{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:QHKGBSBF5HRWRLXJODIEEBUEWN","short_pith_number":"pith:QHKGBSBF","canonical_record":{"source":{"id":"1810.04122","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2018-10-05T02:59:04Z","cross_cats_sorted":["cs.LG","cs.NE","stat.ML"],"title_canon_sha256":"e6e241fe188e90ff960dc4f65dde53f7e9849fdba836000552ea422f825f9b5e","abstract_canon_sha256":"3982b3c63871d1241d3041a3945a9f4a776a34a5f972817bf52a1a4d0a800f36"},"schema_version":"1.0"},"canonical_sha256":"81d460c825e9e368aee970d0420684b36629a7b97b2e6a8ccb03276090671482","source":{"kind":"arxiv","id":"1810.04122","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.04122","created_at":"2026-05-18T00:03:42Z"},{"alias_kind":"arxiv_version","alias_value":"1810.04122v1","created_at":"2026-05-18T00:03:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.04122","created_at":"2026-05-18T00:03:42Z"},{"alias_kind":"pith_short_12","alias_value":"QHKGBSBF5HRW","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_16","alias_value":"QHKGBSBF5HRWRLXJ","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_8","alias_value":"QHKGBSBF","created_at":"2026-05-18T12:32:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:QHKGBSBF5HRWRLXJODIEEBUEWN","target":"record","payload":{"canonical_record":{"source":{"id":"1810.04122","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2018-10-05T02:59:04Z","cross_cats_sorted":["cs.LG","cs.NE","stat.ML"],"title_canon_sha256":"e6e241fe188e90ff960dc4f65dde53f7e9849fdba836000552ea422f825f9b5e","abstract_canon_sha256":"3982b3c63871d1241d3041a3945a9f4a776a34a5f972817bf52a1a4d0a800f36"},"schema_version":"1.0"},"canonical_sha256":"81d460c825e9e368aee970d0420684b36629a7b97b2e6a8ccb03276090671482","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:03:42.232159Z","signature_b64":"BeuYNkYWQUYPHx+C+/KCIcfUCu5+k8/kJ7PSBnkbENDJRsqNHqIeS4+DaoKdd/NsielIIZ6m35iB6AY/mSSoAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"81d460c825e9e368aee970d0420684b36629a7b97b2e6a8ccb03276090671482","last_reissued_at":"2026-05-18T00:03:42.231630Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:03:42.231630Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1810.04122","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:03:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bW5UGFa0FBJlc6M6Ep6+/uBK7+WyAqdhBLDJzgVeg0yRwyyThM4LrpEfs83HyxWTToPOD28q84Xjzd37xzLfBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T03:07:36.057674Z"},"content_sha256":"c81b44a0a9dfcb813a6172c7999b0d8bc0d59bc68970e0bb2a4835fec3802aff","schema_version":"1.0","event_id":"sha256:c81b44a0a9dfcb813a6172c7999b0d8bc0d59bc68970e0bb2a4835fec3802aff"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:QHKGBSBF5HRWRLXJODIEEBUEWN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Convolutional Neural Networks for Noise Detection in ECGs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NE","stat.ML"],"primary_cat":"eess.SP","authors_text":"Alexander Valys, Conner Galloway, Jennifer N. John","submitted_at":"2018-10-05T02:59:04Z","abstract_excerpt":"Mobile electrocardiogram (ECG) recording technologies represent a promising tool to fight the ongoing epidemic of cardiovascular diseases, which are responsible for more deaths globally than any other cause. While the ability to monitor one's heart activity at any time in any place is a crucial advantage of such technologies, it is also the cause of a drawback: signal noise due to environmental factors can render the ECGs illegible. In this work, we develop convolutional neural networks (CNNs) to automatically label ECGs for noise, training them on a novel noise-annotated dataset. By reducing "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.04122","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:03:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"70HbNEcBeof9zvY0X4LfNfNxAPw1vpG7wIYg29jA9vdylknGpRLx3e84fL0QOzLg/HCu6pxSgi1R0KD4hZ4yCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T03:07:36.058203Z"},"content_sha256":"d6440a3f2572b5e839218fc174f84e67d4793c5ff079914591909422052f43e7","schema_version":"1.0","event_id":"sha256:d6440a3f2572b5e839218fc174f84e67d4793c5ff079914591909422052f43e7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QHKGBSBF5HRWRLXJODIEEBUEWN/bundle.json","state_url":"https://pith.science/pith/QHKGBSBF5HRWRLXJODIEEBUEWN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QHKGBSBF5HRWRLXJODIEEBUEWN/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-23T03:07:36Z","links":{"resolver":"https://pith.science/pith/QHKGBSBF5HRWRLXJODIEEBUEWN","bundle":"https://pith.science/pith/QHKGBSBF5HRWRLXJODIEEBUEWN/bundle.json","state":"https://pith.science/pith/QHKGBSBF5HRWRLXJODIEEBUEWN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QHKGBSBF5HRWRLXJODIEEBUEWN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:QHKGBSBF5HRWRLXJODIEEBUEWN","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":"3982b3c63871d1241d3041a3945a9f4a776a34a5f972817bf52a1a4d0a800f36","cross_cats_sorted":["cs.LG","cs.NE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2018-10-05T02:59:04Z","title_canon_sha256":"e6e241fe188e90ff960dc4f65dde53f7e9849fdba836000552ea422f825f9b5e"},"schema_version":"1.0","source":{"id":"1810.04122","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.04122","created_at":"2026-05-18T00:03:42Z"},{"alias_kind":"arxiv_version","alias_value":"1810.04122v1","created_at":"2026-05-18T00:03:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.04122","created_at":"2026-05-18T00:03:42Z"},{"alias_kind":"pith_short_12","alias_value":"QHKGBSBF5HRW","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_16","alias_value":"QHKGBSBF5HRWRLXJ","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_8","alias_value":"QHKGBSBF","created_at":"2026-05-18T12:32:46Z"}],"graph_snapshots":[{"event_id":"sha256:d6440a3f2572b5e839218fc174f84e67d4793c5ff079914591909422052f43e7","target":"graph","created_at":"2026-05-18T00:03:42Z","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"},"paper":{"abstract_excerpt":"Mobile electrocardiogram (ECG) recording technologies represent a promising tool to fight the ongoing epidemic of cardiovascular diseases, which are responsible for more deaths globally than any other cause. While the ability to monitor one's heart activity at any time in any place is a crucial advantage of such technologies, it is also the cause of a drawback: signal noise due to environmental factors can render the ECGs illegible. In this work, we develop convolutional neural networks (CNNs) to automatically label ECGs for noise, training them on a novel noise-annotated dataset. By reducing ","authors_text":"Alexander Valys, Conner Galloway, Jennifer N. John","cross_cats":["cs.LG","cs.NE","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2018-10-05T02:59:04Z","title":"Deep Convolutional Neural Networks for Noise Detection in ECGs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.04122","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:c81b44a0a9dfcb813a6172c7999b0d8bc0d59bc68970e0bb2a4835fec3802aff","target":"record","created_at":"2026-05-18T00:03:42Z","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":"3982b3c63871d1241d3041a3945a9f4a776a34a5f972817bf52a1a4d0a800f36","cross_cats_sorted":["cs.LG","cs.NE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2018-10-05T02:59:04Z","title_canon_sha256":"e6e241fe188e90ff960dc4f65dde53f7e9849fdba836000552ea422f825f9b5e"},"schema_version":"1.0","source":{"id":"1810.04122","kind":"arxiv","version":1}},"canonical_sha256":"81d460c825e9e368aee970d0420684b36629a7b97b2e6a8ccb03276090671482","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"81d460c825e9e368aee970d0420684b36629a7b97b2e6a8ccb03276090671482","first_computed_at":"2026-05-18T00:03:42.231630Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:03:42.231630Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BeuYNkYWQUYPHx+C+/KCIcfUCu5+k8/kJ7PSBnkbENDJRsqNHqIeS4+DaoKdd/NsielIIZ6m35iB6AY/mSSoAA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:03:42.232159Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.04122","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c81b44a0a9dfcb813a6172c7999b0d8bc0d59bc68970e0bb2a4835fec3802aff","sha256:d6440a3f2572b5e839218fc174f84e67d4793c5ff079914591909422052f43e7"],"state_sha256":"79d9f69047ef1568fdcb497a6c52060d8ce2c852f86375f302e449eba818dc88"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oeweCaOsxhEdJDZ+fUEY7C8w/HtYk7nwfP6/BR7ZF672CH/APS5iqOoU8GpF5xQAFe6z5Q0HYkwjWNsG0XjaCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T03:07:36.062874Z","bundle_sha256":"5479fb4768c27c5fdd998ac1e135795aef25c6cf4cb826aa71b371021b766772"}}