{"id":"https://openalex.org/W2949094152","doi":"https://doi.org/10.1109/ncc.2019.8732238","title":"A Weighted SVM Based Approach for Automatic Detection of Posterior Myocardial Infarction Using VCG Signals","display_name":"A Weighted SVM Based Approach for Automatic Detection of Posterior Myocardial Infarction Using VCG Signals","publication_year":2019,"publication_date":"2019-02-01","ids":{"openalex":"https://openalex.org/W2949094152","doi":"https://doi.org/10.1109/ncc.2019.8732238","mag":"2949094152"},"language":"en","primary_location":{"id":"doi:10.1109/ncc.2019.8732238","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ncc.2019.8732238","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 National Conference on Communications (NCC)","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/A5052664987","display_name":"Eedara Prabhakararao","orcid":null},"institutions":[{"id":"https://openalex.org/I1317621060","display_name":"Indian Institute of Technology Guwahati","ror":"https://ror.org/0022nd079","country_code":"IN","type":"education","lineage":["https://openalex.org/I1317621060"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Eedara Prabhakararao","raw_affiliation_strings":["Department of Electrical and Electronics Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronics Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India","institution_ids":["https://openalex.org/I1317621060"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030663279","display_name":"Samarendra Dandapat","orcid":"https://orcid.org/0000-0002-6467-8713"},"institutions":[{"id":"https://openalex.org/I1317621060","display_name":"Indian Institute of Technology Guwahati","ror":"https://ror.org/0022nd079","country_code":"IN","type":"education","lineage":["https://openalex.org/I1317621060"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Samarendra Dandapat","raw_affiliation_strings":["Department of Electrical and Electronics Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronics Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India","institution_ids":["https://openalex.org/I1317621060"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5052664987"],"corresponding_institution_ids":["https://openalex.org/I1317621060"],"apc_list":null,"apc_paid":null,"fwci":1.7629,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.86300186,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"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":0.9997000098228455,"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/T10217","display_name":"Cardiac electrophysiology and arrhythmias","score":0.9843000173568726,"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/T10065","display_name":"Atrial Fibrillation Management and Outcomes","score":0.9749000072479248,"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/support-vector-machine","display_name":"Support vector machine","score":0.6305462718009949},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6015238165855408},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5630532503128052},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.555397093296051},{"id":"https://openalex.org/keywords/myocardial-infarction","display_name":"Myocardial infarction","score":0.4645507037639618},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.4643903374671936},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4410475492477417},{"id":"https://openalex.org/keywords/electrocardiography","display_name":"Electrocardiography","score":0.4241103231906891},{"id":"https://openalex.org/keywords/cardiology","display_name":"Cardiology","score":0.34123876690864563},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.24078208208084106},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.23324042558670044}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6305462718009949},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6015238165855408},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5630532503128052},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.555397093296051},{"id":"https://openalex.org/C500558357","wikidata":"https://www.wikidata.org/wiki/Q12152","display_name":"Myocardial infarction","level":2,"score":0.4645507037639618},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.4643903374671936},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4410475492477417},{"id":"https://openalex.org/C2780040984","wikidata":"https://www.wikidata.org/wiki/Q79785","display_name":"Electrocardiography","level":2,"score":0.4241103231906891},{"id":"https://openalex.org/C164705383","wikidata":"https://www.wikidata.org/wiki/Q10379","display_name":"Cardiology","level":1,"score":0.34123876690864563},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.24078208208084106},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.23324042558670044}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ncc.2019.8732238","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ncc.2019.8732238","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 