Abstract
R-peak detection is the crucial first step in every automatic ECG analysis. Much work has been carried out in this field, using various methods ranging from filtering and threshold methods, through wavelet methods, to neural networks, and others. Performance is generally good, but each method has situations where it fails. In this paper we suggest an approach to automatically combine different algorithms, here the Pan Tompkins and wavelet algorithms, for detection of R-peaks in ECG signals, in order to benefit from the strengths of both algorithms. Experimental results and analysis are provided on the MIT-BIH Arrhythmia Database. We obtained substantial improvements on the test data with respect to the best individual algorithm.
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