Does an optimized knowledge-based QRS detection algorithm achieve high sensitivity and positive predictivity across standard ECG databases compared to existing methods?
A numerically efficient, knowledge-based QRS detection algorithm achieves high accuracy across multiple standard ECG databases, demonstrating suitability for battery-driven and long-term ECG monitoring devices.
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.
Mohamed Elgendi (Mon,) studied this question.
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