Automated discrimination of proximal right coronary artery occlusion from middle-to-distal right coronary artery occlusion and left circumflex occlusion in ST-elevation myocardial infarction

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

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.

Features of electrocardiogram in patients with stenosis of the proximal right coronary artery

BACKGROUND/AIMS

Prediction of lesions of the proximal right coronary artery (pRCA) through electrocardiogram (ECG) is very important because pRCA occlusion has many complications and a high mortality rate, which has frequently been related with right ventricular infarction. The purpose of this study was to devise a screening tool that takes into account multiple leads from a 12-lead ECG to predict the pRCA lesion.

METHODS

A hundred and fifty-eight patients who were diagnosed as acute coronary syndrome and had a pure lesion of RCA or left circumf lex artery (LCX) by ECGs and angiographic findings were enrolled retrospectively. Forty-eight patients with a pure pRCA occlusion were compared to a control group of 110 patients who were diagnosed as having either a pure mid to distal RCA lesion (57 patients) or a pure LCX lesion (53 patients).

RESULTS

ECGs of patients in the pRCA group showed more prominent ST depression in lead I (p = 0.001) and ST elevation in V1 (p = 0.002) than in the control group. The combination of ST depression (≤ 0 mm) in I and ST elevation (> 0.5 mm) in V1 was the best diagnostic tool (area under the curve, 0.84).

CONCLUSIONS

ST changes in leads V1 and I allow more accurate prediction of pRCA occlusion than other criteria, such as the difference between ST elevation of leads II and III or vector direction and amplitude. These variables could help to screen for right ventricular infarction before performing reverse ECG and predicting prognosis.