{"id":"https://openalex.org/W4306317420","doi":"https://doi.org/10.1145/3511808.3557591","title":"Efficient Data Augmentation Policy for Electrocardiograms","display_name":"Efficient Data Augmentation Policy for Electrocardiograms","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4306317420","doi":"https://doi.org/10.1145/3511808.3557591"},"language":"en","primary_location":{"id":"doi:10.1145/3511808.3557591","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3511808.3557591","pdf_url":null,"source":{"id":"https://openalex.org/S4363608762","display_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","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":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","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/A5075656090","display_name":"Byeong Tak Lee","orcid":"https://orcid.org/0000-0002-1802-9915"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Byeong Tak Lee","raw_affiliation_strings":["MedicalAI, Inc., Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MedicalAI, Inc., Seoul, South Korea","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010093998","display_name":"Yong\u2010Yeon Jo","orcid":"https://orcid.org/0000-0002-6755-6850"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yong-Yeon Jo","raw_affiliation_strings":["MedicalAI, Inc., Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MedicalAI, Inc., Seoul, South Korea","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054474745","display_name":"Seon-Yu Lim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Seon-Yu Lim","raw_affiliation_strings":["MedicalAI, Inc., Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MedicalAI, Inc., Seoul, South Korea","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057551093","display_name":"Young-Jae Song","orcid":"https://orcid.org/0000-0002-5278-9715"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Youngjae Song","raw_affiliation_strings":["MedicalAI, Inc., Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MedicalAI, Inc., Seoul, South Korea","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062643671","display_name":"Joon\u2010myoung Kwon","orcid":"https://orcid.org/0000-0001-6754-1010"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Joon-myoung Kwon","raw_affiliation_strings":["MedicalAI, Inc., Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MedicalAI, Inc., Seoul, South Korea","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.9677,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.91577928,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"4153","last_page":"4157"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.9995999932289124,"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":0.9995999932289124,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9789999723434448,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.972599983215332,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/distortion","display_name":"Distortion (music)","score":0.6950284242630005},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6819133758544922},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.447429895401001},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4145965278148651},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36690711975097656},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3251659572124481},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.0562308132648468}],"concepts":[{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.6950284242630005},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6819133758544922},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.447429895401001},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4145965278148651},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36690711975097656},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3251659572124481},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0562308132648468},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.0},{"id":"https://openalex.org/C194257627","wikidata":"https://www.wikidata.org/wiki/Q211554","display_name":"Amplifier","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3511808.3557591","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3511808.3557591","pdf_url":null,"source":{"id":"https://openalex.org/S4363608762","display_name":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","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":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2912522881","https://openalex.org/W2949736877","https://openalex.org/W2953193031","https://openalex.org/W2965520043","https://openalex.org/W2978760586","https://openalex.org/W3011702533","https://openalex.org/W3035682985","https://openalex.org/W3163470764","https://openalex.org/W4206135956","https://openalex.org/W4214699119","https://openalex.org/W4232499617"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4224009465","https://openalex.org/W4286629047","https://openalex.org/W4306321456","https://openalex.org/W4285260836","https://openalex.org/W3046775127","https://openalex.org/W4205958290","https://openalex.org/W3107474891","https://openalex.org/W3209574120"],"abstract_inverted_index":{"We":[0],"present":[1],"the":[2,17,20,24,30,39,43,50,57,61,65,70],"taxonomy":[3],"of":[4,19,26,45,60,67],"data":[5],"augmentation":[6,14,27,84,92],"for":[7],"electrocardiogram":[8],"(ECG)":[9],"after":[10],"reviewing":[11],"various":[12],"ECG":[13,31],"methods.":[15],"On":[16],"basis":[18],"taxonomy,":[21],"we":[22,37,55,80],"demonstrate":[23,87],"effect":[25],"methods":[28],"on":[29,76],"classification":[32],"via":[33],"extensive":[34],"experiments.":[35],"Initially,":[36],"examine":[38],"performance":[40],"trend":[41],"as":[42],"magnitude":[44],"distortion":[46,52],"increases":[47],"and":[48,63,86],"identify":[49,64],"optimal":[51],"magnitude.":[53],"Secondly,":[54],"investigate":[56],"synergistic":[58],"combinations":[59],"transformations":[62,68],"pairs":[66],"with":[69],"greatest":[71],"positive":[72],"effect.":[73],"Finally,":[74],"based":[75],"our":[77],"experimental":[78],"findings,":[79],"propose":[81],"an":[82],"efficient":[83],"policy":[85],"that":[88],"it":[89],"outperforms":[90],"previous":[91],"policies.":[93]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
