{"id":"https://openalex.org/W7130363206","doi":"https://doi.org/10.1109/tencon66050.2025.11375069","title":"Cardiac Care IoT and ML: Portable Home-Based Cardiovascular Monitoring for Early Risk Assessment","display_name":"Cardiac Care IoT and ML: Portable Home-Based Cardiovascular Monitoring for Early Risk Assessment","publication_year":2025,"publication_date":"2025-10-27","ids":{"openalex":"https://openalex.org/W7130363206","doi":"https://doi.org/10.1109/tencon66050.2025.11375069"},"language":null,"primary_location":{"id":"doi:10.1109/tencon66050.2025.11375069","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon66050.2025.11375069","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2025 - 2025 IEEE Region 10 Conference (TENCON)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5126295575","display_name":"Khondoker Ahmed Zubaier","orcid":null},"institutions":[{"id":"https://openalex.org/I157386601","display_name":"North South University","ror":"https://ror.org/05wdbfp45","country_code":"BD","type":"education","lineage":["https://openalex.org/I157386601"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Khondoker Ahmed Zubaier","raw_affiliation_strings":["North South University,Electrical and Computer Engineering,Dhaka,Bangladesh"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"North South University,Electrical and Computer Engineering,Dhaka,Bangladesh","institution_ids":["https://openalex.org/I157386601"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126351257","display_name":"Fatiha Tultul","orcid":null},"institutions":[{"id":"https://openalex.org/I157386601","display_name":"North South University","ror":"https://ror.org/05wdbfp45","country_code":"BD","type":"education","lineage":["https://openalex.org/I157386601"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Fatiha Tultul","raw_affiliation_strings":["North South University,Electrical and Computer Engineering,Dhaka,Bangladesh"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"North South University,Electrical and Computer Engineering,Dhaka,Bangladesh","institution_ids":["https://openalex.org/I157386601"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126357269","display_name":"Intesar Hassan Bhuiyan","orcid":null},"institutions":[{"id":"https://openalex.org/I157386601","display_name":"North South University","ror":"https://ror.org/05wdbfp45","country_code":"BD","type":"education","lineage":["https://openalex.org/I157386601"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Intesar Hassan Bhuiyan","raw_affiliation_strings":["North South University,Electrical and Computer Engineering,Dhaka,Bangladesh"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"North South University,Electrical and Computer Engineering,Dhaka,Bangladesh","institution_ids":["https://openalex.org/I157386601"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028320411","display_name":"Fariah Mahzabeen","orcid":"https://orcid.org/0000-0003-3135-3100"},"institutions":[{"id":"https://openalex.org/I157386601","display_name":"North South University","ror":"https://ror.org/05wdbfp45","country_code":"BD","type":"education","lineage":["https://openalex.org/I157386601"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Fariah Mahzabeen","raw_affiliation_strings":["North South University,Electrical and Computer Engineering,Dhaka,Bangladesh"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"North South University,Electrical and Computer Engineering,Dhaka,Bangladesh","institution_ids":["https://openalex.org/I157386601"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5130660718","display_name":"Riasat Khan","orcid":null},"institutions":[{"id":"https://openalex.org/I157386601","display_name":"North South University","ror":"https://ror.org/05wdbfp45","country_code":"BD","type":"education","lineage":["https://openalex.org/I157386601"]}],"countries":["BD"],"is_corresponding":false,"raw_author_name":"Riasat Khan","raw_affiliation_strings":["North South University,Electrical and Computer Engineering,Dhaka,Bangladesh"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"North South University,Electrical and Computer Engineering,Dhaka,Bangladesh","institution_ids":["https://openalex.org/I157386601"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I157386601"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.78827977,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"978","last_page":"982"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.45590001344680786,"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":0.45590001344680786,"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/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.3862000107765198,"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/T13248","display_name":"Healthcare