{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:SVQU6YFMCVL4G5MHCGK7SCW3TZ","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":"011886616c2a69c96ae007b64bcdb0250cd5db018c81a51ca2a4841f46f59224","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2024-07-12T03:13:52Z","title_canon_sha256":"ea98549a570f1800e547ed122b76bb2f158b49e60d3413991f0119d192c5d558"},"schema_version":"1.0","source":{"id":"2407.11065","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.11065","created_at":"2026-07-05T08:44:17Z"},{"alias_kind":"arxiv_version","alias_value":"2407.11065v1","created_at":"2026-07-05T08:44:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.11065","created_at":"2026-07-05T08:44:17Z"},{"alias_kind":"pith_short_12","alias_value":"SVQU6YFMCVL4","created_at":"2026-07-05T08:44:17Z"},{"alias_kind":"pith_short_16","alias_value":"SVQU6YFMCVL4G5MH","created_at":"2026-07-05T08:44:17Z"},{"alias_kind":"pith_short_8","alias_value":"SVQU6YFM","created_at":"2026-07-05T08:44:17Z"}],"graph_snapshots":[{"event_id":"sha256:608e36f8ceac1d2dd564ecdd44dcec2d71017aab0f3c9ce7c94c05b522c2b555","target":"graph","created_at":"2026-07-05T08:44:17Z","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/2407.11065/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Cardiovascular disease is a major life-threatening condition that is commonly monitored using electrocardiogram (ECG) signals. However, these signals are often contaminated by various types of noise at different intensities, significantly interfering with downstream tasks. Therefore, denoising ECG signals and increasing the signal-to-noise ratio is crucial for cardiovascular monitoring. In this paper, we propose a deep learning method that combines a one-dimensional convolutional layer with transformer architecture for denoising ECG signals. The convolutional layer processes the ECG signal by ","authors_text":"Ding Zhu, Mohammad Mahdi Khalili, Vishnu Kabir Chhabra","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2024-07-12T03:13:52Z","title":"ECG Signal Denoising Using Multi-scale Patch Embedding and Transformers"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.11065","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":"sha256:470d172b3352f04159e824bdf8f09f07b33fbaa5a12dff74ac4a8b89d0d4b35b","target":"record","created_at":"2026-07-05T08:44:17Z","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":"011886616c2a69c96ae007b64bcdb0250cd5db018c81a51ca2a4841f46f59224","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2024-07-12T03:13:52Z","title_canon_sha256":"ea98549a570f1800e547ed122b76bb2f158b49e60d3413991f0119d192c5d558"},"schema_version":"1.0","source":{"id":"2407.11065","kind":"arxiv","version":1}},"canonical_sha256":"95614f60ac1557c375871195f90adb9e699b550fe07703d6af24ce01eaed5d85","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"95614f60ac1557c375871195f90adb9e699b550fe07703d6af24ce01eaed5d85","first_computed_at":"2026-07-05T08:44:17.990297Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:44:17.990297Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QtpFjFdKee3e5RjUVCw2pdW46UenL29d5XwXFciOGUHF+6gYb0uwLtkTYI5+JhZ3ME9yBuoQVobN0aAFUqloAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:44:17.990683Z","signed_message":"canonical_sha256_bytes"},"source_id":"2407.11065","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:470d172b3352f04159e824bdf8f09f07b33fbaa5a12dff74ac4a8b89d0d4b35b","sha256:608e36f8ceac1d2dd564ecdd44dcec2d71017aab0f3c9ce7c94c05b522c2b555"],"state_sha256":"b46ec88dbfc86fbc1cfc00dbb1ecf30a50e553b6367cb7afceae4a5c3a22aa4f"}