Preprocessing Method for Performance Enhancement in CNN-based STEMI Detection from 12-lead ECG

ST elevation myocardial infarction (STEMI) is an acute life-threatening disease. It shows a high mortality risk when a patient is not timely treated within the golden time, prompt diagnosis with limited information such as electrocardiogram (ECG) is crucial. However, previous studies among physicians and paramedics have shown that the accuracy of STEMI diagnosis by the ECG is not sufficient. Thus, we propose a detecting algorithm based on a convolutional neural network (CNN) for detecting the STEMI on 12-lead ECG in order to support physicians, especially in an emergency room. We mostly focus on enhancing the detecting performance using a preprocessing technique. First, we reduce the noise of ECG using a notch filter and high-pass filter. We also segment pulses from ECG to focus on the ST segment. We use 96 normal and 179 STEMI records provided by Seoul National University Bundang Hospital (SNUBH) for the experiment. The sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic (ROC) curve are increased from 0.685, 0.350, and 0.526 to 0.932, 0.896, and 0.943, respectively, depending on the preprocessing technique. As our result shows, the proposed method is effective to enhance STEIM detecting performance. Also, the proposed algorithm would be expected to help timely and the accurate diagnosis of STEMI in clinical practices.