Technology and Patient Monitoring","score":0.032999999821186066,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"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/sudden-cardiac-arrest","display_name":"Sudden cardiac arrest","score":0.6941999793052673},{"id":"https://openalex.org/keywords/triage","display_name":"Triage","score":0.6484000086784363},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.49709999561309814},{"id":"https://openalex.org/keywords/sudden-cardiac-death","display_name":"Sudden cardiac death","score":0.45890000462532043},{"id":"https://openalex.org/keywords/risk-assessment","display_name":"Risk assessment","score":0.3476000130176544},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3463999927043915},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.32199999690055847},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.3070000112056732},{"id":"https://openalex.org/keywords/internet-of-things","display_name":"Internet of Things","score":0.30399999022483826}],"concepts":[{"id":"https://openalex.org/C2778550298","wikidata":"https://www.wikidata.org/wiki/Q202837","display_name":"Sudden cardiac arrest","level":2,"score":0.6941999793052673},{"id":"https://openalex.org/C2777120189","wikidata":"https://www.wikidata.org/wiki/Q780067","display_name":"Triage","level":2,"score":0.6484000086784363},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.6118999719619751},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.49709999561309814},{"id":"https://openalex.org/C2775935837","wikidata":"https://www.wikidata.org/wiki/Q202837","display_name":"Sudden cardiac death","level":2,"score":0.45890000462532043},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44440001249313354},{"id":"https://openalex.org/C545542383","wikidata":"https://www.wikidata.org/wiki/Q2751242","display_name":"Medical emergency","level":1,"score":0.44110000133514404},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4307999908924103},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3684999942779541},{"id":"https://openalex.org/C12174686","wikidata":"https://www.wikidata.org/wiki/Q1058438","display_name":"Risk assessment","level":2,"score":0.3476000130176544},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3463999927043915},{"id":"https://openalex.org/C194828623","wikidata":"https://www.wikidata.org/wiki/Q2861470","display_name":"Emergency medicine","level":1,"score":0.3434999883174896},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.32199999690055847},{"id":"https://openalex.org/C177713679","wikidata":"https://www.wikidata.org/wiki/Q679690","display_name":"Intensive care medicine","level":1,"score":0.3125},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.3070000112056732},{"id":"https://openalex.org/C81860439","wikidata":"https://www.wikidata.org/wiki/Q251212","display_name":"Internet of Things","level":2,"score":0.30399999022483826},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.29280000925064087},{"id":"https://openalex.org/C11783203","wikidata":"https://www.wikidata.org/wiki/Q5478027","display_name":"Framingham Risk Score","level":3,"score":0.29120001196861267},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.2863999903202057},{"id":"https://openalex.org/C3018284874","wikidata":"https://www.wikidata.org/wiki/Q389735","display_name":"Cardiovascular health","level":3,"score":0.28369998931884766},{"id":"https://openalex.org/C2778134438","wikidata":"https://www.wikidata.org/wiki/Q11490631","display_name":"Cardiac monitoring","level":2,"score":0.28349998593330383},{"id":"https://openalex.org/C555175668","wikidata":"https://www.wikidata.org/wiki/Q539690","display_name":"Angiology","level":2,"score":0.28209999203681946},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.28130000829696655},{"id":"https://openalex.org/C2779891985","wikidata":"https://www.wikidata.org/wiki/Q46994","display_name":"Telemedicine","level":3,"score":0.2757999897003174},{"id":"https://openalex.org/C179755657","wikidata":"https://www.wikidata.org/wiki/Q58702","display_name":"Mortality rate","level":2,"score":0.2623000144958496},{"id":"https://openalex.org/C2777055891","wikidata":"https://www.wikidata.org/wiki/Q185325","display_name":"Cardiopulmonary