{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:UY6ZVGUW74QMNQAVFAHA3C6GRF","short_pith_number":"pith:UY6ZVGUW","canonical_record":{"source":{"id":"1904.00138","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-03-30T02:50:23Z","cross_cats_sorted":["cs.LG","eess.SP"],"title_canon_sha256":"dbfe98f8768d97950d7604516c1aa006a44dd1ade64307ab28a043140d4172f7","abstract_canon_sha256":"38bf8e531dec3cfdb95ad65a49cf5514657b9fe69e574864d8337e07a2ca9250"},"schema_version":"1.0"},"canonical_sha256":"a63d9a9a96ff20c6c015280e0d8bc6895543ecd061c5c710d64d37c4151d8132","source":{"kind":"arxiv","id":"1904.00138","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.00138","created_at":"2026-05-17T23:48:49Z"},{"alias_kind":"arxiv_version","alias_value":"1904.00138v4","created_at":"2026-05-17T23:48:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.00138","created_at":"2026-05-17T23:48:49Z"},{"alias_kind":"pith_short_12","alias_value":"UY6ZVGUW74QM","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_16","alias_value":"UY6ZVGUW74QMNQAV","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_8","alias_value":"UY6ZVGUW","created_at":"2026-05-18T12:33:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:UY6ZVGUW74QMNQAVFAHA3C6GRF","target":"record","payload":{"canonical_record":{"source":{"id":"1904.00138","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-03-30T02:50:23Z","cross_cats_sorted":["cs.LG","eess.SP"],"title_canon_sha256":"dbfe98f8768d97950d7604516c1aa006a44dd1ade64307ab28a043140d4172f7","abstract_canon_sha256":"38bf8e531dec3cfdb95ad65a49cf5514657b9fe69e574864d8337e07a2ca9250"},"schema_version":"1.0"},"canonical_sha256":"a63d9a9a96ff20c6c015280e0d8bc6895543ecd061c5c710d64d37c4151d8132","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:49.958915Z","signature_b64":"p9zZFKCIjIsvp0fmHTZE93Ueu/nuz8BGGhfB283CZiv/F1ACHVr1fkr4NLfL5qlun0F/hJvPpPdZ2p+jnlTRBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a63d9a9a96ff20c6c015280e0d8bc6895543ecd061c5c710d64d37c4151d8132","last_reissued_at":"2026-05-17T23:48:49.958187Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:49.958187Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1904.00138","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:48:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0ezQdCpXo/EDQwiJrrZOVx2Jgq0bnRuKHkTDx2lACb9ySmTs8X2/RuFUa8r8mnnTVgH+KN00WJ1Y1jUzN8kCCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T00:23:39.305730Z"},"content_sha256":"2843822687a09480cf22119fd21f96ebb506eada067a5883d5d02b0a88d56096","schema_version":"1.0","event_id":"sha256:2843822687a09480cf22119fd21f96ebb506eada067a5883d5d02b0a88d56096"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:UY6ZVGUW74QMNQAVFAHA3C6GRF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"On Arrhythmia Detection by Deep Learning and Multidimensional Representation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.SP"],"primary_cat":"stat.ML","authors_text":"C. Hao, K.S. Rajput, M. Majmudar, S. Wibowo","submitted_at":"2019-03-30T02:50:23Z","abstract_excerpt":"An electrocardiogram (ECG) is a time-series signal that is represented by one-dimensional (1-D) data. Higher dimensional representation contains more information that is accessible for feature extraction. Hidden variables such as frequency relation and morphology of segment is not directly accessible in the time domain. In this paper, 1-D time series data is converted into multi-dimensional representation in the form of multichannel 2-D images. Following that, deep learning was used to train a deep neural network based classifier to detect arrhythmias. The results of simulation on testing data"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.00138","kind":"arxiv","version":4},"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":"2026-05-17T23:48:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DJ/Eo+F7OwtkkG6oCARuSNAsTMMATg5ahcE03SizRSJ97DlYHe2Fv6AE2/4QdBZatsJj7Wc6tTXuE4ZPYShQAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T00:23:39.306139Z"},"content_sha256":"456ba514ff9f21267666e22348799be153677f5476d1d57ab68848d99bdd77ef","schema_version":"1.0","event_id":"sha256:456ba514ff9f21267666e22348799be153677f5476d1d57ab68848d99bdd77ef"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UY6ZVGUW74QMNQAVFAHA3C6GRF/bundle.json","state_url":"https://pith.science/pith/UY6ZVGUW74QMNQAVFAHA3C6GRF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UY6ZVGUW74QMNQAVFAHA3C6GRF/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":"2026-06-03T00:23:39Z","links":{"resolver":"https://pith.science/pith/UY6ZVGUW74QMNQAVFAHA3C6GRF","bundle":"https://pith.science/pith/UY6ZVGUW74QMNQAVFAHA3C6GRF/bundle.json","state":"https://pith.science/pith/UY6ZVGUW74QMNQAVFAHA3C6GRF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UY6ZVGUW74QMNQAVFAHA3C6GRF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:UY6ZVGUW74QMNQAVFAHA3C6GRF","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":"38bf8e531dec3cfdb95ad65a49cf5514657b9fe69e574864d8337e07a2ca9250","cross_cats_sorted":["cs.LG","eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-03-30T02:50:23Z","title_canon_sha256":"dbfe98f8768d97950d7604516c1aa006a44dd1ade64307ab28a043140d4172f7"},"schema_version":"1.0","source":{"id":"1904.00138","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.00138","created_at":"2026-05-17T23:48:49Z"},{"alias_kind":"arxiv_version","alias_value":"1904.00138v4","created_at":"2026-05-17T23:48:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.00138","created_at":"2026-05-17T23:48:49Z"},{"alias_kind":"pith_short_12","alias_value":"UY6ZVGUW74QM","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_16","alias_value":"UY6ZVGUW74QMNQAV","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_8","alias_value":"UY6ZVGUW","created_at":"2026-05-18T12:33:30Z"}],"graph_snapshots":[{"event_id":"sha256:456ba514ff9f21267666e22348799be153677f5476d1d57ab68848d99bdd77ef","target":"graph","created_at":"2026-05-17T23:48:49Z","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":"An electrocardiogram (ECG) is a time-series signal that is represented by one-dimensional (1-D) data. Higher dimensional representation contains more information that is accessible for feature extraction. Hidden variables such as frequency relation and morphology of segment is not directly accessible in the time domain. In this paper, 1-D time series data is converted into multi-dimensional representation in the form of multichannel 2-D images. Following that, deep learning was used to train a deep neural network based classifier to detect arrhythmias. The results of simulation on testing data","authors_text":"C. Hao, K.S. Rajput, M. Majmudar, S. Wibowo","cross_cats":["cs.LG","eess.SP"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-03-30T02:50:23Z","title":"On Arrhythmia Detection by Deep Learning and Multidimensional Representation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.00138","kind":"arxiv","version":4},"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":"sha256:2843822687a09480cf22119fd21f96ebb506eada067a5883d5d02b0a88d56096","target":"record","created_at":"2026-05-17T23:48:49Z","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":"38bf8e531dec3cfdb95ad65a49cf5514657b9fe69e574864d8337e07a2ca9250","cross_cats_sorted":["cs.LG","eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-03-30T02:50:23Z","title_canon_sha256":"dbfe98f8768d97950d7604516c1aa006a44dd1ade64307ab28a043140d4172f7"},"schema_version":"1.0","source":{"id":"1904.00138","kind":"arxiv","version":4}},"canonical_sha256":"a63d9a9a96ff20c6c015280e0d8bc6895543ecd061c5c710d64d37c4151d8132","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a63d9a9a96ff20c6c015280e0d8bc6895543ecd061c5c710d64d37c4151d8132","first_computed_at":"2026-05-17T23:48:49.958187Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:48:49.958187Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"p9zZFKCIjIsvp0fmHTZE93Ueu/nuz8BGGhfB283CZiv/F1ACHVr1fkr4NLfL5qlun0F/hJvPpPdZ2p+jnlTRBg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:48:49.958915Z","signed_message":"canonical_sha256_bytes"},"source_id":"1904.00138","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2843822687a09480cf22119fd21f96ebb506eada067a5883d5d02b0a88d56096","sha256:456ba514ff9f21267666e22348799be153677f5476d1d57ab68848d99bdd77ef"],"state_sha256":"e7fa890de044536f66f6cc89805ca057c850b8955b6434247bd48e956da851a1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0aTXlhpAKktZ3zPsFFdJQbNOBFQY17Cy+Df/GzGQsCCtyWTN13sJGKC7bA8ib8lNpaU/PMH4fvB7Fg6HcOWUBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T00:23:39.308338Z","bundle_sha256":"a8b1f12552fe81c09d9b9dc30a945a77e0877711365ce27e37a0628d55a53e64"}}