Abstract
The QRS complex is the main wave of the ECG. It is widely used for diagnosing many cardiac diseases. Automatic QRS detection is an essential task of cardiac monitoring and many detection algorithms have been proposed in the literature. Although most of the algorithms perform satisfactorily in normal situations, there are contexts, in the presence of noise or a specific pathology, where one algorithm performs better than the others. We propose a combination method that selects, on line, the detector that is the most adapted to the current context. The selection is done by a decision tree that has been learnt from the performance measures of 7 algorithms in various instances of 130 combinations of arrhythmias and noises. The decision tree is compared to expert rules tested in the framework of the cardiac monitoring system IP-Calicot.
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Portet, F., Quiniou, R., Cordier, MO., Carrault, G. (2007). Learning Decision Tree for Selecting QRS Detectors for Cardiac Monitoring. In: Bellazzi, R., Abu-Hanna, A., Hunter, J. (eds) Artificial Intelligence in Medicine. AIME 2007. Lecture Notes in Computer Science(), vol 4594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73599-1_21
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DOI: https://doi.org/10.1007/978-3-540-73599-1_21
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