Key result
ECGDT software achieves an AUC of ~0.82 for diagnosing six common cardiac abnormalities.
Why the study?
Detection of cardiac abnormalities through early and accurate ECG diagnosis can reduce mortality from cardiovascular diseases.
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ECGDT is a free, graphical software tool that demonstrates good diagnostic accuracy for six common cardiac abnormalities and high user satisfaction.
While cardiovascular diseases are the leading causes of death in developed countries, detection of cardiac abnormalities can reduce mortality rates, through early and accurate diagnosis. One of the main assets used to help in the diagnosis process is the electrocardiogram (ECG). A free software tool for electrocardiogram analysis and diagnosis is presented. The tool, named ECGDT, allows: (1) to detect beats present on the ECG, both in single and multi-channel levels, (2) to identify beat waves, and (3) to diagnose different cardiac abnormalities. System evaluation was performed in two ways: (1) diagnostic capabilities were tested with Receiver Operating Characteristic (ROC) analysis, and (2) Graphical Software Interface (GUI) aspects, such as attraction, efficiency, or novelty, were evaluated employing User Experience Questionnaire (UEQ) scores. For disease diagnosis, the mean Area Under the ROC Curve (AUC) was 0.821. The system was also capable of detecting 100% of several cardiac abnormalities, such as bradycardia or tachycardia. Related to the GUI, all usability estimators scored values ranged between 2.208 and 2.750 (overall positive evaluations are obtained for values over 0.8). ECGDT could serve as an aid in the diagnosis of different medical abnormalities. In addition, the suitability of the developed interface has been proven.
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Mondelo et al. (2023) studied Cardiac abnormalities (n=43). ECGDT software tool vs. Database annotations (ground truth) was evaluated on Mean Area Under the ROC Curve (AUC) for disease diagnosis. The ECGDT software tool achieved a mean Area Under the ROC Curve of 0.821 for diagnosing six common cardiac abnormalities.
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