Key result
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.
May facilitate QRS detector optimization; leaves open prospective clinical validation before practice use.
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.
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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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