dc.contributor.advisor | Lind, Pedro (Hovedveileder) | |
dc.contributor.advisor | Yazidi, Anis (Biveileder) | |
dc.contributor.advisor | Haugerud, Hårek (Biveileder) | |
dc.contributor.author | Adamopoulos, Ioannis | |
dc.date.accessioned | 2022-09-06T09:22:00Z | |
dc.date.available | 2022-09-06T09:22:00Z | |
dc.date.issued | 2022 | |
dc.identifier.uri | https://hdl.handle.net/11250/3015944 | |
dc.description.abstract | The digitization of electrocardiogram (ECG) signals recorded on paper is a very
challenging task usually prone to errors and inaccuracies. Until now there is no
available tool which can perform that task both universally and in a fully automated
way. ECG is a well established medical modality to record the activity of the human
heart which dates to over 100 years ago. Medical experts can diagnose potential heart
irregularities by interpreting the recorded signals on ECG papers. There are millions of
ECGs worldwide and the digitization of them is of paramount importance for research,
analysis and diagnosis in medicine. By recognizing patterns in the digitized ECGs,
artificial intelligence algorithms can predict cardiovascular diseases and help clinicians
to make better medical diagnoses. Therefore, the development or the improvement
of an automated tool that will be able to digitize massively ECG signals at once, is
essential. In this thesis we describe and develop a tool for digitizing ECGs and we
test its performance using ECG scans provided by Akershus University Hospital. In
particular, we introduce some improvements which can make it operate in a more
automated way. The original tool has several parameters in its various steps that
prevent it from being fully automated. However, with proper further improvements,
it has a great potential to become fully automated at least for ECG scans similar to
the ones in the database of Akershus University Hospital. The current master thesis
was held during the last semester of the ACIT’s master program of Oslo Metropolitan
University, namely from January 1st until May 15th. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | OsloMet - storbyuniversitetet | en_US |
dc.relation.ispartofseries | ACIT;2022 | |
dc.subject | Electrocardiogram | en_US |
dc.subject | Digitization | en_US |
dc.subject | Image processing | en_US |
dc.title | Performance assessment of AI tools for digitizing ECG scans | en_US |
dc.type | Master thesis | en_US |
dc.description.version | publishedVersion | en_US |