State Space Least Mean Square Adaptive Filter for Power Line Interference removal from Cardiac Signals

2019 ◽  
Author(s):  
Inam-ur-Rehman ◽  
Nauman Razzaq ◽  
Ehsan Ullah ◽  
Muhammad Salman ◽  
Tahir Zaidi
Keyword(s):  
State Space ◽  
Adaptive Filter ◽  
Power Line ◽  
Mean Square ◽  
Least Mean Square ◽  
Cardiac Signals ◽  
Power Line Interference ◽  
Line Interference

scholarly journals CANCELLATION OF MOTION ARTIFACT NOISE AND POWER LINE INTERFERENCE IN ECG USING ADAPTIVE FILTERS

2014 ◽  
pp. 297-299
Author(s):  
IMTEYAZ AHMAD ◽  
F. ANSARI ◽  
U.K. DEY
Keyword(s):  
Adaptive Filter ◽  
Motion Artifact ◽  
Power Line ◽  
Lms Algorithm ◽  
Recursive Least Squares ◽  
Ecg Signal ◽  
Least Mean Square ◽  
Rls Algorithm ◽  
Power Line Interference ◽  
Line Interference

Background: The electrocardiogram(ECG) has the considerable diagnostic significance, and applications of ECG monitoring are diverse and in wide use. Noises that commonly disturb the basic electrocardiogram are power line interference(PLI), instrumentation noise, external electromagnetic field interference, noise due to random body movements and respiration movements. These noises can be classified according to their frequency content. It is essential to reduce these disturbances in ECG signal to improve accuracy and reliability. The bandwidth of the noise overlaps that of wanted signals, so that simple filtering cannot sufficiently enhance the signal to noise ratio. It is difficult to apply filters with fixed filter co-efficients to reduce these noise. Adaptive filter technique is required to overcome this problem as the filter coefficients can be varied to track the dynamic variations of the signals. Adaptive filter based on the least mean square (LMS) algorithm and recursive least squares (RLS) algorithm are applied to noisy ECG to reduce 50 Hz power line noise and motion artifact noise. Method: ECG signal is taken from physionet database. A ECG signal (without noise) was mixed with constant 0.1 mVp-p 50 Hz interference and motion artifact noise processed with Adaptive filter based on the least mean square (LMS) algorithm and recursive least squares (RLS) algorithm. Simulation results are also shown. Performance of filters are analyzed based on SNR and MSE.

scholarly journals Effect of adaptive line enhancement filters on noise cancellation in ECG signals

2021 ◽  
Vol 18 (3) ◽  
pp. 291-302
Author(s):  
George Karraz
Keyword(s):  
Adaptive Filter ◽  
Adaptive Filters ◽  
Reference Signal ◽  
Power Line ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Filter Performance ◽  
Power Line Interference ◽  
Adaptive Line Enhancement ◽  
Line Interference

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.

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