{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:MMYV3SQ4GORZI6UDGFTFEA3WZX","short_pith_number":"pith:MMYV3SQ4","schema_version":"1.0","canonical_sha256":"63315dca1c33a3947a833166520376cdde4d69a17669b7a18ec607b778e3357e","source":{"kind":"arxiv","id":"2509.08961","version":1},"attestation_state":"computed","paper":{"title":"FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Md. Golam Raibul Alam, Md Jobayer, Md Mehedi Hasan Shawon, Md. Sajeebul Islam Sk.","submitted_at":"2025-09-10T19:48:07Z","abstract_excerpt":"Cardiovascular diseases (CVDs) remain a leading cause of mortality worldwide, underscoring the importance of accurate and scalable diagnostic systems. Electrocardiogram (ECG) analysis is central to detecting cardiac abnormalities, yet challenges such as noise, class imbalance, and dataset heterogeneity limit current methods. To address these issues, we propose FoundationalECGNet, a foundational framework for automated ECG classification. The model integrates a dual-stage denoising by Morlet and Daubechies wavelets transformation, Convolutional Block Attention Module (CBAM), Graph Attention Net"},"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":"2509.08961","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-09-10T19:48:07Z","cross_cats_sorted":[],"title_canon_sha256":"af062c0006064a787e3308dac4276790c311f99872be175867258b0ff8f8b472","abstract_canon_sha256":"cd892c2e12d705687396a40bddccf7607321fd6339b254c6df43f1c3a6408085"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:09:26.246145Z","signature_b64":"uN921ht8/RePvqsC3WqBENh98/DonFWlQWAuLCrDi7UNM+vwG5B9omKh59OtY83n3lveuX9BwptZBD/Rr6QvAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"63315dca1c33a3947a833166520376cdde4d69a17669b7a18ec607b778e3357e","last_reissued_at":"2026-07-05T12:09:26.245610Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:09:26.245610Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Md. Golam Raibul Alam, Md Jobayer, Md Mehedi Hasan Shawon, Md. Sajeebul Islam Sk.","submitted_at":"2025-09-10T19:48:07Z","abstract_excerpt":"Cardiovascular diseases (CVDs) remain a leading cause of mortality worldwide, underscoring the importance of accurate and scalable diagnostic systems. Electrocardiogram (ECG) analysis is central to detecting cardiac abnormalities, yet challenges such as noise, class imbalance, and dataset heterogeneity limit current methods. To address these issues, we propose FoundationalECGNet, a foundational framework for automated ECG classification. The model integrates a dual-stage denoising by Morlet and Daubechies wavelets transformation, Convolutional Block Attention Module (CBAM), Graph Attention Net"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.08961","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2509.08961/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2509.08961","created_at":"2026-07-05T12:09:26.245673+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.08961v1","created_at":"2026-07-05T12:09:26.245673+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.08961","created_at":"2026-07-05T12:09:26.245673+00:00"},{"alias_kind":"pith_short_12","alias_value":"MMYV3SQ4GORZ","created_at":"2026-07-05T12:09:26.245673+00:00"},{"alias_kind":"pith_short_16","alias_value":"MMYV3SQ4GORZI6UD","created_at":"2026-07-05T12:09:26.245673+00:00"},{"alias_kind":"pith_short_8","alias_value":"MMYV3SQ4","created_at":"2026-07-05T12:09:26.245673+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.02502","citing_title":"An Explainable Vision-Language Model Framework with Adaptive PID-Tversky Loss for Lumbar Spinal Stenosis Diagnosis","ref_index":38,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MMYV3SQ4GORZI6UDGFTFEA3WZX","json":"https://pith.science/pith/MMYV3SQ4GORZI6UDGFTFEA3WZX.json","graph_json":"https://pith.science/api/pith-number/MMYV3SQ4GORZI6UDGFTFEA3WZX/graph.json","events_json":"https://pith.science/api/pith-number/MMYV3SQ4GORZI6UDGFTFEA3WZX/events.json","paper":"https://pith.science/paper/MMYV3SQ4"},"agent_actions":{"view_html":"https://pith.science/pith/MMYV3SQ4GORZI6UDGFTFEA3WZX","download_json":"https://pith.science/pith/MMYV3SQ4GORZI6UDGFTFEA3WZX.json","view_paper":"https://pith.science/paper/MMYV3SQ4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.08961&json=true","fetch_graph":"https://pith.science/api/pith-number/MMYV3SQ4GORZI6UDGFTFEA3WZX/graph.json","fetch_events":"https://pith.science/api/pith-number/MMYV3SQ4GORZI6UDGFTFEA3WZX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MMYV3SQ4GORZI6UDGFTFEA3WZX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MMYV3SQ4GORZI6UDGFTFEA3WZX/action/storage_attestation","attest_author":"https://pith.science/pith/MMYV3SQ4GORZI6UDGFTFEA3WZX/action/author_attestation","sign_citation":"https://pith.science/pith/MMYV3SQ4GORZI6UDGFTFEA3WZX/action/citation_signature","submit_replication":"https://pith.science/pith/MMYV3SQ4GORZI6UDGFTFEA3WZX/action/replication_record"}},"created_at":"2026-07-05T12:09:26.245673+00:00","updated_at":"2026-07-05T12:09:26.245673+00:00"}