{"id":"https://openalex.org/W3119910168","doi":"https://doi.org/10.1145/3431943.3431956","title":"Performance Characterization of Binary Classifiers for Automatic Annotation of Aortic Valve Opening in Seismocardiogram Signals","display_name":"Performance Characterization of Binary Classifiers for Automatic Annotation of Aortic Valve Opening in Seismocardiogram Signals","publication_year":2020,"publication_date":"2020-10-16","ids":{"openalex":"https://openalex.org/W3119910168","doi":"https://doi.org/10.1145/3431943.3431956","mag":"3119910168"},"language":"en","primary_location":{"id":"doi:10.1145/3431943.3431956","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3431943.3431956","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 9th International Conference on Bioinformatics and Biomedical Science","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/A5074629782","display_name":"Deepak Rai","orcid":"https://orcid.org/0000-0002-7635-3801"},"institutions":[{"id":"https://openalex.org/I3129773123","display_name":"Bennett University","ror":"https://ror.org/00an5hx75","country_code":"IN","type":"education","lineage":["https://openalex.org/I3129773123"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Deepak Rai","raw_affiliation_strings":["Bennett University, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bennett University, India","institution_ids":["https://openalex.org/I3129773123"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050290363","display_name":"Hiren Kumar Thakkar","orcid":"https://orcid.org/0000-0002-4196-7651"},"institutions":[{"id":"https://openalex.org/I3129773123","display_name":"Bennett University","ror":"https://ror.org/00an5hx75","country_code":"IN","type":"education","lineage":["https://openalex.org/I3129773123"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Hiren Kumar Thakkar","raw_affiliation_strings":["Bennett University, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bennett University, India","institution_ids":["https://openalex.org/I3129773123"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091167597","display_name":"Shyam Singh Rajput","orcid":"https://orcid.org/0000-0002-1244-7366"},"institutions":[{"id":"https://openalex.org/I91277730","display_name":"Maulana Azad National Institute of Technology","ror":"https://ror.org/026vtd268","country_code":"IN","type":"education","lineage":["https://openalex.org/I91277730"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Shyam Singh Rajput","raw_affiliation_strings":["National Institute of Technology, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Institute of Technology, India","institution_ids":["https://openalex.org/I91277730"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7842,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.69569989,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"77","last_page":"82"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.9911999702453613,"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/T12419","display_name":"Phonocardiography and Auscultation Techniques","score":0.9894000291824341,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory 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/annotation","display_name":"Annotation","score":0.7944800853729248},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7848654389381409},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.7035261988639832},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.6553241610527039},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.566720724105835},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5618471503257751},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.4797267019748688},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.4790681302547455},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47392019629478455},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.42166438698768616},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.35832810401916504},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3234155774116516},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1600005328655243}],"concepts":[{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.7944800853729248},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7848654389381409},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.7035261988639832},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.6553241610527039},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.566720724105835},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5618471503257751},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.4797267019748688},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.4790681302547455},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47392019629478455},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.42166438698768616},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.35832810401916504},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3234155774116516},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1600005328655243},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3431943.3431956","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3431943.3431956","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 9th International Conference on Bioinformatics and Biomedical Science","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5600000023841858,"display_name":"Responsible consumption and production","id":"https://metadata.un.org/sdg/12"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2066481362","https://openalex.org/W2530643226","https://openalex.org/W2538315610","https://openalex.org/W2562153080","https://openalex.org/W2581494515","https://openalex.org/W2592923127","https://openalex.org/W2603156040","https://openalex.org/W2790457091","https://openalex.org/W2803038464","https://openalex.org/W2986588121","https://openalex.org/W2997591727","https://openalex.org/W4242831755"],"related_works":["https://openalex.org/W4389954502","https://openalex.org/W2771255398","https://openalex.org/W2930428186","https://openalex.org/W3200027047","https://openalex.org/W4224922629","https://openalex.org/W4385770464","https://openalex.org/W3125536479","https://openalex.org/W4214820172","https://openalex.org/W3120363735","https://openalex.org/W2394323384"],"abstract_inverted_index":{"In":[0,69],"the":[1,11,58,63,75,89,150],"recent":[2],"past":[3],"seismocardiogram":[4],"(SCG)":[5],"has":[6,42],"emerged":[7],"as":[8,47,92,101,128],"one":[9],"of":[10,39,66,74,132,137],"potential":[12,43],"non-invasive":[13],"modalities":[14],"to":[15,56,62],"estimate":[16],"cardiac":[17,31,49],"health":[18,50],"parameters.":[19],"Each":[20],"SCG":[21,25,40,59,67,78,146],"cycle":[22],"contains":[23],"specific":[24,30],"peaks":[26,41],"that":[27,170],"help":[28],"identify":[29],"mechanical":[32],"events.":[33],"The":[34,134,155,166],"accurate":[35],"and":[36,112,122,130,159,172],"automatic":[37,72],"annotation":[38,60,73,91,121],"daily-life":[44],"applications":[45],"such":[46,100,127],"continuous":[48],"monitoring.":[51],"However,":[52],"it":[53],"is":[54,85,141],"challenging":[55],"automate":[57],"due":[61],"morphological":[64],"variations":[65],"signals.":[68],"this":[70],"paper,":[71],"most":[76],"important":[77],"peak":[79],"called":[80],"Aortic":[81],"valve":[82],"Opening":[83],"(AO)":[84],"explored":[86],"by":[87,124,163],"formulating":[88],"AO":[90,120],"a":[93],"binary":[94,98,139],"classification":[95],"problem.":[96],"Four":[97],"classifiers":[99,140,156],"Logistic":[102],"Regression":[103],"(LR),":[104],"Support":[105],"Vector":[106],"Machine":[107],"(SVM),":[108],"Decision":[109],"Tree":[110],"(DT),":[111],"Gaussian":[113],"Na\u00efve":[114],"Bayes":[115],"(GNB)":[116],"are":[117,157],"used":[118],"for":[119],"supported":[123],"empirical":[125],"features":[126],"\u201dAmplitude\u201d":[129],"\u201dTime":[131],"Appearance\u201d.":[133],"performance":[135,180],"comparison":[136],"these":[138],"carried":[142],"out":[143],"using":[144],"759":[145],"signals":[147],"acquired":[148],"from":[149],"Physionet":[151],"public":[152],"repository":[153],"\u201ccebsdb\u201d.":[154],"trained":[158],"comprehensively":[160],"tested":[161],"followed":[162],"5-fold":[164],"cross-validation.":[165],"experimental":[167],"results":[168],"show":[169],"GNB":[171],"DT":[173],"consistently":[174],"perform":[175],"well":[176],"on":[177],"established":[178],"seven":[179],"metrics.":[181]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
