{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:FRVN2XJOGJ75Q7ZYNC4M4X3TMJ","short_pith_number":"pith:FRVN2XJO","schema_version":"1.0","canonical_sha256":"2c6add5d2e327fd87f3868b8ce5f7362488f852b61e42c190dc3ef0307fb75b1","source":{"kind":"arxiv","id":"2306.12098","version":1},"attestation_state":"computed","paper":{"title":"MSW-Transformer: Multi-Scale Shifted Windows Transformer Networks for 12-Lead ECG Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.SP","authors_text":"Jingfeng Guo, Lei Xie, Renjie Cheng, Shuxin Zhuang, Zhemin Zhuang","submitted_at":"2023-06-21T08:27:26Z","abstract_excerpt":"Automatic classification of electrocardiogram (ECG) signals plays a crucial role in the early prevention and diagnosis of cardiovascular diseases. While ECG signals can be used for the diagnosis of various diseases, their pathological characteristics exhibit minimal variations, posing a challenge to automatic classification models. Existing methods primarily utilize convolutional neural networks to extract ECG signal features for classification, which may not fully capture the pathological feature differences of different diseases. Transformer networks have advantages in feature extraction for"},"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":"2306.12098","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2023-06-21T08:27:26Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"73bc9bfed7c3e3d487505ca8fda7c1282381bce8581895505406178008865471","abstract_canon_sha256":"910a44385b4d22b03799e779d4e0d6ea72928a5894f3c8d7c92ea446fc2dff47"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:23:10.551815Z","signature_b64":"4TvrTXWZPbsAsPie8QvJt6HRc7jBfFVTFpAbzx2MMweUedc/HnJoYAlQxz+aLCxgVUaQ1XJHkzGNz/HgTQG2DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2c6add5d2e327fd87f3868b8ce5f7362488f852b61e42c190dc3ef0307fb75b1","last_reissued_at":"2026-07-05T06:23:10.551484Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:23:10.551484Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MSW-Transformer: Multi-Scale Shifted Windows Transformer Networks for 12-Lead ECG Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.SP","authors_text":"Jingfeng Guo, Lei Xie, Renjie Cheng, Shuxin Zhuang, Zhemin Zhuang","submitted_at":"2023-06-21T08:27:26Z","abstract_excerpt":"Automatic classification of electrocardiogram (ECG) signals plays a crucial role in the early prevention and diagnosis of cardiovascular diseases. While ECG signals can be used for the diagnosis of various diseases, their pathological characteristics exhibit minimal variations, posing a challenge to automatic classification models. Existing methods primarily utilize convolutional neural networks to extract ECG signal features for classification, which may not fully capture the pathological feature differences of different diseases. Transformer networks have advantages in feature extraction for"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.12098","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/2306.12098/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":"2306.12098","created_at":"2026-07-05T06:23:10.551535+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.12098v1","created_at":"2026-07-05T06:23:10.551535+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.12098","created_at":"2026-07-05T06:23:10.551535+00:00"},{"alias_kind":"pith_short_12","alias_value":"FRVN2XJOGJ75","created_at":"2026-07-05T06:23:10.551535+00:00"},{"alias_kind":"pith_short_16","alias_value":"FRVN2XJOGJ75Q7ZY","created_at":"2026-07-05T06:23:10.551535+00:00"},{"alias_kind":"pith_short_8","alias_value":"FRVN2XJO","created_at":"2026-07-05T06:23:10.551535+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.06718","citing_title":"MSAIC-Net: A Multi-Scale Attention and Imbalance-Aware Contrastive Network for ECG-Based Myocardial Substrate Abnormality Detection","ref_index":58,"is_internal_anchor":false},{"citing_arxiv_id":"2605.13194","citing_title":"ECG-NAT: A Self-supervised Neighborhood Attention Transformer for Multi-lead Electrocardiogram Classification","ref_index":16,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FRVN2XJOGJ75Q7ZYNC4M4X3TMJ","json":"https://pith.science/pith/FRVN2XJOGJ75Q7ZYNC4M4X3TMJ.json","graph_json":"https://pith.science/api/pith-number/FRVN2XJOGJ75Q7ZYNC4M4X3TMJ/graph.json","events_json":"https://pith.science/api/pith-number/FRVN2XJOGJ75Q7ZYNC4M4X3TMJ/events.json","paper":"https://pith.science/paper/FRVN2XJO"},"agent_actions":{"view_html":"https://pith.science/pith/FRVN2XJOGJ75Q7ZYNC4M4X3TMJ","download_json":"https://pith.science/pith/FRVN2XJOGJ75Q7ZYNC4M4X3TMJ.json","view_paper":"https://pith.science/paper/FRVN2XJO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.12098&json=true","fetch_graph":"https://pith.science/api/pith-number/FRVN2XJOGJ75Q7ZYNC4M4X3TMJ/graph.json","fetch_events":"https://pith.science/api/pith-number/FRVN2XJOGJ75Q7ZYNC4M4X3TMJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FRVN2XJOGJ75Q7ZYNC4M4X3TMJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FRVN2XJOGJ75Q7ZYNC4M4X3TMJ/action/storage_attestation","attest_author":"https://pith.science/pith/FRVN2XJOGJ75Q7ZYNC4M4X3TMJ/action/author_attestation","sign_citation":"https://pith.science/pith/FRVN2XJOGJ75Q7ZYNC4M4X3TMJ/action/citation_signature","submit_replication":"https://pith.science/pith/FRVN2XJOGJ75Q7ZYNC4M4X3TMJ/action/replication_record"}},"created_at":"2026-07-05T06:23:10.551535+00:00","updated_at":"2026-07-05T06:23:10.551535+00:00"}