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How do I implement feature selection for training a neural network on time-dependent data?

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Hi, I am trying to train a LSTM neural network to classify time-dependent 2-dimensional input signals derived from ECG's. I have used this example to train the network on raw data first but this took about an hour with a 77% classification accuracy. The example suggests using time-frequency analysis for feature extraction to speed up the training process and improve accuracy but this involves altering the method for one-dimensional data.
Is there any way of doing this feature extraction using 2-dimensional input signals. If not this method of feature extraction, is there another method for feature extraction that might be more suitable?
Any suggestions would be greatly appreciated, thanks in advance.

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