Abstract: A medical data and diagnostics management system for processing classified electrogram (EGM) datasets includes a server system that receives transmissions of classified EGM datasets, each corresponding to an arrhythmic episode detected by an implantable medical device (IMD), and applies a machine-learning model to each classified EGM dataset, thereby determining confidence indicator(s) relating to the IMD classification for each arrhythmic episode. Based upon the confidence indicator(s), the server system generates a set of machine-adjudicated EGM datasets, assigns a ranking score to each machine-adjudicated EGM dataset, and selects for display (for clinical analysis) a subset of the machine-adjudicated EGM datasets based upon their ranking scores. The machine-adjudicated EGM datasets are also stored in a database and further processed to generate diagnostic information and/or diagnostic alerts relating to the arrhythmic episodes detected by the IMD over time.

Abstract: A computer implemented method and system for declaring arrhythmias in cardiac activity are provided. The method and system are under control of one or more processors that are configured with specific executable instructions. The method and system obtain far field cardiac activity (CA) signals for a series of beats and builds an N-dimensional data set from data values for features of interest from the CA signals. The method and system utilize a manifold structure to map the N-dimensional data set, through nonlinear dimensional reduction, onto an M-dimensional data set and declares an atrial fibrillation (AFL) episode based on a relation between the M-dimensional data set and one or more AFL classification criteria.