{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:7V2VI5YEWSVDRXFLHZH4Q7MFFQ","short_pith_number":"pith:7V2VI5YE","schema_version":"1.0","canonical_sha256":"fd75547704b4aa38dcab3e4fc87d852c2be575bfa81103ab242c107095a9167c","source":{"kind":"arxiv","id":"1701.08968","version":1},"attestation_state":"computed","paper":{"title":"Supervised Learning in Automatic Channel Selection for Epileptic Seizure Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrew Faulks, Jiawei Yang, Levin Kuhlmann, Mohammad Reza Bonyadi, Nhan Truong, Omid Kavehei","submitted_at":"2017-01-31T10:01:45Z","abstract_excerpt":"Detecting seizure using brain neuroactivations recorded by intracranial electroencephalogram (iEEG) has been widely used for monitoring, diagnosing, and closed-loop therapy of epileptic patients, however, computational efficiency gains are needed if state-of-the-art methods are to be implemented in implanted devices. We present a novel method for automatic seizure detection based on iEEG data that outperforms current state-of-the-art seizure detection methods in terms of computational efficiency while maintaining the accuracy. The proposed algorithm incorporates an automatic channel selection "},"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":"1701.08968","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-01-31T10:01:45Z","cross_cats_sorted":[],"title_canon_sha256":"af9dc0cfca1513d0db8d0093e33335e5a1496d3aacfd290813f4d355b0385f67","abstract_canon_sha256":"ff5d0097f1b45932faa2b13e67ce57d224b3294e4b6bb7c2b3723c5c92c8d2c8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:51:38.783778Z","signature_b64":"Sm1pSB61HJt0TyJQeVKQUpH4kN/EYWKeCWk3IvkLvLrLrPc/FPdYfnaw4DYbb6vBh8vIEXsXWqrovVaID2PrAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fd75547704b4aa38dcab3e4fc87d852c2be575bfa81103ab242c107095a9167c","last_reissued_at":"2026-05-18T00:51:38.783378Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:51:38.783378Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Supervised Learning in Automatic Channel Selection for Epileptic Seizure Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrew Faulks, Jiawei Yang, Levin Kuhlmann, Mohammad Reza Bonyadi, Nhan Truong, Omid Kavehei","submitted_at":"2017-01-31T10:01:45Z","abstract_excerpt":"Detecting seizure using brain neuroactivations recorded by intracranial electroencephalogram (iEEG) has been widely used for monitoring, diagnosing, and closed-loop therapy of epileptic patients, however, computational efficiency gains are needed if state-of-the-art methods are to be implemented in implanted devices. We present a novel method for automatic seizure detection based on iEEG data that outperforms current state-of-the-art seizure detection methods in terms of computational efficiency while maintaining the accuracy. The proposed algorithm incorporates an automatic channel selection "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.08968","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":"1701.08968","created_at":"2026-05-18T00:51:38.783445+00:00"},{"alias_kind":"arxiv_version","alias_value":"1701.08968v1","created_at":"2026-05-18T00:51:38.783445+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.08968","created_at":"2026-05-18T00:51:38.783445+00:00"},{"alias_kind":"pith_short_12","alias_value":"7V2VI5YEWSVD","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_16","alias_value":"7V2VI5YEWSVDRXFL","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_8","alias_value":"7V2VI5YE","created_at":"2026-05-18T12:31:05.417338+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/7V2VI5YEWSVDRXFLHZH4Q7MFFQ","json":"https://pith.science/pith/7V2VI5YEWSVDRXFLHZH4Q7MFFQ.json","graph_json":"https://pith.science/api/pith-number/7V2VI5YEWSVDRXFLHZH4Q7MFFQ/graph.json","events_json":"https://pith.science/api/pith-number/7V2VI5YEWSVDRXFLHZH4Q7MFFQ/events.json","paper":"https://pith.science/paper/7V2VI5YE"},"agent_actions":{"view_html":"https://pith.science/pith/7V2VI5YEWSVDRXFLHZH4Q7MFFQ","download_json":"https://pith.science/pith/7V2VI5YEWSVDRXFLHZH4Q7MFFQ.json","view_paper":"https://pith.science/paper/7V2VI5YE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1701.08968&json=true","fetch_graph":"https://pith.science/api/pith-number/7V2VI5YEWSVDRXFLHZH4Q7MFFQ/graph.json","fetch_events":"https://pith.science/api/pith-number/7V2VI5YEWSVDRXFLHZH4Q7MFFQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7V2VI5YEWSVDRXFLHZH4Q7MFFQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7V2VI5YEWSVDRXFLHZH4Q7MFFQ/action/storage_attestation","attest_author":"https://pith.science/pith/7V2VI5YEWSVDRXFLHZH4Q7MFFQ/action/author_attestation","sign_citation":"https://pith.science/pith/7V2VI5YEWSVDRXFLHZH4Q7MFFQ/action/citation_signature","submit_replication":"https://pith.science/pith/7V2VI5YEWSVDRXFLHZH4Q7MFFQ/action/replication_record"}},"created_at":"2026-05-18T00:51:38.783445+00:00","updated_at":"2026-05-18T00:51:38.783445+00:00"}