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Description:
A 12-lead electrocardiogram (ECG), a common examination tool in cardiology, represents the electrical activity of the heart as waveforms. Predictions and classifications with deep learning (DL) algorithms show great potential to aid clinicians in the diagnosis and treatment of patients. However, since clinicians are responsible for the treatment and thus the outcome of single patients, they need to understand the reasoning behind these model’s decisions. Important criteria for the acceptance of DL models in clinical settings are covered by aspects of trustworthiness, such as safety and privacy. In this work, new methods and tools are developed to evaluate and quantify technical aspects of trustworthiness on a pre-trained deep neural network (DNN) for 12-lead ECG classification of six clinically relevant abnormalities. The open source DNN by Ribeiro et al. indicated a good performance on test data and was trained on a large data set. It is systematically analyzed for its reproducibility, explainability, robustness, and generalizability with multiple public and clinical data sets. For this, F1-scores are calculated and evaluated for different groups, and quantitative measurements for relevance scores of post-hoc explainable artificial intelligence (XAI) methods are analyzed. Moreover, raw ECG data recorded in clinical routine is exported and integrated into the local research infrastructure to evaluate the generalizability of the model in clinical settings. The results of the DNN with the original test data set can be reproduced with errors in the range of rounding errors. The DNN exhibits similarly high performance on the PTB-XL and CPSC 2018 public data sets, as well as on a large export of resting ECGs from Schiller devices acquired at the University Medical Center G¨ottingen. Applying XAI to the DNN reveals features similar to cardiological textbook knowledge, such as lead V1 being most important and missing P-waves in atrial fibrillation, and this is validated on all data sets. The noise annotations of PTB-XL ...