title: Combining algorithms in automatic detection of QRS complexes in ECG signals.
abstract: 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.
authors.affiliation: Philips Research Laboratories, Aachen, Germany. carsten.meyer@philips.com
authors.forename: Carsten
authors.initials: C
authors.lastname: Meyer
authors.forename: José
authors.initials: J
authors.lastname: Fernández Gavela
authors.forename: Matthew
authors.initials: M
authors.lastname: Harris
mesh_terms: D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000175:diagnosis* / Q000503:physiopathology; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003198:Computer Simulation; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006321:Heart / Q000503:physiopathology; D006339:Heart Rate; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D008955:Models, Cardiovascular; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity