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We compare the capability of wavelet functions used for noise removal inpreprocessing step of a QRS detection algorithm in the electrocardiogram (ECG)signal. The QRS signal to noise ratio enhancement and the detection accuracy ofeach wavelet function are evaluated using three measures: (1) the ratio of themaximum beat amplitude to the minimum beat amplitude (RMM), (2) the mean ofabsolute of time error (MATE), and (3) the figure of merit (FOM). Three waveletfunctions from previous well-known publications are explored, i.e., Bior1.3,Db10, and Mexican hat wavelet functions. Results evaluated with the ECG signalfrom MIT-BIH arrhythmia database show that the Mexican hat wavelet function isbetter than the others. While the scale 8 of Mexican hat wavelet function canprovide the best enhancement in QRS signal to noise ratio, the scale 4 ofMexican hat wavelet function can provide the best detection accuracy. Theseresults may be combined and may enable the use of a single fixed threshold forall ECG records leading to the reduction in computational complexity of the QRSdetection algorithm.

Comparisons of wavelet functions in QRS signal to noise ratio enhancement and detection accuracy