{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:IEM4BN6ETOMC346GIB7VTVGSPL","short_pith_number":"pith:IEM4BN6E","canonical_record":{"source":{"id":"1710.06122","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-10-17T06:50:58Z","cross_cats_sorted":[],"title_canon_sha256":"f41fb9a73b39f1d8838b7ac8ec7089572ffae43bb7b171a168592feccc5b478a","abstract_canon_sha256":"37d028a80a6929118c0f316629c62a5a9ec21767930d21a690792f2d6a76f66f"},"schema_version":"1.0"},"canonical_sha256":"4119c0b7c49b982df3c6407f59d4d27ad909886f118b17975140060cf3e9d4d6","source":{"kind":"arxiv","id":"1710.06122","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.06122","created_at":"2026-05-18T00:19:03Z"},{"alias_kind":"arxiv_version","alias_value":"1710.06122v2","created_at":"2026-05-18T00:19:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.06122","created_at":"2026-05-18T00:19:03Z"},{"alias_kind":"pith_short_12","alias_value":"IEM4BN6ETOMC","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_16","alias_value":"IEM4BN6ETOMC346G","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_8","alias_value":"IEM4BN6E","created_at":"2026-05-18T12:31:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:IEM4BN6ETOMC346GIB7VTVGSPL","target":"record","payload":{"canonical_record":{"source":{"id":"1710.06122","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-10-17T06:50:58Z","cross_cats_sorted":[],"title_canon_sha256":"f41fb9a73b39f1d8838b7ac8ec7089572ffae43bb7b171a168592feccc5b478a","abstract_canon_sha256":"37d028a80a6929118c0f316629c62a5a9ec21767930d21a690792f2d6a76f66f"},"schema_version":"1.0"},"canonical_sha256":"4119c0b7c49b982df3c6407f59d4d27ad909886f118b17975140060cf3e9d4d6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:19:03.311798Z","signature_b64":"yDoBrgHhcaoSCZbmgoTvOkOxUQuwetdUMMKFo2Zttk83wI016IIZOtgV039n3hf6H5Y08haqJn53omH4FI3fAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4119c0b7c49b982df3c6407f59d4d27ad909886f118b17975140060cf3e9d4d6","last_reissued_at":"2026-05-18T00:19:03.311104Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:19:03.311104Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1710.06122","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:19:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"a9EwTIWoAal6xNSIUdnlXLJwAhzM5Iak90TuzbZd0EvBXykfc39Sh8sWWQCsmsxN3bvSPslREEWxHueVxFPvBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T14:30:26.574605Z"},"content_sha256":"3fedd5b8f65b5fe7fec8be6c45aca2efa44e86b7e38b73e67d2386e8e8272f90","schema_version":"1.0","event_id":"sha256:3fedd5b8f65b5fe7fec8be6c45aca2efa44e86b7e38b73e67d2386e8e8272f90"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:IEM4BN6ETOMC346GIB7VTVGSPL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Convolutional Recurrent Neural Networks for Electrocardiogram Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Dmytro Perekrestenko, Martin Zihlmann, Michael Tschannen","submitted_at":"2017-10-17T06:50:58Z","abstract_excerpt":"We propose two deep neural network architectures for classification of arbitrary-length electrocardiogram (ECG) recordings and evaluate them on the atrial fibrillation (AF) classification data set provided by the PhysioNet/CinC Challenge 2017. The first architecture is a deep convolutional neural network (CNN) with averaging-based feature aggregation across time. The second architecture combines convolutional layers for feature extraction with long-short term memory (LSTM) layers for temporal aggregation of features. As a key ingredient of our training procedure we introduce a simple data augm"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.06122","kind":"arxiv","version":2},"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":"2026-05-18T00:19:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3pVfADjewLXUH+xXcMeREuJhNC41bHr/UfgwJGqt3ruDQ3XjkfpGBK9MMb26HfTIG3gTByp67f6DNAPmBgAoAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T14:30:26.575180Z"},"content_sha256":"f587c6a3e6416b90e1056a141323e549fec60b697284859f4cc94c13f8bb00c5","schema_version":"1.0","event_id":"sha256:f587c6a3e6416b90e1056a141323e549fec60b697284859f4cc94c13f8bb00c5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IEM4BN6ETOMC346GIB7VTVGSPL/bundle.json","state_url":"https://pith.science/pith