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This paper presents combination of wavelet match filtering and neural network approach in QRS detection. In development, a particular emphasis is put on low signal-to-noise ratio and low computational complexity. Morlet wavelet is used for artifact removal and MLP is then used for QRS classification. Testing on MIT/BIH arrhythmia database, with added artifacts, shows above 90% accuracy in QRS detection in worst case scenario.