Fig. 4: The GradCAM plots for the DL model in diagnosis of different cardiovascular conditions. | npj Digital Medicine

Fig. 4: The GradCAM plots for the DL model in diagnosis of different cardiovascular conditions.

From: Development and validation of machine learning algorithms based on electrocardiograms for cardiovascular diagnoses at the population level

Fig. 4

Representative ECG traces were chosen for a selected group of diagnoses. GradCAM results do not extend to the entire population, but indicative of the DL model’s prediction for a single representative case. The darker areas in each trace on GradCAM denote the areas with the most contribution to DL model’s diagnostic prediction. PR intervals and QRS complexes in STEMI, T waves in NSTEMI, QRS complexes in PHTN, VT beats in patients with non-sustained VT, QRS complexes in AS, p waves in AVB, and ST segment region in HF contributed the most to the diagnosis of each condition. AS aortic stenosis, AVB atrioventricular block, DL deep learning, ECG electrocardiogram, HF heart failure, NSTEMI non-ST-elevation myocardial infarction, STEMI ST-elevation myocardial infarction, PHTN pulmonary hypertension, VT ventricular tachycardia.

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