Abstract 13745: Exploring the Segment-Wise Explainability of Electrocardiogram on Deep Learning-Based Models for Acute Myocardial Infarction

Abstract

Background: While DL models for AMI detection using ECGs have advanced, their 'black box' nature hinders seamless integration into clinical practice, as it limits our understanding of their decision-making process. Research Questions: Which specific ECG segments (P, QRS, T) are considered important by the DL model for the detecting AMI, and does it fit into the ECG criteria of AMI? Aims: We aimed to examine the importance of each ECG segment in the detection of DL model for AMI. Methods: We examined the impact of various ECG segments by replacing their values with Gaussian noise and verify the decrease in AUC of a DL model for detecting AMI. The importance of each ECG segment in diagnosing AMI using a DL model was assessed by observing the performance drop in AUC of the model. DL-based model detecting AMI trained with 17,907 ECGs from 5,872 coronary angiography-validated AMI patients and 705,482 ECGs from 294,755 non-AMI patients at Chonnam National University Hospital (May 2013 - December 2020). External validation was performed at Hallym University Kangnam Sacred Heart Hospital using 357,072 ECGs from 163,870 patients (January 2006 - December 2020). For ECG segmentation, a U-Net architecture trained on LUDB was employed. Results: The test set was divided into STEMI and NSTEMI groups within internal and external sets. The occurrence rates of events in ECGs were 0.498% (1895/380149) for STEMI and 0.67% (2550/380804) for NSTEMI. In the STEMI group, the AUC values were as follows: 12-lead raw ECG - 0.988, P-wave masking ECG - 0.986, QRS-complex masking ECG - 0.943, and T-wave masking ECG - 0.939. In the NSTEMI group, the AUC values were 0.943, 0.936, 0.835, and 0.867, respectively. Conclusion: The DL model assigns varying degrees of importance to individual ECG segments when determining AMI. The results show that the AMI model effectively utilizes ECG characteristics such as QRS and T waves, which are also highly valued in clinical practice, to determine the presence of AMI.