{"id":"https://openalex.org/W4311224545","doi":"https://doi.org/10.1109/icecs202256217.2022.9970834","title":"Binary ECG Classification Using Explainable Boosting Machines for IoT Edge Devices","display_name":"Binary ECG Classification Using Explainable Boosting Machines for IoT Edge Devices","publication_year":2022,"publication_date":"2022-10-24","ids":{"openalex":"https://openalex.org/W4311224545","doi":"https://doi.org/10.1109/icecs202256217.2022.9970834"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/icecs202256217.2022.9970834","pdf_url":null,"source":{"id":"https://openalex.org/S4363608220","display_name":"2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS)","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":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5100353732","display_name":"Xiaolin Li","orcid":null},"institutions":[{"id":"https://openalex.org/I100930933","display_name":"University College Dublin","ror":"https://ror.org/05m7pjf47","country_code":"IE","type":"education","lineage":["https://openalex.org/I100930933"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"Li Xiaolin","raw_affiliation_strings":["University College Dublin"],"affiliations":[{"raw_affiliation_string":"University College Dublin","institution_ids":["https://openalex.org/I100930933"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062719337","display_name":"Qingyuan Wang","orcid":"https://orcid.org/0000-0002-7879-4328"},"institutions":[{"id":"https://openalex.org/I100930933","display_name":"University College Dublin","ror":"https://ror.org/05m7pjf47","country_code":"IE","type":"education","lineage":["https://openalex.org/I100930933"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"Wang Qingyuan","raw_affiliation_strings":["University College Dublin"],"affiliations":[{"raw_affiliation_string":"University College Dublin","institution_ids":["https://openalex.org/I100930933"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046881927","display_name":"Rajesh C. Panicker","orcid":null},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Rajesh C. Panicker","raw_affiliation_strings":["National University of Singapore"],"affiliations":[{"raw_affiliation_string":"National University of Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072460590","display_name":"Barry Cardiff","orcid":"https://orcid.org/0000-0003-1303-8115"},"institutions":[{"id":"https://openalex.org/I100930933","display_name":"University College Dublin","ror":"https://ror.org/05m7pjf47","country_code":"IE","type":"education","lineage":["https://openalex.org/I100930933"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"Barry Cardiff","raw_affiliation_strings":["University College Dublin"],"affiliations":[{"raw_affiliation_string":"University College Dublin","institution_ids":["https://openalex.org/I100930933"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029098934","display_name":"Deepu John","orcid":"https://orcid.org/0000-0002-6139-1100"},"institutions":[{"id":"https://openalex.org/I100930933","display_name":"University College Dublin","ror":"https://ror.org/05m7pjf47","country_code":"IE","type":"education","lineage":["https://openalex.org/I100930933"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"Deepu John","raw_affiliation_strings":["University College Dublin"],"affiliations":[{"raw_affiliation_string":"University College Dublin","institution_ids":["https://openalex.org/I100930933"]}]}],"institution_assertions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.023,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.777597,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":85,"max":87},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":1.0,"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":1.0,"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":0.9978,"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/T12419","display_name":"Phonocardiography and Auscultation Techniques","score":0.9783,"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/boosting","display_name":"Boosting","score":0.75934184},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.68923634},{"id":"https://openalex.org/keywords/binary-decision-diagram","display_name":"Binary decision diagram","score":0.54044235},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.50728554},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.4854758}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.75934184},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.75501764},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.68923634},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6656897},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.6267053},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6063646},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.55340886},{"id":"https://openalex.org/C3309909","wikidata":"https://www.wikidata.org/wiki/Q864155","display_name":"Binary decision diagram","level":2,"score":0.54044235},{"id":"https://openalex.org/C150594956","wikidata":"https://www.wikidata.org/wiki/Q1334829","display_name":"Wearable