  Nowadays, the electrocardiogram (ECG) is still the most widely used signal
for the diagnosis of cardiac pathologies. However, this recording is often
disturbed by the powerline interference (PLI), its removal being mandatory to
avoid misdiagnosis. Although a broad variety of methods have been proposed for
that purpose, often they substantially alter the original signal morphology or
are computationally expensive. Hence, the present work introduces a simple and
efficient algorithm to suppress the PLI from the ECG. Briefly, the input signal
is decomposed into four Wavelet levels and the resulting coefficients are
thresholded to remove the PLI estimated from the TQ intervals. The denoised ECG
signal is then reconstructed by computing the inverse Wavelet transform. The
method has been validated making use of fifty 10-min length clean ECG segments
obtained from the MIT BIH Normal Sinus Rhythm database, which were contaminated
with a sinusoidal signal of 50 Hz and variable harmonic content. Comparing the
original and denoised ECG signals through a signed correlation index,
improvements between 10 - 72% have been observed with respect to common
adaptive notch filtering, implemented for comparison. These results suggest
that the proposed method is featured by an enhanced trade-off between noise
reduction and signal morphology preservation.
