Identification of Coronary Culprit Lesion in ST Elevation Myocardial Infarction by Using Deep Learning

Li-Ming Tseng; Cheng-Yen Chuang; Su-Kiat Chua; Vincent S Tseng

doi:10.1109/JTEHM.2022.3227204

Identification of Coronary Culprit Lesion in ST Elevation Myocardial Infarction by Using Deep Learning

IEEE J Transl Eng Health Med. 2022 Dec 8:11:70-79. doi: 10.1109/JTEHM.2022.3227204. eCollection 2023.

Authors

Li-Ming Tseng^{1

2

3}, Cheng-Yen Chuang⁴, Su-Kiat Chua^{4

3}, Vincent S Tseng²

Affiliations

¹ Department of Emergency MedicineShin Kong Wu Ho-Su Memorial Hospital Taipei 11101 Taiwan.
² Department of Computer ScienceNational Yang Ming Chiao Tung University Hsinchu 30010 Taiwan.
³ School of Medicine, College of MedicineFu Jen Catholic University New Taipei 24205 Taiwan.
⁴ Division of CardiologyDepartment of Internal MedicineShin Kong Wu Ho-Su Memorial Hospital Taipei 11101 Taiwan.

Abstract

Objective: Early revascularization of the occluded coronary artery in patients with ST elevation myocardial infarction (STEMI) has been demonstrated to decrease mortality and morbidity. Currently, physicians rely on features of electrocardiograms (ECGs) to identify the most likely location of coronary arteries related to an infarct. We sought to predict these culprit arteries more accurately by using deep learning.

Methods: A deep learning model with a convolutional neural network (CNN) that incorporated ECG signals was trained on 384 patients with STEMI who underwent primary percutaneous coronary intervention (PCI) at a medical center. The performances of various signal preprocessing methods (short-time Fourier transform [STFT] and continuous wavelet transform [CWT]) with different lengths of input ECG signals were compared. The sensitivity and specificity for predicting each infarct-related artery and the overall accuracy were evaluated.

Results: ECG signal preprocessing with STFT achieved fair overall prediction accuracy (79.3%). The sensitivity and specificity for predicting the left anterior descending artery (LAD) as the culprit vessel were 85.7% and 88.4%, respectively. The sensitivity and specificity for predicting the left circumflex artery (LCX) were 37% and 99%, respectively, and the sensitivity and specificity for predicting the right coronary artery (RCA) were 88.4% and 82.4%, respectively. Using CWT (Morlet wavelet) for signal preprocessing resulted in better overall accuracy (83.7%) compared with STFT preprocessing. The sensitivity and specificity were 93.46% and 80.39% for LAD, 56% and 99.7% for LCX, and 85.9% and 92.9% for RCA, respectively.

Conclusion: Our study demonstrated that deep learning with a CNN could facilitate the identification of the culprit coronary artery in patients with STEMI. Preprocessing ECG signals with CWT was demonstrated to be superior to doing so with STFT.

Keywords: Convolutional neural network; STEMI; electrocardiography; machine learning; percutaneous coronary intervention.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Coronary Vessels / surgery
Deep Learning*
Heart
Humans
Percutaneous Coronary Intervention*
ST Elevation Myocardial Infarction* / diagnosis

Grants and funding

This work was supported in part by the National Science and Technology Council Taiwan under Grant 110-2634-FA49-002 and Grant 111-2634-F-A49-011.