RT info:eu-repo/semantics/article
T1 ECGMiner: A flexible software for accurately digitizing ECG
A1 Fernández Santamónica, Adolfo
A1 Carratalá Sáez, Rocío
A1 Larriba González, Yolanda
A1 Pérez Castellanos, Alberto
A1 Rueda Sabater, María Cristina
K1 ECG
K1 Images
K1 Digitization
K1 Software
K1 FMM model
AB Background and objective: The electrocardiogram (ECG) is the most important non-invasive method for elucidating information about heart and cardiovascular disease diagnosis. Typically, the ECG system manufacturing companies provide ECG images, but store the numerical data in a proprietary format that is not interpretable and is not therefore useful for automatic diagnosis. There have been many efforts to digitize paper-based ECGs. The main limitations of previous works in ECG digitization are that they require manual selection of the regions of interest, only partly provide signal digitization, and offer limited accuracy.Methods: We have developed the ECGMiner, an open-source software to digitize ECG images. It is precise, fast, and simple to use. This software digitizes ECGs in four steps: 1) recognizing the image composition; 2) removing the gridline; 3) extracting the signals; 4) post-processing and storing the data.Results: We have evaluated the ECGMiner digitization capabilities using the Pearson Correlation Coefficient (PCC) and the Root Mean Square Error (RMSE) measures, and we consider ECG from two large, public, and widely used databases, LUDB and PTB-XL. The actual and digitized values of signals in both databases have been compared. The software's ability to correctly identify the location of characteristic waves has also been validated. Specifically, the PCC values are between 0.971 and 0.995, and the RMSE values are between 0.011 and 0.031 mV.Conclusions: The ECGMiner software presented in this paper is open access, easy to install, easy to use, and capable of precisely recovering the paper-based/digital ECG signal data, regardless of the input format and signal complexity. ECGMiner outperforms existing digitization algorithms in terms of PCC and RMSE values.
PB Elsevier
SN 0169-2607
YR 2024
FD 2024
LK https://uvadoc.uva.es/handle/10324/72927
UL https://uvadoc.uva.es/handle/10324/72927
LA eng
NO Computer Methods and Programs in Biomedicine, 2024, vol. 246, 108053
NO Producción Científica
DS UVaDOC
RD 26-dic-2024