{"id":"https://openalex.org/W3094430732","doi":"https://doi.org/10.1109/mlsp49062.2020.9231814","title":"Pulse ID: The Case for Robustness of ECG as a Biometric Identifier","display_name":"Pulse ID: The Case for Robustness of ECG as a Biometric Identifier","publication_year":2020,"publication_date":"2020-09-01","ids":{"openalex":"https://openalex.org/W3094430732","doi":"https://doi.org/10.1109/mlsp49062.2020.9231814","mag":"3094430732"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp49062.2020.9231814","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp49062.2020.9231814","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5022702877","display_name":"Vishnu Chandrashekhar","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Vishnu Chandrashekhar","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Carnegie Mellon University"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040930648","display_name":"Prerna Singh","orcid":"https://orcid.org/0000-0003-2770-9493"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Prerna Singh","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Carnegie Mellon University"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044051422","display_name":"Mihir P. Paralkar","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mihir Paralkar","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Carnegie Mellon University"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088788482","display_name":"O.K. Tonguz","orcid":"https://orcid.org/0000-0002-6834-3371"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ozan K. Tonguz","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Carnegie Mellon University"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5022702877"],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":0.8962,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.77633595,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9929999709129333,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.992900013923645,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/biometrics","display_name":"Biometrics","score":0.8564134240150452},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7869498133659363},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7216953039169312},{"id":"https://openalex.org/keywords/identifier","display_name":"Identifier","score":0.6576683521270752},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5937116146087646},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5621828436851501},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5375295877456665},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.5300011038780212},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.5231332182884216},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.48458993434906006},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3960597515106201},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3578607141971588}],"concepts":[{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.8564134240150452},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7869498133659363},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7216953039169312},{"id":"https://openalex.org/C154504017","wikidata":"https://www.wikidata.org/wiki/Q853614","display_name":"Identifier","level":2,"score":0.6576683521270752},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5937116146087646},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5621828436851501},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5375295877456665},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.5300011038780212},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.5231332182884216},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.48458993434906006},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3960597515106201},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3578607141971588},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlsp49062.2020.9231814","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp49062.2020.9231814","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1891173449","https://openalex.org/W2037678283","https://openalex.org/W2037986891","https://openalex.org/W2101234009","https://openalex.org/W2131030544","https://openalex.org/W2161621370","https://openalex.org/W2181523240","https://openalex.org/W2191779130","https://openalex.org/W2407642697","https://openalex.org/W2518538812","https://openalex.org/W2563173059","https://openalex.org/W6685767691","https://openalex.org/W6714029921"],"related_works":["https://openalex.org/W2468279273","https://openalex.org/W2354198838","https://openalex.org/W2149037710","https://openalex.org/W2149281258","https://openalex.org/W2322340370","https://openalex.org/W1995418324","https://openalex.org/W2211301776","https://openalex.org/W2127079445","https://openalex.org/W2060905804","https://openalex.org/W2125926329"],"abstract_inverted_index":{"Electrocardiogram":[0],"(ECG)":[1],"signals":[2],"are":[3],"known":[4],"to":[5,18,53,76,124,148],"encode":[6],"unique":[7],"signatures":[8],"based":[9],"on":[10,69],"the":[11,15,59,78,126],"geometrical":[12],"characteristics":[13],"of":[14,80,101,109],"heart.":[16],"Due":[17],"other":[19,71],"advantages":[20],"-":[21,31],"such":[22],"as":[23],"continuity":[24],"and":[25,103,152],"accessibility":[26],"(now":[27],"via":[28],"smartwatch":[29],"technology)":[30],"ECG":[32,45,62,72,146],"could":[33],"make":[34],"for":[35],"a":[36,98],"robust":[37],"biometric":[38],"ID":[39],"system.":[40],"We":[41,111,130],"show":[42,144],"that":[43,137,145],"single-node":[44],"measurements":[46],"through":[47],"an":[48,55],"Apple":[49,60],"Watch":[50,61],"would":[51],"suffice":[52],"identify":[54,125],"individual.":[56],"Apart":[57],"from":[58,74],"data,":[63],"we":[64,88],"have":[65,112],"also":[66,113],"performed":[67],"analysis":[68],"two":[70,84],"datasets":[73],"PhysioNet":[75],"test":[77],"robustness":[79],"our":[81],"methods":[82],"in":[83,86,118],"situations:":[85],"particular,":[87],"tested":[89],"how":[90],"it":[91],"holds":[92],"up":[93],"against":[94],"high":[95,104],"volume":[96],"(across":[97,106],"large":[99],"number":[100],"individuals)":[102],"variability":[105],"different":[107,121],"states":[108],"activity).":[110],"compared":[114],"multiple":[115],"classifier":[116],"models":[117],"combination":[119],"with":[120],"feature":[122],"sets":[123],"most":[127],"superior":[128],"combination.":[129],"observed":[131],"Equal":[132],"Error":[133],"Rate":[134],"(EER)":[135],"values":[136],"were":[138],"consistently":[139],"<;":[140],"3%.":[141],"Our":[142],"results":[143],"proves":[147],"be":[149],"very":[150],"effective":[151],"robust.":[153]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
