Fast QRS Detection with an Optimized Knowledge-Based Method: Evaluation on 11 Standard ECG Databases

Mohamed Elgendi

PLOS ONE, 2013, vol. 8, issue 9, 1-18

Abstract: 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.

Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0073557

DOI: 10.1371/journal.pone.0073557

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