ECG Statement Classification and Lead Reconstruction Using CNN-Based Models.

Abstract

ECG is an essential diagnostic tool that offers important insight into a person's cardiac and general health. The rise of intelligent wearable devices has opened a new avenue for clinicians and individuals to capture long term ECG data-albeit with fewer leads than the 12 leads that are typically used clinically, which can be vital for identifying and addressing health concerns. In this work, a multi-task convolutional neural network (CNN) classifier was used to study the influence of various combinations of ECG leads in interpretation of 71 cardiac statements spanning cardiac diagnostics, form, and rhythm. Results of this analysis suggest that the subset of limb leads I and II and chest leads V1, V3, and V6 can be used to identify several cardiac statements without loss of performance (average macro AUC of 0.903) when compared to a model trained using all 12-leads (average macro AUC of 0.905; p = 1). A hybrid CNN-LSTM (long short-term memory) model was developed to reconstruct the missing chest leads. The highest performing lead reconstructor achieved an average R² score of 0.835 when reconstructing three chest leads. This architecture was proposed as the foundation for a wearable system that could record a limited number of ECG leads while also providing a 12-lead ECG for clinical applications.