source":{"kind":"arxiv","id":"2309.07145","version":1},"attestation_state":"computed","paper":{"title":"ETP: Learning Transferable ECG Representations via ECG-Text Pre-training","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"eess.SP","authors_text":"Che Liu, Mi Zhang, Rossella Arcucci, Sibo Cheng, Zhongwei Wan","submitted_at":"2023-09-06T19:19:26Z","abstract_excerpt":"In the domain of cardiovascular healthcare, the Electrocardiogram (ECG) serves as a critical, non-invasive diagnostic tool. Although recent strides in self-supervised learning (SSL) have been promising for ECG representation learning, these techniques often require annotated samples and struggle with classes not present in the fine-tuning stages. To address these limitations, we introduce ECG-Text Pre-training (ETP), an innovative framework designed to learn cross-modal representations that link ECG signals with textual reports. For the first time, this framework leverages the zero-shot classi"},"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":"2309.07145","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2023-09-06T19:19:26Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"4113a1351c2f082e0f3bc8d021324592d7fe983f7a671ffd3c44951e3f185a52","abstract_canon_sha256":"b61d03ff40bbabf84c5a8a40039f83a1fe2a174d17b17c1d71e3ab2700e0d33f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:50:31.161248Z","signature_b64":"awPog0rh0UU+m6BpDzmMnNtFSky8eQN+PhJ1VKFqxqkpFVELrOM7melBm0h9npgTR5H1+E74dw/VqQGHloMRDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7fbe25b38ed47053705b1433faa80e06134a89b50f188d487dcdd038ecb7ad7f","last_reissued_at":"2026-07-05T06:50:31.160865Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:50:31.160865Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ETP: Learning Transferable ECG Representations via ECG-Text Pre-training","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"eess.SP","authors_text":"Che Liu, Mi Zhang, Rossella Arcucci, Sibo Cheng, Zhongwei Wan","submitted_at":"2023-09-06T19:19:26Z","abstract_excerpt":"In the domain of cardiovascular healthcare, the Electrocardiogram (ECG) serves as a critical, non-invasive diagnostic tool. Although recent strides in self-supervised learning (SSL) have been promising for ECG representation learning, these techniques often require annotated samples and struggle with classes not present in the fine-tuning stages. To address these limitations, we introduce ECG-Text Pre-training (ETP), an innovative framework designed to learn cross-modal representations that link ECG signals with textual reports. For the first time, this framework leverages the zero-shot classi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.07145","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/2309.07145/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":"2309.07145","created_at":"2026-07-05T06:50:31.160920+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.07145v1","created_at":"2026-07-05T06:50:31.160920+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.07145","created_at":"2026-07-05T06:50:31.160920+00:00"},{"alias_kind":"pith_short_12","alias_value":"P67CLM4O2RYF","created_at":"2026-07-05T06:50:31.160920+00:00"},{"alias_kind":"pith_short_16","alias_value":"P67CLM4O2RYFG4C3","created_at":"2026-07-05T06:50:31.160920+00:00"},{"alias_kind":"pith_short_8","alias_value":"P67CLM4O","created_at":"2026-07-05T06:50:31.160920+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/P67CLM4O2RYFG4C3CQZ7VKAOAY","json":"https://pith.science/pith/P67CLM4O2RYFG4C3CQZ7VKAOAY.json","graph_json":"https://pith.science/api/pith-number/P67CLM4O2RYFG4C3CQZ7VKAOAY/graph.json","events_json":"https://pith.science/api/pith-number/P67CLM4O2RYFG4C3CQZ7VKAOAY/events.json","paper":"https://pith.science/paper/P67CLM4O"},"agent_actions":{"view_html":"https://pith.science/pith/P67CLM4O2RYFG4C3CQZ7VKAOAY","download_json":"https://pith.science/pith/P67CLM4O2RYFG4C3CQZ7VKAOAY.json","view_paper":"https://pith.science/paper/P67CLM4O","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.07145&json=true","fetch_graph":"https://pith.science/api/pith-number/P67CLM4O2RYFG4C3CQZ7VKAOAY/graph.json","fetch_events":"https://pith.science/api/pith-number/P67CLM4O2RYFG4C3CQZ7VKAOAY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/P67CLM4O2RYFG4C3CQZ7VKAOAY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/P67CLM4O2RYFG4C3CQZ7VKAOAY/action/storage_attestation","attest_author":"https://pith.science/pith/P67CLM4O2RYFG4C3CQZ7VKAOAY/action/author_attestation","sign_citation":"https://pith.science/pith/P67CLM4O2RYFG4C3CQZ7VKAOAY/action/citation_signature","submit_replication":"https://pith.science/pith/P67CLM4O2RYFG4C3CQZ7VKAOAY/action/replication_record"}},"created_at":"2026-07-05T06:50:31.160920+00:00","updated_at":"2026-07-05T06:50:31.160920+00:00"}