Prediction of Imminent Battery Depletion in Implantable Cardioverter-Defibrillator

Implantable Cardioverter-Defibrillators (ICDs) are life-sustaining devices used in the prevention of sudden death in patients suffering from advanced cardiac diseases. Although improvements in ICD technology and monitoring capabilities have been made, existing techniques are still not effective in predicting accelerated battery drain, thereby causing unplanned generator replacement and clinically significant device-related events. In this study, machine learning techniques were employed in the assessment of the early detection of ICD battery depletion risk using the collected device interrogation reports. The dataset used consisted of 32 devices in the training set and nine in the testing set. In order to mitigate the problem of a small sample size, a GMM-based data augmentation technique was applied solely to the training data, and actual devices were used for the testing data. Five supervised models, including Logistic Regression, Random Forest, SVM, CatBoost, and a Neural Network (MLP), have been utilized using a repeated stratified cross-validation and a separate held-out test data set. All the models have been tested for their performance using classification metrics. All models demonstrated variable performance with wide confidence intervals due to limited sample size. Support vector machines showed the highest cross-validation discrimination 0.889 ± 0.203, though uncertainty remains substantial given the small datasets (n = 41). From the feature analysis, it was found that atrial sensing amplitude, RV/LV capture threshold, output settings, and implant duration were the most important features for the prediction of high battery depletion risk. These findings suggest that changes in device parameters and implant age are associated with elevated battery depletion risk, supporting the feasibility of telemetry-driven risk stratification for device management.