ECG-SMART-NET: A Deep Learning Architecture for Precise ECG Diagnosis of Occlusion Myocardial Infarction
<|reference_start|>ECG-SMART-NET: A Deep Learning Architecture for Precise ECG Diagnosis of Occlusion Myocardial Infarction: In this paper we describe ECG-SMART-NET for identification of occlusion myocardial infarction (OMI). OMI is a severe form of heart attack characterized by complete blockage of one or more coronary arteries requiring immediate referral for cardiac catheterization to restore blood flow to the heart. Two thirds of OMI cases are difficult to visually identify from a 12-lead electrocardiogram (ECG) and can be potentially fatal if not identified in a timely fashion. Previous works on this topic are scarce, and current state-of-the-art evidence suggests that both random forests with engineered features and convolutional neural networks (CNNs) are promising approaches to improve the ECG detection of OMI. While the ResNet architecture has been successfully adapted for use with ECG recordings, it is not ideally suited to capture informative temporal features within each lead and the spatial concordance or discordance across leads. We propose a clinically informed modification of the ResNet-18 architecture. The model first learns temporal features through temporal convolutional layers with 1xk kernels followed by a spatial convolutional layer, after the residual blocks, with 12x1 kernels to learn spatial features. The new ECG-SMART-NET was benchmarked against the original ResNet-18 and other state-of-the-art models on a multisite real-word clinical dataset that consists of 10,893 ECGs from 7,297 unique patients (rate of OMI = 6.5%). ECG-SMART-NET outperformed other models in the classification of OMI with a test AUC score of 0.889 +/- 0.027 and a test average precision score of 0.587 +/- 0.087.<|reference_end|>
@article{riek2024ecg-smart-net:,
    title={ECG-SMART-NET: A Deep Learning Architecture for Precise ECG Diagnosis of
  Occlusion Myocardial Infarction},
    author={Nathan T. Riek, Murat Akcakaya, Zeineb Bouzid, Tanmay Gokhale,
  Stephanie Helman, Karina Kraevsky-Philips, Rui Qi Ji, Ervin Sejdic, Jessica
  K. Z`egre-Hemsey, Christian Martin-Gill, Clifton W. Callaway, Samir Saba,
  Salah Al-Zaiti},
    journal={arXiv preprint arXiv:2405.09567},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.09567},
    primaryClass={eess.SP cs.AI cs.LG}
}