{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:XH763TIDQ6N7RR6JZHIPPRPRIN","short_pith_number":"pith:XH763TID","schema_version":"1.0","canonical_sha256":"b9ffedcd03879bf8c7c9c9d0f7c5f143579e2518a157802415427ce882a2e40d","source":{"kind":"arxiv","id":"2502.16255","version":1},"attestation_state":"computed","paper":{"title":"rECGnition_v2.0: Self-Attentive Canonical Fusion of ECG and Patient Data using deep learning for effective Cardiac Diagnostics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"eess.SP","authors_text":"Deepak Sharma, Durgesh Kumar, Ram Jiwari, Sandeep Seth, Shreya Srivastava","submitted_at":"2025-02-22T15:16:46Z","abstract_excerpt":"The variability in ECG readings influenced by individual patient characteristics has posed a considerable challenge to adopting automated ECG analysis in clinical settings. A novel feature fusion technique termed SACC (Self Attentive Canonical Correlation) was proposed to address this. This technique is combined with DPN (Dual Pathway Network) and depth-wise separable convolution to create a robust, interpretable, and fast end-to-end arrhythmia classification model named rECGnition_v2.0 (robust ECG abnormality detection). This study uses MIT-BIH, INCARTDB and EDB dataset to evaluate the effici"},"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":"2502.16255","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2025-02-22T15:16:46Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"013f47df116c468e5ffa0db4720b864b2f189b268ccc444d2d7e94c183386a69","abstract_canon_sha256":"efe9d1c348ec007c4166be40a7af94e35c68b3e3e80917509cddbd4b5f328925"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:18:23.777229Z","signature_b64":"15ncRwgJiDBkQq7eyYAPIcH1VQXEEm0PYaRXoC1KwXgveEcIrM5AfY+qCQXUQmmg2nnMkXQzMmwlIbJkVdscCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b9ffedcd03879bf8c7c9c9d0f7c5f143579e2518a157802415427ce882a2e40d","last_reissued_at":"2026-07-05T10:18:23.776726Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:18:23.776726Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"rECGnition_v2.0: Self-Attentive Canonical Fusion of ECG and Patient Data using deep learning for effective Cardiac Diagnostics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"eess.SP","authors_text":"Deepak Sharma, Durgesh Kumar, Ram Jiwari, Sandeep Seth, Shreya Srivastava","submitted_at":"2025-02-22T15:16:46Z","abstract_excerpt":"The variability in ECG readings influenced by individual patient characteristics has posed a considerable challenge to adopting automated ECG analysis in clinical settings. A novel feature fusion technique termed SACC (Self Attentive Canonical Correlation) was proposed to address this. This technique is combined with DPN (Dual Pathway Network) and depth-wise separable convolution to create a robust, interpretable, and fast end-to-end arrhythmia classification model named rECGnition_v2.0 (robust ECG abnormality detection). This study uses MIT-BIH, INCARTDB and EDB dataset to evaluate the effici"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.16255","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/2502.16255/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":"2502.16255","created_at":"2026-07-05T10:18:23.776784+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.16255v1","created_at":"2026-07-05T10:18:23.776784+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.16255","created_at":"2026-07-05T10:18:23.776784+00:00"},{"alias_kind":"pith_short_12","alias_value":"XH763TIDQ6N7","created_at":"2026-07-05T10:18:23.776784+00:00"},{"alias_kind":"pith_short_16","alias_value":"XH763TIDQ6N7RR6J","created_at":"2026-07-05T10:18:23.776784+00:00"},{"alias_kind":"pith_short_8","alias_value":"XH763TID","created_at":"2026-07-05T10:18:23.776784+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/XH763TIDQ6N7RR6JZHIPPRPRIN","json":"https://pith.science/pith/XH763TIDQ6N7RR6JZHIPPRPRIN.json","graph_json":"https://pith.science/api/pith-number/XH763TIDQ6N7RR6JZHIPPRPRIN/graph.json","events_json":"https://pith.science/api/pith-number/XH763TIDQ6N7RR6JZHIPPRPRIN/events.json","paper":"https://pith.science/paper/XH763TID"},"agent_actions":{"view_html":"https://pith.science/pith/XH763TIDQ6N7RR6JZHIPPRPRIN","download_json":"https://pith.science/pith/XH763TIDQ6N7RR6JZHIPPRPRIN.json","view_paper":"https://pith.science/paper/XH763TID","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.16255&json=true","fetch_graph":"https://pith.science/api/pith-number/XH763TIDQ6N7RR6JZHIPPRPRIN/graph.json","fetch_events":"https://pith.science/api/pith-number/XH763TIDQ6N7RR6JZHIPPRPRIN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XH763TIDQ6N7RR6JZHIPPRPRIN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XH763TIDQ6N7RR6JZHIPPRPRIN/action/storage_attestation","attest_author":"https://pith.science/pith/XH763TIDQ6N7RR6JZHIPPRPRIN/action/author_attestation","sign_citation":"https://pith.science/pith/XH763TIDQ6N7RR6JZHIPPRPRIN/action/citation_signature","submit_replication":"https://pith.science/pith/XH763TIDQ6N7RR6JZHIPPRPRIN/action/replication_record"}},"created_at":"2026-07-05T10:18:23.776784+00:00","updated_at":"2026-07-05T10:18:23.776784+00:00"}