PrepAnnECG: A user friendly MATLAB ECG preprocessing and annotation GUI for health professionals
| dc.contributor.author | Nalan Ǒzkurt | |
| dc.date.accessioned | 2025-10-06T17:49:20Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Electrocardiography (ECG) is an indispensable tool for diagnosing heart diseases. However long ECG records should be annotated by clinicians for training the Artificial Intelligence (AI) algorithms. Furthermore raw ECG should be processed for better learning. This paper introduces an open-source user-friendly Matlab preprocessing and annotation interface. preprocessECG offers a GUI for cropping manual artifact removal filtering and segmentation. Also custom preprocessing software can be invoked from the interface. PrepAnnECG is an easy-to-use ECG annotation GUI that is suitable for long records. The outputs of both GUI are comma-separated value (csv) files that any machine learning algorithm can read. © 2023 Elsevier B.V. All rights reserved. | |
| dc.identifier.doi | 10.1016/j.softx.2023.101589 | |
| dc.identifier.issn | 23527110 | |
| dc.identifier.issn | 2352-7110 | |
| dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178662129&doi=10.1016%2Fj.softx.2023.101589&partnerID=40&md5=f2f01d48ae83643ece709970e466d128 | |
| dc.identifier.uri | https://gcris.yasar.edu.tr/handle/123456789/8372 | |
| dc.language.iso | English | |
| dc.publisher | Elsevier B.V. | |
| dc.relation.ispartof | SoftwareX | |
| dc.source | SoftwareX | |
| dc.subject | Data Preparation For Machine Learning, Ecg Annotation, Preprocessing, Electrocardiography, Graphical User Interfaces, Learning Algorithms, Matlab, Open Source Software, Artificial Intelligence Algorithms, Data Preparation, Data Preparation For Machine Learning, Electrocardiography Annotation, Health Professionals, Heart Disease, Indispensable Tools, Machine-learning, Preprocessing, User Friendly, Machine Learning | |
| dc.subject | Electrocardiography, Graphical user interfaces, Learning algorithms, MATLAB, Open source software, Artificial intelligence algorithms, Data preparation, Data preparation for machine learning, Electrocardiography annotation, Health professionals, Heart disease, Indispensable tools, Machine-learning, Preprocessing, User friendly, Machine learning | |
| dc.title | PrepAnnECG: A user friendly MATLAB ECG preprocessing and annotation GUI for health professionals | |
| dc.type | Article | |
| dspace.entity.type | Publication | |
| gdc.bip.impulseclass | C4 | |
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| gdc.bip.popularityclass | C4 | |
| gdc.coar.type | text::journal::journal article | |
| gdc.collaboration.industrial | false | |
| gdc.description.startpage | 101589 | |
| gdc.description.volume | 24 | |
| gdc.identifier.openalex | W4389209330 | |
| gdc.index.type | Scopus | |
| gdc.oaire.accesstype | GOLD | |
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| gdc.oaire.influence | 2.7884532E-9 | |
| gdc.oaire.isgreen | false | |
| gdc.oaire.keywords | ECG annotation | |
| gdc.oaire.keywords | QA76.75-76.765 | |
| gdc.oaire.keywords | Computer software | |
| gdc.oaire.keywords | Data preparation for machine learning | |
| gdc.oaire.keywords | Preprocessing | |
| gdc.oaire.popularity | 4.9337388E-9 | |
| gdc.oaire.publicfunded | false | |
| gdc.openalex.collaboration | National | |
| gdc.openalex.fwci | 1.0981 | |
| gdc.openalex.normalizedpercentile | 0.81 | |
| gdc.opencitations.count | 3 | |
| gdc.plumx.crossrefcites | 4 | |
| gdc.plumx.mendeley | 9 | |
| gdc.plumx.newscount | 1 | |
| gdc.plumx.scopuscites | 4 | |
| person.identifier.scopus-author-id | Ǒzkurt- Nalan (8546186400) | |
| project.funder.name | Funding text 1: This study has been supported by the The Scientific and Technological Research Council of Turkiye TÜBİTAK 1001–121E119 Research Project., Funding text 2: The author thanks Assoc.Prof.Dr. Evrim Şimsek Dept. of Cardiology Ege University Medical School Turkey for his support during concept development. I also would thank Ege University Medical School students Özlem Memiş and Nurbanu Dedebağı for testing the software reporting bugs and proposing improvements. This study has been supported by the The Scientific and Technological Research Council of Turkiye TÜBİTAK 1001–121E119 Research Project. | |
| publicationvolume.volumeNumber | 24 | |
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