{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:2TH4K5UL3FJVKNACEYIJVBXT76","short_pith_number":"pith:2TH4K5UL","schema_version":"1.0","canonical_sha256":"d4cfc5768bd95355340226109a86f3ff89648109d8963bcee2220807628a9e0d","source":{"kind":"arxiv","id":"1708.05356","version":1},"attestation_state":"computed","paper":{"title":"Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NE","authors_text":"Anup Das, Chris Van Hoof, Francky Catthoor, Jeffrey L. Krichmar, Nikil Dutt, Paruthi Pradhapan, Prathyusha Adiraju, Raj Thilak Rajan, Siebren Schaafsma, Willemijn Groenendaal","submitted_at":"2017-07-18T13:53:55Z","abstract_excerpt":"Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine intelligent approach for heart-rate estimation from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a sub"},"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":"1708.05356","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-07-18T13:53:55Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0e938b4e3da0e7935ae87e328704ea7e2ef726026b98c6ca050dc23fe4a4a228","abstract_canon_sha256":"c050d09bf6cf6bf35b7dc0f4b9f1c4307ef6ab78545ceac4c1a6ed5682f22cb8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:23:53.181478Z","signature_b64":"0UG/N40KPDYbQWCDuQrG9xWTIAEV8kEO2Ac+7POHKYluc5Y8eP6byX00Q4pG0RGsWxFats1Gpt3J0ZCWO72/Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d4cfc5768bd95355340226109a86f3ff89648109d8963bcee2220807628a9e0d","last_reissued_at":"2026-05-18T00:23:53.180862Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:23:53.180862Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NE","authors_text":"Anup Das, Chris Van Hoof, Francky Catthoor, Jeffrey L. Krichmar, Nikil Dutt, Paruthi Pradhapan, Prathyusha Adiraju, Raj Thilak Rajan, Siebren Schaafsma, Willemijn Groenendaal","submitted_at":"2017-07-18T13:53:55Z","abstract_excerpt":"Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine intelligent approach for heart-rate estimation from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a sub"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.05356","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":"1708.05356","created_at":"2026-05-18T00:23:53.180933+00:00"},{"alias_kind":"arxiv_version","alias_value":"1708.05356v1","created_at":"2026-05-18T00:23:53.180933+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.05356","created_at":"2026-05-18T00:23:53.180933+00:00"},{"alias_kind":"pith_short_12","alias_value":"2TH4K5UL3FJV","created_at":"2026-05-18T12:30:55.937587+00:00"},{"alias_kind":"pith_short_16","alias_value":"2TH4K5UL3FJVKNAC","created_at":"2026-05-18T12:30:55.937587+00:00"},{"alias_kind":"pith_short_8","alias_value":"2TH4K5UL","created_at":"2026-05-18T12:30:55.937587+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/2TH4K5UL3FJVKNACEYIJVBXT76","json":"https://pith.science/pith/2TH4K5UL3FJVKNACEYIJVBXT76.json","graph_json":"https://pith.science/api/pith-number/2TH4K5UL3FJVKNACEYIJVBXT76/graph.json","events_json":"https://pith.science/api/pith-number/2TH4K5UL3FJVKNACEYIJVBXT76/events.json","paper":"https://pith.science/paper/2TH4K5UL"},"agent_actions":{"view_html":"https://pith.science/pith/2TH4K5UL3FJVKNACEYIJVBXT76","download_json":"https://pith.science/pith/2TH4K5UL3FJVKNACEYIJVBXT76.json","view_paper":"https://pith.science/paper/2TH4K5UL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1708.05356&json=true","fetch_graph":"https://pith.science/api/pith-number/2TH4K5UL3FJVKNACEYIJVBXT76/graph.json","fetch_events":"https://pith.science/api/pith-number/2TH4K5UL3FJVKNACEYIJVBXT76/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2TH4K5UL3FJVKNACEYIJVBXT76/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2TH4K5UL3FJVKNACEYIJVBXT76/action/storage_attestation","attest_author":"https://pith.science/pith/2TH4K5UL3FJVKNACEYIJVBXT76/action/author_attestation","sign_citation":"https://pith.science/pith/2TH4K5UL3FJVKNACEYIJVBXT76/action/citation_signature","submit_replication":"https://pith.science/pith/2TH4K5UL3FJVKNACEYIJVBXT76/action/replication_record"}},"created_at":"2026-05-18T00:23:53.180933+00:00","updated_at":"2026-05-18T00:23:53.180933+00:00"}