DCA Detection of cardiac arrhythmias in a 12-lead ECG dataset of more than 10,000 patients: a preliminary study using clustering algorithms

Abstract

The groupings of cardiac arrhythmias allow the identification of common patterns, distinctive characteristics, and similarities between different cases. In datasets where less common types of arrhythmias are identified, these grouping tools can better classify each subtype. This research was carried out on electrocardiogram records from a data set with more than 10,000 patients, previously labeled by cardiology specialists into 11 heart rhythms and grouped according to medical guidelines into four groups. A preliminary analysis of an ongoing project for detecting cardiac arrhythmias using unsupervised learning tools: clustering is presented. Feature selection was performed using filter tools, and the RR interval was extracted from the ECG records to be incorporated into the dataset under analysis as a new attribute. Internal validation metrics are used to check the quality of the selected clustering methods.