Detecting malignant ventricular arrhythmias in electrocardiograms by Gaussian process classification

Ventricular tachycardia, ventricular flutter, and ventricular fibrillation are malignant forms of cardiac arrhythmias, whose occurrence may be a life-threatening event. Several methods exist for detecting these arrhythmias in the electrocardiogram. However, the use of Gaussian process classifiers in this context has not been reported in the current literature. In comparison to the popular support vector machines, Gaussian processes have the advantage of being fully probabilistic, they can be re-casted in Bayesian filtering compatible state-space form, and they can be flexibly combined with first-principles physical models. In this paper we use Gaussian process classification to detect malignant ventricular arrhythmias in the electrocardiogram. We describe how Gaussian process classifiers can be used to solve the detection problem, and show that the proposed classifiers achieve a performance that is comparable to that of the state-of-the-art methods henceforth laying down promising foundations for more general electrocardiogram-based arrhythmia detection framework.