Deep Learning Based QRS Multilead Delineator in Electrocardiogram Signals

Abstract

The surface electrocardiogram (ECG) is the most widely adopted test to diagnose cardiac diseases. Extracting critical biomarkers from these signals, such as the QRS width, requires delineating the fundamental waves in them. However, even though ECG signals significantly change depending on the recording methodology and cardiac condition, the available QRS delineators are hard to adapt to non-considered cases. We present a deep learning-based multilead ECG delineation method which can successfully delineate QRS complexes. Our approach reached root-mean-square errors (RMSE) of 12.1±0.5 and 18.5±1.1 ms for QRS onset and offset, respectively, when evaluated on the QT database; thus, demonstrating to be comparable to the state-of-the-art. Moreover, these results are similar to the RMSE calculated from differences between the two cardiologists that annotated this database, namely, 14.7 ms for the QRS onset and 17.2 ms for the offset.