/IEM4BN6ETOMC346GIB7VTVGSPL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IEM4BN6ETOMC346GIB7VTVGSPL/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":"2026-06-07T14:30:26Z","links":{"resolver":"https://pith.science/pith/IEM4BN6ETOMC346GIB7VTVGSPL","bundle":"https://pith.science/pith/IEM4BN6ETOMC346GIB7VTVGSPL/bundle.json","state":"https://pith.science/pith/IEM4BN6ETOMC346GIB7VTVGSPL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IEM4BN6ETOMC346GIB7VTVGSPL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:IEM4BN6ETOMC346GIB7VTVGSPL","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":"37d028a80a6929118c0f316629c62a5a9ec21767930d21a690792f2d6a76f66f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-10-17T06:50:58Z","title_canon_sha256":"f41fb9a73b39f1d8838b7ac8ec7089572ffae43bb7b171a168592feccc5b478a"},"schema_version":"1.0","source":{"id":"1710.06122","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.06122","created_at":"2026-05-18T00:19:03Z"},{"alias_kind":"arxiv_version","alias_value":"1710.06122v2","created_at":"2026-05-18T00:19:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.06122","created_at":"2026-05-18T00:19:03Z"},{"alias_kind":"pith_short_12","alias_value":"IEM4BN6ETOMC","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_16","alias_value":"IEM4BN6ETOMC346G","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_8","alias_value":"IEM4BN6E","created_at":"2026-05-18T12:31:21Z"}],"graph_snapshots":[{"event_id":"sha256:f587c6a3e6416b90e1056a141323e549fec60b697284859f4cc94c13f8bb00c5","target":"graph","created_at":"2026-05-18T00:19:03Z","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":"We propose two deep neural network architectures for classification of arbitrary-length electrocardiogram (ECG) recordings and evaluate them on the atrial fibrillation (AF) classification data set provided by the PhysioNet/CinC Challenge 2017. The first architecture is a deep convolutional neural network (CNN) with averaging-based feature aggregation across time. The second architecture combines convolutional layers for feature extraction with long-short term memory (LSTM) layers for temporal aggregation of features. As a key ingredient of our training procedure we introduce a simple data augm","authors_text":"Dmytro Perekrestenko, Martin Zihlmann, Michael Tschannen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-10-17T06:50:58Z","title":"Convolutional Recurrent Neural Networks for Electrocardiogram Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.06122","kind":"arxiv","version":2},"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":"sha256:3fedd5b8f65b5fe7fec8be6c45aca2efa44e86b7e38b73e67d2386e8e8272f90","target":"record","created_at":"2026-05-18T00:19:03Z","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":"37d028a80a6929118c0f316629c62a5a9ec21767930d21a690792f2d6a76f66f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-10-17T06:50:58Z","title_canon_sha256":"f41fb9a73b39f1d8838b7ac8ec7089572ffae43bb7b171a168592feccc5b478a"},"schema_version":"1.0","source":{"id":"1710.06122","kind":"arxiv","version":2}},"canonical_sha256":"4119c0b7c49b982df3c6407f59d4d27ad909886f118b17975140060cf3e9d4d6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4119c0b7c49b982df3c6407f59d4d27ad909886f118b17975140060cf3e9d4d6","first_computed_at":"2026-05-18T00:19:03.311104Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:19:03.311104Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yDoBrgHhcaoSCZbmgoTvOkOxUQuwetdUMMKFo2Zttk83wI016IIZOtgV039n3hf6H5Y08haqJn53omH4FI3fAw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:19:03.311798Z","signed_message":"canonical_sha256_bytes"},"source_id":"1710.06122","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3fedd5b8f65b5fe7fec8be6c45aca2efa44e86b7e38b73e67d2386e8e8272f90","sha256:f587c6a3e6416b90e1056a141323e549fec60b697284859f4cc94c13f8bb00c5"],"state_sha256":"c81e69e255c4f3acc2e3b50c31aab4afc4034c181af81f8fbeeea0bdff02baf0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dzOyOc81UzBf8eQgVZDLL4C4SV/dI8q5dUabyDc75E2r6d05yrNxgHdRr0LPJmwU4o88CdAt5DT9K7uAmdupDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T14:30:26.578192Z","bundle_sha256":"e600d95e58f04df9e3a250a863935954e3b15d9875a58154163c6c454b5458dc"}}