{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:R2RK7IHTQSMH3XAKF2EFXDHXR7","short_pith_number":"pith:R2RK7IHT","schema_version":"1.0","canonical_sha256":"8ea2afa0f384987ddc0a2e885b8cf78ffb6bf1ecee230275e7000c03d52a7a55","source":{"kind":"arxiv","id":"1804.06812","version":1},"attestation_state":"computed","paper":{"title":"ECG arrhythmia classification using a 2-D convolutional neural network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Daeyoung Kim, Daeyoun Kang, Dohyeun Kim, Hoang Minh Nguyen, Tae Joon Jun, Young-Hak Kim","submitted_at":"2018-04-18T16:54:57Z","abstract_excerpt":"In this paper, we propose an effective electrocardiogram (ECG) arrhythmia classification method using a deep two-dimensional convolutional neural network (CNN) which recently shows outstanding performance in the field of pattern recognition. Every ECG beat was transformed into a two-dimensional grayscale image as an input data for the CNN classifier. Optimization of the proposed CNN classifier includes various deep learning techniques such as batch normalization, data augmentation, Xavier initialization, and dropout. In addition, we compared our proposed classifier with two well-known CNN mode"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1804.06812","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-04-18T16:54:57Z","cross_cats_sorted":[],"title_canon_sha256":"5040618e44502ee6efeb23c3cbf1155ece64573d14db703b8c29cf44002269a5","abstract_canon_sha256":"5683ac106d648947145b36db30ddaa575d11676ee65a4f1853152dc6772db0a7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:18:05.693230Z","signature_b64":"xNLLC1zE11qwzAzLsN/rqIJ42sNNiXxQHBEwNBpy+us0RTX9g9yCTeZXNZ65cBHz2CCV9nb5XHrWGcTzpt8hBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8ea2afa0f384987ddc0a2e885b8cf78ffb6bf1ecee230275e7000c03d52a7a55","last_reissued_at":"2026-05-18T00:18:05.692611Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:18:05.692611Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ECG arrhythmia classification using a 2-D convolutional neural network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Daeyoung Kim, Daeyoun Kang, Dohyeun Kim, Hoang Minh Nguyen, Tae Joon Jun, Young-Hak Kim","submitted_at":"2018-04-18T16:54:57Z","abstract_excerpt":"In this paper, we propose an effective electrocardiogram (ECG) arrhythmia classification method using a deep two-dimensional convolutional neural network (CNN) which recently shows outstanding performance in the field of pattern recognition. Every ECG beat was transformed into a two-dimensional grayscale image as an input data for the CNN classifier. Optimization of the proposed CNN classifier includes various deep learning techniques such as batch normalization, data augmentation, Xavier initialization, and dropout. In addition, we compared our proposed classifier with two well-known CNN mode"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.06812","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1804.06812","created_at":"2026-05-18T00:18:05.692722+00:00"},{"alias_kind":"arxiv_version","alias_value":"1804.06812v1","created_at":"2026-05-18T00:18:05.692722+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.06812","created_at":"2026-05-18T00:18:05.692722+00:00"},{"alias_kind":"pith_short_12","alias_value":"R2RK7IHTQSMH","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_16","alias_value":"R2RK7IHTQSMH3XAK","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_8","alias_value":"R2RK7IHT","created_at":"2026-05-18T12:32:50.500415+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.27259","citing_title":"VTBench: A Multimodal Framework for Time-Series Classification with Chart-Based Representations","ref_index":24,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/R2RK7IHTQSMH3XAKF2EFXDHXR7","json":"https://pith.science/pith/R2RK7IHTQSMH3XAKF2EFXDHXR7.json","graph_json":"https://pith.science/api/pith-number/R2RK7IHTQSMH3XAKF2EFXDHXR7/graph.json","events_json":"https://pith.science/api/pith-number/R2RK7IHTQSMH3XAKF2EFXDHXR7/events.json","paper":"https://pith.science/paper/R2RK7IHT"},"agent_actions":{"view_html":"https://pith.science/pith/R2RK7IHTQSMH3XAKF2EFXDHXR7","download_json":"https://pith.science/pith/R2RK7IHTQSMH3XAKF2EFXDHXR7.json","view_paper":"https://pith.science/paper/R2RK7IHT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1804.06812&json=true","fetch_graph":"https://pith.science/api/pith-number/R2RK7IHTQSMH3XAKF2EFXDHXR7/graph.json","fetch_events":"https://pith.science/api/pith-number/R2RK7IHTQSMH3XAKF2EFXDHXR7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R2RK7IHTQSMH3XAKF2EFXDHXR7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R2RK7IHTQSMH3XAKF2EFXDHXR7/action/storage_attestation","attest_author":"https://pith.science/pith/R2RK7IHTQSMH3XAKF2EFXDHXR7/action/author_attestation","sign_citation":"https://pith.science/pith/R2RK7IHTQSMH3XAKF2EFXDHXR7/action/citation_signature","submit_replication":"https://pith.science/pith/R2RK7IHTQSMH3XAKF2EFXDHXR7/action/replication_record"}},"created_at":"2026-05-18T00:18:05.692722+00:00","updated_at":"2026-05-18T00:18:05.692722+00:00"}