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I am facing quite a strange behaviour. As far as I have understood, especially when dealing with CNN, feature normalisation/ standardisation, should help the model at converging faster. Now, I am working with ECG signals, which are characterized by high variability intra and inter subjects. I am dealing with them using CNNs. My model performs quite well when I do not normalise the data. Otherwise, when I do apply either standardisation or normalisation, performance downgrade (higher loss, and 10% less accuracy and F1). This is true if I apply the preprocessing globally as well as if normalize/standardise each signal per se.

Any clue about what could cause this behaviour? My guesses are:

  • presence of outliers that causes too large "compression" of the data with low values
  • as before but with numerical problems caused by the usage of float32, that could cause (?) potential loss of data and thus of information.
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