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I'm trying to predict Freezing of Gait (FoG) for Parkinson's patients using EMG signals recorded from three types of muscles of the subjects - tibialis anterior muscle of right leg, gastrocnemius muscle of right leg, and tibialis anterior muscle of left leg. It's a two class classification problem.

Should I concatenate the data of these three columns to a single column before applying some window function to them? Or should I store the data from these three muscles in three different columns and process them separately because data from different muscles have different distributions hence putting them in a single column may confuse the deep learning model we are going to build?

I've shared signals (4000 samples) from three muscles for a particular subject below:

enter image description here

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  • $\begingroup$ What are the x- and y-axes in the plots you give. Do you observe one outcome per subject or many? $\endgroup$
    – psboonstra
    Aug 24 at 21:39
  • $\begingroup$ X -axis : time, Y-axis : amplitude, One outcome/subject $\endgroup$
    – Debbie
    Aug 24 at 23:49
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    $\begingroup$ I encourage you to use three columns. You tagged this ANN. You may already know this, but for ANN, scaling the data is essential. $\endgroup$
    – Kat
    Aug 27 at 23:43
  • $\begingroup$ yeah. You are right. I shouldn't normalize different types of data altogether. $\endgroup$
    – Debbie
    Aug 30 at 18:37
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The three signals definitely need to be treated separately.

Also, you haven't specified how you intend to model this, but I'd suggest some dimensionality reduction technique. As I see in the figure, the right muscles have very abrupt jumps, while L-TA has more natural oscillations. This would likely be easy to see in the Fourier transform, where the abrupt jumps should translate into high power in high frequencies. If you're lucky, maybe even just two features per signal, high and low frequency power, might suffice for classification.

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