{"id":"https://openalex.org/W3201498483","doi":"https://doi.org/10.1109/mwscas47672.2021.9531733","title":"A Low-Power ECG Readout Circuit Integrated with Machine Learning Based ECG Heartbeat Classifier","display_name":"A Low-Power ECG Readout Circuit Integrated with Machine Learning Based ECG Heartbeat Classifier","publication_year":2021,"publication_date":"2021-08-09","ids":{"openalex":"https://openalex.org/W3201498483","doi":"https://doi.org/10.1109/mwscas47672.2021.9531733","mag":"3201498483"},"language":"en","primary_location":{"id":"doi:10.1109/mwscas47672.2021.9531733","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mwscas47672.2021.9531733","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)","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/A5049644654","display_name":"Deepa Kota","orcid":"https://orcid.org/0000-0001-5436-8044"},"institutions":[{"id":"https://openalex.org/I123534392","display_name":"University of North Texas","ror":"https://ror.org/00v97ad02","country_code":"US","type":"education","lineage":["https://openalex.org/I123534392"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Deepa Kota","raw_affiliation_strings":["University of North Texas, Denton, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of North Texas, Denton, TX, USA","institution_ids":["https://openalex.org/I123534392"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041993740","display_name":"Ifana Mahbub","orcid":"https://orcid.org/0000-0002-5561-0880"},"institutions":[{"id":"https://openalex.org/I123534392","display_name":"University of North Texas","ror":"https://ror.org/00v97ad02","country_code":"US","type":"education","lineage":["https://openalex.org/I123534392"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ifana Mahbub","raw_affiliation_strings":["University of North Texas, Denton, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of North Texas, Denton, TX, USA","institution_ids":["https://openalex.org/I123534392"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6356,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.7162612,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"101","issue":null,"first_page":"639","last_page":"643"},"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":0.9984999895095825,"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/T10323","display_name":"Analog and Mixed-Signal Circuit Design","score":0.9858999848365784,"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":0.6829129457473755},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6651501655578613},{"id":"https://openalex.org/keywords/heartbeat","display_name":"Heartbeat","score":0.5712441802024841},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5528209805488586},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.47732219099998474},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43091779947280884},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4235681891441345},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4116877615451813},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.38288548588752747},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.18790289759635925}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6829129457473755},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6651501655578613},{"id":"https://openalex.org/C13852961","wikidata":"https://www.wikidata.org/wiki/Q17021880","display_name":"Heartbeat","level":2,"score":0.5712441802024841},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5528209805488586},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.47732219099998474},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43091779947280884},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4235681891441345},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4116877615451813},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.38288548588752747},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.18790289759635925},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mwscas47672.2021.9531733","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mwscas47672.2021.9531733","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.44999998807907104,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1994670735","https://openalex.org/W2134351284","https://openalex.org/W2143516309","https://openalex.org/W2162800060","https://openalex.org/W2251133041","https://openalex.org/W2482102801","https://openalex.org/W2765825637","https://openalex.org/W2785993669","https://openalex.org/W2919620237","https://openalex.org/W3015802815","https://openalex.org/W3034935078","https://openalex.org/W3112866804","https://openalex.org/W6787159562"],"related_works":["https://openalex.org/W4385543909","https://openalex.org/W3039320222","https://openalex.org/W3199640442","https://openalex.org/W1898280036","https://openalex.org/W2315807364","https://openalex.org/W2382278803","https://openalex.org/W2376695684","https://openalex.org/W2803040299","https://openalex.org/W2034075638","https://openalex.org/W1982967776"],"abstract_inverted_index":{"Dry":[0],"electrodes":[1],"have":[2],"been":[3],"a":[4,61,76,103,108,114,119,126,149,164,187],"popular":[5],"mechanism":[6],"for":[7,23,159],"ECG":[8,25,51,143,177],"acquisition":[9],"due":[10],"to":[11,18,27,135,211],"their":[12],"ease":[13],"of":[14,36,80,86,105,122,170,230],"use":[15],"and":[16,39,83,113,157,179,204,232],"ability":[17],"secure":[19],"longer":[20],"signals.":[21],"However,":[22],"wearable":[24],"monitoring":[26],"be":[28],"effective,":[29],"high-fidelity":[30],"signal":[31],"processing":[32,41],"is":[33,163,237],"imperative.":[34],"Lack":[35],"low-cost,":[37],"compact,":[38],"effective":[40],"options":[42],"at":[43],"the":[44,129,139,184,213,221,226,233,238],"subject":[45],"end":[46],"could":[47],"result":[48],"in":[49,186],"noisy":[50],"data":[52,144,178,185],"transmission.":[53],"To":[54],"overcome":[55],"this":[56,58,191],"constraint":[57],"paper":[59,192],"presents":[60],"four-layered":[62],"Printed":[63],"Circuit":[64],"Board":[65],"(PCB)":[66],"based":[67],"readout":[68,96,140],"circuit":[69,97],"using":[70,138],"commercial":[71],"off-the-shelf":[72],"components":[73,98],"(COTS),":[74],"having":[75],"low":[77,115],"power":[78],"consumption":[79],"1":[81],"\u00b5W":[82],"an":[84,100],"area":[85],"2.1":[87],"x":[88],"1.8":[89],"cm":[90],"<sup":[91],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[92],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>":[93],".":[94],"The":[95,168,217],"are":[99,209],"amplifier":[101],"with":[102,118,240],"gain":[104],"40":[106],"dB,":[107],"60":[109],"Hz":[110],"notch":[111],"filter,":[112],"pass":[116],"filter":[117],"cut-off":[120],"frequency":[121],"100":[123],"Hz.":[124],"For":[125],"test":[127],"signal,":[128],"SNR":[130],"improved":[131],"from":[132,146],"\u22129":[133],"dB":[134,137],"29.3":[136],"circuit.":[141],"Voluminous":[142],"acquired":[145],"patients":[147],"over":[148],"long":[150],"period":[151],"requires":[152],"deep":[153],"analysis,":[154],"feature":[155],"extraction,":[156],"annotation":[158],"pattern":[160],"recognition,":[161],"which":[162],"significantly":[165],"time-consuming":[166],"task.":[167],"development":[169],"machine":[171,194],"learning":[172,195],"programs":[173],"can":[174],"analyze":[175],"hard-to-read":[176],"identify":[180],"unique":[181],"characteristics":[182],"within":[183],"short":[188],"period.":[189],"In":[190],"three":[193],"models,":[196],"namely":[197],"Decision":[198,235],"Trees":[199],"(DT),":[200],"K-Nearest":[201],"Neighbors":[202],"(KNN),":[203],"Support":[205],"Vector":[206],"Machine":[207],"(SVM)":[208],"built":[210],"classify":[212],"MIT-BIH":[214],"Arrhythmia":[215],"Database.":[216],"results":[218],"show":[219],"that":[220],"Weighted":[222],"KNN":[223],"model":[224],"has":[225],"highest":[227],"training":[228],"accuracy":[229],"88.7%":[231],"Medium":[234],"tree":[236],"fastest":[239],"23.9":[241],"seconds.":[242]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
