{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:WVMJMGSRZTGSQMSE6OAWMIM5QL","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":"c0cead67c931d48a723df2fde3f7f17a27c01bcc3ad5c56556797e28704b7d8d","cross_cats_sorted":["cs.CV","eess.SP","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-08-27T19:14:32Z","title_canon_sha256":"a3278975aa8f9801769d889122d57552e8c815b4621f85e2400cd74216f29d52"},"schema_version":"1.0","source":{"id":"1908.10417","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.10417","created_at":"2026-07-05T01:12:22Z"},{"alias_kind":"arxiv_version","alias_value":"1908.10417v3","created_at":"2026-07-05T01:12:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.10417","created_at":"2026-07-05T01:12:22Z"},{"alias_kind":"pith_short_12","alias_value":"WVMJMGSRZTGS","created_at":"2026-07-05T01:12:22Z"},{"alias_kind":"pith_short_16","alias_value":"WVMJMGSRZTGSQMSE","created_at":"2026-07-05T01:12:22Z"},{"alias_kind":"pith_short_8","alias_value":"WVMJMGSR","created_at":"2026-07-05T01:12:22Z"}],"graph_snapshots":[{"event_id":"sha256:3a68e7cea788c83d063b9c666d950c0347a281fe4217bf81784abd11a028523c","target":"graph","created_at":"2026-07-05T01:12:22Z","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/1908.10417/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Effective and powerful methods for denoising real electrocardiogram (ECG) signals are important for wearable sensors and devices. Deep Learning (DL) models have been used extensively in image processing and other domains with great success but only very recently have been used in processing ECG signals. This paper presents several DL models namely Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Restricted Boltzmann Machine (RBM) together with the more conventional filtering methods (low pass filtering, high pass filtering, Notch filtering) and the standard wavelet-based te","authors_text":"Corneliu Arsene","cross_cats":["cs.CV","eess.SP","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-08-27T19:14:32Z","title":"Complex Deep Learning Models for Denoising of Human Heart ECG signals"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.10417","kind":"arxiv","version":3},"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:c426d8408dd08eb67c1410bf1275acf8f04675ee0d5b83496af355d6f91ff892","target":"record","created_at":"2026-07-05T01:12:22Z","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":"c0cead67c931d48a723df2fde3f7f17a27c01bcc3ad5c56556797e28704b7d8d","cross_cats_sorted":["cs.CV","eess.SP","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-08-27T19:14:32Z","title_canon_sha256":"a3278975aa8f9801769d889122d57552e8c815b4621f85e2400cd74216f29d52"},"schema_version":"1.0","source":{"id":"1908.10417","kind":"arxiv","version":3}},"canonical_sha256":"b558961a51cccd283244f38166219d82f4a53fe2ac8e305d69ad38182e7d77b8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b558961a51cccd283244f38166219d82f4a53fe2ac8e305d69ad38182e7d77b8","first_computed_at":"2026-07-05T01:12:22.857459Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:12:22.857459Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MqTuNZnksdWGPz2EkW4WHtT2+3U/QszD9jx29BvBe9k2UPWrLZMq2tPbbla56EYMWUcPgBIk2NOa0J0htSTTCg==","signature_status":"signed_v1","signed_at":"2026-07-05T01:12:22.857950Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.10417","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c426d8408dd08eb67c1410bf1275acf8f04675ee0d5b83496af355d6f91ff892","sha256:3a68e7cea788c83d063b9c666d950c0347a281fe4217bf81784abd11a028523c"],"state_sha256":"bace6e904b2de6056bd06493fe0b173dac7201bbbb1d5c5c025bbaffcd106ed9"}