{"id":"https://openalex.org/W2765961575","doi":"https://doi.org/10.4018/ijehmc.2018010103","title":"A Comparative Study of FFT, DCT, and DWT for Efficient Arrhytmia Classification in RP-RF Framework","display_name":"A Comparative Study of FFT, DCT, and DWT for Efficient Arrhytmia Classification in RP-RF Framework","publication_year":2017,"publication_date":"2017-10-23","ids":{"openalex":"https://openalex.org/W2765961575","doi":"https://doi.org/10.4018/ijehmc.2018010103","mag":"2765961575"},"language":"en","primary_location":{"id":"doi:10.4018/ijehmc.2018010103","is_oa":false,"landing_page_url":"https://doi.org/10.4018/ijehmc.2018010103","pdf_url":null,"source":{"id":"https://openalex.org/S18047094","display_name":"International Journal of E-Health and Medical Communications","issn_l":"1947-315X","issn":["1947-315X","1947-3168"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of E-Health and Medical Communications","raw_type":"journal-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/A5044744377","display_name":"Tea Marasovi\u0107","orcid":"https://orcid.org/0000-0002-1737-5680"},"institutions":[{"id":"https://openalex.org/I92251255","display_name":"University of Split","ror":"https://ror.org/00m31ft63","country_code":"HR","type":"education","lineage":["https://openalex.org/I92251255"]}],"countries":["HR"],"is_corresponding":false,"raw_author_name":"Tea Marasovi\u0107","raw_affiliation_strings":["Faculty of Electrical Engineering, Mechanical Engineering, and Naval Architecture (FESB), University of Split, Split, Croatia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Electrical Engineering, Mechanical Engineering, and Naval Architecture (FESB), University of Split, Split, Croatia","institution_ids":["https://openalex.org/I92251255"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013504831","display_name":"Vladan Papi\u0107","orcid":"https://orcid.org/0000-0002-1873-6353"},"institutions":[{"id":"https://openalex.org/I92251255","display_name":"University of Split","ror":"https://ror.org/00m31ft63","country_code":"HR","type":"education","lineage":["https://openalex.org/I92251255"]}],"countries":["HR"],"is_corresponding":false,"raw_author_name":"Vladan Papi\u0107","raw_affiliation_strings":["Faculty of Electrical Engineering, Mechanical Engineering, and Naval Architecture (FESB), University of Split, Split, Croatia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Electrical Engineering, Mechanical Engineering, and Naval Architecture (FESB), University of Split, Split, Croatia","institution_ids":["https://openalex.org/I92251255"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I92251255"],"apc_list":null,"apc_paid":null,"fwci":0.1815,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.60517886,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"9","issue":"1","first_page":"35","last_page":"49"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"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/T10323","display_name":"Analog and Mixed-Signal Circuit Design","score":0.9962999820709229,"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/T11032","display_name":"VLSI and Analog Circuit Testing","score":0.9883999824523926,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/discrete-cosine-transform","display_name":"Discrete cosine transform","score":0.7510810494422913},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6945432424545288},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.653028130531311},{"id":"https://openalex.org/keywords/fast-fourier-transform","display_name":"Fast Fourier transform","score":0.6036468744277954},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5677266716957092},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5422990322113037},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5081593990325928},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.5031799674034119},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4893431067466736},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4644154906272888},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.4564649164676666},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4333980083465576},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37272435426712036},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33008283376693726},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.20933941006660461},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.08634904026985168}],"concepts":[{"id":"https://openalex.org/C2221639","wikidata":"https://www.wikidata.org/wiki/Q2877","display_name":"Discrete cosine transform","level":3,"score":0.7510810494422913},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6945432424545288},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.653028130531311},{"id":"https://openalex.org/C75172450","wikidata":"https://www.wikidata.org/wiki/Q623950","display_name":"Fast