resuscitation","level":3,"score":0.26010000705718994},{"id":"https://openalex.org/C147494362","wikidata":"https://www.wikidata.org/wiki/Q2078905","display_name":"Troubleshooting","level":2,"score":0.2583000063896179},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2558000087738037},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.2513999938964844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tencon66050.2025.11375069","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon66050.2025.11375069","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2025 - 2025 IEEE Region 10 Conference (TENCON)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.9109603762626648,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1995625741","https://openalex.org/W2914902556","https://openalex.org/W4200003377","https://openalex.org/W4223442335","https://openalex.org/W4322765178","https://openalex.org/W4323644141","https://openalex.org/W4365517060","https://openalex.org/W4380272221","https://openalex.org/W4388775240","https://openalex.org/W4388931882","https://openalex.org/W4390588437","https://openalex.org/W4390670395","https://openalex.org/W4392169308","https://openalex.org/W4396508338","https://openalex.org/W4401585598","https://openalex.org/W4402634687","https://openalex.org/W4403894534"],"related_works":[],"abstract_inverted_index":{"Sudden":[0],"cardiac":[1,12,28,57,74,241],"arrest":[2,75],"is":[3,24,38,114,181],"a":[4,27,55,90,222,232],"significant":[5],"global":[6],"health":[7,36,97,123],"concern;":[8],"however,":[9],"triage":[10],"of":[11,72,210,216],"events":[13],"typically":[14],"occurs":[15],"only":[16],"in":[17,63,173],"clinical":[18],"settings,":[19],"often":[20],"when":[21],"the":[22,68,108,142,174,198],"patient":[23],"already":[25],"experiencing":[26],"episode.":[29],"Effective":[30],"and":[31,87,119,138,164,188,212,225],"efficient":[32],"at-home":[33],"cardiovascular":[34,96,127],"virtual":[35],"monitoring":[37,234],"essential":[39],"for":[40,116,161,167],"early":[41],"intervention,":[42],"potentially":[43],"preventing":[44],"sudden":[45,73],"fatalities.":[46],"A":[47,99,178],"highly":[48],"accurate":[49],"machine":[50],"learning":[51],"model":[52,92,117,206],"combined":[53,100],"with":[54,195],"Veroboardintegrated":[56],"care":[58],"kit":[59],"has":[60],"been":[61,171],"developed":[62],"this":[64],"work,":[65],"which":[66],"addresses":[67],"high":[69,226],"mortality":[70],"rate":[71],"through":[76],"home-based":[77],"monitoring.":[78],"This":[79,218],"device":[80,220],"integrates":[81],"three":[82],"critical":[83,126],"parameters-ECG,":[84],"blood":[85,132],"pressure,":[86],"heart":[88],"rate-into":[89],"machine-learning":[91],"to":[93,157,236],"analyze":[94],"real-time":[95],"data.":[98],"dataset":[101,143,175],"of$\\mathbf{1,":[102],"6":[103],"9":[104],"0}$cardiac":[105],"patients":[106],"from":[107,239],"UC":[109],"Irvine":[110],"Machine":[111],"Learning":[112],"Repository":[113],"used":[115],"training":[118],"testing,":[120],"encompassing":[121],"11":[122],"features,":[124],"including":[125],"indicators":[128],"such":[129],"as":[130,192,197,231],"fasting":[131],"sugar,":[133],"ECG":[134],"results,":[135],"exercise-induced":[136],"angina,":[137],"ST":[139],"slope.":[140],"While":[141],"includes":[144],"individuals":[145,154],"across":[146],"different":[147],"age":[148],"groups,":[149],"it":[150,229],"primarily":[151],"focuses":[152],"on":[153],"aged":[155],"40":[156],"60.":[158],"Min-max":[159],"scaling":[160],"continuous":[162],"features":[163,169],"one-hot":[165],"encoding":[166],"categorical":[168],"have":[170],"applied":[172,203],"preprocessing":[176],"stage.":[177],"Stacking":[179,204],"classifier":[180],"implemented,":[182],"using":[183],"Decision":[184],"Tree,":[185],"Random":[186],"Forest,":[187],"Gradient":[189],"Boost":[190],"classifiers":[191],"base":[193],"estimators,":[194],"KNN":[196],"final":[199],"meta":[200],"estimator.":[201],"The":[202],"ensemble":[205],"achieves":[207],"an":[208,213],"accuracy":[209],"95.6%":[211],"F1":[214],"score":[215],"95.9%.":[217],"proposed":[219],"ensures":[221],"user-friendly":[223],"interface":[224],"accuracy,":[227],"making":[228],"suitable":[230],"household":[233],"tool":[235],"reduce":[237],"fatalities":[238],"unanticipated":[240],"events.":[242]},"counts_by_year":[],"updated_date":"2026-08-12T21:12:35.861297","created_date":"2026-02-19T00:00:00"}
