{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:CRI7POO772XD4I6KR5NYQPIOQJ","short_pith_number":"pith:CRI7POO7","schema_version":"1.0","canonical_sha256":"1451f7b9dffeae3e23ca8f5b883d0e826a403ba4e1beef5b84a1b0a987ebb42b","source":{"kind":"arxiv","id":"1709.05849","version":1},"attestation_state":"computed","paper":{"title":"Neonatal Seizure Detection using Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Alison O'Shea, Andriy Temko, Geraldine Boylan, Gordon Lightbody","submitted_at":"2017-09-18T10:30:43Z","abstract_excerpt":"This study presents a novel end-to-end architecture that learns hierarchical representations from raw EEG data using fully convolutional deep neural networks for the task of neonatal seizure detection. The deep neural network acts as both feature extractor and classifier, allowing for end-to-end optimization of the seizure detector. The designed system is evaluated on a large dataset of continuous unedited multi-channel neonatal EEG totaling 835 hours and comprising of 1389 seizures. The proposed deep architecture, with sample-level filters, achieves an accuracy that is comparable to the state"},"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":"1709.05849","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-09-18T10:30:43Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"2f4196ddff2dadd1031c70ee42e15052216cca4b38ad93c23fa89533f3acb57d","abstract_canon_sha256":"d0fc7863cba167c2585406a735f633b2277d9415d7c295055a22248c67aa8b67"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:34:59.141298Z","signature_b64":"ukNMQIhsrjxkl9asba7C+SAEENwkYznohZsf2EsP61xDf9oyxjd0l2/wFJxD3UDZgiF2TQXnOE8gbGZopuu9Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1451f7b9dffeae3e23ca8f5b883d0e826a403ba4e1beef5b84a1b0a987ebb42b","last_reissued_at":"2026-05-18T00:34:59.140609Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:34:59.140609Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neonatal Seizure Detection using Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Alison O'Shea, Andriy Temko, Geraldine Boylan, Gordon Lightbody","submitted_at":"2017-09-18T10:30:43Z","abstract_excerpt":"This study presents a novel end-to-end architecture that learns hierarchical representations from raw EEG data using fully convolutional deep neural networks for the task of neonatal seizure detection. The deep neural network acts as both feature extractor and classifier, allowing for end-to-end optimization of the seizure detector. The designed system is evaluated on a large dataset of continuous unedited multi-channel neonatal EEG totaling 835 hours and comprising of 1389 seizures. The proposed deep architecture, with sample-level filters, achieves an accuracy that is comparable to the state"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.05849","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":"1709.05849","created_at":"2026-05-18T00:34:59.140758+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.05849v1","created_at":"2026-05-18T00:34:59.140758+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.05849","created_at":"2026-05-18T00:34:59.140758+00:00"},{"alias_kind":"pith_short_12","alias_value":"CRI7POO772XD","created_at":"2026-05-18T12:31:10.602751+00:00"},{"alias_kind":"pith_short_16","alias_value":"CRI7POO772XD4I6K","created_at":"2026-05-18T12:31:10.602751+00:00"},{"alias_kind":"pith_short_8","alias_value":"CRI7POO7","created_at":"2026-05-18T12:31:10.602751+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CRI7POO772XD4I6KR5NYQPIOQJ","json":"https://pith.science/pith/CRI7POO772XD4I6KR5NYQPIOQJ.json","graph_json":"https://pith.science/api/pith-number/CRI7POO772XD4I6KR5NYQPIOQJ/graph.json","events_json":"https://pith.science/api/pith-number/CRI7POO772XD4I6KR5NYQPIOQJ/events.json","paper":"https://pith.science/paper/CRI7POO7"},"agent_actions":{"view_html":"https://pith.science/pith/CRI7POO772XD4I6KR5NYQPIOQJ","download_json":"https://pith.science/pith/CRI7POO772XD4I6KR5NYQPIOQJ.json","view_paper":"https://pith.science/paper/CRI7POO7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.05849&json=true","fetch_graph":"https://pith.science/api/pith-number/CRI7POO772XD4I6KR5NYQPIOQJ/graph.json","fetch_events":"https://pith.science/api/pith-number/CRI7POO772XD4I6KR5NYQPIOQJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CRI7POO772XD4I6KR5NYQPIOQJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CRI7POO772XD4I6KR5NYQPIOQJ/action/storage_attestation","attest_author":"https://pith.science/pith/CRI7POO772XD4I6KR5NYQPIOQJ/action/author_attestation","sign_citation":"https://pith.science/pith/CRI7POO772XD4I6KR5NYQPIOQJ/action/citation_signature","submit_replication":"https://pith.science/pith/CRI7POO772XD4I6KR5NYQPIOQJ/action/replication_record"}},"created_at":"2026-05-18T00:34:59.140758+00:00","updated_at":"2026-05-18T00:34:59.140758+00:00"}