Why the study?
Digital notch filters used to suppress power line interference in ECG signals cause transient interferences and ringing effects, particularly when digitization does not meet full period sampling.
A novel adaptive notch filter algorithm effectively removes power line interference from ECG signals while preserving essential QRS-complex features without requiring manual parameter tuning.
Adaptive notch filter may reduce ECG PLI artifacts without ringing; leaves open clinical validation before routine adoption.
The noise cancellation in electrocardiogram (ECG) signal is very influential to distinguish the essential signal features masked by noises. The power line interference (PLI) is the main source of noise in most of bio-electric signals. Digital notch filters can be used to suppress the PLI in ECG signals. However, the problems of transient interferences and the ringing effect occur, especially when the digitization of PLI does not meet the condition of full period sampling. In this paper, to obtain a better cancellation of the PLI, a designing approach, generating adaptive notch filter (ANF) of sharp resolution, is proposed. The proposed method is concise in algorithm and achieves a more comprehensive reduction of the PLI. It only requires on one fast Fourier transform on the input signal. The spectrum correction method, based on the information from the FFT spectrum of the corrupted signal, is utilized to estimate the harmonic parameters of the PLI. The information of a few main lobe spectral bins in the FFT spectrum is merged such that a compensation signal can be synthesized. By subtracting the compensational signal from the original measurement, the PLI within the investigated signal can substantially reduced. A distinguished advantage of the proposed ANF lies in the fact that no parameters are required to be specified, making the algorithm easier to be implemented. The proposed ANF outperforms conventional notch filters because it not only alleviates the undesirable effects but also better preserves the QRS-complex features in the filtered signal.
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Chen et al. (2019) studied this question.
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