  Continuous follow-up of heart condition through long-term electrocardiogram
monitoring is an invaluable tool for diagnosing some cardiac arrhythmias. In
such context, providing tools for fast locating alterations of normal
conduction patterns is mandatory and still remains an open issue. This work
presents a real-time method for adaptive clustering QRS complexes from
multilead ECG signals that provides the set of QRS morphologies that appear
during an ECG recording. The method processes the QRS complexes sequentially,
grouping them into a dynamic set of clusters based on the information content
of the temporal context. The clusters are represented by templates which evolve
over time and adapt to the QRS morphology changes. Rules to create, merge and
remove clusters are defined along with techniques for noise detection in order
to avoid their proliferation. To cope with beat misalignment, Derivative
Dynamic Time Warping is used. The proposed method has been validated against
the MIT-BIH Arrhythmia Database and the AHA ECG Database showing a global
purity of 98.56% and 99.56%, respectively. Results show that our proposal not
only provides better results than previous offline solutions but also fulfills
real-time requirements.
