While the signal-to-noise ratio (SNR) is a commonly used metric for evaluating the quality of digitized ECG signals, it may not fully capture the clinical significance of the recovered ECG details. Therefore, several alternative evaluation metrics can be considered:
Weighted Signal-to-Noise Ratio (WSNR): This metric can be tailored to emphasize specific components of the ECG signal that are clinically relevant, such as the QRS complex, T-wave, and ST segment. By applying different weights to these components based on their diagnostic importance, WSNR can provide a more nuanced assessment of the quality of the digitized ECG.
Root Mean Square Error (RMSE): RMSE can be used to quantify the difference between the original time-series data and the reconstructed signal from the digitized image. This metric provides a direct measure of the accuracy of the digitization process, highlighting discrepancies that may affect clinical interpretation.
Correlation Coefficient: The correlation coefficient can be employed to assess the degree of similarity between the original and digitized ECG signals. A high correlation indicates that the essential features of the ECG have been preserved, which is critical for accurate diagnosis.
Clinical Diagnostic Accuracy: Evaluating the performance of digitized ECGs in terms of their ability to support clinical diagnoses can be a powerful metric. This could involve comparing the diagnostic outcomes derived from digitized images against those obtained from original time-series data, using metrics such as sensitivity, specificity, and overall accuracy.
Feature Extraction Metrics: Metrics that focus on the preservation of key ECG features, such as heart rate variability, QT interval measurements, and other clinically relevant parameters, can provide insights into the effectiveness of the digitization process. These metrics can help determine whether the digitized ECG retains the necessary information for clinical decision-making.
Visual Inspection and Expert Review: Incorporating qualitative assessments from trained cardiologists or ECG technicians can provide valuable insights into the clinical usability of digitized ECGs. Expert reviews can identify subtle details that may be clinically significant but not captured by quantitative metrics.
By utilizing these alternative evaluation metrics, researchers can gain a more comprehensive understanding of the clinical significance of recovered ECG details from digitized images, ultimately improving the quality and reliability of ECG digitization models.