A genetic algorithm was successfully used to design optimum linear and nonlinear QRS detectors, minimizing detection errors on the MIT-BIH Arrhythmia Database.
A genetic algorithm approach can be used to design optimum linear and nonlinear QRS detectors to minimize detection errors on ECG signals.
This paper describes an approach to the design of optimum QRS detectors. We report on detectors including a linear or nonlinear polynomial filter, which enhances and rectifies the QRS complex, and a simple, adaptive maxima detector. The parameters of the filter and the detector, and the samples to be processed are selected by a genetic algorithm which minimizes the detection errors made on a set of reference ECG signals. Three different architectures and the experimental results achieved on the MIT-BIH Arrhythmia Database are described.
Poli et al. (1995) studied Arrhythmia / ECG signal processing. Genetic algorithm for QRS detector design was evaluated on Detection errors. A genetic algorithm was successfully used to design optimum linear and nonlinear QRS detectors, minimizing detection errors on the MIT-BIH Arrhythmia Database.
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