Key result
ECGMiner accurately digitized paper-based ECGs, achieving Pearson Correlation Coefficients between 0.971 and 0.995 and Root Mean Square Errors between 0.011 and 0.031 mV across all leads.
Why the study?
Existing ECG digitization methods require manual selection of regions of interest, only partly provide signal digitization, and offer limited accuracy.
Effect estimate: PCC 0.971 to 0.995; RMSE 0.011 to 0.031 mV
p-value: p=<0.001
ECGMiner is an accurate, open-source software tool capable of digitizing paper-based or image-format ECGs into numerical data with high fidelity, facilitating automated analysis.
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.
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Fernández-Santamónica et al. (2024) studied Electrocardiogram (ECG) digitization (n=2,048). ECGMiner software vs. Actual digital ECG signals (ground truth) was evaluated on Pearson Correlation Coefficient (PCC) and Root Mean Square Error (RMSE) between actual and digitized signals (PCC 0.971 to 0.995; RMSE 0.011 to 0.031 mV, p=<0.001). ECGMiner accurately digitized paper-based ECGs, achieving Pearson Correlation Coefficients between 0.971 and 0.995 and Root Mean Square Errors between 0.011 and 0.031 mV across all leads.
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