Delineating 12-lead ECG for automated ST-elevation and ST depression detection using deep learning

Abstract

ST-elevation or ST-depression are markers of an abnormal heart condition detected through an electrocardiogram (ECG) where the tracing in the ST-segment is unusually elevated above the TP-segment (baseline). Identifying the localization of the ST-segment on an ECG is difficult because even a minor change in the ST-segment can be obscured by filtering processes. The 12-lead ECG signal is a non-invasive tool in the early detection of ST-elevation based on ST- and TP-segment, with quick and accurate interpretation. This study proposes a standard 12-lead ECG delineation model using deep learning (DL). The ECG signal has been segmented to Pstart–Pend, Pend–QRSstart, QRSstart–Rpeak, Rpeak–QRSend, QRSend-Tstart, Tstart–Tend, and Tend–Pstart. The study interpreted ST-elevation or -depression using an ECG delineation approach guided by medical rules. The findings revealed that the DL model achieved an average accuracy of 99.18%, sensitivity of 92.55%,specificity of 99.55%, precision of 92.61%, and F1-score of 92.52% in limb leads. Similarly, in chest leads, the DL model attained an accuracy of 99.16%, sensitivity of 93.10%, specificity of 99.53%, precision of 93.32%, and F1-score of 93.11%. This study also validated the DL-predicted results by a cardiologist from Mohammad Hoesin Hospital, Indonesia.