Removing Baseline Wandering

[@ProgrammingCradle]

Thank you so much Rami for this video. It helped me to understand the basic concepts involved in removing baseline wandering.

[@ali.al-timemy]

Thank you Dr Rami for this great video

[@hassanqassim8945]

Thank you very much Dr for the valued information. I think the wavelet transform approach is beneficial for identifying unknown noises and their frequencies. In the example of this video, the ECG has already known frequency, so using suitable band pass filter with appropriate order would also give good result. However, wavelet transform for baseline wandering gives me a lot of ideas to apply in the future. Thanks again Dr

[@AlaphBeth]

Thank you for your feedback, I am glad it helped.

[@the-hanhpham8950]

Thank you very much for your teaching. May I know if you have any recommendations for removing the powerline interference in ECG signals? Thank you again.

[@AlaphBeth]

Thank you for your feedback. The choice of the method depends pretty much on the platform in which you are applying the baseline wandering methods. If you are running these kind of analysis on a PC then you can use whatever method there and specifically explore the deep learning approaches as the literature is promoting these methods. However, if you are considering running these analyses on a wearable device, then it’s all about the computational power of your wearable device that will determine what method(s) you can use. As for the selection of the methods themselves, I have explored a number of these in the video specifically for ECG signals, and there could be more methods out there, but it all depends again on what you want specifically, that is to extract some features without caring much about the shape or pattern or you want very nice:clean representations for exploring:plotting or other purposes.

[@BioniChaos]

great content, highly recommended!

[@monashaaban2337]

Thank you so much dr Rami Khushaba for the information. what about the detrend function to removing baseline wandering from ECG signal? please dr continue analysis of ECG signal.

[@AlaphBeth]

Thank you for your feedback. If you mean the detrend function in Matlab, that one is based on curve fitting, I.e., fitting a curve to the data and then subtracting that curve away. This is not explicitly looking at filtering a range of frequencies like the FFT, classical filters, or wavelet approaches. So the methods in the video look at frequency domain analysis or time-frequency analysis versus detrend that does curve fitting. On the other hand, the deep learning approach learn how to act as a robust filter based on how much data you show it (can be thought of on both sides).

[@monashaaban2337]

@@AlaphBeth Hi, dr.. If using DWT db4 level 10 to ECG Signal to removing noise Fs= 360 baseline wander (0 - 0.5hz) = [a10+d10+d9+d8+d7+d6+d5+d4+d3+d2+d1] =for feq between (0- 0.35hz) and for power line interference (50 / 60 hz) PL= [a4+ d4+ d3+d2+ d1], is the correct dr.

[@AlaphBeth]

@@monashaaban2337 there is no correct and wrong here, it’s about what works the best for your signals. Just run your code and see how good it is denoised. For the baseline wander that range seems to be within the limits so it should give you some good approximation there. For the power line interference, 4 levels of decomposition will give you ranges of nearly 22.5Hz and that is a bit wide range if you want to filter 50Hz. As you are trying to do multiple ranges of frequencies, how about you try the same idea in the video but with wavelet packet transform? That will give you more control over the ranges.

[@monashaaban2337]

@@AlaphBeth thank you dr.

[@AlaphBeth]

@@monashaaban2337 happy to help anytime, keep the questions coming if you have more :)

[@slembcke]

I've used IIR filters for real-time use cases in the past, but would be interested in trying something better. I don't have much experience with the DWT, so it's not clear to me how you would apply it in real time. Doesn't it require the entire signal? With an IIR filter you can just process each sample as it comes in, or use a short buffer for FIR/FFT filters.

[@AlaphBeth]

Here is one paper that discusses the implementation details for denoising with wavelets in real-time www.researchgate.net/publication/4174293_A_real-time_system_for_denoising_of_signals_in_continuous_streams_through_the_wavelet_transform. A more recent experiment with a DSP kit is also available here akulmalhotra.github.io/posts/2020/05/waveletecg/. Myself, I have used the DWT for EMG feature extraction (not ECG denoising) in real-time experiments, working on windows of 256ms worth of data. As for your question, a short buffer is suggested.

[@slembcke]

Thanks! I'll give this a read.

[@SSS0401]

Great video Rami :-). Do you have any python code for your method that was in the IEEE article?

[@AlaphBeth]

Thanks for your feedback. Yes, the repo link is shown on slide 13, github.com/mitbal/py-bwr