Key result
The proposed real-time context-based adaptive QRS clustering method achieved a global purity of 98.56% on the MIT-BIH Arrhythmia Database and 99.56% on the AHA ECG Database.
A novel real-time adaptive QRS clustering algorithm demonstrates high global purity on standard ECG databases, outperforming previous offline solutions.
May support real-time arrhythmia monitoring; leaves open prospective clinical validation.
Continuous followup 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 paper 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 by 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.
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A 2014 study studied Cardiac arrhythmias. Context-based adaptive QRS clustering algorithm vs. Expert annotations (ground truth) was evaluated on Global purity. The proposed real-time context-based adaptive QRS clustering method achieved a global purity of 98.56% on the MIT-BIH Arrhythmia Database and 99.56% on the AHA ECG Database.
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