Discrimination between RA and LA Sinus Rhythms using machine learning approach

Atrial fibrillation (AF) is a common cardiac disease that potentially leads to fatal conditions. Machine Learning (ML) classification methods are widely used to distinguish between sinus rhythm and AF for post-ablation rhythms in ECG. However, intracardiac electrograms (iEGMs) recorded in the left atrium (LA) and right atrium (RA) might have different sinus rhythms characteristics. In this work, we demonstrate a method to evaluate the iEGMs in the high-dimensional parameter space and effectively discriminate between the sinus rhythms recorded from LA and RA by extracting the features from the time series and using Support Vector Machine (SVM) and K-means clustering. We also demonstrate that the rhythms in LA post ablations exhibit a similar distribution in feature space to that of the sinus RA. The classification has achieved an accuracy of 90.15% for the non-supervised K-Means cluster. It marks the difference between LA and RA baseline and provides insights into signal identification using iEGMs.