Fast QRS detection with an optimized knowledge-based method: evaluation on 11 standard ECG databases.
The current state-of-the-art in automatic QRS detection methods show high robustness and almost negligible error rates. In return, the methods are usually based on machine-learning approaches that require sufficient computational resources. However, simple-fast methods can also achieve high detection rates. There is a need to develop numerically efficient algorithms to accommodate the new trend towards battery-driven ECG devices and to analyze long-term recorded signals in a time-efficient manner. A typical QRS detection method has been reduced to a basic approach consisting of two moving averages that are calibrated by a knowledge base using only two parameters. In contrast to high-accuracy methods, the proposed method can be easily implemented in a digital filter design.
Department of Computing Science, University of Alberta, Edmonton, Canada.
Mohamed
M
Elgendi
D000465:Algorithms; D001185:Artificial Intelligence; D016208:Databases, Factual; D004562:Electrocardiography / Q000379:methods*; D012815:Signal Processing, Computer-Assisted