A method for context-based adaptive QRS clustering in real-time
This work addresses the need for fast detection of heart condition alterations in long-term ECG monitoring, which is crucial for diagnosing cardiac arrhythmias, though it appears incremental as it builds on existing clustering methods with real-time adaptation.
The paper tackles the problem of real-time clustering of QRS complexes from ECG signals for cardiac monitoring, achieving a global purity of 98.56% on the MIT-BIH Arrhythmia Database and 99.56% on the AHA ECG Database, outperforming previous offline solutions while meeting real-time requirements.
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.