Learning Domain Shift in Simulated and Clinical Data: Localizing the Origin of Ventricular Activation From 12-Lead Electrocardiograms

Abstract

Building a data-driven model to localize the origin of ventricular activation from 12-lead electrocardiograms (ECG) requires addressing the challenge of large anatomical and physiological variations across individuals. The alternative of a patient-specific model is, however, difficult to implement in clinical practice because the training data must be obtained through invasive procedures. In this paper, we present a novel approach that overcomes this problem of the scarcity of clinical data by transferring the knowledge from a large set of patient-specific simulation data while utilizing domain adaptation to address the discrepancy between the simulation and clinical data. The method that we have developed quantifies non-uniformly distributed simulation errors, which are then incorporated into the process of domain adaptation in the context of both classification and regression. This yields a quantitative model that, with the addition of 12-lead ECG data from each patient, provides progressively improved patient-specific localizations of the origin of ventricular activation. We evaluated the performance of the presented method in localizing 75 pacing sites on three in-vivo premature ventricular contraction (PVC) patients. We found that the presented model showed an improvement in localization accuracy relative to a model trained on clinical ECG data alone or a model trained on combined simulation and clinical data without considering domain shift. Furthermore, we demonstrated the ability of the presented model to improve the real-time prediction of the origin of ventricular activation with each added clinical ECG data, progressively guiding the clinician towards the target site.

Fig. 1

Mapping function. This modified sigmoid function transforms ρ_i ≥ 0.5 to a similar output score ψ_i, but it transforms ρ_i < 0.5 to a drastically decreasing output score ψ_i. Th red dotted line shows an example when the input score is 0.5 the output estimated score will be 0.88

Fig. 2

Illustration of the change in the learned similarity map as the number of clinical data increases (middle panel), in comparison with examples of clinical versus simulated ECG data with their actual correlation coefficients (CC) at selected sites (A, B, C, and D). This provides an example of the agreement between the actual simulation quality at selected sites and the learned similarity map as more clinical data are used for training.

Fig. 3

Representative examples of histograms of features extracted from simulated versus clinical ECG data showing the discrepancy of distribution between the two datasets.

Fig. 4

Schematics of the pre-defined 26-segment model.

Fig. 5

Comparison of classification results (top-one, top-two hits) among alternative models on each of the three subjects.

Fig. 6

Comparison of regression results (mean and standard deviation) among alternative models on each subject.

Fig. 7

Results of retrospective emulation of the presented scheme of progressive prediction. This schematic shows the mean reduction in prediction error with each added clinical data point, along with the number of cases (N) tested in each step.

Fig. 8

Two examples emulating how the presented scheme of progressive prediction would guide pace-mapping. Orange dots mark the targets, and yellow dots mark the models predictions in the annotated order.

Fig. 9

Comparison of localization accuracy between the presented method and the ECGI method in [11] (Subject 1, 2 and 3 respectively). STD(A): standard deviation of localization accuracy associated with different ECG beats when the same training data are used (i.e., the same trial), averaged across all 20 trials. STD(B): standard deviation of localization accuracy associated with the use of different training data (all 20 trials) for each beat, averaged across all ECG beats from the same pacing site.

Fig. 10

Comparison of classification accuracy when 14 versus 26 segments are used for localizing the activation origin.

Fig. 11

The effect of keeping or removing extremity leads from the calculation of similarity scores on exact segment prediction for subject 1 and subject 2.

Fig. 12

Improvement in accuracy achieved by the presented domain adaptation methods when simulation data from Subject 2 or Subject 3 are adapted to clinical data from Subject 1.