{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:SHG2B5ZHNL36FS5FBLCB7S3AJZ","short_pith_number":"pith:SHG2B5ZH","schema_version":"1.0","canonical_sha256":"91cda0f7276af7e2cba50ac41fcb604e79bd32fd35193666d3f34c91167b0c8a","source":{"kind":"arxiv","id":"1809.10932","version":3},"attestation_state":"computed","paper":{"title":"SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP","stat.ML"],"primary_cat":"cs.LG","authors_text":"Fernando Andreotti, Huy Phan, Maarten De Vos, Navin Cooray, Oliver Y. Ch\\'en","submitted_at":"2018-09-28T09:37:48Z","abstract_excerpt":"Automatic sleep staging has been often treated as a simple classification problem that aims at determining the label of individual target polysomnography (PSG) epochs one at a time. In this work, we tackle the task as a sequence-to-sequence classification problem that receives a sequence of multiple epochs as input and classifies all of their labels at once. For this purpose, we propose a hierarchical recurrent neural network named SeqSleepNet. At the epoch processing level, the network consists of a filterbank layer tailored to learn frequency-domain filters for preprocessing and an attention"},"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":"1809.10932","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-28T09:37:48Z","cross_cats_sorted":["eess.SP","stat.ML"],"title_canon_sha256":"0c1d061fb57ae2c5f9a4d4f4045843ff7fbe8ba31d4c78de2b15280a2bc440ac","abstract_canon_sha256":"6444cd95c0b17a71400ad3631eb3015d939e11cd6ef3d45c11c69beaa9d4e9b6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:54:53.718522Z","signature_b64":"OpMUdn7FtjEfZULot1InqZ778xLY89GokS5dKy+VfK00O5qGWWGaj4l5tlqZkBVvzu4uGIH3dESoihcQ1qUUBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"91cda0f7276af7e2cba50ac41fcb604e79bd32fd35193666d3f34c91167b0c8a","last_reissued_at":"2026-05-17T23:54:53.718016Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:54:53.718016Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP","stat.ML"],"primary_cat":"cs.LG","authors_text":"Fernando Andreotti, Huy Phan, Maarten De Vos, Navin Cooray, Oliver Y. Ch\\'en","submitted_at":"2018-09-28T09:37:48Z","abstract_excerpt":"Automatic sleep staging has been often treated as a simple classification problem that aims at determining the label of individual target polysomnography (PSG) epochs one at a time. In this work, we tackle the task as a sequence-to-sequence classification problem that receives a sequence of multiple epochs as input and classifies all of their labels at once. For this purpose, we propose a hierarchical recurrent neural network named SeqSleepNet. At the epoch processing level, the network consists of a filterbank layer tailored to learn frequency-domain filters for preprocessing and an attention"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.10932","kind":"arxiv","version":3},"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":"1809.10932","created_at":"2026-05-17T23:54:53.718103+00:00"},{"alias_kind":"arxiv_version","alias_value":"1809.10932v3","created_at":"2026-05-17T23:54:53.718103+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.10932","created_at":"2026-05-17T23:54:53.718103+00:00"},{"alias_kind":"pith_short_12","alias_value":"SHG2B5ZHNL36","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_16","alias_value":"SHG2B5ZHNL36FS5F","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_8","alias_value":"SHG2B5ZH","created_at":"2026-05-18T12:32:53.628368+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/SHG2B5ZHNL36FS5FBLCB7S3AJZ","json":"https://pith.science/pith/SHG2B5ZHNL36FS5FBLCB7S3AJZ.json","graph_json":"https://pith.science/api/pith-number/SHG2B5ZHNL36FS5FBLCB7S3AJZ/graph.json","events_json":"https://pith.science/api/pith-number/SHG2B5ZHNL36FS5FBLCB7S3AJZ/events.json","paper":"https://pith.science/paper/SHG2B5ZH"},"agent_actions":{"view_html":"https://pith.science/pith/SHG2B5ZHNL36FS5FBLCB7S3AJZ","download_json":"https://pith.science/pith/SHG2B5ZHNL36FS5FBLCB7S3AJZ.json","view_paper":"https://pith.science/paper/SHG2B5ZH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1809.10932&json=true","fetch_graph":"https://pith.science/api/pith-number/SHG2B5ZHNL36FS5FBLCB7S3AJZ/graph.json","fetch_events":"https://pith.science/api/pith-number/SHG2B5ZHNL36FS5FBLCB7S3AJZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SHG2B5ZHNL36FS5FBLCB7S3AJZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SHG2B5ZHNL36FS5FBLCB7S3AJZ/action/storage_attestation","attest_author":"https://pith.science/pith/SHG2B5ZHNL36FS5FBLCB7S3AJZ/action/author_attestation","sign_citation":"https://pith.science/pith/SHG2B5ZHNL36FS5FBLCB7S3AJZ/action/citation_signature","submit_replication":"https://pith.science/pith/SHG2B5ZHNL36FS5FBLCB7S3AJZ/action/replication_record"}},"created_at":"2026-05-17T23:54:53.718103+00:00","updated_at":"2026-05-17T23:54:53.718103+00:00"}