Detecting and interpreting myocardial infarction using fully convolutional neural networks

Objective : We aim to provide an algorithm for the detection of myocardial infarction that operates
directly on ECG data without any preprocessing and to investigate its decision criteria. Approach :
We train an ensemble of fully convolutional neural networks on the PTB ECG dataset and apply
state-of-the-art attribution methods. Main results : Our classifier reaches 93.3% sensitivity and
89.7% specificity evaluated using 10-fold cross-validation with sampling based on patients. The
presented method outperforms state-of-the-art approaches and reaches the performance level of human
cardiologists for detection of myocardial infarction. We are able to discriminate channel-specific
regions that contribute most significantly to the neural network ’s decision. Interestingly, the
network ’s decision is influenced by signs also recognized by human cardiologists as indicative of
myocardial infarction. Significance : Our results demonstrate the high prospects of algo...

Source: Physiological Measurement - January 15, 2019 Category: Physiology Authors: Nils Strodthoff and Claas Strodthoff Source Type: research