{"id":"https://openalex.org/W4294975615","doi":"https://doi.org/10.1109/embc48229.2022.9871266","title":"Fast and Sample Accurate R-Peak Detection for Noisy ECG Using Visibility Graphs","display_name":"Fast and Sample Accurate R-Peak Detection for Noisy ECG Using Visibility Graphs","publication_year":2022,"publication_date":"2022-07-11","ids":{"openalex":"https://openalex.org/W4294975615","doi":"https://doi.org/10.1109/embc48229.2022.9871266","pmid":"https://pubmed.ncbi.nlm.nih.gov/36086455"},"language":"en","primary_location":{"id":"doi:10.1109/embc48229.2022.9871266","is_oa":false,"landing_page_url":"https://doi.org/10.1109/embc48229.2022.9871266","pdf_url":null,"source":{"id":"https://openalex.org/S4363607706","display_name":"2022 44th Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 44th Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5049568697","display_name":"Taulant Koka","orcid":"https://orcid.org/0000-0001-6377-3439"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Taulant Koka","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5068601055","display_name":"Michael Muma","orcid":"https://orcid.org/0000-0002-7983-1944"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Michael Muma","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5049568697"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.5813,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.95777027,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"2022","issue":null,"first_page":"121","last_page":"126"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.9997000098228455,"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":0.9997000098228455,"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9940000176429749,"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"}},{"id":"https://openalex.org/T10745","display_name":"Heart Rate Variability and Autonomic Control","score":0.9914000034332275,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.7770471572875977},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6979210376739502},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.6330822110176086},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5587217807769775},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5573305487632751},{"id":"https://openalex.org/keywords/step-detection","display_name":"Step detection","score":0.5364603996276855},{"id":"https://openalex.org/keywords/visibility-graph","display_name":"Visibility graph","score":0.5189116597175598},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4700948894023895},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45513689517974854},{"id":"https://openalex.org/keywords/visibility","display_name":"Visibility","score":0.4509647488594055},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.443952351808548},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4402840733528137},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.41698119044303894},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.273149311542511},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22442740201950073},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.16796985268592834},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.12233883142471313},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.11037704348564148},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.09053921699523926},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.08522126078605652}],"concepts":[{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.7770471572875977},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6979210376739502},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.6330822110176086},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5587217807769775},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5573305487632751},{"id":"https://openalex.org/C293773","wikidata":"https://www.wikidata.org/wiki/Q7608015","display_name":"Step detection","level":3,"score":0.5364603996276855},{"id":"https://openalex.org/C173362246","wikidata":"https://www.wikidata.org/wiki/Q8216024","display_name":"Visibility graph","level":3,"score":0.5189116597175598},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4700948894023895},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45513689517974854},{"id":"https://openalex.org/C123403432","wikidata":"https://www.wikidata.org/wiki/Q654068","display_name":"Visibility","level":2,"score":0.4509647488594055},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.443952351808548},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4402840733528137},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41698119044303894},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.273149311542511},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22442740201950073},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.16796985268592834},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.12233883142471313},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.11037704348564148},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.09053921699523926},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.08522126078605652},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D004562","descriptor_name":"Electrocardiography","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D004562","descriptor_name":"Electrocardiography","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D004562","descriptor_name":"Electrocardiography","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D009622","descriptor_name":"Noise","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D009622","descriptor_name":"Noise","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D009622","descriptor_name":"Noise","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012815","descriptor_name":"Signal