Key result
A deep learning model using a convolutional neural network with continuous wavelet transform preprocessing of ECG signals achieved an overall accuracy of 83.7% in identifying the culprit coronary artery in patients with STEMI.
Why the study?
Physicians rely on ECG features to identify infarct-related coronary arteries in STEMI, and more accurate prediction using deep learning was sought.
Does a deep learning model using ECG signals accurately predict the culprit coronary artery in patients with STEMI?
Observational (n=384)
No
Does a deep learning model using ECG signals accurately predict the culprit coronary artery in patients with STEMI?
Absolute Event Rate: 83.7% vs 79.3%
A deep learning model using continuous wavelet transform preprocessing of ECG signals achieved 83.7% overall accuracy in identifying the culprit coronary artery in STEMI patients.
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.
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Tseng et al. (2022) conducted an observational in ST elevation myocardial infarction (STEMI) (n=384). Convolutional neural network with continuous wavelet transform (CWT) preprocessing of ECG signals vs. Short-time Fourier transform (STFT) preprocessing was evaluated on Overall accuracy for predicting the infarct-related artery. A deep learning model using a convolutional neural network with continuous wavelet transform preprocessing of ECG signals achieved an overall accuracy of 83.7% in identifying the culprit coronary artery in patients with STEMI.
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