Malignant ventricular arrhythmias prediction based on cardiorespiratory signals analysis and machine learning approach

This study aims to evaluate the potential of machine learning (ML) algorithms in predicting malignant ventricular arrhythmias based on RR intervals and respiratory signals in heart failure patients. A total of 26 distinct features are extracted from both signals and systematically categorized into three groups: cardiac, respiratory, and their interaction-based metrics. The data comprise 82 heart failure patients, all of whom underwent screening prior to cardioverter defibrillator implantation, ensuring a clinically relevant population for evaluation. Despite the absence of statistically significant differences in features identified by traditional analysis, the integration of advanced ML models, specifically Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) ML algorithms, confirms a separation between patient groups. Moreover, the valuable application of Random Over-Sampling Examples (ROSE) as a data augmentation technique is successful in addressing limitations inherent in the small and imbalanced dataset. Our results suggest that the ML algorithms can detect subtle, nonlinear relationships embedded within the complex interplay of cardiac and respiratory dynamics. The best predictive outcomes are achieved through a strategic combination of pseudo-random oversampling and undersampling, as well as through application of ensemble learning algorithms. These findings underscore the promise of ML approaches—not only in improving the predictive potential of cardiorespiratory features, but also in illuminating the critical role of respiratory features and cardiorespiratory interactions in the early identification of malignant ventricular arrhythmias among heart failure patients.