Department of Emergency MedicineShin Kong Wu Ho-Su Memorial Hospital Taipei 11101 Taiwan.
Department of Computer ScienceNational Yang Ming Chiao Tung University Hsinchu 30010 Taiwan.
IEEE J Transl Eng Health Med. 2022 Dec 8;11:70-79. doi: 10.1109/JTEHM.2022.3227204. eCollection 2023.
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.
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.
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.
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.
ST 段抬高型心肌梗死（STEMI）患者闭塞冠状动脉的早期血运重建已被证明可降低死亡率和发病率。目前，医生依赖心电图（ECG）的特征来确定与梗死相关的冠状动脉的最可能位置。我们试图通过深度学习更准确地预测这些罪犯动脉。
在一家医疗中心接受经皮冠状动脉介入治疗（PCI）的 384 例 STEMI 患者中，使用包含 ECG 信号的卷积神经网络（CNN）训练了一个深度学习模型。比较了不同长度输入 ECG 信号的各种信号预处理方法（短时傅里叶变换[STFT]和连续小波变换[CWT]）的性能。评估了预测每条相关梗死动脉和整体准确性的敏感性和特异性。
使用 STFT 进行 ECG 信号预处理可实现良好的整体预测准确性（79.3%）。预测罪犯血管为左前降支（LAD）的敏感性和特异性分别为 85.7%和 88.4%。预测左旋支（LCX）的敏感性和特异性分别为 37%和 99%，预测右冠状动脉（RCA）的敏感性和特异性分别为 88.4%和 82.4%。与 STFT 预处理相比，使用 CWT（Morlet 小波）进行信号预处理可获得更好的整体准确性（83.7%）。LAD 的敏感性和特异性分别为 93.46%和 80.39%，LCX 为 56%和 99.7%，RCA 为 85.9%和 92.9%。
我们的研究表明，CNN 深度学习可有助于识别 STEMI 患者的罪犯冠状动脉。与 STFT 相比，使用 CWT 预处理 ECG 信号效果更佳。