National Conference on Communications (NCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5099999904632568,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1521872049","https://openalex.org/W1527699425","https://openalex.org/W1548678411","https://openalex.org/W1555148682","https://openalex.org/W1586692934","https://openalex.org/W1999318832","https://openalex.org/W2011360192","https://openalex.org/W2047181473","https://openalex.org/W2053468861","https://openalex.org/W2060114499","https://openalex.org/W2105811964","https://openalex.org/W2115356273","https://openalex.org/W2118978333","https://openalex.org/W2125202294","https://openalex.org/W2153635508","https://openalex.org/W2274452222","https://openalex.org/W2330905130","https://openalex.org/W2527977052","https://openalex.org/W3120421331","https://openalex.org/W6632828075","https://openalex.org/W6678546190","https://openalex.org/W6694608216"],"related_works":["https://openalex.org/W1574414179","https://openalex.org/W4362597605","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4297676672","https://openalex.org/W4281702477","https://openalex.org/W4378510483","https://openalex.org/W4376166922","https://openalex.org/W2490526372","https://openalex.org/W4221142204"],"abstract_inverted_index":{"Myocardial":[0],"infarction":[1],"(MI),":[2],"commonly":[3],"known":[4],"as":[5,60,89],"heart":[6,20,127],"attack":[7],"is":[8,34,57,87,173],"a":[9,35,159,174,198],"life-threatening":[10],"arrhythmia":[11],"occurs":[12],"due":[13],"to":[14,18,97,168,191],"insufficient":[15],"oxygen":[16],"supply":[17],"the":[19,50,193],"tissues":[21,55,128],"resulted":[22],"from":[23],"formation":[24],"of":[25,43,53,76,126,151,200,215,218,223],"clots":[26],"in":[27,122,139,177],"one":[28],"or":[29],"more":[30],"coronary":[31],"arteries.":[32],"There":[33],"growing":[36],"interest":[37],"among":[38],"researchers":[39],"for":[40,108,132],"automatic":[41,74,106],"detection":[42,75,110],"MI":[44,56,68,78],"using":[45,111,197],"computer":[46],"algorithms.":[47],"Based":[48],"on":[49],"spatial":[51],"location":[52],"damaged":[54],"further":[58],"categorized":[59],"anterior":[61],"MI,":[62,64,66],"septal":[63],"lateral":[65],"inferior":[67],"and":[69,136,203,220],"posterior":[70,77,96],"MI.":[71],"Among":[72],"all,":[73],"(PMI)":[79],"with":[80,230],"standard":[81],"12-lead":[82],"electrocardiogram":[83],"(12-lead":[84],"ECG)":[85],"signal":[86],"challenging":[88],"it":[90],"does":[91],"not":[92],"have":[93],"monitoring":[94],"electrodes":[95],"human":[98],"body.":[99],"In":[100],"this":[101],"paper,":[102],"we":[103,157],"propose":[104,158],"an":[105],"method":[107,195],"PMI":[109,137,205],"3-lead":[112,208],"vectorcardiogram":[113],"(3-lead":[114],"VCG)":[115],"signal.":[116],"The":[117,182,211],"proposed":[118,194],"approach":[119],"exploits":[120],"changes":[121,146],"electrical":[123],"conduction":[124],"properties":[125],"during":[129],"cardiac":[130],"activity":[131],"healthy":[133],"control":[134],"(HC)":[135],"subjects":[138],"three-dimensional":[140],"(3D)":[141],"space.":[142],"To":[143],"quantify":[144],"these":[145],"multiscale":[147],"eigen":[148],"features":[149],"(MSEF)":[150],"subband":[152],"matrices":[153],"are":[154,225],"used.":[155],"Furthermore,":[156],"cost":[160],"sensitive":[161],"weighted":[162],"support":[163],"vector":[164],"machine":[165],"(WSVM)":[166],"classifier":[167,229],"combat":[169],"class":[170],"imbalance,":[171],"which":[172],"common":[175],"problem":[176],"real-world":[178],"disease":[179],"data":[180],"classification.":[181],"publicly":[183],"available":[184],"PhysioNet/PTBDB":[185],"diagnostic":[186],"database":[187],"has":[188],"been":[189],"used":[190],"validate":[192],"by":[196,227],"total":[199],"1463":[201],"HC,":[202],"148":[204],"4":[206],"sec":[207],"VCG":[209],"signals.":[210],"best":[212],"test":[213],"accuracy":[214],"96.69%,":[216],"sensitivity":[217],"80%,":[219],"geometric":[221],"mean":[222],"88.72%":[224],"achieved":[226],"WSVM":[228],"radial":[231],"basis":[232],"function":[233],"(RBF)":[234],"kernel.":[235]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1}],"updated_date":"2026-01-08T20:05:33.558190","created_date":"2025-10-10T00:00:00"}