{"id":"https://openalex.org/W7065089080","doi":null,"title":"Deep Learning Based Electrocardiogram Delineation","display_name":"Deep Learning Based Electrocardiogram Delineation","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W7065089080"},"language":"en","primary_location":{"id":"pmh:oai:etd.ohiolink.edu:ucin1563525992210273","doi":null,"is_oa":true,"landing_page_url":"http://rave.ohiolink.edu/etdc/view?acc_num=ucin1563525992210273","pdf_url":"http://rave.ohiolink.edu/etdc/view?acc_num=ucin1563525992210273","source":{"id":"https://openalex.org/S4377196344","display_name":"OhioLink ETD Center (Ohio Library and Information Network)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4210155183","host_organization_name":"Ohio Library and Information Network","host_organization_lineage":["https://openalex.org/I4210155183"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://rave.ohiolink.edu/etdc/view?acc_num=ucin1563525992210273","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://rave.ohiolink.edu/etdc/view?acc_num=ucin1563525992210273","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5027894662","display_name":"Hedayat Abrishami","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I63135867","display_name":"University of Cincinnati","ror":"https://ror.org/01e3m7079","country_code":"US","type":"education","lineage":["https://openalex.org/I63135867"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Abrishami, Hedayat","raw_affiliation_strings":["University of Cincinnati"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Cincinnati","institution_ids":["https://openalex.org/I63135867"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5027894662"],"corresponding_institution_ids":["https://openalex.org/I63135867"],"apc_list":null,"apc_paid":null,"fwci":0.1793,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.52940278,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":93},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"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/T10217","display_name":"Cardiac electrophysiology and arrhythmias","score":0.0,"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/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.0,"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/electrocardiography","display_name":"electrocardiogram","score":0.8870000243186951},{"id":"https://openalex.org/keywords/ecg-segmentation","display_name":"ECG segmentation","score":0.8759999871253967},{"id":"https://openalex.org/keywords/automatic-ecg-analysis","display_name":"automated ECG analysis","score":0.9020000100135803},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.9860000014305115},{"id":"https://openalex.org/keywords/drug-induced-ecg-changes","display_name":"drug-induced ECG changes","score":0.6359999775886536},{"id":"https://openalex.org/keywords/arrhythmia-detection","display_name":"arrhythmia detection","score":0.5730000138282776},{"id":"https://openalex.org/keywords/ecg-screening","display_name":"ECG screening","score":0.4830000102519989}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7419999837875366},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6184999942779541},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5694000124931335},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5667999982833862},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5358999967575073},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5098000168800354},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4909999966621399},{"id":"https://openalex.org/C2988455589","wikidata":"https://www.wikidata.org/wiki/Q189331","display_name":"Cardiac arrhythmia","level":3,"score":0.3896999955177307},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3808000087738037},{"id":"https://openalex.org/C2780040984","wikidata":"https://www.wikidata.org/wiki/Q79785","display_name":"Electrocardiography","level":2,"score":0.3614000082015991},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.35580000281333923},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.3246000111103058},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.323199987411499},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3172000050544739},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.31520000100135803},{"id":"https://openalex.org/C192244848","wikidata":"https://www.wikidata.org/wiki/Q7672706","display_name":"T wave","level":3,"score":0.3084000051021576},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.28049999475479126},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.2752000093460083},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.25699999928474426},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.25529998540878296},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.25200000405311584}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:etd.ohiolink.edu:ucin1563525992210273","doi":null,"is_oa":true,"landing_page_url":"http://rave.ohiolink.edu/etdc/view?acc_num=ucin1563525992210273","pdf_url":"http://rave.ohiolink.edu/etdc/view?acc_num=ucin1563525992210273","source":{"id":"https://openalex.org/S4377196344","display_name":"OhioLink ETD Center (Ohio Library and Information Network)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4210155183","host_organization_name":"Ohio Library and Information