{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:EFOXF56G2NFG24ZKADP5E57Z3Y","short_pith_number":"pith:EFOXF56G","schema_version":"1.0","canonical_sha256":"215d72f7c6d34a6d732a00dfd277f9de0f97ae32148db8504de8fa856f8ee051","source":{"kind":"arxiv","id":"1907.09296","version":1},"attestation_state":"computed","paper":{"title":"A-Phase classification using convolutional neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","eess.IV"],"primary_cat":"eess.SP","authors_text":"Alfonso Alba, Edgar R. Arce-Santana, Martin O. Mendez, Valdemar Arce-Guevara","submitted_at":"2019-07-22T13:10:38Z","abstract_excerpt":"A series of short events, called A-phases, can be observed in the human electroencephalogram during NREM sleep. These events can be classified in three groups (A1, A2 and A3) according to their spectral contents, and are thought to play a role in the transitions between the different sleep stages. A-phase detection and classification is usually performed manually by a trained expert, but it is a tedious and time-consuming task. In the past two decades, various researchers have designed algorithms to automatically detect and classify the A-phases with varying degrees of success, but the problem"},"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":"1907.09296","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2019-07-22T13:10:38Z","cross_cats_sorted":["cs.CV","eess.IV"],"title_canon_sha256":"ec455d08ccc5c61731b1c75b151a6ce8e8bdf3a0dc2e596fab9e7b36bf020f4b","abstract_canon_sha256":"8366df9a6810574538f382e128db5fbe07b02da6e01e152882f8116b440515ac"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:59.578532Z","signature_b64":"UUBJfarv8Ps9J70v5sMkWzZD3ytNfASEmCCOu26rHh/ealLkei0F5eu3uoeke+ru6godXHgTag+D33Rc4MXEDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"215d72f7c6d34a6d732a00dfd277f9de0f97ae32148db8504de8fa856f8ee051","last_reissued_at":"2026-05-17T23:39:59.577950Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:59.577950Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A-Phase classification using convolutional neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","eess.IV"],"primary_cat":"eess.SP","authors_text":"Alfonso Alba, Edgar R. Arce-Santana, Martin O. Mendez, Valdemar Arce-Guevara","submitted_at":"2019-07-22T13:10:38Z","abstract_excerpt":"A series of short events, called A-phases, can be observed in the human electroencephalogram during NREM sleep. These events can be classified in three groups (A1, A2 and A3) according to their spectral contents, and are thought to play a role in the transitions between the different sleep stages. A-phase detection and classification is usually performed manually by a trained expert, but it is a tedious and time-consuming task. In the past two decades, various researchers have designed algorithms to automatically detect and classify the A-phases with varying degrees of success, but the problem"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.09296","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":"1907.09296","created_at":"2026-05-17T23:39:59.578063+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.09296v1","created_at":"2026-05-17T23:39:59.578063+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.09296","created_at":"2026-05-17T23:39:59.578063+00:00"},{"alias_kind":"pith_short_12","alias_value":"EFOXF56G2NFG","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_16","alias_value":"EFOXF56G2NFG24ZK","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_8","alias_value":"EFOXF56G","created_at":"2026-05-18T12:33:15.570797+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/EFOXF56G2NFG24ZKADP5E57Z3Y","json":"https://pith.science/pith/EFOXF56G2NFG24ZKADP5E57Z3Y.json","graph_json":"https://pith.science/api/pith-number/EFOXF56G2NFG24ZKADP5E57Z3Y/graph.json","events_json":"https://pith.science/api/pith-number/EFOXF56G2NFG24ZKADP5E57Z3Y/events.json","paper":"https://pith.science/paper/EFOXF56G"},"agent_actions":{"view_html":"https://pith.science/pith/EFOXF56G2NFG24ZKADP5E57Z3Y","download_json":"https://pith.science/pith/EFOXF56G2NFG24ZKADP5E57Z3Y.json","view_paper":"https://pith.science/paper/EFOXF56G","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.09296&json=true","fetch_graph":"https://pith.science/api/pith-number/EFOXF56G2NFG24ZKADP5E57Z3Y/graph.json","fetch_events":"https://pith.science/api/pith-number/EFOXF56G2NFG24ZKADP5E57Z3Y/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EFOXF56G2NFG24ZKADP5E57Z3Y/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EFOXF56G2NFG24ZKADP5E57Z3Y/action/storage_attestation","attest_author":"https://pith.science/pith/EFOXF56G2NFG24ZKADP5E57Z3Y/action/author_attestation","sign_citation":"https://pith.science/pith/EFOXF56G2NFG24ZKADP5E57Z3Y/action/citation_signature","submit_replication":"https://pith.science/pith/EFOXF56G2NFG24ZKADP5E57Z3Y/action/replication_record"}},"created_at":"2026-05-17T23:39:59.578063+00:00","updated_at":"2026-05-17T23:39:59.578063+00:00"}