{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:G6MIGSXKB3JEWSZLPEVPBMCJPR","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":"b03699c6adb318d345a730f2bfa0c7fa4a9eb77aea5b33a4f7cc79d91b21a2b8","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2023-06-02T00:08:38Z","title_canon_sha256":"19a80f1400dec4302d5cd53ddcafbecbb24784d7dc43e3b6ab12aa2f0304014c"},"schema_version":"1.0","source":{"id":"2306.01210","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.01210","created_at":"2026-07-05T06:16:39Z"},{"alias_kind":"arxiv_version","alias_value":"2306.01210v1","created_at":"2026-07-05T06:16:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.01210","created_at":"2026-07-05T06:16:39Z"},{"alias_kind":"pith_short_12","alias_value":"G6MIGSXKB3JE","created_at":"2026-07-05T06:16:39Z"},{"alias_kind":"pith_short_16","alias_value":"G6MIGSXKB3JEWSZL","created_at":"2026-07-05T06:16:39Z"},{"alias_kind":"pith_short_8","alias_value":"G6MIGSXK","created_at":"2026-07-05T06:16:39Z"}],"graph_snapshots":[{"event_id":"sha256:28c26e6a9b0ba8de799b7b6d5e798dfbf3bc876e80096e8ed83e81dab88a2876","target":"graph","created_at":"2026-07-05T06:16:39Z","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/2306.01210/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Background: Cardiac resynchronization therapy (CRT) has emerged as an effective treatment for heart failure patients with electrical dyssynchrony. However, accurately predicting which patients will respond to CRT remains a challenge. This study explores the application of deep transfer learning techniques to train a predictive model for CRT response. Methods: In this study, the short-time Fourier transform (STFT) technique was employed to transform ECG signals into two-dimensional images. A transfer learning approach was then applied on the MIT-BIT ECG database to pre-train a convolutional neu","authors_text":"Hongjin Si, Jiangang Zou, Qing-Hui Chen, Weihua Zhou, Xinwei Zhang, Zhuo He","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2023-06-02T00:08:38Z","title":"A new method using deep transfer learning on ECG to predict the response to cardiac resynchronization therapy"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.01210","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":"sha256:4f8e9d1e23891a6bbe0893634f67174d82f58129dc70d5e69511450ee74ab633","target":"record","created_at":"2026-07-05T06:16:39Z","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":"b03699c6adb318d345a730f2bfa0c7fa4a9eb77aea5b33a4f7cc79d91b21a2b8","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2023-06-02T00:08:38Z","title_canon_sha256":"19a80f1400dec4302d5cd53ddcafbecbb24784d7dc43e3b6ab12aa2f0304014c"},"schema_version":"1.0","source":{"id":"2306.01210","kind":"arxiv","version":1}},"canonical_sha256":"3798834aea0ed24b4b2b792af0b0497c7ae6e86fb66c9e18460519a2143b66e9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3798834aea0ed24b4b2b792af0b0497c7ae6e86fb66c9e18460519a2143b66e9","first_computed_at":"2026-07-05T06:16:39.417787Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:16:39.417787Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6INd3/o4u9T+9dmYURI1HPhPgnnC/z9SqOTOtUv3XT6VKPEhC5GoYui0JyLBcRIgeCo4ney2vYa6VVmk6pHIAA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:16:39.418188Z","signed_message":"canonical_sha256_bytes"},"source_id":"2306.01210","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4f8e9d1e23891a6bbe0893634f67174d82f58129dc70d5e69511450ee74ab633","sha256:28c26e6a9b0ba8de799b7b6d5e798dfbf3bc876e80096e8ed83e81dab88a2876"],"state_sha256":"bd8c7cfa991172576c97ca14be167d154946834e1fc599ebbe3ac650adc04063"}