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

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  • Mohamed Elgendi

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.

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  • Mohamed Elgendi, 2013. "Fast QRS Detection with an Optimized Knowledge-Based Method: Evaluation on 11 Standard ECG Databases," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-18, September.
    • Handle: RePEc:plo:pone00:0073557
    DOI: 10.1371/journal.pone.0073557
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    Cited by:

    1. Mohamed Elgendi & Shine Kumar & Long Guo & Jennifer Rutledge & James Y Coe & Roger Zemp & Dale Schuurmans & Ian Adatia, 2015. "Detection of Heart Sounds in Children with and without Pulmonary Arterial Hypertension―Daubechies Wavelets Approach," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-22, December.

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