Discriminating electrocardiographic responses to His-bundle pacing using machine learning

Abstract

BACKGROUND His-bundle pacing (HBP) has emerged as an alternative to conventional ventricular pacing because of its ability to deliver physiological ventricular activation. Pacing at the His bundle produces different electrocardiographic (ECG) responses: selective His-bundle pacing (S-HBP), non-selective His bundle pacing (NS-HBP), and myocardium-only capture (MOC). These 3 capture types must be distinguished from each other, which can be challenging and time-consuming even for experts. OBJECTIVE The purpose of this study was to use artificial intelligence (AI) in the form of supervised machine learning using a convolutional neural network (CNN) to automate HBP ECG interpretation. METHODS We identified patients who had undergone HBP and extracted raw 12-lead ECG data during S-HBP, NS-HBP, and MOC. A CNN was trained, using 3-fold cross-validation, on 75% of the segmented QRS complexes labeled with their capture type. The remaining 25% was kept aside as a testing dataset. RESULTS The CNN was trained with 1297 QRS complexes from 59 patients. Cohen kappa for the neural network's performance on the 17-patient testing set was 0.59 (95% confidence interval 0.30 to 0.88; P ,.0001), with an overall accuracy of 75%. The CNN's accuracy in the 17-patient testing set was 67% for S-HBP, 71% for NS-HBP, and 84% for MOC. CONCLUSION We demonstrated proof of concept that a neural network can be trained to automate discrimination between HBP ECG responses. When a larger dataset is trained to higher accuracy, automated AI ECG analysis could facilitate HBP implantation and follow-up and prevent complications resulting from incorrect HBP ECG analysis.

ECG and Pacing Criteria for Differentiating Conduction System Pacing from Myocardial Pacing

“…Arnold et al applied machine learning to diagnose loss of HB capture. 29 Although the overall accuracy of 75% during differentiation of various forms of HB area capture was disappointing, this is a promising method. Perhaps simply including V6RWPT and V1RWPT in automated ECG interpretation reports would be helpful.…”

Left ventricular activation time and pattern are preserved with both selective and nonselective His bundle pacing

“…S-HBP and NS-HBP were defined according to internationally recognized criteria (set out in the Online Appendix). 1 , 2 , 3 Patients with various indications for HBP were recruited: resynchronization of bundle branch block (BBB), optimization of atrioventricular interval in heart failure, or prevention of pacing-induced cardiomyopathy in atrioventricular block. All patients gave written, informed consent and the study was approved by the local ethics committee (13/LO/1440).…”

Electrocardiographic predictors of successful resynchronization of left bundle branch block by His bundle pacing

“…The lead was not actively fixated unless BVP failed, in which case the SelectSecure 3830 lead was deployed for permanent HBP. Selective and nonselective His bundle capture was determined using standard criteria 15 . BVP was performed using the standard technique and AAI pacing was performed from the right atrial appendage.…”

“…Selective and nonselective His bundle capture was determined using standard criteria. 15 BVP was performed using the standard technique and AAI pacing was performed from the right atrial appendage.…”