Identification of Myocardial Infarction by High Frequency Serial ECG Measurement

dc.contributor.authorSandelin Jonas
dc.contributor.authorSirkiä Jukka-Pekka
dc.contributor.authorAnzanpour Arman
dc.contributor.authorKoivisto Tero
dc.contributor.organizationfi=terveysteknologia|en=Health Technology|
dc.contributor.organization-code1.2.246.10.2458963.20.28696315432
dc.converis.publication-id178084055
dc.converis.urlhttps://research.utu.fi/converis/portal/Publication/178084055
dc.date.accessioned2025-08-27T23:20:10Z
dc.date.available2025-08-27T23:20:10Z
dc.description.abstract<p>The purpose of this study is to attempt to identify acute myocardial infarction with high frequency serial electrocardiogram which both are ECG analyzing techniques. The idea is to combine these two techniques and see if changes between different ECGs from the same person can provide us with some information, whether it being in the high frequency or normal frequency range of ECG. A heart attack can occur at any time and therefore the possibility of using a wearable device was also researched. <br></p><p>To answer the questions, an existing database which contained multiple ECGs for each person with high sampling frequency was used. On top of this, a new serial ECG database was gathered using a wearable device designed by the University of Turku. Using multiple ECGs, features were extracted from the signals and then used in different machine learning methods in order to classify the subjects. <br></p><p>All of the methods seem to be relevant. High frequency ECG was found to be useful, while serial ECG provided us good results with both databases. The device was also found to be able to produce good quality ECG.</p>
dc.identifier.issn2325-8861
dc.identifier.jour-issn2325-8861
dc.identifier.olddbid203822
dc.identifier.oldhandle10024/186849
dc.identifier.urihttps://www.utupub.fi/handle/11111/49598
dc.identifier.urlhttps://cinc.org/archives/2022/pdf/CinC2022-185.pdf
dc.identifier.urnURN:NBN:fi-fe202301265919
dc.language.isoen
dc.okm.affiliatedauthorSandelin, Jonas
dc.okm.affiliatedauthorSirkiä, Jukka-Pekka
dc.okm.affiliatedauthorAnzanpour, Arman
dc.okm.affiliatedauthorKoivisto, Tero
dc.okm.discipline113 Computer and information sciencesen_GB
dc.okm.discipline217 Medical engineeringen_GB
dc.okm.discipline3141 Health care scienceen_GB
dc.okm.discipline113 Tietojenkäsittely ja informaatiotieteetfi_FI
dc.okm.discipline217 Lääketieteen tekniikkafi_FI
dc.okm.discipline3141 Terveystiedefi_FI
dc.okm.internationalcopublicationnot an international co-publication
dc.okm.internationalityInternational publication
dc.okm.typeA4 Conference Article
dc.publisher.countryUnited Statesen_GB
dc.publisher.countryYhdysvallat (USA)fi_FI
dc.publisher.country-codeUS
dc.relation.conferenceComputing in Cardiology
dc.relation.doi10.22489/CinC.2022.185
dc.relation.ispartofjournalComputing in Cardiology
dc.relation.ispartofseriesComputing in Cardiology
dc.relation.volume49
dc.source.identifierhttps://www.utupub.fi/handle/10024/186849
dc.titleIdentification of Myocardial Infarction by High Frequency Serial ECG Measurement
dc.title.bookComputing in Cardiology 2022
dc.year.issued2022

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