PO-03-007 CROSS VALIDATION OF A MACHINE LEARNING ALGORITHM DESIGNED TO IDENTIFY PULMONARY VEIN SIGNALS USING COMPETING TRAINING DATA

Abstract

State of the art electrophysiology (EP) recording systems allow recording of both the surface electrocardiograms and intracardiac electrograms (EGMs) in an unfiltered fashion with improved quality, lower noise and higher sample rate. Legacy EP recording systems sample and store irreversibly pre-filtered EGMs, often susceptible to noise and interference. This study hypothesized that EGM quality impacts the performance of machine learning algorithms later trained on these data. This study compares the performance of a previously validated machine learning algorithm that classifies pulmonary vein potentials trained on simultaneous recorded legacy and novel EP recording system datasets. Data from pulmonary vein isolation procedures were simultaneously collected using a conventional EP recording system (EP-Tracer, CardioTek and LabSystem Pro, Boston Scientific) and a novel electrophysiology recording system (ECGenius System, CathVision, Denmark) connected in parallel. A machine learning classification algorithm, originally designed for legacy data, was adapted to use data from both the conventional and ECGenius EP recording systems. The performance of the algorithm was evaluated using k-fold validation without overlap in patient data between folds. Classified labels across all folds were summarized and compared to the common ground truth labels of the two datasets. In total, 1536 samples from 521 EGMs were extracted from 89 patients prospectively enrolled in the PVISION study. The trained algorithm using the ECGenius recorded EGMs resulted in a higher predictive value (91% vs 84%), sensitivity (93% vs 90%) and overall accuracy (91% vs 86%) compared to the algorithm trained with EGMs from the conventional dataset, resulting in superiority of the ECGenius System algorithm (p <0.05). Receiver operating characteristic (ROC) curves show superiority of the ECGenius-trained algorithm at all relevant operating points (Fig. 1). Performance of machine learning algorithms using intracardiac EP data are dependent on EGM quality and sample rate. Using high quality, unfiltered unipolar EGMs from a novel EP recording system resulted in superior machine learning algorithm performance compared to the use of EGMs recorded with legacy EP systems.