Clustering of atrial fibrillation based on surface ECG measurements

Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical research. In particular, the study of AF types or sub-classes is a very interesting research topic. In this paper we present a preliminary study to find sub-classes of AF from real 12-lead ECG recordings using k-means and hierarchical clustering algorithms. We applied blind source separation to an initial set of 218 recordings from which we extracted a subset of 136 atrial activity signals displaying known properties of AF. As features for clustering we proposed the peak frequency mean value (PFM), peak frequency standard deviation (PFSD) and the spectral concentration (SC). We computed the silhouette coefficient to obtain an optimal number of clusters of k=5, and conducted preliminary feature selection to evaluate clustering quality. We observed that the separability increases if we discard SC as a feature. The proposed method is the first stage to a future AF classification method, which combined with specialist advice, should help in the clinical field.