Nowadays, analog electrocardiographs that deliver only paper printout are ubiquitous in medical institutions. Doctors do visual analysis of electrocardiograms (ECG), occasionally using measurement tools. This article reviews approaches to automatic analysis of electrocardiogram images, including the signal conversion from paper to digital format. The following methods are presented: digitizing graphs from images, determination of signal nodes, and preparation of final report. Various methods of computer vision were tested on electrocardiogram images in order to highlight the graph and transfer coordinates to millimeters. Their limitations are identified and described. Based on the evaluation, a suitable electrocardiogram analysis method has been developed. It includes color filtering of the background grid. Methods of signal analysis and reading of indicators, and their further analysis, are also given. The text conclusion is based on decision trees traversal. As a result, the architecture of measuring system software for electrocardiogram analysis was developed. The system is described considering that the electrocardiogram evaluation unit does not depend on external implementation and can be reused in other systems performing electrocardiogram analysis.
|Название основной публикации||2020 Cognitive Sciences, Genomics and Bioinformatics (CSGB)|
|Место публикации||IEEE Xplore|
|Издатель||IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC|
|ISBN (электронное издание)||978-1-7281-9597-1|
|ISBN (печатное издание)||978-1-7281-9596-4, 978-1-7281-9598-8|
|Состояние||Опубликовано - 7 окт 2020|
Хазанкин, Г. Р. (2020). ECG printout interpretation system for clinical decision support. В 2020 Cognitive Sciences, Genomics and Bioinformatics (CSGB) (стр. 19-22). IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. https://doi.org/10.1109/CSGB51356.2020.9214740