A data-driven approach combining Pan-Tompkins and wavelet algorithms outperformed both individual algorithms for automatic detection of QRS complexes in ECG signals.
Does a combined approach of Pan-Tompkins and wavelet algorithms improve QRS complex detection in ECG signals compared to individual algorithms?
Combining Pan-Tompkins and wavelet algorithms improves the automatic detection of QRS complexes in ECG signals.
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.
Meyer et al. (Sat,) conducted a other in Arrhythmia (ECG signals). Combination of Pan-Tompkins and wavelet algorithms vs. Individual algorithms (Pan-Tompkins and wavelet) was evaluated on QRS complex detection performance. A data-driven approach combining Pan-Tompkins and wavelet algorithms outperformed both individual algorithms for automatic detection of QRS complexes in ECG signals.
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