Establishment and validation of an electrocardiogram vector-based machine learning model for the conversion of prone position electrocardiograms into standard electrocardiograms

The prone electrocardiogram (ECG) presents challenges in detecting anterior ST-segment elevated myocardial infarction (STEMI). This study aims to develop a method to convert prone ECGs to standard ECGs to facilitate physician diagnosis of STEMI and other cardiovascular diseases (CVD).

The standard ECGs, vectorcardiograms (VCGs), and prone ECGs were prospectively examined for model development. Three conversion approaches were developed: direct lead matching by linear regression (Approach 1), conversion from prone ECGs to VCGs via regression and then to standard ECGs (Approach 2), and machine-learning (ML)-based models (Approach 3). External validation was done with a separate cohort, and a hybrid model was created by integrating the best-performing morphology and amplitude models. The diagnostic performance of the converted ECGs was reviewed by nine cardiologists and benchmarked against the original ECG interpretations. Five hundred and ninety prone ECG cardiac cycles from seventy participants [median age 64 years, interquartile range (IQR) 27.0–70.0] were analysed for model development. The external validation cohort had 94 patients (median age 56.5 years, IQR 39.3–67.0). Approach 3 had the best morphology accuracy, and Approach 2 had the best amplitude similarity. These two models were combined into a hybrid model. In the external validation dataset, the AUCs (95% confidence intervals) for detecting normal ECGs, anterior ST-segment elevation/depression, old anterior myocardial infarction, and bundle branch blocks were 0.835 (0.734–0.908), 0.825 (0.693–0.923), 0.898 (0.799–0.957), 0.867 (0.622–0.956), and 0.910 (0.714–0.953), respectively.

The successful development of models for converting prone ECGs to standard ECGs demonstrated good and robust diagnostic performance for CVD.