Choosing real-time predictors for ventricular arrhythmia detection

The risk of developing life-threatening ventricular arrhythmias in patients with structural heart disease is higher with increased occurrence of premature ventricular complex (PVC). Therefore, reliable detection of these arrhythmias is a challenge for a cardiovascular diagnosis system. While early diagnosis is critical, the task of its automatic detection and classification becomes crucial. Therefore, the underlying models should be efficient, albeit ensuring robustness. Although neural networks (NN) have proven successful in this setting, we show that kernel-based learning algorithms achieve superior performance. In particular, recently developed sparse Bayesian methods, such as, Relevance Vector Machines (RVM), present a parsimonious solution when compared with Support Vector Machines (SVM), yet revealing competitive accuracy. This can lead to significant reduction in the computational complexity of the decision function, thereby making RVM more suitable for real-time applications.