PLL-based adaptive power line interference canceller for ECG signal

Author(s):  
T Li ◽  
T Li
2006 ◽  
Vol 53 (11) ◽  
pp. 2220-2231 ◽  
Author(s):  
S.M.M. Martens ◽  
M. Mischi ◽  
S.G. Oei ◽  
J.W.M. Bergmans

2014 ◽  
Vol 556-562 ◽  
pp. 1506-1509
Author(s):  
Bao Jie Wang ◽  
Yan Men ◽  
Gang Zheng

Power line interference (PLI) may lead to the signal-to-noise ratio (SNR) decline sharply on biomedical signals, including the electrocardiogram (ECG). The proposed method employs the relationship of frequency and weights in adaptive filter to track the frequency variation of PLI. Real ECG signals from MIT-BIH database was used in the experiment, and they were corrupt by an artificial PLI signal for experiment. Correction performances of the proposed method and traditional adaptive method were compared by SNR in the paper. The results showed that the proposed method is consistently superior to the traditional one when the power line interference is vary with time, and the proposed method can track the variation of power line interference effectively.


Author(s):  
Martina Ladrova ◽  
Radek Martinek ◽  
René Jaros

The recordings of electrocardiogram (ECG), as an important biological signal which provides a valuable basis for the clinical diagnosis and treatment, are often corrupted by the wide range of artifacts. One important of them is power line interference (PLI). The overlapping interference affects the quality of ECG waveform, leading to the false detection and recognition of wave groups, and thus causing faulty treatment or diagnosis. The study deals with some of the signal processing approaches frequently used for elimination of PLI in ECG signal and compares the accuracy of methods by evaluation of the power of the remaining noise and comparing a filtered ECG signal with an original. The results are compared for three levels of interference and each tested method: Butterworth filter (BF), notch filter, moving average filter (MA), adaptive noise canceller (ANC), wavelet transform (WT) and empirical mode decomposition (EMD).


Big Data ◽  
2021 ◽  
Author(s):  
Suleman Tahir ◽  
Muneeb Masood Raja ◽  
Nauman Razzaq ◽  
Alina Mirza ◽  
Wazir Zada Khan ◽  
...  

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