Fourier transform","level":2,"score":0.6036468744277954},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5677266716957092},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5422990322113037},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5081593990325928},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.5031799674034119},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4893431067466736},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4644154906272888},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.4564649164676666},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4333980083465576},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37272435426712036},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33008283376693726},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.20933941006660461},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.08634904026985168},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.4018/ijehmc.2018010103","is_oa":false,"landing_page_url":"https://doi.org/10.4018/ijehmc.2018010103","pdf_url":null,"source":{"id":"https://openalex.org/S18047094","display_name":"International Journal of E-Health and Medical Communications","issn_l":"1947-315X","issn":["1947-315X","1947-3168"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of E-Health and Medical Communications","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:igg:jehmc0:v:9:y:2018:i:1:p:35-49","is_oa":false,"landing_page_url":"https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJEHMC.2018010103","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life in Land","score":0.6899999976158142,"id":"https://metadata.un.org/sdg/15"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W848176222","https://openalex.org/W2015651164","https://openalex.org/W2032304886","https://openalex.org/W2037757210","https://openalex.org/W2095409369","https://openalex.org/W2118922542","https://openalex.org/W2129131372","https://openalex.org/W2130129780","https://openalex.org/W2132300419","https://openalex.org/W2145044818","https://openalex.org/W2151223362","https://openalex.org/W2162273778","https://openalex.org/W2162800060","https://openalex.org/W2181608983","https://openalex.org/W2221431013","https://openalex.org/W2251133041","https://openalex.org/W2334754775","https://openalex.org/W2515773207","https://openalex.org/W2600602178","https://openalex.org/W2979473749","https://openalex.org/W4212896145"],"related_works":["https://openalex.org/W1995622179","https://openalex.org/W1484111231","https://openalex.org/W1552543208","https://openalex.org/W2074396517","https://openalex.org/W2166963679","https://openalex.org/W2187269125","https://openalex.org/W1641615907","https://openalex.org/W3089231081","https://openalex.org/W2093956241","https://openalex.org/W2354420595"],"abstract_inverted_index":{"Computer-aided":[0],"ECG":[1,140],"classification":[2,65],"is":[3],"an":[4],"important":[5],"tool":[6],"for":[7,102,135],"timely":[8],"diagnosis":[9],"of":[10,24,44,107,118,138],"abnormal":[11],"heart":[12],"conditions.":[13],"This":[14],"paper":[15,39],"proposes":[16],"a":[17,103],"novel":[18],"framework":[19],"that":[20,57,88,128],"combines":[21],"the":[22,38,41,60,64,78,119,129],"theory":[23],"compressive":[25],"sensing":[26],"with":[27],"random":[28,108],"forests":[29],"to":[30,53,63,96,143],"achieve":[31],"reliable":[32],"automatic":[33],"cardiac":[34],"arrhythmia":[35,75],"detection.":[36],"Furthermore,":[37],"evaluates":[40],"characterization":[42],"power":[43],"FFT,":[45],"DCT":[46],"and":[47,80],"DWT":[48,89],"data":[49],"transformations":[50],"in":[51],"order":[52],"extract":[54],"significant":[55],"features":[56,91],"will":[58],"bring":[59],"additional":[61],"boost":[62],"performance.":[66],"The":[67,122],"experiments":[68],"\u2013":[69,86],"carried":[70],"out":[71],"over":[72],"MIT-BIH":[73],"benchmark":[74],"database,":[76],"following":[77],"standards":[79],"recommended":[81],"practices":[82],"provided":[83],"by":[84],"AAMI":[85],"demonstrate":[87],"based":[90],"exhibit":[92],"better":[93],"performances":[94],"compared":[95],"other":[97],"two":[98],"feature":[99],"extraction":[100],"techniques":[101],"relatively":[104],"small":[105],"number":[106],"projected":[109],"coefficients,":[110],"i.e.":[111],"after":[112],"considerable":[113],"(approx.":[114],"85%)":[115],"dimensionality":[116],"reduction":[117],"input":[120],"signal.":[121],"results":[123],"are":[124],"very":[125],"promising,":[126],"suggesting":[127],"proposed":[130],"model":[131],"could":[132],"be":[133],"implemented":[134],"practical":[136],"applications":[137],"real-time":[139],"monitoring,":[141],"due":[142],"its":[144],"low-complexity.":[145]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
