Fuzzy clustering: application to the diagnosis of ventricular arrhythmias

The authors examine the problem of reliably detecting life-threatening ventricular arrhythmias, discriminating them from other rhythms and imitative artifacts, making use of spectral parameters. They try to show the existence of a clusters´ structure in the defined features space, one of each cluster representing a kind of rhythm or artifacts. They used a design set of ECG (electrocardiogram) records labeled by a cardiologist. Even when the hard classification labels of these records indicates that they are reasonably separated, they overlap considerably in some regions of the features space. For this reason, the authors use fuzzy clustering algorithms for a statistical characterization of the groups. The membership-functions matrix of the prototypes of each of the clusters and the matrices that induce the norms in their environment give useful descriptions over the training set for constructing a feasible classifier for detecting ventricular arrhythmias.<>