Exploring attribution methods explaining atrial fibrillation predictions from sinus ECGs : Attributions in Scale, Time and Frequency

Sammanfattning: Deep Learning models are ubiquitous in machine learning. They offer state-of- the-art performance on tasks ranging from natural language processing to image classification. The drawback of these complex models is their black box nature. It is difficult for the end-user to understand how a model arrives at its prediction from the input. This is especially pertinent in domains such as medicine, where being able to trust a model is paramount. In this thesis, ways of explaining a model predicting paroxysmal atrial fibrillation from sinus electrocardiogram (ECG) data are explored. Building on the concept of feature attributions, the problem is approached from three distinct perspectives: time, scale, and frequency. Specifically, one method based on the Integrated Gradients framework and one method based on Shapley values are used. By perturbing the data, retraining the model, and evaluating the retrained model on the perturbed data, the degree of correspondence between the attributions and the meaningful information in the data is evaluated. Results indicate that the attributions in scale and frequency are somewhat consistent with the meaningful information in the data, while the attributions in time are not. The conclusion drawn from the results is that the task of predicting atrial fibrillation for the model in question becomes easier as the level of scale is increased slightly, and that high-frequency information is either not meaningful for the task of predicting atrial fibrillation, or that if it is, the model is unable to learn from it.