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