Heartbeat characterization is an important issue in cardiac assistance diagnosis systems. In particular, wide sets of features are commonly used in long term electrocardiographic signals. Then, if such a feature space does not represent properly the arrhythmias to be grouped, classification or clustering process may fail. In this work a suitable feature set for different heartbeat types is studied, involving morphology, representation and time-frequency features. To determine what kind of features generate better clusters, feature selection procedure is used and assessed by means clustering validity measures. Then the feature subset is shown to produce fine clustering that yields into high sensitivity and specificity values for a broad range of heartbeat types.
Cardiac arrhythmia analysis on Holter recordings is an important issue in clinical settings, however such issue implicitly involves attending other problems related to the large amount of unlabelled data which means a high computational cost. In this work an unsupervised methodology based in a segment framework is presented, which consists of dividing the raw data into a balanced number of segments in order to identify fiducial points, characterize and cluster the heartbeats in each segment separately. The resulting clusters are merged or split according to an assumed criterion of homogeneity. This framework compensates the high computational cost employed in Holter analysis, being possible its implementation for further real time applications. The performance of the method is measure over the records from the MIT/BIH arrhythmia database and achieves high values of sensibility and specificity, taking advantage of database labels, for a broad kind of heartbeats types recommended by the AAMI.