Detecting and interpreting myocardial infarctions using fully convolutional neural networks

We consider the detection of myocardial infarction in electrocardiography (ECG) data as provided by the PTB ECG database without non-trivial preprocessing. The classification is carried out using deep neural networks in a comparative study involving convolutional as well as recurrent neural network architectures. The best architecture, an ensemble of fully convolutional architectures, beats state-of-the-art results on this dataset and reaches 93.3% sensitivity and 89.7% specificity evaluated with 10-fold crossvalidation, which is the performance level of human cardiologists for this task. We investigate questions relevant for clinical applications such as the dependence of the classification results on the considered data channels and the considered subdiagnoses. Finally, we apply attribution methods to gain an understanding of the network's decision criteria on an exemplary basis.