Machine Learning for Prediction of Ventricular Arrhythmia Episodes from Intracardiac Electrograms of Automatic Implantable Cardioverter-Defibrillators

Background Despite the implantable cardioverter defibrillator’s (ICD) effectiveness in saving patients with life-threatening ventricular arrhythmias (VAs), the temporal occurrence of VA following ICD implantation is unpredictable. Objective Apply machine learning (ML) to intracardiac electrograms (IEGMs) recorded by ICDs as a unique biomarker for predicting impending VAs. Methods The study included 13,516 patients who received BIOTRONIK ICDs and enrolled in the CERTITUDE registry between 01/01/2010 to 12/31/2020. Database extraction included IEGMs from standard quarterly transmissions and VA event episodes. The processed IEGM data were pulled from device transmissions stored in a centralized Home Monitoring Service Center and reformatted into an analyzable format. Long- (baseline or first scheduled remote recording), mid-(scheduled remote recording every 90 days), or short-range predictions (IEGM within 5 seconds before the VA onset) were used to determine whether ML-processed IEGMs predicted impending VA events. Convolutional neural network classifiers using ResNet architecture were employed. Results Of 13,516 patients (male 72%, age 67.5 ± 11.9 years), 301,647 IEGM recordings were collected; 27,845 episodes of sustained VT/VF were observed in 4,467 patients (33.0%). Neural networks based on CNN using ResNet-like architectures on far-field IEGMs yielded an AUC of 0.83 with a 95% confidence interval of [0.79, 0.87] in the short-term, while the long- and mid-range analyses had minimal predictive value for VA events. Conclusion In this study, applying ML to ICD-acquired IEGMs predicted impending VT/VF events seconds before they occurred, whereas mid- to long-term predictions were not successful. This could have important implications for future device therapies.