{"id":"https://openalex.org/W4214903986","doi":"https://doi.org/10.1109/ccwc54503.2022.9720851","title":"ECG Data Analysis with IoT and Machine Learning","display_name":"ECG Data Analysis with IoT and Machine Learning","publication_year":2022,"publication_date":"2022-01-26","ids":{"openalex":"https://openalex.org/W4214903986","doi":"https://doi.org/10.1109/ccwc54503.2022.9720851"},"language":"en","primary_location":{"id":"doi:10.1109/ccwc54503.2022.9720851","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccwc54503.2022.9720851","pdf_url":null,"source":{"id":"https://openalex.org/S4363608098","display_name":"2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC)","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 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC)","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/A5072961734","display_name":"Abhigya Pote Shrestha","orcid":null},"institutions":[{"id":"https://openalex.org/I198034347","display_name":"Wentworth Institute of Technology","ror":"https://ror.org/03tqeft14","country_code":"US","type":"education","lineage":["https://openalex.org/I198034347"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abhigya Pote Shrestha","raw_affiliation_strings":["School of Engineering Wentworth Institute of Technology,Boston,MA,U.S.A","School of Engineering Wentworth Institute of Technology, Boston, MA, U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Engineering Wentworth Institute of Technology,Boston,MA,U.S.A","institution_ids":["https://openalex.org/I198034347"]},{"raw_affiliation_string":"School of Engineering Wentworth Institute of Technology, Boston, MA, U.S.A","institution_ids":["https://openalex.org/I198034347"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102706365","display_name":"Chen-Hsiang Yu","orcid":"https://orcid.org/0000-0002-8261-2849"},"institutions":[{"id":"https://openalex.org/I198034347","display_name":"Wentworth Institute of Technology","ror":"https://ror.org/03tqeft14","country_code":"US","type":"education","lineage":["https://openalex.org/I198034347"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chen-Hsiang Yu","raw_affiliation_strings":["School of Computing and Data Science Wentworth Institute of Technology,Boston,MA,U.S.A","School of Computing and Data Science Wentworth Institute of Technology, Boston, MA, U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing and Data Science Wentworth Institute of Technology,Boston,MA,U.S.A","institution_ids":["https://openalex.org/I198034347"]},{"raw_affiliation_string":"School of Computing and Data Science Wentworth Institute of Technology, Boston, MA, U.S.A","institution_ids":["https://openalex.org/I198034347"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.3497,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.88006757,"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":"0323","last_page":"0327"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9962000250816345,"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/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9957000017166138,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7417949438095093},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.660870373249054},{"id":"https://openalex.org/keywords/heartbeat","display_name":"Heartbeat","score":0.6321645975112915},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6030678749084473},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3286620080471039},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.08527752757072449}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7417949438095093},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.660870373249054},{"id":"https://openalex.org/C13852961","wikidata":"https://www.wikidata.org/wiki/Q17021880","display_name":"Heartbeat","level":2,"score":0.6321645975112915},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6030678749084473},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3286620080471039},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.08527752757072449}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ccwc54503.2022.9720851","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccwc54503.2022.9720851","pdf_url":null,"source":{"id":"https://openalex.org/S4363608098","display_name":"2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC)","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 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W2047098613","https://openalex.org/W2162800060","https://openalex.org/W2908758223","https://openalex.org/W2975698043","https://openalex.org/W4211159574"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4224009465","https://openalex.org/W2973489423","https://openalex.org/W4286629047","https://openalex.org/W4306321456","https://openalex.org/W4285260836","https://openalex.org/W3046775127","https://openalex.org/W3170094116","https://openalex.org/W4205958290"],"abstract_inverted_index":{"Regularity":[0],"and":[1,25,60,72,134,165,170],"irregularity":[2],"in":[3,33,175],"heartbeat":[4],"rhythms":[5,64,154],"are":[6],"widely":[7],"diagnosed":[8],"through":[9,119],"the":[10,17,34,138,156,176,183,193,196,212],"analysis":[11,21],"of":[12,16,36,41,68,195,211],"electrocardiogram":[13],"(ECG)":[14],"recordings":[15],"heart.":[18],"However,":[19],"clinical":[20],"comes":[22],"with":[23,84,234],"financial":[24],"scheduling":[26],"costs":[27],"that":[28,55,215],"may":[29],"not":[30,218],"be":[31],"necessary":[32],"absence":[35],"heart":[37,63,153],"illnesses.":[38],"The":[39,94,124,144,168,209],"goal":[40],"this":[42,92],"research":[43],"is":[44],"to":[45,58,78,90,106,114,130,137,191,222],"investigate":[46],"if":[47],"we":[48],"can":[49],"design":[50],"a":[51,85,98,115,131,185,206,235],"mobile":[52,186],"health":[53],"system":[54,82,96],"allows":[56],"users":[57,221],"monitor":[59,223],"analyze":[61,230],"their":[62,224],"by":[65],"using":[66],"Internet":[67],"Things":[69],"(IoT)":[70],"techniques":[71,217],"machine":[73,87,139,145,237],"learning":[74,88,140,146,238],"methods.":[75],"We":[76],"proposed":[77,95],"have":[79],"an":[80,120],"IoT-based":[81],"embedded":[83],"trained":[86,149,236],"model":[89,141,147,239],"address":[91],"issue.":[93],"contains":[97],"few":[99],"parts.":[100],"An":[101],"AD8232":[102],"sensor":[103],"was":[104,148,189],"used":[105],"collect":[107],"ECG":[108,126,203,232],"signals,":[109],"which":[110],"were":[111,128,173],"then":[112,135],"sent":[113,136],"minicomputer,":[116],"Raspberry":[117],"Pi,":[118],"analog-to-digital":[121],"(ADC)":[122],"converter.":[123],"digital":[125],"signals":[127,233],"fed":[129],"Flask":[132],"application":[133,188],"for":[142,182,240],"classification.":[143],"on":[150],"three":[151],"different":[152],"from":[155],"PhysioNet,":[157],"including":[158],"Normal":[159],"Sinus":[160],"Rhythm,":[161],"Congestive":[162],"Heart":[163],"Failure,":[164],"Atrial":[166],"Fibrillation.":[167],"training":[169],"testing":[171],"processes":[172],"also":[174,229],"IoT":[177,216],"device.":[178],"To":[179],"support":[180],"interaction":[181],"public,":[184],"web":[187],"created":[190],"show":[192],"result":[194,210],"classification":[197],"as":[198,200,205],"well":[199],"presenting":[201],"real-time":[202,231],"data":[204],"continuous":[207],"graph.":[208],"work":[213],"demonstrates":[214],"only":[219],"allowed":[220],"heartbeats,":[225],"but":[226],"it":[227],"could":[228],"anomaly":[241],"detection.":[242]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-13T07:54:00.901334","created_date":"2025-10-10T00:00:00"}
