Power line interference is the main noise source that contaminates
Electrocardiogram (ECG) signals and measurements. In recent years, adaptive
filters with different approaches have been investigated to eliminate power
line interference in ECG waveforms. Adaptive line enhancement filter is a
special type of adaptive filter that, unlike other adaptive filters, does
not require a reference signal and has potential application in ECG signal
filtering. In this paper, a selflearning filter based on an adaptive line
enhancement (ALE) filter is proposed to remove power line interference in
ECG signals. We simulate the adaptive filter in MATLwith a noisy ECG
signal and analyze the performance of algorithms in terms of signal-to-noise
ratio (SNR) improvement. The proposed algorithm is validated with
Physikalisch-Technische Bundesanstalt (PTB) ECG signals database. Additive
white gaussian noise is added to the raw ECG signal. Influential parameters
on the ALE filter performance such as filter delay, the convergence factor,
and the filter length are analyzed and discussed.
Effect of adaptive line enhancement filters on noise cancellation in ECG signals
