1. Detection of myocardial ischemia by intracoronary ECG using convolutional neural networks.
- Author
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Bigler MR and Seiler C
- Subjects
- Aged, Coronary Artery Disease diagnostic imaging, Coronary Artery Disease pathology, Coronary Occlusion diagnostic imaging, Coronary Occlusion pathology, Coronary Vessels diagnostic imaging, Coronary Vessels pathology, Deep Learning, Female, Heart diagnostic imaging, Humans, Male, Myocardial Ischemia diagnostic imaging, Myocardial Ischemia pathology, Neural Networks, Computer, Coronary Artery Disease diagnosis, Coronary Occlusion diagnosis, Electrocardiography statistics & numerical data, Myocardial Ischemia diagnosis
- Abstract
Introduction: The electrocardiogram (ECG) is a valuable tool for the diagnosis of myocardial ischemia as it presents distinctive ischemic patterns. Deep learning methods such as convolutional neural networks (CNN) are employed to extract data-derived features and to recognize natural patterns. Hence, CNN enable an unbiased view on well-known clinical phenomenon, e.g., myocardial ischemia. This study tested a novel, hypothesis-generating approach using pre-trained CNN to determine the optimal ischemic parameter as obtained from the highly susceptible intracoronary ECG (icECG)., Method: This was a retrospective observational study in 228 patients with chronic coronary syndrome. Each patient had participated in clinical trials with icECG recording and ST-segment shift measurement at the beginning (i.e., non-ischemic) and the end (i.e., ischemic) of a one-minute proximal coronary artery balloon occlusion establishing the reference. Using these data (893 icECGs in total), two pre-trained, open-access CNN (GoogLeNet/ResNet101) were trained to recognize ischemia. The best performing CNN during training were compared with the icECG ST-segment shift for diagnostic accuracy in the detection of artificially induced myocardial ischemia., Results: Using coronary patency or occlusion as reference for absent or present myocardial ischemia, receiver-operating-characteristics (ROC)-analysis of manually obtained icECG ST-segment shift (mV) showed an area under the ROC-curve (AUC) of 0.903±0.043 (p<0.0001, sensitivity 80%, specificity 92% at a cut-off of 0.279mV). The best performing CNN showed an AUC of 0.924 (sensitivity 93%, specificity 92%). DeLong-Test of the ROC-curves showed no significant difference between the AUCs. The underlying morphology responsible for the network prediction differed between the trained networks but was focused on the ST-segment and the T-wave for myocardial ischemia detection., Conclusions: When tested in an experimental setting with artificially induced coronary artery occlusion, quantitative icECG ST-segment shift and CNN using pathophysiologic prediction criteria detect myocardial ischemia with similarly high accuracy., Competing Interests: The authors have declared that no competing interests exist.
- Published
- 2021
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