Removing baseline drift from ECG signal

Question

I am trying to design a high pass filter to remove baseline drift from an ECG signal. the baseline drift is of very low frequency like 0.3Hz or so with an amplitude of 25% of the ECG signal. But the bandwidth of ECG signal itself is 0.5Hz to 150Hz. I have been trying fdatoolbox in matlab to design the HPF but I was not able to remove the baseline drift accurately.Please help on how can i achieve a low order filter to eliminate baseline drift. I have got it using higher order filters of order above 1000. But I'm looking for a way if I can get lower order filter,like order of less than 30, to do the same.Thanks for your help..

Answer 1

You usually use a DC notch filter. This is a recursive filter that should kill a very narrow band of frequencies around DC. One possible implementation is a first order DC notch with transfer function

$$\frac{1 - z^{-1}}{1 - \lambda z^{-1}},$$

where $\lambda$ is some number very close to 1, say 0.99. A to demonstrate this in MATLAB,

b = [1 -1];
a = [1 -0.99];
freqz(b,a);

Notice that the phase response around DC gets pretty non-linear. Such is the nature of these recursive filters.

The closer you make $\lambda$ to 1, the narrower the notch gets and the more distorted your phase response becomes around DC.

For ECG signals, this will usually do. Experiment with $\lambda$ values that will suit your needs. Sometimes, if you make the notch too narrow, you won't get all off the drift, so 0.99 is usually a good starting point.

Answer 2

Recently, we have released a baseline filtering and noise removal method that takes into account the sparsity (and the potential asymmetry) of one-dimensional signals. It was initially applied to analytical chemistry data (chromatograms), but could possibly be used of ECG. It relies on a recursive low-pass filter, an asymmetry parameter, and a penalization related to the noise level and the strength of the signal and its derivatives.

One illustration for BEADS background removal is:

The code for BEADS (Baseline Estimation And Denoising with Sparsity) is available in Matlab, R, C++. It has been used for instance in the following papers:

• The unsupervised pattern recognition for the ECG signal features detection, 2022, Biomedical Signal Processing and Control
• Epileptic seizure onset detection based on EEG and ECG data fusion
• Shannon’s Energy Based Algorithm in ECG Signal Processing
• Detection of Atrial Fibrillation in ECG Hand-held Devices Using a Random Forest Classifier
• Combined matching pursuit and Wigner-Ville Distribution analysis for the discrimination of ictal heart rate variability
• Signal processing using sparse derivatives with applications to chromatograms and ECG
• Training System Methodology Using ECG Signal