Unknown

Dataset Information

0

Detection of myocardial ischemia by intracoronary ECG using convolutional neural networks.


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.

SUBMITTER: Bigler MR 

PROVIDER: S-EPMC8202932 | biostudies-literature |

REPOSITORIES: biostudies-literature

Similar Datasets

| S-EPMC6925141 | biostudies-literature
| S-EPMC6236889 | biostudies-other
| S-EPMC9075394 | biostudies-literature
| S-EPMC9933989 | biostudies-literature
| S-EPMC10909211 | biostudies-literature
| S-EPMC6499237 | biostudies-literature
| S-EPMC5701651 | biostudies-literature
| S-EPMC6614282 | biostudies-literature
| S-EPMC5314376 | biostudies-literature
| S-EPMC7177575 | biostudies-literature