Processing, Computer-Assisted","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D012815","descriptor_name":"Signal Processing, Computer-Assisted","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D012815","descriptor_name":"Signal Processing, Computer-Assisted","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016208","descriptor_name":"Databases, Factual","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016208","descriptor_name":"Databases, Factual","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016208","descriptor_name":"Databases, Factual","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":3,"locations":[{"id":"doi:10.1109/embc48229.2022.9871266","is_oa":false,"landing_page_url":"https://doi.org/10.1109/embc48229.2022.9871266","pdf_url":null,"source":{"id":"https://openalex.org/S4363607706","display_name":"2022 44th Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 44th Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC)","raw_type":"proceedings-article"},{"id":"pmid:36086455","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/36086455","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","raw_type":null},{"id":"pmh:oai:tubiblio.ulb.tu-darmstadt.de:129962","is_oa":false,"landing_page_url":"http://tubiblio.ulb.tu-darmstadt.de/129962/","pdf_url":null,"source":{"id":"https://openalex.org/S4377196390","display_name":"TUbilio (Technical University of Darmstadt)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I31512782","host_organization_name":"Technische Universit\u00e4t Darmstadt","host_organization_lineage":["https://openalex.org/I31512782"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Konferenzver\u00f6ffentlichung"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.46000000834465027,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1605480097","https://openalex.org/W1895255156","https://openalex.org/W1971937094","https://openalex.org/W2005897501","https://openalex.org/W2055538060","https://openalex.org/W2056944867","https://openalex.org/W2063875884","https://openalex.org/W2083812645","https://openalex.org/W2089947864","https://openalex.org/W2104563593","https://openalex.org/W2106822551","https://openalex.org/W2114950466","https://openalex.org/W2119433462","https://openalex.org/W2145044818","https://openalex.org/W2162273778","https://openalex.org/W2197473678","https://openalex.org/W2287472077","https://openalex.org/W2537943564","https://openalex.org/W2605942112","https://openalex.org/W2783767524","https://openalex.org/W2811420366","https://openalex.org/W2899125647","https://openalex.org/W2922165552","https://openalex.org/W2963768879","https://openalex.org/W2963832159","https://openalex.org/W3033523981","https://openalex.org/W3127637041","https://openalex.org/W3159712939","https://openalex.org/W3208266734","https://openalex.org/W4250772482"],"related_works":["https://openalex.org/W2743652671","https://openalex.org/W2032709970","https://openalex.org/W2066813664","https://openalex.org/W2963183586","https://openalex.org/W2159176266","https://openalex.org/W2094166299","https://openalex.org/W2360664695","https://openalex.org/W137371565","https://openalex.org/W1608416195","https://openalex.org/W1585675989"],"abstract_inverted_index":{"More":[0],"than":[1],"a":[2,28,47,61,66,73,171,191],"century":[3],"has":[4,37],"passed":[5],"since":[6],"Einthoven":[7],"laid":[8],"the":[9,20,55,87,96,118,132,145,154,159,167,180,183],"foundation":[10],"of":[11,22,32,86,90,141,156,182],"modern":[12],"electrocardiography":[13],"and":[14,24,75,108,124,144,173],"in":[15,34,153],"recent":[16],"years,":[17],"driven":[18],"by":[19,68,122,190],"advance":[21],"wearable":[23],"low":[25],"budget":[26],"devices,":[27],"sample":[29,71,174],"accurate":[30,175],"detection":[31,50],"R-peaks":[33,101],"noisy":[35,172],"ECG-signals":[36],"become":[38],"increasingly":[39],"important.":[40],"To":[41],"accommodate":[42],"these":[43],"demands,":[44],"we":[45],"propose":[46],"new":[48],"R-peak":[49],"approach":[51],"that":[52,100],"builds":[53],"upon":[54],"visibility":[56],"graph":[57,67],"transformation,":[58],"which":[59,186],"maps":[60],"discrete":[62],"time":[63,146],"series":[64],"to":[65,94,129,150],"expressing":[69],"each":[70],"as":[72,117],"node":[74],"assigning":[76],"edges":[77],"between":[78],"intervisible":[79],"samples.":[80],"The":[81,134,177],"proposed":[82,184],"method":[83,160],"takes":[84],"advantage":[85],"high":[88],"connectivity":[89],"large,":[91],"isolated":[92],"values":[93],"weight":[95],"original":[97],"signal":[98,106],"so":[99],"are":[102,110,136],"amplified":[103],"while":[104],"other":[105],"components":[107],"noise":[109],"suppressed.":[111],"A":[112],"simple":[113],"thresholding":[114,169],"procedure,":[115],"such":[116],"widely":[119],"used":[120],"one":[121],"Pan":[123],"Tompkins,":[125],"is":[126,148,161],"then":[127],"sufficient":[128],"accurately":[130],"detect":[131],"R-peaks.":[133],"weights":[135],"computed":[137],"for":[138],"overlapping":[139],"segments":[140],"equal":[142],"size":[143],"complexity":[147],"shown":[149],"be":[151],"linear":[152],"number":[155],"segments.":[157],"Finally,":[158],"benchmarked":[162],"against":[163],"existing":[164],"methods":[165],"using":[166],"same":[168],"on":[170],"database.":[176],"results":[178],"illustrate":[179],"potential":[181],"method,":[185],"outperforms":[187],"common":[188],"detectors":[189],"significant":[192],"margin.":[193]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":4}],"updated_date":"2026-02-26T08:16:20.718346","created_date":"2025-10-10T00:00:00"}
