IEEE Transactions on Information Technology in Biomedicine · 2006 · 110 citations · 16 references
QRS complex detection is a critical first step in ECG analysis, with many methods such as filtering, wavelet, and neural networks achieving good performance yet each failing in certain situations. The study proposes automatically combining Pan–Tompkins and wavelet QRS detection algorithms to leverage their complementary strengths. The authors introduce data‑driven parameters to balance the Pan–Tompkins and wavelet algorithms and evaluate the approach on the MIT‑BIH Arrhythmia Database. The combined method outperforms each individual algorithm.
QRS complex and specifically R-Peak detection is the crucial first step in every automatic electrocardiogram 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 QRS complex detection algorithms, here the Pan–Tompkins and wavelet algorithms, to benefit from the strengths of both methods. In particular, we introduce parameters allowing to balance the contribution of the individual algorithms; these parameters are estimated in a data-driven way. Experimental results and analysis are provided on the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia Database. We show that our combination approach outperforms both individual algorithms.
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A Real-Time QRS Detection Algorithm
Jiapu Pan, W.J. Tompkins · IEEE Transactions on Biomedical Engineering · 1985 · 7.6K citations