Abstract
Sudden cardiac death causes multiple deaths annually, and T-wave alternans are a reliable predictor of this fatal event. Detecting alternans is crucial for reducing disease incidence, and electrocardiographic imaging is a promising tool, providing spatial-temporal insights. The absence of references and segmentation methods specific to these data may complicate progress in the field. Therefore, this work aimed to develop a reference for evaluating estimation methods. Initially, a novel T-wave segmentation procedure specific to these data was introduced and compared with a commonly used method. Subsequently, a reference for assessing alternans estimation methods was created by integrating alternans into epicardial signals through a spatial-temporal Gaussian function. Finally, a bootstrap-based classifier for detecting alternans was developed. Results underscored the superiority of the novel T-wave segmentation procedure, with the lowest 95% confidence interval being [ 16.57 μV, 18.80 μV ], indicating significant disparities between the two segmentation methodologies. Furthermore, the generated reference demonstrated the distinguishability of T-wave alternans with an amplitude of approximately 55 μV from noise. Additionally, the classifier exhibited consistency with previous findings, demonstrating its ability to detect alternans with amplitudes around 50 μV . In conclusion, this study provides a spatial-temporal reference for proper evaluation of estimation methods, contributing to establishing a gold standard.