%0 Journal Article %1 strodthoff2019detecting %A Strodthoff, Nils %A Strodthoff, Claas %D 2019 %I IOP Publishing %J Physiological Measurement %K convnet deep ecg infarction learning myocardial %N 1 %P 015001 %R 10.1088/1361-6579/aaf34d %T Detecting and interpreting myocardial infarction using fully convolutional neural networks %U https://doi.org/10.1088%2F1361-6579%2Faaf34d %V 40 %X 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 algorithmic ECG analysis for future clinical applications considering both its quantitative performance as well as the possibility of assessing decision criteria on a per-example basis, which enhances the comprehensibility of the approach.

@article{strodthoff2019detecting, abstract = {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 algorithmic ECG analysis for future clinical applications considering both its quantitative performance as well as the possibility of assessing decision criteria on a per-example basis, which enhances the comprehensibility of the approach.}, added-at = {2020-04-23T15:52:44.000+0200}, author = {Strodthoff, Nils and Strodthoff, Claas}, biburl = {https://www.bibsonomy.org/bibtex/2e6fc3839b9d159194e5eac2d074a8b92/nosebrain}, doi = {10.1088/1361-6579/aaf34d}, interhash = {3620fd4092d685dde400e33f2e0a789a}, intrahash = {e6fc3839b9d159194e5eac2d074a8b92}, journal = {Physiological Measurement}, keywords = {convnet deep ecg infarction learning myocardial}, month = jan, number = 1, pages = 015001, publisher = {{IOP} Publishing}, timestamp = {2020-04-23T15:53:42.000+0200}, title = {Detecting and interpreting myocardial infarction using fully convolutional neural networks}, url = {https://doi.org/10.1088%2F1361-6579%2Faaf34d}, volume = 40, year = 2019 }