computer","level":2,"score":0.52049965},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5201069},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5106178},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.50728554},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.4854758},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4773084},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.43465954},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36775774},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.34509236},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.10299134},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/icecs202256217.2022.9970834","pdf_url":null,"source":{"id":"https://openalex.org/S4363608220","display_name":"2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS)","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":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[],"grants":[{"funder":"https://openalex.org/F4320320847","funder_display_name":"Science Foundation Ireland","award_id":"18/CRT/6183"},{"funder":"https://openalex.org/F4320321056","funder_display_name":"Irish Research Council","award_id":null},{"funder":"https://openalex.org/F4320322725","funder_display_name":"China Scholarship Council","award_id":null}],"datasets":[],"versions":[],"referenced_works_count":16,"referenced_works":["https://openalex.org/W1530181845","https://openalex.org/W2034128732","https://openalex.org/W2046945713","https://openalex.org/W2162800060","https://openalex.org/W2519152515","https://openalex.org/W2748902594","https://openalex.org/W2792543406","https://openalex.org/W2972073115","https://openalex.org/W3082355596","https://openalex.org/W3109650690","https://openalex.org/W3116399583","https://openalex.org/W3198436897","https://openalex.org/W4205987779","https://openalex.org/W4226082372","https://openalex.org/W4233275825","https://openalex.org/W4383501757"],"related_works":["https://openalex.org/W4310492845","https://openalex.org/W4310224730","https://openalex.org/W4289703016","https://openalex.org/W3204430031","https://openalex.org/W3137904399","https://openalex.org/W3094138326","https://openalex.org/W2967733078","https://openalex.org/W2885778889","https://openalex.org/W2885516856","https://openalex.org/W2766514146"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"an":[3,29,72,181],"explainable,":[4],"low-complexity":[5],"binary":[6,81],"electrocardiogram":[7],"(ECG)":[8],"classifier":[9,77,179,199],"to":[10,135,142,155,207],"be":[11,23,85],"deployed":[12],"in":[13,33,65,117,195,215,221],"a":[14,34,40,50,88,131,196],"resource-limited":[15],"wearable":[16,223],"edge":[17,30,48,224],"device.":[18,225],"The":[19],"presented":[20],"technique":[21],"could":[22],"used":[24,71,112,167,194],"as":[25],"stand":[26],"alone":[27],"on":[28,46,57],"device":[31],"or":[32],"two-stage":[35],"distributed":[36,197],"edge-cloud":[37,198],"classifier,":[38],"where":[39],"preliminary":[41,80],"two-class":[42],"classification":[43,54,164],"is":[44,55,96,147],"done":[45,56],"the":[47,58,61,79,122,137,151,163,168,177,211,218,222],"and":[49,93,103,130,160,176,189],"more":[51],"comprehensive":[52],"multi-class":[53],"cloud.":[59],"Considering":[60],"importance":[62],"of":[63,125,149,183,187,191,210],"interpretability":[64],"clinical":[66],"decision":[67,89],"support":[68],"systems,":[69],"we":[70,111],"Explainable":[73],"Boosting":[74],"Machine":[75],"(EBM)":[76],"for":[78,173],"classification.":[82],"EBMs":[83],"can":[84],"implemented":[86],"using":[87],"tree":[90],"like":[91],"structure":[92],"therefore":[94],"complexity":[95,110],"much":[97],"lower":[98],"than":[99],"deep":[100],"learning":[101,150],"models":[102],"many":[104],"traditional":[105],"classifiers.":[106],"To":[107],"further":[108],"limit":[109],"only":[113,208],"limited":[114],"ECG":[115],"features":[116,154],"this":[118],"work,":[119],"which":[120,214],"include":[121],"peak":[123],"amplitudes":[124,134],"signal":[126,133,138],"segments,":[127],"time":[128],"intervals,":[129],"few":[132],"represent":[136],"morphology.":[139],"In":[140],"addition":[141],"these":[143],"direct":[144],"features,":[145],"EBM":[146,178],"capable":[148],"interactions":[152],"between":[153],"derive":[156],"intermediate":[157],"feature":[158],"combinations":[159],"thus":[161],"improve":[162],"performance.":[165],"We":[166],"Physionet":[169],"MIT-BIH":[170],"Arrhythmia":[171],"dataset":[172],"performance":[174],"evaluation":[175],"achieves":[180],"accuracy":[182],"96.84%,":[184],"F1":[185],"score":[186],"91.38%,":[188],"sensitivity":[190],"96.83%.":[192],"When":[193],"configuration,":[200],"our":[201],"proposed":[202],"work":[203],"limits":[204],"cloud":[205],"transmission":[206],"19.37%":[209],"total":[212],"data,":[213],"turn":[216],"reduces":[217],"power":[219],"consumed":[220]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4311224545","counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":5}],"updated_date":"2024-12-09T06:29:48.998351","created_date":"2022-12-24"}