How do I implement feature extraction using time-frequency analysis

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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.

Answers (1)

Vinayak Choyyan
Vinayak Choyyan on 12 Oct 2022
Hi,
As per my understanding, you have a 2-dimensional data from an ECG that you are trying to pass through an LSTM network. You are also trying to apply feature extraction to improve the accuracy and decrease training time.
You could try dimensionality reduction methods on your data to reduce the 2-dimensional data into 1-dimensional data. This will generate a 1-dimensional data which approximates your 2-dimensional data. You can read more about the available dimensionality reduction functions in MATLAB here Dimensionality Reduction and Feature Extraction - MATLAB & Simulink - MathWorks India.

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