Network","host_organization_lineage":["https://openalex.org/I4210155183"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://rave.ohiolink.edu/etdc/view?acc_num=ucin1563525992210273","raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:etd.ohiolink.edu:ucin1563525992210273","doi":null,"is_oa":true,"landing_page_url":"http://rave.ohiolink.edu/etdc/view?acc_num=ucin1563525992210273","pdf_url":"http://rave.ohiolink.edu/etdc/view?acc_num=ucin1563525992210273","source":{"id":"https://openalex.org/S4377196344","display_name":"OhioLink ETD Center (Ohio Library and Information Network)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4210155183","host_organization_name":"Ohio Library and Information Network","host_organization_lineage":["https://openalex.org/I4210155183"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://rave.ohiolink.edu/etdc/view?acc_num=ucin1563525992210273","raw_type":"text"},"sustainable_development_goals":[{"score":0.504800021648407,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7065089080.pdf","grobid_xml":"https://content.openalex.org/works/W7065089080.grobid-xml"},"referenced_works_count":53,"referenced_works":["https://openalex.org/W1034159276","https://openalex.org/W1531333757","https://openalex.org/W1975050716","https://openalex.org/W1989268751","https://openalex.org/W1997227106","https://openalex.org/W2000909451","https://openalex.org/W2052494071","https://openalex.org/W2064675550","https://openalex.org/W2072566913","https://openalex.org/W2090472274","https://openalex.org/W2107878631","https://openalex.org/W2110235300","https://openalex.org/W2112796928","https://openalex.org/W2116261113","https://openalex.org/W2117539524","https://openalex.org/W2127498682","https://openalex.org/W2127510558","https://openalex.org/W2131774270","https://openalex.org/W2132300419","https://openalex.org/W2133441640","https://openalex.org/W2136922672","https://openalex.org/W2146950261","https://openalex.org/W2162273778","https://openalex.org/W2163430278","https://openalex.org/W2168085666","https://openalex.org/W2291961022","https://openalex.org/W2395611524","https://openalex.org/W2471230540","https://openalex.org/W2523246573","https://openalex.org/W2540347367","https://openalex.org/W2551779716","https://openalex.org/W2587455741","https://openalex.org/W2600620086","https://openalex.org/W2605056515","https://openalex.org/W2610184543","https://openalex.org/W2611191801","https://openalex.org/W2731010577","https://openalex.org/W2735582123","https://openalex.org/W2747849569","https://openalex.org/W2751777443","https://openalex.org/W2775127214","https://openalex.org/W2785654587","https://openalex.org/W2792954102","https://openalex.org/W2798146972","https://openalex.org/W2911296969","https://openalex.org/W2949335953","https://openalex.org/W2952186574","https://openalex.org/W2962902328","https://openalex.org/W2963212833","https://openalex.org/W2963993992","https://openalex.org/W2964121744","https://openalex.org/W4239745429","https://openalex.org/W6982273903"],"related_works":["https://openalex.org/W4391284568","https://openalex.org/W2979765299","https://openalex.org/W4414273124","https://openalex.org/W4391266547","https://openalex.org/W3183465802","https://openalex.org/W3021060239","https://openalex.org/W3183761764","https://openalex.org/W4233156859","https://openalex.org/W2972163396","https://openalex.org/W6948954353"],"abstract_inverted_index":{"Cardiac":[0],"waves":[1,24,75],"in":[2,29,47,99],"Electrocardiogram":[3],"(ECG)":[4],"provide":[5],"important":[6],"information":[7],"for":[8],"heart":[9],"conditions":[10],"and":[11,16,20,32,43,90,96],"effects":[12],"of":[13,18,26,78,116],"cardiac":[14,23,74,117],"medications.Segmentation":[15],"analysis":[17,36],"ECG":[19,35,50,68,88,100],"its":[21],"constituent":[22],"are":[25],"high":[27,79],"significance":[28],"cardiology":[30],"diagnosis":[31],"pharmaceutical":[33],"studies.Traditionally":[34],"has":[37],"been":[38],"performed":[39],"by":[40],"well-trained":[41],"clinicians":[42,46],"cardiologists.However,":[44],"utilizing":[45],"large":[48],"scale":[49],"screening":[51,59],"such":[52],"as":[53],"drug":[54],"test":[55],"phases":[56],"or":[57,108],"populationbased":[58],"programs":[60],"is":[61,77],"simply":[62],"not":[63],"economically":[64],"feasible.Thus,":[65],"an":[66],"automated":[67,87],"segmentation":[69,89],"approach":[70],"that":[71],"can":[72,138],"segment":[73],"accurately":[76],"importance.This":[80],"dissertation":[81],"studies":[82],"Deep":[83],"Learning":[84],"(DL)":[85],"based":[86],"delineation":[91],"methods.Due":[92],"to":[93,112,130,141],"various":[94],"shapes":[95],"abnormalities":[97],"presented":[98],"signal,":[101],"the":[102,114,128,135,152],"traditional":[103],"simple":[104],"feature":[105,125],"filters":[106,126],"(i.e.correlators,":[107],"derivative-based":[109],"filters)":[110],"fail":[111],"extract":[113,131,142],"variety":[115],"wave":[118],"formations.Convolutional":[119],"Neural":[120],"Network":[121],"(ConvNet)":[122],"applies":[123],"multilayer":[124],"on":[127],"input":[129],"complex":[132],"features":[133],"from":[134],"signal.Thus,":[136],"ConvNets":[137],"be":[139],"utilized":[140],"hierarchical":[143],"i":[144],"Ph.D.":[145],"studies.Also,":[146],"I":[147],"thank":[148],"my":[149],"mentors":[150],"at":[151],"Simulation":[153],"Center,":[154],"Dr.":[155]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2025-10-10T00:00:00"}
